The Dissertation Report

Entitled

Design and Control of Solar Energy Based

E-Rickshaw
Submitted in
Partial fulfilment for the award of the Degree of

Master of Technology
in
Electrical Engineering
(Power Electronics and Electrical Drives)

By
Mr. ARJUN. T. C.
(P19EL015)

Under the guidance of

Dr. S. R. ARYA
(Associate Professor)

DEPARTMENT OF ELECTRICAL ENGINEERING

SARDAR VALLABHBHAI NATIONAL INSTITUTE OF
TECHNOLOGY
SURAT – 395007
June 2021
 DECLARATION
“I hereby declare that the Dissertation report entitled “ DESIGN AND CONTROL OF
SOLAR ENERGY BASED E-RICKSHAW ”is being submitted in the partial fulfilment for the
award of the degree of “Master of Technology in Electrical Engineering (Power Electronics and
Electrical Drives)’’ to Sardar Vallabhbhai National Institute of Technology, Surat is an authentic
record of my own work done under the guidance of Dr. S. R. Arya, in Electrical Engineering
Department. The matter reported in this Dissertation has not been submitted at any other place for
award of any degree or diploma, except where due acknowledgment has been made in the text.

Date: Signature :
Place: SVNIT, Surat. Name: Mr. ARJUN T.C.
Roll No: P19EL015
 CERTIFICATE

This is to certify that the dissertation report entitled “DESIGN AND CONTROL OF
SOLAR ENERGY BASED E-RICKSHAW “submitted by Mr. ARJUN .T.C., P19EL015 is
a record of bonafide work carried out by him in partial fulfilment of the requirement for the
award of the degree of “MASTER OF TECHNOLOGY IN ELECTRICAL
ENGINEERING with specialization POWER ELECTRONICS AND ELECTRICAL
DRIVES

Date:

Place: SURAT

Dr. S.R.Arya

Associate Professor
Department of Electrical Engineering
S. V. National Institute of Technology
Surat, India-395 007
 EXAMINER’S CERTIFICATE OF APPROVAL

The Dissertation entitled “DESIGN AND CONTROL OF SOLAR ENERGY BASED E-

RICKSHAW” submitted by Mr. ARJUNT.C. (P19EL015) in the partial fulfilment of the
requirement for the award of the degree in “Master of Technology in Electrical
Engineering with specialization POWER ELECTRONICS AND ELECTRICAL
DRIVES” of the Sardar Vallabhbhai National Institute of Technology, Surat is hereby
approved for the award of the degree.

Date:
Place: SURAT

Dr. S. R. Arya

(Supervisor) (External Examiner)

(Chairman)
 ACKNOWLEDGEMENT
This is to place on record my appreciation and deep gratitude to the persons without
whose support this dissertation work would never see the light of day.

I would like to begin by thanking Dr. S. R. Arya, Associate Professor, Electrical

Engineering, for his efforts and endeavour in guiding and helping me for my dissertation work
and also I express my heartfelt gratitude towards all department staffs who have contributed
their precious time in helping me complete my preliminary dissertation works.

I am also grateful to Dr. R. Chudamani, Professor and Head of the Department,

Electrical Engineering, for providing necessary facilities in the department.

I am indebted to Dr. Rakesh Maurya, Associate Professor and P.G. Incharge of the
Electrical Engineering Department, for her consent, guidance and permission to utilize the
facilities of the department for my study.

An assemblage of this nature could never have been attempted without reference to
and inspiration from the works of others whose details are mentioned in the reference section.
I acknowledge my indebtedness to all of them.

Finally, I would like to thank my family and all my friends for their continuous love
and support.

Mr. Arjun T.C.

(P19EL015)

5
 ABSTRACT
In all Over the world Energy crisis is a prime item, Fossil fuels are considered to be energy
source over the past few years. But they are not renewable in nature, once we used them it will
take time to recreate again. Fossil fuels are harmful for environment as well. In order to limit
the usage of fossil-fuels by some extend, we have to rely on renewable energy source. It has
been shown by researches that the global energy demand can be fully meet by using
renewable energy sources. Amazingly the sun is an infinite source of energy to fulfil all
energy requirement for ever. In this report solar energy is used to power an e-rickshaw is
proposed. The objective of this report is to design various control schemes for induction motor
and also to charge the battery of the e-rikshaw by using different MPPT algorithm such as
perturb and observe method, incremental conductance method and adaptive MPPT. Induction
Motor is the one of the best choices for e rickshaw. By using advanced vector controls we can
control induction motor as like separately exited dc motor. Since it doesn’t require any
permanent magnet or rare earth material this motor is cheap and easily available. Full load
power factor is around 0.8 and efficiency is also high. Various control algorithms such as
direct vector control, indirect vector control, Model reference adaptive control and stator flux
oriented control is proposed in this report. In spite of atmospheric variation MPPT algorithms
will track the maximum power from solar panel and the battery is charged. By using different
vector control algorithms induction motor is controlled for e rickshaw application.

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Table of Contents

ACKNOWLEDGEMENT .......................................................................................................... 5
ABSTRACT ............................................................................................................................... 6
List of Figures ....................................................................................................................... 11
List of Tables ........................................................................................................................ 14
List of Abbreviations ............................................................................................................ 15
Chapter 1 ................................................................................................................................... 17
Introduction............................................................................................................................... 17
1.1. Electric Vehicle .............................................................................................................. 18
1.2. Solar Chargers and Maximum Power Point Tracking ................................................... 18
1.3. Scalar Control or v/f Control ......................................................................................... 18
1. 4.Field Oriented Control ................................................................................................... 19
1.5. Literature Review: ......................................................................................................... 20
1.6 Dissertation Objectives ................................................................................................... 24
1.7 Organisation of the Report .............................................................................................. 25
1.8 Conclusion ...................................................................................................................... 25
Chapter 2 ................................................................................................................................... 26
Solar Powered E Rickshaw ....................................................................................................... 26
2.1. Solar Irradiation in India [51] ........................................................................................ 26
2.2. Single Diode Model of a Solar Cel ................................................................................ 27
2.3. Losses in Solar Cells ...................................................................................................... 29
2.4. Solar Panel Calculation .................................................................................................. 30
2.5. Electric Vehicle Modelling [49] ........................................................................................ 31
2.6. Charger for E rickshaw ...................................................................................................... 32
2.6.1. On Board Chargers ..................................................................................................... 32
2.6.2. Off Board Chargers ..................................................................................................... 32
2.6.3. PWM Solar Chargers. ................................................................................................. 33
2.6.4. MPPT Based Solar Chargers ...................................................................................... 34
2.7. Summery ............................................................................................................................ 34
Chapter 3 ................................................................................................................................... 35
Solar Battery Chargers Topologies........................................................................................... 35

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 3.1 Electric Vehicle Charger ................................................................................................. 35
3.2 Solar Boost Converter with Perturb and Observe MPPT ............................................... 35
3.2.1. Flow Chart of Perturb and Observe Method ........................................................... 36
3.2.2. Design of Solar Fed Boost Converter [50] .............................................................. 37
3.2.3Advantageous and Disadvantageous ......................................................................... 38
3.2.4. Simulation Diagram of Non Isolated Boost Converter with Perturb and Observe
MPPT ................................................................................................................................ 38
3.2.5. Simulation Result of Non Isolated Boost Converter ............................................... 40
3.3 Solar Boost Charger with Incremental Conductance MPPT .......................................... 41
3.3.1. Flow Chart of Incremental Conductance Based MPPT .......................................... 41
3.3.2 Design of Solar Fed Boost Converter[50] ................................................................ 42
3.3.3Advantages and disadvantageous .............................................................................. 43
3.3.4. Simulation Diagram: Non Isolated Boost Converter with Incremental Conductance
MPPT ................................................................................................................................ 43
3.3.5. Simulation Result of Non Isolated Boost Converter with Incremental Conductance
Method .............................................................................................................................. 45
3.4 Perturb and Observe MPPT using Isolated Forward Converter ..................................... 46
3.4.1 Advantageous and Disadvantageous ........................................................................ 47
3.4.2. Design of Solar Fed Forward Converter [50] .......................................................... 47
3.4.3 Simulation Diagram: Forward Converter with Perturb and Observe MPPT ........... 49
3.4.4. Simulation Result of Isolated Forward Converter with Perturb and Observe MPPT50
3.5 Adaptive MPPT using Non Isolated Converter .............................................................. 51
3.5.1. Advanced Adaptive MPPT...................................................................................... 54
3.5.2 Advantageous and Disadvantageous ........................................................................ 55
3.5.3 Simulation Diagram: Adaptive MPPT using Non Isolated Boost Converter ..... 55
3.5.4. Simulation Result of Non Isolated Boost Converter with Adaptive MPPT ............ 56
3.6 Adaptive MPPT using Isolated Forward Converter ........................................................ 57
3.6.1. Design of Solar Fed Forward Converter [50] .......................................................... 59
3.6.2. Simulation Diagram : Forward Converter with Adaptive MPPT............................ 60
3.6.3. Simulation Result of Isolated Forward Converter with Adaptive MPPT ............... 61
3.7. Solar Charger with SOC Indication of the Battery ............................................................ 63
3.7.1Design of Solar Fed Buck Converter [50] .................................................................... 64
3.7.2. Simulation Diagram Battery Charger with SOC Indication ....................................... 65
3.7.3. Simulation Diagram of Buck Based Charger with MPPT .......................................... 66
3.7.4. Cluster Details of Non Isolated Charger with SOC Indication ................................... 68

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3.8. Summery ............................................................................................................................ 68
Chapter 4 ................................................................................................................................... 69
Basics of Vector Control .......................................................................................................... 69
4.1 Clarke’s and Park’s Transform ....................................................................................... 70
4.2 Summery ......................................................................................................................... 74
Chapter 5 ................................................................................................................................... 75
Field Oriented Control for Induction motor ............................................................................. 75
5.1 Principles of Vector Control ........................................................................................... 75
5.2 Direct or Feedback Vector Control ................................................................................. 76
5.2.1 Voltage Model for Flux Estimation.......................................................................... 76
5.2.2 Current Model for Flux Estimation .......................................................................... 77
5.3 Simulation of Direct Vector Control ............................................................................... 78
5.3.1 Machine Rating and Parameters ............................................................................... 78
5.3.2 Simulation Diagram: Direct Vector Control ( Current Based)................................. 79
5.3.3. Simulation Result .................................................................................................... 82
5.3.4 Simulation Result of Voltage Based Model ............................................................. 85
5.3.5 Simulation Result ..................................................................................................... 86
5.4 Indirect or Feed forward Vector control ......................................................................... 89
5.5Simulation of Indirect Vector Control ............................................................................. 92
5.5.1. Machine Rating and Parameters .............................................................................. 92
5.5.2. Simulation Diagram of Indirect Vector Control...................................................... 92
5.5.3. Simulation Result of IDVC ..................................................................................... 94
5.6 Indirect Vector Control with Slip Gain Tuning .............................................................. 96
5.6.1. Simulink Model and Result ..................................................................................... 99
5.7 Stator Flux Oriented Control ........................................................................................ 104
5.7.1.Vector Diagram of Stator Flux Oriented Control ................................................... 106
5.7.2. Decoupling Scheme of Stator Flux Oriented Control ........................................... 106
5.7.3. Simulink Model and Result for Stator Flux Oriented Vector Control .................. 107
5.8 Summery ....................................................................................................................... 111
Chapter 6 ................................................................................................................................. 113
Conclusion and Future Work .................................................................................................. 113
1.Conclusion ....................................................................................................................... 113
2. Future Work .................................................................................................................... 113

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References............................................................................................................................... 114

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List of Figures
Fig. 1.1 Block diagram of scalar control of Induction motor drive…………………………...19
Fig. 1.2 Block diagram of vector control AC drive…………………………………………...20
Fig. 2.1 Annual Solar Irradiance in India……………………………………………….….…26
Fig. 2.2 Solar Irradiance in the Month of March……………………………….………….….27
Fig. 2.3 Single Diode Model of Solar Cell……….…………………………………………...27
Fig. 2.4 Sectional View of Solar Cell………………………………………………………...28
Fig. 2.5 Losses in Sol.ar Cell…………………………………………………………….........29
Fig. 2.6 On Board Charger……………………………………………………………………32
Fig. 2.7Off Board Charger…………………………….….…………………………………...33
Fig. 2.8PWM Charger………………………………………………………………………...33
Fig. 2.9MPPT Charger………………………………………………………………………...34
Fig. 3.1Boost Converter…………………………………………………………………….…36
Fig. 3.2Flow Chart of Perturb and Observe MPPT…………………………………………...37
Fig 3.3. Simulation Diagram of Non Isolated Boost Converter with P&O Method………….39
Fig 3.4. Simulation Diagram of Non isolated Boost Converter with P&O method……….….39
Fig 3.5. Performance of Boost Converter with Perturb and Observe Method…………….….40
Fig 3.6. Boost Converter……………………………………………………….……….….….41
Fig 3.7. Flowchart of Incremental Conductance Based MPPT………………….……….…...42
Fig. 3.8Simulation Diagram of Incremental Conductance Based MPPT………………….….44
Fig. 3.9. Simulation Diagram of Incremental Conductance Based MPPT…………………....44
Fig. 3.10. Performance of Incremental Conductance Based MPPT………………….……….45
Fig. 3.11Forward Converter……………………………………………………………….….46
Fig. 3.12Simulation Diagram of Forward Converter with P&O method………………….….49
Fig 3.13. Simulation Diagram of Forward Converter with P&O method…………….………50
Fig. 3.14Performance of Isolated Forward Converter with P&O Method……………………51
Fig. 3.15Boost Converter……………………………………………………………………...52
Fig. 3.16PV Curve of Solar Panel………………………………………………….…………52
Fig. 3.17. Flow Chart of Adaptive MPPT…………………………………………………….54
Fig 3.18. Simulation Diagram of Adaptive MPPT………………………………...….………55
Fig.3.19. Simulation Diagram of Adaptive MPPT. ………………………………………….56
Fig. 3.20Performance of Non Isolated Boost Converter with Adaptive MPPT………………57
Fig. 3. 21.. Forward Converter………………………….………………………………....….58

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Fig. 3.22.PV Curve……………………………………………………………………………58
Fig. 3.23.Simulation Diagram of Forward Converter with Adaptive MPPT…………………61
Fig. 3.24Simulation Diagram of Forward Converter with Adaptive MPPT ………………....61
Fig. 3.25Performance of Isolated Forward Converter with Adaptive MPPT………...……….62
Fig. 3.26Automatic Charging Algorithm………………………………………………….….63
Fig. 3.27Simulation Diagram of Buck converter with P&O method with SOC indication......65
Fig. 3.28Automatic Battery Charging Algorithm………………………………......................66
Fig. 3.29. Performance of Buck Based Charger with P&O method……………………….….67
Fig. 3.30.Vehicle Cluster view when SOC greeter than 40%………………………………...68
Fig. 3.31.Vehicle Cluster view when SOC less than 40%…………………………………...68
Fig4.1 Separately exited dc motor…………………………………………………………….70
Fig. 4.2Vector Control of Induction Motor Drive…………………………………………….70
Fig. 4.3Vector diagram of Clarke transformation…………………………………………….71
Fig4.4.Vector Diagram of Park’s Transformation…………………………………………….71
Fig. 4.5Vectore diagram of ab to dqo…………………………………………………………72
Fig. 4.6.Equvalnat circuit of Induction motor in dqo frame…………………………………..72
Fig. 5.1Block Diagram of Vector Control of AC Drive.……………………………………...75
Fig. 5.2ds qs Model of Induction motor………………………………………………............77
Fig. 5.3DVC Simulation Diagram…….....................................................................................80
Fig. 5.4Subsystem of DVC Diagram………………………………………………………….81
Fig. 5.5Simulation Diagram of DVC………………………………………………………….82
Fig. 5.6Performance of Current Based Vector Control…………………………………….....83
Fig. 5.7Performance of Current Based Vector Control ………………………………………84
Fig. 5.8.Simulation Diagram of Voltage Based Vector Control………………………...........85
Fig 5.9Theta generation circuit of voltage based vector control……………………………...86
Fig. 5.10Performance of Voltage based vector control……………………………………….87
Fig. 5.11.Performance of Voltage based vector control ……………………………………...88
Fig. 5.12.Phasor Diagram of Indirect Vector Control………………………………………...89
Fig. 5.13.Block Diagram of Indirect Vector Control…………………………………............91
Fig. 5.14IDVC Simulation Diagram…………………………………………………..............93
Fig. 5.15Subsystem of IDVC Control……………...................................................................94
Fig. 5.16Performance of Indirect Vector Control………………………………………….….95
Fig. 5.17.Performance of Indirect Vector Control.....................................................................96
Fig. 5.18. Detuning of KS due to rotor resistance………………………………………….....97

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Fig 5.19. Effect of Detuning on response…………………………………………………......97
Fig. 5.20Block Diagram of MRAS……………………………………………………………98
Fig. 5.21Simulation diagram of Model Reverence Adaptive Control………………………...99
Fig. 5.22Theta generation in MRAS………………………………………………………...100
Fig. 5.23Reference Model………………………………………………….……………......100
Fig. 5.24Adaptive Model…………………………………………………………………….101
Fig. 5.25Performance of Model Reference adaptive Control……………………………......102
Fig. 5.26.Performance of Model Reference adaptive Control.................................................103
Fig. 5.27. Block Diagram of stator flux oriented Control…………………………………...104
Fig. 5.28Vector diagram of stator flux oriented Control…………………………………….106
Fig. 5.29Decoupler………………………………….……….................................................106
Fig. 5.30Simulation Diagram of Stator Flux Oriented Control……………………………...108
Fig. 5.31. Simulation Diagram of Stator Flux Oriented Control ……………………………109
Fig. 5.32.Decouple……………………………………………….…….……………….........109
Fig. 5.33Performance of stator flux oriented Control……………………………………….110
Fig 5.34.Performance of stator flux oriented Control……………………………………….111

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List of Tables
TABLE-I. Solar panel details…………………………………………………………………31
TABLE -2 Performance of Boost Converter with Perturb and Observe Algorithm………….40
TABLE- 3 Performance of Boost Converter with Incremental Conductance Algorithm…….45
TABLE -4 Performance of Isolated Forward Converter with P&O Algorithm………………51
TABLE- 5 Performance of Non Isolated Boost Converter with Adaptive MPPT……………57
TABLE -6 Performance of Isolated Forward Converter with Perturb and Observe MPPT….62
TABLE -7 Performance of Non Isolated Buck Converter with P&OMPPT………………...67
TABLE -8 Performance of Current Based Direct Vector Control……………………………85
TABLE -9 Performance of Voltage Based Direct Vector Control…………………………...88
TABLE- 10 Performance of Indirect Vector Control………………………………………...96
TABLE -11 Performance of Model Reference Adaptive Control…………………………..103
TABLE- 12 Performance of Stator Flux Oriented Control………………………………….111

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List of Abbreviations
Symbols
Ls Stator inductance
Lr Rotor inductance
Rs stator resistance
Rr Rotor resistance
Is Stator current
Vs Stator voltage
Ѱr Rotor flux linkage
P No. of poles
Te Electromagnetic torque
Θ Rotor position
Ꞷe Synchronous speed
Ꞷr Rotor speed
Ks Slip gain
Abbreviations
FOC Field oriented control

DVC Direct vector control

IDVC Indirect vector control

ANN Artificial neural network

MPPT Maximum Power Point Tracking

P&O Perturb and observe

IC Incremental Conductance

APO Advanced Perturb and Observe

EPP Extended Perturb-Perturb

ADC Analog to Digital Converter

MRAS Model reference adaptive system

D Duty ratio

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fs Switching frequency

DSO Digital Storage Oscilloscope

Ƞ Efficiency

Pmp Highest power obtained from the solar module

Vmp Voltage corresponding to highest power of the solar module

Imp Current corresponding to highest power of the solar module

P Proportional controller

I Integral controller

G Gate pulses
ESR Equivalent Series Resistance

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 Chapter 1
Introduction
The automotive industry engaged an important place in many countries given its place in the
lifecycle of the different communities. Conventional vehicles are harmful to environment as it
emits toxic gases to atmosphere [1]. For sustainable and efficient means of transit EVs are the
best option and they are increasingly accepted in different cities.[2]. Due to large no of EVs it
will increase the energy consumption of the countries in addition to all other electrical
equipment present. In a country where there is no alternate renewable energy option for
producing electrical energy, it will lead to emission of the overall greenhouse gases.[3].
Different form of energy storage such as lead acid Ni Cd and Lithium ion can be used as
battery for e rickshaw application, out of which lithium ion is widely accepted due to large
energy density higher efficiency and longer life span. If we can use solar panel for charging
the battery that can reduce the overall energy consumption significantly and can extract
maximum power from the solar panel by using various MPPT techniques.[4]. MPPT monitors
the output voltage and current from the solar panel and determine operating point that will
give maximum power.[5] Different kind of fixed step MPPT methods are there they have their
own advantage and disadvantages. if we use a MPPT with good tracking in varying
environment, we can track more clearly in varying environment as well. Induction motor is
considered to be one of the best options for e rickshaw application. About fifty years later
from Faraday’s initial discovery of electro-magnetic induction in 1831, Nikola Tesla
developed first induction machine in 1888. He succeeded, after many years, at developing an
electrical machine that did not need any commutator and brushes. As we know at present
Induction motor are the machines used in almost all industries because it has good efficiency,
least maintenance due to absence of brushes and commutator(like a DC machine), robust
construction. Since 3-ɸ Induction motors are self-starting and singly excited (required only
AC) therefore no extra arrangement for supply and starting equipment are required. On the
basis of construction Induction motor can be divided into two categories namely squirrel cage
Induction motor and wound rotor Induction motor. Since Induction motor are simple in
construction and easy to operate because of that it is used in most of the industries. And as the
technologies developed industries required high quality Torque and Speed control. To full fill
their requirements three techniques were developed which are namely ‘Scalar control’ ‘Vector
control’& ‘Direct torque control’.

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1.1. Electric Vehicle
An electric vehicle is one operate on electric motor. In order to address the issue of global
warming, pollution depleting natural resources and other economic benefits electric vehicle
has to be used. fuelling with electricity offers some advantageous not available with
conventional internal combustion engine based vehicle. Electric vehicle reacts more quickly
as compared to internal combustion engine based vehicle. Electric vehicles are very
responsive and good at torque. efficiency of EV is high as compared to conventional IC
engine based vehicle. In electric vehicle gear train is absent so the efficiency of the electric
vehicle is more than conventional internal combustion engine used vehicle. Another
advantage is that we can use electric vehicle as an energy source as well. electric vehicle can
act as distributed generators also.

1.2. Solar Chargers and Maximum Power Point Tracking

Battery of the electric vehicle has to be charged, In solar chargers battery of the vehicle is
charged by solar energy. Provision of conventional grid based charging option is also given,
solar charger should be provided as an add on. So vehicle get charged during normal running
as well as parking of the vehicle. This will increase the effective range of the vehicle. In
conventional vehicle PWM based charge controllers and normal MPPT based algorithm is
implemented along with grid chargers. While inserting solar panel we have to take care about
the weight of the solar panel as well. If higher the capacity the weight of the solar panel is also
high so this will reduce the effective range of the vehicle significantly. So, we have to find
balance between these two. Now a days vehicle supports both onboard chargers and off board
chargers. Off board chargers are nothing but fast charger in which charging current is high so
charging time is less. In on board chargers, charges are present inside the vehicle so plugs are
provided for charging. Maximum power point tracking is used to extract maximum power
from the given solar panel under the given atmospheric condition. It will basically change the
load impedance same as source impudence so solar panel can deliverer maximum power
under that atmospheric condition. in this report different MPPT methods such as perturb and
observe method, Incremental conductance method and Adaptive MPPT are proposed.

1.3. Scalar Control or v/f Control

As the name indicates there are basically two control parameters one is ‘voltage’ and other is
‘frequency’. We can control the flux of the machine by controlling the voltage in the machine
and by controlling frequency it can control the torque and slip. But there exist some coupling

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effect between torque and flux. By using scalar control if we want to change torque, flux of
the machine will change, similarly if we want to change flux, Torque of the of the machine
will change. Torque equation of induction motor is given by–

R2
Vs *
3 S
Te = * ( 1.1)
s  R2 2
  + X2
2

 
s

Where: R2=rotor resistance, S= Slip, X2= Rotor reactance, ws = Synchronous speed. Speed of
the machine can by controlled by changing the frequency of the machine. But merely
changing frequency flux of the machine also changes. To maintain flux (V/F) ratio is
maintained constant.

Fig. 1.1 Block Diagram of Scalar Control of Induction Motor Drive

1. 4. Field Oriented Control

In this case controlling of induction motor is done like separately exited DC motors In
which field produced by the armature winding and filed produced by the field winding are
orthogonal in nature and they are decupled too. So, we can control torque and flux
individually. But In induction motor coupling effect is present between torque and flux. As we
need to get decoupled control in induction motor as in dc machine, we need to convert control
variable in synchronously rotating frame.

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 Fig. 1.2 Block Diagram of Vector Control of Induction Motor Drive

1.5. Literature Review:

Electric vehicle are getting poplar day by day. The main advantage of electric vehicle is
that absence of IC engine and transmission system, By replacing gear train efficiency
improved scientifically. Usually, a battery is used to power electric motor. [1]. explains about
various battery topologies out of which lithium ion battery has more energy density [3]in this
paper it is explained about additional advantageous of electric vehicle such as vehicle to grid
application. it also explain about new improvement in electric vehicle and what is the impact
of electric vehicle in environment. If we charge electric vehicle by renewable energy it will
reduce the electrical energy requirement and also reduce the pollution.[4]. this paper explains
about various PV modelling scheme other than single diode model. There are various MPPT
algorithms are present.[5]. this paper compares about various MPPT techniques such as
perturb and observe, Incremental conductance and Fuzzy based methods. High performance
control and estimation technology for AC drives has experienced rapid growth in recent years.
Now they get increasing acceptance in industrial drives for applications such as steel mills,
paper mills, servos, machine tools, robotics, elevators and transportation system.
Traditionally, AC machines were considered ideal for constant speed applications. In past few
decades intense research is carried out in the area of power electronics and several new things
and techniques are developed like solid state devices, various converters (AC to AC, AC to
DC, DC to DC, DC to AC) which are used in control system of various electrical machine in
industries. With the development of inverter the first technique for speed control of induction
motor is introduced which is known as open loop V/F control or scalar control [6]
.Unfortunately, induction machine machines are nonlinear, parameter-varying, multi-variable

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with coupling effect, and have complex dynamics of higher order because of it does not give
satisfactory fast response for sudden change of load and speed.

To improve the performance of an induction, motor the first thing which is required is to
run the motor in a closed loop manner. In 1970’s Germany invented a new controlling
technique known as field oriented control or vector control of induction motor [7]. A vector-
controlled induction motor drive behaves just like a separately excited de motor. The vector
control is also known as decoupling or orthogonal control. It is a comprehensive invention in
ac drives technology. The higher order and coupling model of the machine which gives
complex stability and sluggish response problems occur in a scalar controlled drive tend to
disappear with vector control. Conventional vector control has so many limitations such as
accuracy is highly depends upon parameter variation and no of sensor requirement is very
high this can be reduced by this approach[8].in this paper instead of taking torque reference
and flux reference here real power reference and reactive power reference is taken where
reactive power reference is corresponds to flux reference that case transient response is also
improved as compared to conventional method .[9]In this paper it is explained about the
senseless vector control. by suing which we can avoid speed sensor. In this paper slip speed is
calculated based on motor equations and as is constant for a particular motor if frequency of
operation is constant in that way it calculates the rotor speed and avoid requirement of speed
sensor and he tested the r result for 05kw motor vacuum pump and is working perfectly.[10]in
this paper it is explained about advanced direct torque control in which wavelet transform is
used to calculate stator resistance they are claiming this method is more accurate as compared
to conventional method. By using this method current ripple and torque ripple reduces
significantly.[11]in this paper tis is explained about SVM advanced DTC in conventional
DTC torque ripple and current ripple is very high due to reduce no of voltage vectors. So in
this paper by using SVM they could reduce torque ripple and current ripple significantly .By
using above method Better dc bus utilization less current ripple and less torque ripple is
obtained.[12]in this paper various flux sensing algorithms are compared, if we go for
conventional method like voltage based flux estimation integrators are used for calculations of
the flux. The accuracy of integrators is less especially at low-speed region due to drift. So, we
cannot use voltage based flux estimation at low speed region. This problem can be eliminated
if are suing adaptive low pass filter based flux estimation. in that case accuracy can be further
improved if we take stator resistance into account.[9]in this paper fuzzy logic controller based
vector controller is proposed. In this method high quality regulation is obtained by FLC based

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algorithm since this method are less prone to change in parameter variation the main
difference between this methods and conventional method is that in this methods pi controller
is replaced by an FLC based controller.[15]in this method advanced senseless control is
proposed they have implemented a new speed sensing algorithm using current differential
.The estimated speed is used to derive the torque component of the motor .They have tested
using 3KW,380V,50Hz induction motor and verified the output.[20]in this paper a new
method of slip frequency detections is proposed. If we are using indirect vector control, we
need to know about the slip frequency estimation. In that equation to rotor resistance is
present .so if temperature changes resistance also changes so estimated slip frequency will not
able accurate if temperature changes. In this method a high frequency signal is applied to
motor and by detecting zero crossing of the signal slip frequency is estimated. This method is
also prone to motor parameter variation.[21].In this paper it is explained about indirect vector
control with stator flux oriented control. They have measured speed and stator resistance by
injecting the current. They have verified it by 3kw induction motor with dspace DS1102 based
controller board.[23]in this paper sensor less control is proposed so we don’t want to use
resolver or any kind of speed sensor. Here reference model and adaptive model is used for
calculation of the speed.[24]in this paper RF MRAS and BEMF MRAS are compared. Both
models are good enough with respect to parameter variation. But BEMF based approach is
more superior especially in transient analysis.[25]in this method speed regulation of various
MRAS method is controlled especially transient response that how quickly it can adapt
change in speed. By using proposed approach, it can adapt change in speed quickly as
compared to conventional methods.

In order to extend the range of electric vehicles solar panels are used While considering
solar panels it hast to take care the dimension of the solar panel as well because to get
optimum output we increase the panel capacity it will increase the weight of the vehicle and
which will eventually reduce the range of the vehicle.[26] in this paper maximum power point
tracking for parallel connected system and battery charging is proposed. If we are using load
as battery we need to check SOC(state of charge ) of the battery and based on that we need to
control the discharge of the battery, if soc is very less we can’t directly use that battery again
for that in that purpose what us proposed is that it will turn off discharging circuit when soc is
too low. In that paper explains normal MPPT only. [27]in this paper voltage based
MPPT(VMPPT)and current based (IMPPT)is used. A micro controller based tracker will track
the online voltage and current through the module and they are clamming voltage based mppt

22
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model is more energy efficient model for MPPT application.[28]in this paper an improved
variable step incremental conductance algorithm is proposed. If we are going for fixed step
algorithm it cannot track maximum power point if environmental condition is varying in
nature ,because it will take some time to reach the final value if step size is large it will take
too much time to reach final value and if step size is large it will reach final position quickly
but the oscillation would be large. so the proposed method will limit that problem into
particular extend.[29]in this paper MPPT and incremental conductance algorithm are
compared they concluded that if we are using incremental conductance used algorithm it is
less susceptible to system noise but its performance is not good for fast changing
environment. But MPPT methods are not good for system noise but are comparatively more
suitable for fast varying environment.[30]in this method MPPT method is implemented for
fourth order converter circuit. Overall efficiency can be improved significantly by using
fourth order circuit.[31]in this paper an improved MPPT algorithm with genetic algorithm is
proposed in conventional MPPT if environmental condition changes rapidly it cant rack this
problem can be restricted to some level by using proposed method.[31]in this paper it is
explained perturb and observe method for cuk converter and they compare two model in such
a way that in one model normal pwm based approach is used and other MPPT is used.[32]in
this paper an improved delta incremental conductance algorithm is proposed. The main
difference between this method and conventional is that in conventional method delta is
constant, here space shuttle algorithm and song based algorithm is used to find the change in
delta for a particular atmospheric condition. But this way they can reduce torque ripple
tremendously with respect to variable environmental conditions.[33]in this paper incremental
and perturb and observe algorithm is used for buck boost converter and compared the
performance. According to them incremental conductance based algorithms has less steady
state oscillation as compared to MPPT algorithm.[34]in this method an improved MPPT
algorithm is proposed that along with incremental conductance algorithm it will check the
limit within in 70% of the VOC .so in this method what is done is that it will first open circuit
the panel and first calculate the VOC of the panel and once it is calculated VOC it sill check
maximum power point at the vicinity of the 70%of VOC.[35]this method is similar to [34]this
method is modified perturb and observe method. in this case also open circuit voltage is
calculated for particulate irradiance than it will do perturb and observe based algorithm in the
vicinity of the 70%of the VOC of the panel.[36]in this method particular swam optimization is
utilized for MPPT algorithm and they have compared the result with conventional MPPT

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algorithm. PSO algorithm is superior in terms of ripples in the output.[37]In this paper a
comprehensive MPPT algorithm is proposed .normal MPPT algorithm will not give accurate
result under partial shading condition there exist two maximum power point so conventional
method cannot track accurately .so proposed method will take care about partial shading as
well.[38]in this paper it is proposed an advanced MPPT algorithms in conventional MPPT
algorithms short circuit current is not taken into account. But in this case during each iteration
short circuit current is taken into account and it will check maximum PowerPoint in the
vicinity of maximum PowerPoint area.[39]in this paper various variable step MPPT methods
are compared.[40]in this paper thy have proposed an optimum MPPT algorithms for low
power application such as wireless sensors. This method will track whether given power is
available and if it is not available it will turn of the converter and it will turn on the converter
if and only if sufficient maximum power is available for tracking.[41]in this method
generalized perturb and observe algorithm is used which will work effectively in both partial
shading case and normal case. Here what is done is that along with perturb and observe
algorithms during each iteration it will calculate the power for some random voltage and
check previous value and it will try to find MPPT at that random voltage and it will give
maximum power point under partial shading conditions as well.[42]in this paper various
photovoltaic model is compared. They have competed conventional one diode model with two
diode proposed model and they have compared with actual solar panel.[43]in this paper
advanced MPPT approach is proposed this method uses variable step size and hysterics band
for finding duty ratio this method is 0.5% superior than conventional methods.[44] in this
method one variable MPPT is proposed. Here only current is used to find the MPPT point in
conventional case it is required to sense both voltage and current of the panel. Here they have
sensed only panel voltage.[45]in this paper a fuzzy based controller based approach for
maximum power point tracking is used for induction motor, separately exited dc motor and
PMSM and result is compared.[47]in this paper particular swam optimization is used for
MPPT with partial shading, so the proposed method is used to find exact peak under partial
loading condition.[48] .this paper explains how to get PV and IV curve of an instrument using
a virtual instrument. PV panel coupled with a converter is connected to a virtual instrument
which will give PV and IV curves of the panel.

1.6 Dissertation Objectives

After analysing the literature review studies about motor control algorithms and different solar
fed chargers

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 1. To Study about the different motors and selection of motor for E-Rickshaws.
2. To study about solar energy and its application in E rickshaws
3. Designing of battery chargers and MPPT techniques
4. Designing of controls for motor in E-Rickshaw applications

1.7 Organisation of the Report

This report is organised into seven chapters including the introduction, the concept of
renewable energy, different types of power electronic converters and solar panel modelling in

Chapter 1.Explains about Electrical vehicles, Solar chargers, Maximum PowerPoint tracking
and basic control of induction motor drives such as scalar control and vector control

Chapter 2:Explains about modelling of a solar cell, Various Losses of solar cell and
calculation of solar panel size. It also explains about the modelling of electric vehicle and
various charges for electric vehicles such as on board chargers, off board chargers and MPPT
based chargers for E rickshaw.

Chapter 3: This section explains about Different solar charger topologies such as non isolated
boost converter with perturb and observe algorithms, Non isolated Boost converter with
Incremental conductance algorithms, Non isolated boost converter with Adaptive MPPT,
Isolated forward converter with Perturb and Observe and Adaptive MPPT methods and also
proposed a battery charger for E rickshaw with SOC indication and DTE status.

Chapter 4: This section discusses about Basics of vector control and also explains about
various reference frame. It is also explained about Clark’s and Park’s Transform.

Chapter 5: This chapter explains about different vector control methods such as Direct vector
control, Indirect vector control, Model reference Adaptive control and Stator Flux oriented
Control.

Chapter 6: In this chapter, conclusion and scope of future work is explained.

1.8 Conclusion
Here it is explained about conventional schemes of motor control for electric vehicle
application and how it is working and what is the drawback of existing methods. It also
compare various solar fed chargers available for charging electric vehicle and what are the
existing method for tracking maximum power from a solar panel.

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 Chapter 2
Solar Powered E Rickshaw
2.1. Solar Irradiation in India [51]
Before calculating exact solar panel capacity, one must know about the average solar
irradiance on a particular place. To get exact value of solar irradiance of a particular place one
can refer NASA’s website. In that website if we put latitude and longitude of a particular
place, we will get irradiation at that particular place. There are numerous options like we can
put in between zones and also zone wise irradiation data is also available. It will give all
details regarding solar insolation. The website is power.larc.nasa/data acces viewer

Fig. 2.1 Annual solar irradiance in India

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 Fig2.2 Solar Irradiance in the Month of March

From the data’s maximum solar irradiance in India is about 5.25kwh/m2/day and average
insolation is about4kwh/m2/day. Average availability of solar irradiance is 8hours a day. So
will get an average of 400 to 500w/mw. So the panel what we are choosing has to deliver
required power at this irradiation.

Typical battery used in e rickshaw are 48V 100Ah Li polymer battery. So, if solar panel is
used to charge the battery in normal rate. We can reduce the panel weight Because if we add
fast chargers that will increase the panel capacity and weight of the panel which will
adversely affect the range of the battery.

2.2. Single Diode Model of a Solar Cell

The model of a single diode solar cell is shown below. Here ID represents the
diode current. Basically, solar cell is a PN junction so it is following diode
equation which is shown in fig 2.3

Fig. 2.3 Single diode model of a solar cell

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A Solar cell can be represented by a current source and an anti-parallel diode. Current source
represents the light generated current or photon current .Net current available at output is the
difference between light generated current and diode current. In diode equation V represents
the voltage across the diode and I is the external load current.as the voltage across the external
circuit.
The diode current is represented as,

qv
I D = I O (e akT
− 1) (2.1)

Where,
T = Temperature in K
q = Charge in coulombs of an electron (1.602e-19)
k = Boltzmann constant (1.38e-23 J/K)
Io = Dark current or reverse saturation current of the diode at T
a = Diode ideality factor (normally between 1 and 2)
The main non idiality in the solar cell is the parasitic resistance. It includes shunt and series
resistance of the solar cell. Series resistance represents the all contact resistance of the solar
cell. Higher the value higher the losses across them. Similarly shunt resistance represent the
various leakage current inside the solar cell. This resistance should be as high as possible. If
this resistance is low than than power loss also becomes larger..

Fig. 2.4 Sectional views of a Solar cell

The three specifications for generating electricity from solar lights are (1) Material capable of
absorbing sunlight energy and generating electrons, (2) Process for facilitating the motion of
electrons through the internal circuit to generate the current, and (3) sufficiently big voltage to
generate usable power. The active part of a solar cell is a wafer made up of silicon

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semiconducting material. A solar cell has three layers. The upper layer is made up of silicon
and a very tiny amount of phosphorous which have a lot of free electrons. The bottom layer
contains silicon with fewer amount of boron. The thicker middle layer contains only a few
electrons. It also has an anti-reflecting coating to minimize energy loss due to reflection. It is
shown in Fig.2.4
If the incident sunlight vigour is superior to the material's band gap energy and it can release
an electron from the valence band into the conductive band

2.3. Losses in Solar Cells

The PV modules maximum efficiency is represented by Pmp which is equal to product of Vmp
and Imp.The loss occurring in the solar cell can be classified into optical losses and electrical
losses. Optical losses include reflection, partial shading and non-absorbed radiation. Electrical
losses can be further classified into two. Ohmic losses and Recombination losses. Ohmic
resistance includes all contact resistance. Recombination occurs in emitter and base region of
the solar cell. This recombination reduces the net output of the solar cell.Fig.2.5 represents the
various losses in solar cell.

Fig 2.5. The losses in a Solar cell

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2.4. Solar Panel Calculation
In order to calculate the capacity of the solar panel following equations are used. If we want to
calculate the capacity of the solar panel, we need to know the average irradiance of that
particular place, Average solar energy availably time. We also need to know the rating of the
apparatus to use, in this case it is induction motor and we need to know the usage time of the
apparatus.

Rating of the motor =5HP(3.6kw)

Average travelling time of vehicle=7h

Unit needed=3.6*7=25.2kwh

Average solar energy time=7h/day

We can select 4kw panel

But for electric vehicle application typical roof are is around 4m2.So we can’t fully run an
electric vehicle with solar panel. Instead, we can charge the battery using solar panel.
Typical recommended panel rating is 215w panel. We can connect them into series parallel
for battery charging. Here polycrystalline panels are uses which will reduce the cost but
efficiency is also low.

Table 1. Solar panel details

SLOTCH 215p

Manufacture Slotch

Model Number ISTH 250p

Type Poly crystalline

Power at STC 250

Open Circuit Voltage 36.3 V

Short Circuit Current 7.84 A

Vmp 29

Imp 7.35

Panel Efficiency 13.70%

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 Fill Factor 75%

Maximum System Voltage 600V

Temperature Coefficient of Isc 0.01%

Temperature Coefficient of Voc -0.36%

Temperature Coefficient of Pmp -0.50%

Cell Size 156mm * 156mm

Weight 20 Kg

Other Approach is that Battery used in E rickshaw are 48V,150Ah Batteries, if we can charge
battery by 13A it will take almost 11 hours and typical charging voltage for 48V Battery is
around 53 V o we can choose 630W panel only for battery charging.

2.5. Electric Vehicle Modelling [49]

In order to model electric vehicle, we need to know the torque speed characteristics of the
electric vehicle and also we need to know the torque at various instants. Here weight of the
vehicle is chosen as 400Kg. We need to consider in which plane vehicle is moving and what is
the application of the vehicle.

Frolling = Cn*m*g (2.2)

Typical Cn= 0.012

Let mass of vehicle m=400kg

F rolling =47.15N

P rolling =Frolling *v/3600 (2.3)

Where V is the velocity for vehicle in km/hr

P rolling = 44.15*100/3600
= 1.326kw

P gradinat = m*g*sinӨ (2.4)

Faero = 0.5*Cd*Af*p*(v)^2 (2.5)

Paero= 1.2kw

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P total = 1.226+1.2
= 2.426kw

Pin = Ptotal/efficiency (2.6)

Efficiency = 0.8

Pin = 2.42/0.8
Approx.3.6kw motor is selected

2.6. Charger for E rickshaw

In order to Charge Battery of rickshaw chargers are used. There are so many times of chargers
are available. They are mainly classified into two one is onboard chargers and other is off
board chargers. In onboard chargers all chargers are accommodated inside the vehicle and two
terminals are provided for charging. They are slow chargers. In off bord chargers battery
terminals are taken out and connected to external chargers. They are fast chargers So it
requires less charging time as compared to on board chargers. Here charging current is high as
compared to on board chargers.

2.6.1. On Board Chargers

Fig 2.6 shows the On board charger of an e rickshaw. On board chargers are present inside the
vehicle. Only two plug points are provided to connect to external circuits. Usually, rating is
small for this type of chargers. Charging time is also large for this type of chargers. Here PFC
is provided inside the charger so it will have less harmonics than convention non PFC type
chargers. Here it is given isolated chargers for charging the battery. It will enhance the safety
of the person as well. It is providing galvanic isolation.

Fig.2.6 On board charger

2.6.2. Off Board Chargers
Fig 2.7 Shows the off board charger of an e rickshaw. In this case chargers are connected
externally. Battery terminals are taken out and connected to external chargers. Usually
charging current is high as compared to previous case sp charging time is very less. Charging
stations are provided with this type of chargers. This comes with large variation of voltage and
current. Typical charges would be 48V to 500V are commonly available.48V is used for e
bikes and 500V typically used for electric car. Now a days vehicles comes with both option

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that is both on board chargers and off board chargers are available we can use on board
chargers inside home and by using fast chargers at charging stations.

Fig. 2.7 off board charger

2.6.3. PWM Solar Chargers.

Fig 2.8 shows the PWM chargers for charging battery from solar energy. This is a solar
charger it will extract available emery from solar panel. The power extracted from pwm
controller is less than that of MPPT charge controller. Current drawn from the panel is
depends upon the battery voltage. PWM based chargers will not take care about maximum
PowerPoint. It will merely charge the battery from the solar panel. PWM chargers are more
economical as compared to MPPT based chargers. So if cost is the first priory we have to go
for PWM based chargers.

Fig. 2.8 PWM charger

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2.6.4. MPPT Based Solar Chargers
Fig 2.9 shows MPPT based charger. In MPPT solar charges maximum power available from
the solar panel is extracted. It will continuously change duty ratio to find maximum power
point with respect to given atmospheric conditions. Perturb and observe and incremental
conductance are commonly used method for MPPT. The main problem with perturb and
observe is the oscillation at final position. The oscillations are comparatively lesser in
incremental conductance method. In MPPT duty ratio of the controller is varied such that load
impedance is same as source impedance. But the main problem with MPPT based charger is
that it is not that economical. Cost of MPPT based charger is higher than that of PWM based
charger.

Fig. 2.9 MPPT charger

2.7. Summery
This chapter explains about basics of solar cell, how it is modelled and what are the major
losses in solar cell. It also explains how to calculate solar panel for given applications. It also
explains about how to model an electric vehicle it also explains about various chargers for E-
rickshaw. It also explains about on board chargers ,off board chargers, PWM charger and
MPPT based chargers. It also explains how this chargers are different. In solar chargers we
can use MPPT or PWM based chargers depending upon the requirement.

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 Chapter 3
Solar Battery Chargers Topologies

3.1 Electric Vehicle Charger

charging station, also known as electric vehicle charging station, electric recharging zone
is the place where electric vehicles are charged in commercial manner—includes car ,bus
,trucks and other vehicles. Some electrical vehicle has in build charging convertors and
some has only battery terminals and other have both convertors and battery terminals
which support both fast charging and normal charging. If you use solar for charging
battery we can extend the range of the electric vehicle significantly. Due to uncertainty of
global energy pattern and severity of pollution renewable energy such as solar energy is
widly used. These methods have benefits which includes the reduction in energy
consumption and emissions of toxic gases and it will decrease the battery recharge time
also. Inclusion of PV panels in vehicles will facilitate vehicle to grid option. Solar energy
for vehicle application is already been used year ago. Nuna 3 was one of the racing car
powered by solar energy. It was developed by Delfit University. It could achieve a speed
of 100km/hr. If we are adding solar panels to electric vehicle, if we increase capacity of
the panels we can increase the range of the vehicle at the same time it will increase the
weight of the vehicle which will reduce the range of the vehicle so we have to find
balance between these two. Here it is discussed about both isolated and non-isolated solar
converter with different MPPT algorithm.

3.2 Solar Boost Converter with Perturb and Observe MPPT

Here boost converter is used to charge the battery. Solar panel is used as source to boost
converter. Boost converter is converter in which output voltage is greater than input voltage. It
is a type of switched-mode power supply (SMPS).It consist of minimum two semiconductor
device such as diode and mosfet or any type of switch. It consists of minimum one storage
element such as inductor or capacitor or combination of both. To reduce voltage ripple and
current ripple filters made with either capacitor or inductor or sometimes combination is used.
which is shown in Fig 3.1.

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 Fig. 3.1 Boost converter

Boost converter can be powered by DC sources, It could be batteries, rectifiers solar panels,
and DC generators. A method that changes one DC voltage to another DC voltage is called
DC to DC conversion. A boost converter is a DC to DC converter with an output voltage
greater than the input voltage. A boost converter is sometimes called a step-up converter
because it is "steps up" the input voltage. Since power is conserved, the output current is less
than the input current.

An MPPT, or maximum power point tracker is basically a DC to DC converter that optimise

the match between the solar panel (PV panels), and the battery or the external grid. They will
reduce or increase the battery voltage from solar panels to charge the battery. The perturb and
observe method is commonly adopted due to simplicity in implementation. Initially, the
voltage across the PV panel and current through the PV panels are sensed to obtain PV power.
In the P&O algorithm, the voltage of the PV array output is perturbed by a small increment
which results in a change of power which is equal to ∆P. If the value of ∆P is positive, then
perturbation will move in the same direction and the operating voltage toward the maximum
power point or MPP. Then, the perturbation size is again made in the same direction. If
change in power ∆P is negative than perturbation is done in opposite direction.

3.2.1. Flow Chart of Perturb and Observe Method

Fig3.2 shows the flow chart of MPPT, initially we have to measure voltage and current from
the solar panel. Based on that power is calculated. Then change in power is calculated. Than
change in voltage (del V)is calculated. We need to decrease the duty ratio of the converter if
dP/dV is positive. To do this dP is measured after that dV is measured, Flowchart will check
weather both dP and dV has same sign than dP/dV is positive. So it will decrease the duty
ratio of the converter to small delta value which is predefined. Similarly, if dP/dV is negative

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we need to increase the duty ratio of the converter. To ensure this flowchart will check
weather dP and dV has opposite sign if dP and dV has opposite sign than dP/dV is negative
than duty ratio has to be incresed

Fig. 3.2 Flowchart of Perturb and Observe Method

3.2.2. Design of Solar Fed Boost Converter [50]

Here converter is designed in such a way that it should operate in continues conduction
(CCM) mode all the time. To get so we need to find minimum inductor current ant put that to
zero.

ILmin=0 (3.1)

IL-ΔIL/2=0 (3.2)

Io/(1-D)=1/2*(Vs/L)*D*T (3.3)

T=1/F

Where f = switching frequency

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L=D*(1-D)2*R/2f (3.4)

While design we have to consider the worst case scenario

Dmax is taken as 0.9

Switching frequency, F = 5Khz

L>20μH

So inductor value chosen should be greater than 20uH (3.5)

Voltage ripple While designing capacitor main consideration is voltage ripple

ΔVo/vo=DT/RC (3.6)

=D/RCf (3.7)

Where D=Duty Ratio

R=Load Resistance

C=Capacitance

Taking voltage ripple as 1% of the output voltage

C=100μf

So 100μF Capacitor is chosen

3.2.3Advantageous and Disadvantageous

Perturb and observe method is lesser complicated algorithm to find the maximum PowerPoint
of the solar panel. Here transient response is not optimum as compared to incremental
conductance method. But complexity is less than incremental conductance method.

3.2.4. Simulation Diagram of Non Isolated Boost Converter with Perturb and
Observe MPPT
Fig 3.3 shows the simulation diagram of non isolated boost converter with MPPT Here signal
builder block is used to give temperature and solar irradiance of the solar panel. Resistive load
is connected to Load. Here IGBT is used as switch. Here ESR (Effective Series Resistance) of
the capacitor and series resistance of inductor is also considered. The feedback block will give
input to MPPT, Feedback block consist of Panel voltage and Panel current measurement
which is fed to input of MPPT.

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 Fig. 3.3 simulation diagram of non isolated boost converter with Perturb and Observe
Algorithm
Fig 3.4 shows the block which calculates the duty ratio corresponding to maximum power
point of the solar panel. It will read the panel voltage and current based on that it will
calculate the duty ratio corresponds to maximum power point of the solar panel. The feedback
from the solar panel is connected to duty ratio calculation block. Panel voltage and Panel
current is given as feedback signal

Fig. 3.4 Simulation diagram of non isolated boost converter with Perturb and Observe MPPT

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3.2.5. Simulation Result of Non Isolated Boost Converter
Fig 3.5 shows the performance of non isolated boost converter with Perturb and Observe
maximum power point tracking. First fig shows the temperature and second fig shows the
irradiance of solar panel. In this case step size is not a function of atmospheric condition so it
is also called fixed step size MPPT. Here temperature of 25˚C is applied to solar panel. We
can change this value by signal builder block. Initially solar irradiance of 1000w/m2applied
and after some time irradiation of the solar panel input is changed by using signal builder
block. Third fig shows the output voltage of the converter and fourth fig represent output
current of the converter and fifth fig represent the input voltage and sixth fig represents the
input current of the converter. Here polycrystalline based solar panel is connected to input of
the converter. It has a panel efficiency of around 13.5% and fill factor values is around 75%

Fig.3.5 Performance of Boost Converter with Perturb and Observe MPPT

Table 2 Performance of The Boost Converter with Perturb and Observe Algorithm
Temperature(˚C) 25
Irradiance(w/m2) 1000

Pin(w) 636.73

Pout(w) 628.86

Efficiency (%) 98.7

Fill Factor 75
Panel Efficiency 13.7

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3.3 Solar Boost Charger with Incremental Conductance MPPT
Here boost converter is used to charge the battery solar panel is used as source to boost
converter. Boost converter is converter in which output voltage is greater than input voltage. It
is a class of switched-mode power supply (SMPS).It consist of minimum two semiconductor
device such as diode and mosfet or any type of switch. It consists of minimum one storage
element such as inductor or capacitor or combination of both. To reduce voltage ripple and
current ripple filters made with either capacitor or inductor or sometimes combination is used.
Boost converter can be powered by DC sources, it could be batteries, rectifiers solar panels,
and DC generators. A method that changes one DC voltage to another DC voltage is called
DC to DC transformation. A boost converter is a DC-to-DC converter with an output voltage
greater than the input voltage. A boost converter is sometimes called a step-up converter
because it "steps up" the input voltage. Here power is conserved, the output current is less
than the input current

Fig. 3.6 Boost Converter

Boost converter is used to charge the battery and solar panel is used as source. Incremental
conductance method is used to charge the battery. In MPPT controller will adjust the duty
ratio of the converter in such a way that it will extract maximum power from the solar panel.
It will operate with a duty ratio in which load impedance is same as solar panel impedance.
Here ratio of change in current with respect to change in voltage is calculated and if this value
is absolute than duty ratio is not changed and if this value is not absolute value than duty ratio
is changed until it reaches maximum power point.

3.3.1. Flow Chart of Incremental Conductance Based MPPT

Fig3.7 shows the flow chart of Incremental conductance method, initially we have to measure
voltage and current from the solar panel. Based on that change in voltage and change in
current is calculated. If change in current with respect to change in voltage is same as ratio of

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current to voltage (ie dI/dV is same as I/V) than duty ratio of the converter is not changed. It
also checks whether both dI and dV is not changed than also controller will not change the
duty ratio of the converter. If dI/dV is not absolute controller has to change the value of the
duty ratio of the converter, to ensure this controller will check weather dI / dV greater than
absolute value than it will increase the duty ratio of the converter. Similarly, if ratio of dI and
dV is less than absolute value. it will reduce the duty ratio of the converter. Here also time to
reach study state value it will be determined by the initial step size

Fig 3.7 Flow Chart of Incremental Conductance MPPT

3.3.2 Design of Solar Fed Boost Converter[50]

Here converter is designed in such a way that it should operate in continues conduction
(CCM) mode all the time. To get so we need to find minimum inductor current ant put that to
zero.

ILmin=0 (3.8)

IL-ΔIL/2=0 (3.9)

Io/(1-D)=1/2*(Vs/L)*D*T (3.10)

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T=1/F (3.11)

Where f= switching frequency

L=D*(1-D)^2*R/2f (3.12)

While design we have to consider the worst case scenario

Dmax is taken as 0.9

Switching frequency, F = 5Khz

L>20uH

So inductor value chosen should be greater than 20uH

Voltage ripple

While designing capacitor main consideration is voltage ripple

ΔVo/vo =DT/RC (3.13)

=D/RCf (3.14)

Where D=Duty Ratio

R=Load Resistance

C=Capacitance

Taking voltage ripple as 1% of the output voltage

C=100uF

So 100uf capacitor is chosen

3.3.3Advantages and disadvantageous

In incremental conductance method it will reach final position more quickly as compared to
perturb and observe method. But the problem is it is more complex as compared to perturb
and observe method.

3.3.4. Simulation Diagram: Non Isolated Boost Converter with Incremental

Conductance MPPT
Fig 3.8 shows the simulation diagram of non isolated boost converter with incremental
conductance algorithm. Here signal builder block is used to give solar irradiance and
temperature of the solar panel. In this case step size is not a function of atmospheric condition

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so it is also called fixed step size MPPT.. Resistive load is connected to Load. Here IGBT is
used as switch. Here ESR (Effective Series Resistance) of the capacitor and series resistance
of inductor is also considered. The feedback block will give input to MPPT, Feedback block
consist of Panel voltage and Panel current measurement which is fed to input of MPPT.

Fig. 3.8Simulation Diagram of Non Isolated Boost Converter with Incremental Conductance
MPPT
Fig 3.9 shows the block which calculate the duty ratio corresponds to maximum PowerPoint
tracking. Here it will read panel voltage and panel current based on that it will calculate the
duty ratio corresponds to maximum power point.

Fig. 3.9 Simulation Diagram of non isolated Boost Converter with Incremental Conductance
MPPT

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3.3.5. Simulation Result of Non Isolated Boost Converter with Incremental
Conductance Method
Fig 3.10 shows the performance of incremental conductance based boost converter. Here
incremental conductance is used to calculate the maximum power point of the solar panel. The
problem with this methods is that here transient response is not good as compared with
conventional MPPT. Here temperature of 25˚C is applied to solar panel. We can change this
value by signal builder block. Initially solar irradiance of 1000w/m2applied and after some
time irradiation of the solar panel input is changed by using signal builder block. Third fig
shows the output voltage of the converter and fourth fig represent output current of the
converter and fifth fig represent the input voltage and sixth fig represents the input current of
the converter. Here polycrystalline based solar panel is connected to input of the convertdr.it
has a panel efficiency of around 13.5% and fill factor values is around 75%.

3.10. Performance of Incremental Conductance Based MPPT

Table 3 Performance of Boost Converter with Incremental Conductance algorithms
Temperature(˚C) 25
Irradiance(w/m2) 1000
Pin(w) 636.67
Pout(w) 629.94
Efficiency (%) 98.9
Fill factor(%) 75
Panel efficiency 13.7

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3.4 Perturb and Observe MPPT using Isolated Forward
Converter
Isolated power converter isolates the input from the output by electrically and
physically separates the circuit in such a way that there is no direct connection from input
to output. Usually if humans are directly involved with operation of the converter
isolation is recommended since isolation has separate ground. Ground connection from
source to lead is not continues.

Fig. 3.11 Forward Converter

The forward converter is a DC to DC converter. Here transformer is used to increase or

decrease the output voltage which is depend on the transformation ratio. It also ensures
galvanic isolation between source and load. By using multiple winding it ensures high
and low voltage at the secondary of the transformer. In flyback converter energy is stored
in inductor during turn on of switch. When the switch turns off, the stored magnetic field
will get collapsed and the energy is transferred to the output of the converter. The flyback
converter is fundamentally two inductors sharing a common core with opposite polarity
windings. In forward converter (transformer with same-polarity windings, larger
magnetizing inductance and no air gap) does not store energy during the on time of
switch – Transformer will not store energy like inductor Instead, energy is passed directly
to the output of the forward converter by transformer action during the switch on time.
While the output voltage of a flyback converter is theoretically infinite, the maximum
output voltage of the forward converter is limited by the transformer turns ratio.

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 (3.15)

Where Vout is the Output Voltage and D is duty ratio of the converter, Ns and Np are primary
and secondary turns respectively.

In maximum PowerPoint tracking initially change in power is calculated than change in

voltage is calculated, Than ratio of dp to dv is calculated and controller will change the duty
ratio of the converter until dP/dV is zero. If we plot the dP vs dV of typical solar panel we
want to get maximum value of the function we need to calculate dP/dV and equate to zero.
This is what is done in maximum power point tracking.

3.4.1 Advantageous and Disadvantageous

In forward converter galvanic isolation is provided, So it will improve the overall safety of the
end user. Here by varying the turns ratio and duty ratio we can vary the output voltage. By
adding transformer it will increase the overall size of the equipment.

3.4.2. Design of Solar Fed Forward Converter [50]

Here converter is designed in such a way that it should operate in continues conduction
(CCM) mode all the time. To get so we need to find minim inductor current ant put that to
zero.

Input Voltage (Max) = 200V (3.16)

Output Voltage = 60V (3.17)

Vo/Vin=N3/N2*D (3.18)

D is the Duty ratio of the converter

F=switching frequency

Switching frequency is taken as 100kHz

For the above specification ETD based core is choosed

ETD cores are economical core which has less mean length of the turn so the coil resistance is
less and efficiency of the core is improved

ETD 34 as chosen as core

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N1>Vinmax*Dmax/Bsat*Ae*Fs (3.19)

N1>34.1

Where Dmax is the maximum duty ratio

Bmax is the maximum flux density in wb/m2

Bsat is taken as 0.3T for ferrites from datasheet

Ae = 97.1*10-6m2

Vo/Vin = N3/N1*D (3.20)

N3/N1=0.6

N3>23

For this design Dmax=0.5

Inductor design

Here it is designed in such a way that converter should work in CCM even at 10% of the load

ILdc(constant)=10%*Imax

In order to reach inductor current not zero peak to peak ripple must be less than twice the Ildc
value

ΔIL<4A

ΔIL=(1/2)*(Vin*(N3/N1)-vo)/L)*Ton (3.21)

ΔIL=(1/2)*(Vin*(N3/N1)-vo)/L)*DT (3.22)

Where D=(N1/N3)*(Vo/Vin) (3.23)

T=1/fs (3.24)

Were fs = switching frequency

L>(1-1/vin*N1/N3*vo)*1/ΔIL)*vo*(1/fs) (3.25)

L>6μH

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In order to get continues conduction mode at 10% of the load value of inductor should be
greater than 6μH

3.4.3 Simulation Diagram: Forward Converter with Perturb and Observe MPPT
Fig 3.12 shows the simulation diagram of forward converter with conventional MPPT. Here
signal builder block is used to give temperature and irradiance of the solar panel. Here
feedback block will give voltage across the panel and current through the panel. The panel
voltage and Panel current from the feedback bloc is connected to MPPT block.

Fig. 3.12Simulation Diagram of Forward Converter with Perturb and Observe MPPT
Fig 3.13 shows the block to calculate the duty ratio of the maximum power point tracking. It
will read the voltage and current of the solar panel and based on that it will calculate the duty
ratio. The feedback from the panel give voltage across the panel and current through the
panel. This feedback element is connected to MPPT input block.

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 Fig. 3.13. Simulation Diagram of Forward Converter with Perturb and Observe MPPT
3.4.4. Simulation Result of Isolated Forward Converter with Perturb and Observe
MPPT
Fig 3.14 show the output and input waveform of forward converter with conventional MPPT.
First and second fig represents the temperature and irradiance (w/m2) of the solar panel. Here
isolated converter is used to give galvanic isolation. Here it will not consider the atmospheric
condition for calculating the maximum power of the solar panel. Here First fig represents the
temperature of the solar panel. Here 25˚C is applied to solar panel through signal builder
block. Second fig represents the solar irradiance o the solar panel. Here also signal builder
block is used to apply the solar irradiance to solar panel. Third fig represents the input voltage
of the forward converter, fourth fig represents the input current through the forward converter.
Fifth fig represents the output voltage of the converter, seventh fig represents the output
current of the convertibly using forward converter ground is isolated from input to output.
This will enhance the safety of the person who is using the equipment. Here solar panel used
is having an efficiency of 13.5% and fill factor which indicates how effectively panel is used
is about 75%.

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 Fig.3.14 Performance of Isolated Forward Converter with Perturb and observe MPPT

Table 4Performance of Isolated Forward Converter with Perturb and Observe Algorithm
Temperature(˚C) 25
Irradiance(w/m2) 600
Pin(w) 743.9
Pout(w) 737.23
Efficiency (%) 98.9
Fill Factor (%) 75
Panel Efficiency (%) 13.7

3.5 Adaptive MPPT using Non Isolated Converter

In conventional maximum power point tracking step size of the iteration is
constant and is independent of the solar irradiance and step size is also constant for all
iteration. This will increase the convergence time for maximum power point tracking. The
same thing is applicable for incremental conductance method as well. If atmospheric
condition is changes fast, if MPPT method is not fast enough what will happen is that it
P19EL015 51
cannot lively track the maximum power point of the solar panel to avoid this problem
adaptive method is used.

Fig. 3.15 Boost Converter

In fig 3.15 boost converter is used to charge the battery from solar panel. Here solar panel
is used as source to boost converter. Boost converter is converter in which output voltage
is greater than input voltage. It is a class or type of switched-mode power supply (SMPS).
It consist of minimum two semiconductor device such as diode and mosfet or any type of
switch. It consist of minimum one storage element such as inductor or capacitor or
combination of both. To reduce voltage ripple and current ripple filters made with either
capacitor or inductor or sometimes combination is used. Boost converter can be powered
by DC sources, It could be batteries, rectifiers solar panels, and DC generators. A process
that changes one DC voltage to another DC voltage is called DC to DC transformation. A
boost converter is a DC to DC converter with an output voltage greater than the source
voltage. A boost converter is also called a step-up converter because it "steps up" the
input voltage. Since power is conserved, the output current is less than the input current.

Fig. 3.16.PV curve of Solar Panel

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If we observe PV curve of a typical solar panel, we will understand that maximum power
that can be tracked by the solar panel is function of solar irradiance. Maximum power
that can be tracked by the solar panel is directly proportional to solar irradiance. If solar
irradiance is high maximum power available from the panel is high and if solar irradiance
is less maximum power available for tracking is less. So if we change the step size of the
controller with respect to irradiance it can reach the final value quickly. The problem with
adaptive method is that since step size is varying continually change of transients are very
high this can be overcomes by using direct MPPT in which controller will solve the non
linear equation and directly calculate the duty ratio of the controller

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3.5.1. Advanced Adaptive MPPT

N Y

Y N
Y N

Fig 3.17. Flow Chart of Adaptive MPPT

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3.5.2 Advantageous and Disadvantageous
In conventional MPPT step size is constant. It will not change with atmospheric conditions.
But in adaptive MPPT step size is not constant. It will vary with atmospheric conditions. Here
it is little bit complicated as compared to conventional MPPT.

3.5.3 Simulation Diagram: Adaptive MPPT using Non Isolated Boost Converter
Fig 3.18 shows the simulation diagram of adaptive MPPT using non isolated boost converter.
Here signal builder block is given to apply temperature and irradiance of the solar panel. Here
ESR(Effective Series Resistance) of the capacitor and coil resistance of the inductor is also
taken into account. Feedback block consist of Panel voltage and Panel current measurement
which is fed to input of MPPT.

Fig. 3.18Simulation Diagram of Adaptive MPPT using Boost Converter.

Fig 3.19 shows the block which calculated duty ratio for the maximum power point tracking.
Here voltage and current of the panel is taken as input and based on that it will calculate the
duty ratio. Feedback block will measure the voltage across the panel and current through the
panel and solar irradiance data. This block is applied to the MPPT duty ratio calculation
block.

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 Fig. 3.19. Simulation Diagram of Adaptive MPPT using Boost Converter
3.5.4. Simulation Result of Non Isolated Boost Converter with Adaptive MPPT
Fig 3.20 shows Performance waveform of non isolated boost converter with adaptive MPPT.
Here First fig shows the solar panel temperature and second fig shows the solar panel
irradiance and respective output is shown. In this case voltage and current ripple at the output
is less as compared to conventional MPPT. Here first fig represents the temperature of the
solar panel. Here 25˚C is applied to the solar panel by using signal builder block. Second fig
represents the solar irradiance initially solar irradiance of 1000w/m2 is applied. After t=1s
solar irradiance is varied by using signal builder block. Third fig represents the output voltage
of the converter and fourth fig represents the output current of the converter, fifth fig
represents the input voltage of the converter and seventh fig represents the input current of the
converter.

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 Fig. 3.20Performance of NonIsolated Boost Converter with Adaptive MPPT

Table5 Performance of Non Isolated Boost Converter with Adaptive MPPT

Temperature(˚C) 25

Irradiance(w/m2) 1000

Pin(w) 637.12

Pout(w) 626.96

Efficiency (%) 98.40

Fill factor 75

Panel efficiency(%) 13.7

3.6 Adaptive MPPT using Isolated Forward Converter

In conventional maximum power point tracking step size of the iteration is constant and is
independent of the solar irradiance and step size is also constant for all iteration. This will
increase the convergence time for maximum power point tracking. The same thing is
applicable for incremental conductance method as well. In normal incremental
conductance method step size is constant this problem can be overcome by the adaptive
MPPT of adaptive incremental conductance method..

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 Fig. 3.21 Forward Converter

Here it is explained about explaining adaptive maximum power point tracking with
forward converter. From the PV curve of a typically solar panel it is obvious that power
out from the solar panel is a function of the irradiance. More accurately power output is
directly proportional to solar irradiance

Fig. 3.22 PV curve

If higher the solar irradiance higher the power output and if it is lower power output from
the panels is also reduced. In adaptive method, step size is taken as large when intensity is
large and it will reduce if intercity is reduced usually step size is taken and maximum for
solar radiance of 1000w/mm2.Another thing is that if we reduce step size for each

58
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itraction we will reach the final value more quickly. This is what is done in adaptive
mppt.

3.6.1. Design of Solar Fed Forward Converter [50]

Here converter is designed in such a way that it should operate in continues conduction
(CCM) mode all the time. To get so we need to find minimum inductor current ant put that to
zero.

Input Voltage (Max)=200V

Output Voltage =60V

Vo/Vin=N3/N2*D (3.26)

D is the Duty ratio of the converter

F=switching frequency

Switching frequency is taken as 100 kHz

For the above specification ETD based core is taken.

ETD cores are economical core which has less mean length of the turn so the coil resistance is
less and efficiency of the core is improved

ETD 34 as chosen as core

N1>Vinmax*Dmax/Bsat*Ae*Fs (3.27)

N1>34.1

Bsat is the saturation value of flux density in wb/m2

Bsat is taken as 0.3T for ferrites from datasheet

Ae=97.1*10-6m2

Vo/Vin=n3/n1*D (3.28)

N3/N1=0.6

N3>23

For this design Dmax=0.5

Inductor design

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Here it is designed in such a way that converter should work in ccm even at 10% of the load

ILdc(constant)=10%*Imax

In order to reach inductor current not zero peak to peak ripple must be less than twice the ILdc
value

ΔIL<4A (3.29)

ΔIL=(1/2)*(Vin*(N3/N1)-vo)/L)*Ton (3.30)

ΔIL=(1/2)*(Vin*(N3/N1)-vo)/L)*DT (3.31)

Where D=(N1/N3)*(Vo/Vin) (3.32)

T=1/fs

Were fs = switching frequency

L>(1-1/vin*N1/N3*vo)*1/ΔIL)*vo*(1/fs) (3.33)

L>6μH

Inductor value greater than 6μH ensures CCM mode of operation at 10% of the load.

3.6.2. Simulation Diagram : Forward Converter with Adaptive MPPT

Fig 3.23 shows the simulation diagram of forward converter with adaptive MPPT .Signal
builder block is used to apply temperature of the solar panel and solar irradiance of the
solar panel. In adaptive MPPT. step size is not constant with respect to temperature and
step size will change in such a way that it will converge to final value more quickly. Here
feedback block will give voltage across the panel and current through the panel. The
panel voltage and Panel current from the feedback bloc is connected to MPPT block.

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 Fig.3.23Simulation Diagram of Forward Converter with Adaptive MPPT
Here MPPT algorithm is implemented using the above block. This block will read the voltage
and current of the MPPT and based on that it will calculate dP/dV and it will converge to a
point where dp/dv is zero or maximum power point of the given solar panel.The feedback
from the panel give voltage across the panel, current through the panel and irradiance details.
This feedback element is connected to MPPT input block

Fig. 3.24. Simulation Diagram of Forward Converter with Adaptive MPPT

3.6.3. Simulation Result of Isolated Forward Converter with Adaptive MPPT
Fig shows the simulation result of isolated forward converter with adaptive MPPT. Isolated
converter will provide galvanic isolation by mans of transformer. By using transformer by
controlling turns ratio we can control output voltage as well. Here First fig represents the

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temperature of the solar panel. Here 25˚C is applied to solar panel through signal builder
block. Second fig represents the solar irradiance of the solar panel. Here also signal builder
block is used to apply the solar irradiance to solar panel. Third fig represents the input voltage
of the forward converter, fourth fig represents the input current through the forward converter.
Fifth fig represents the output voltage of the converter, seventh fig represents the output
current of the convertibly using forward converter ground is isolated from input to output.
This will enhance the safety of the person who is using the equipment. Here solar panel used
is having an efficiency of 13.5% and fill factor which indicates how effectively panel is used
is about 75%.

Fig. 3.25Perfomance of Isolated Forward Converter with Adaptive MPPT

Table6 performance of Isolated Forward Converter with Adaptive MPPT
Temperature(˚C) 25

Irradiance(w/m2) 600

Pin(w) 743.9

Pout(w) 727.07

Efficiency (%) 97.73

Fill Factor (%) 75

Panel efficiency (%) 13.7

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 3.7. Solar Charger with SOC Indication of the Battery
Here boost converter is used to charge the battery and solar panel is used maximum power
point tracking is used to charge the battery. In MPPT controller will adjust the duty ratio of
the converter in such a wa that it will extract maximum power from the solar panel. It will
operate with a duty ratio in which load impedance is same as solar panel impedance. Here
battery SOC will also displayed, based on converter output we can calculate existing range
of the electric vehicle. Battery SOC will also displays in vehicle cluster

Fig. 3.26 Automatic charging algorithm

Along with battery charger here a Automatic charging scheme is also implemented. Controller
will continually check the SOC of the battery and battery voltage. If SOC of the battery is less
than 100 percentage and if voltage of the battery is less than full charged voltage it will
initiate automatic charging. Automatic charging enable is accomplished by normal multiplier
block. Battery voltage status check block will give a logic high(1) if battery voltage is less
than fully charged voltage. If it is full charged than this voltage is logic low(0).So output of
the enable 1 circuit is high only if battery voltage is less than fully charged voltage. Output of
the SOC status block is logic high (1) when soc is less than 100 percentage. So enable 2 out
put would be high only if soc is less than 100 percentage both condition is satisfied than only
duty ratio calculated from MPPT circuit will reach to MOSFET circuit.

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3.7.1Design of Solar Fed Buck Converter [50]
Here converter is designed in such a way that it should operate in continues conduction
(CCM) mode all the time. To get so we need to find minimum inductor current ant put that to
zero.

ILmin=0 (3.34)

IL-ΔIL/2=0 (3.35)

IL= ΔIL/2 (3.36)

Io = (1/2)*(Vin-Vo)/L)*D*T (3.37)

T = 1/F (3.38)

Where f = switching frequency

L= (1-D)*R/2f (3.39)

While design we have to consider the worst case scenario

Dmax is taken as 0.9

Switching frequency, F = 5Khz

L=10μH

10μH inductor is selected

Voltage ripple

While designing capacitor main consideration is voltage ripple

ΔVo/vo = DT/RC (3.40)

= D/RCf (3.41)

Where D=Duty Ratio

R=Load Resistance

C=Capacitance

Taking voltage ripple as 1% of the output voltage

C=100uF

Capacitor is chosen as 100μF

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3.7.2. Simulation Diagram Battery Charger with SOC Indication
Fig shows the simulation diagram of buck based MPPT charger, Here signal builder block will
give the irradiation and temperature of the solar panel. Battery soc is displayed on the vehicle
cluster. Sufficient and critical battery indications are also given. Here polycrystalline based
solar panel is used where the primary concern is minimisation of the cost at the same time
efficiency is also less as compared to monocrystalline based panel. Here feedback block is
used to measure voltage across the panel and current through the panel. This block is further
connected to MPPT input block.

Fig. 3.27Simulation Diagram of Buck Based Perturb and Observe Charger with SOC
Indication
Fig 3.28 shows the automatic charging scheme for buck based MPPT charger. Here whenever
battery SOC is less than 100% .it will check the voltage of the battery and if voltage is less
than specific value and SOC is less than 100% it will enable MPPT circuit.

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 Fig. 3.28 Automatic Battery charging Algorithm
3.7.3. Simulation Diagram of Buck Based Charger with MPPT
Fig 3.29 shows the output of the buck converter based MPPT charger for electric vehicle.
Charger will automatically turn on when soc of the battery is less than 100%. Here
temperature an irradiation of the solar panel is considered as constant during operation. When
battery soc is less than 40 % it will show critical battery indication and it will show distance to
empty also, similarly when battery soc is greater than 40% it will shows sufficient indication.
Here first fig represents the temperature of the solar panel. Here it is applied about
25˚C.Second fig represents the irradiance of the solar panel in (w/m2).Third fig represents the
output voltage and foruth fig rpersent sthe output current of the converter, Fifth fig represents
the input voltage of the converter and seventh fig repress the input current of the converter.

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 Fig. 3.29 Performance of Buck Based Charger with Perturb and Observe MPPT

Table7 Performance of Non Isolated Buck Based Battery charger with Perturb and Observe
Algorithm
Temperature(˚C) 25

Irradiance(w/m2) 750

Pin(w) 787.2

Pout(w) 746.09

Efficiency (%) 94.7

Fill Factor (%) 75

Panel efficiency 13.5

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3.7.4. Cluster Details of Non Isolated Charger with SOC Indication
Fig shows the cluster details of the battery and it also shows the distance to empty based on
SOC

Fig.3.30Vehicle Cluster view when SOC greater than 40 percentage

Fig. 3.31Vehicle Cluster view when SOC lesser than 40 percentage

3.8. Summery
This chapter explains about how to track maximum power from given solar panel and also
explains about how to reduce the oscillations in conventional MPPT techniques. It also
explains about a complete solar charger for electric vehicle. Here both isolated and non
isolated converter with maximum PowerPoint tracking is proposed.

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 Chapter 4
Basics of Vector Control
As we know for field oriented control we have to model the Induction motor like a dc motor
and also we have to convert all the control parameters which are in a-b-c reference frame into
synchronously rotating reference frame by mathematical equations known as Clarke’s
transformation and Park’s transformation. After converting all the parameters which are time
arying in nature appears as a DC quantities. For a DC machine if we neglect armature reaction
then the torque developed in the machine is given by:

Te = Z Ia * P / 2 A (4.1)

Te = KI a I f (4.2)

Where ,

Ia = armature current

If = field current

The construction of a dc machine is like that the field flux Ѱf produced by the field current If,
is normal or quadrature to the armature flux Ѱa which is produced by the current in the
armature or armature current Ia. These space vectors which are stationary in space and they ,
are orthogonal or decoupled in nature. This gives if torque is changed by controlling the
armature current la, the flux Ѱf is not changed and we transient response would be fast and
large torque/ampere ratio with the rated voltage. Because of this decoupling, when the field
current If is changed, it changes the field flux only and armature flux remain constant. But
because of the inherent coupling nature present in induction motor it cannot generally give
such fast response. To achieve fast response like DC motor from an Induction motor the
machine control is considered in a synchronously rotating reference frame where the
sinusoidal variables which are fundamental appear as dc quantities in steady state. Fig. (4.1)
shows a separately excited DC motor and Fig. (4.2) show vector controlled based induction
motor.

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 Fig. 4.1 Separately excited DC motor

Fig. 4.2 Vector control of Induction motor drive

Where ids & iqs are the components of armature current in synchronously rotating frame. The
direct axis current ids is similar to field current of DC motor while the quadrature axis current
component iqs is similar to armature current of DC motor and we shows that these current
components are in synchronously rotating frame.

4.1 Clarke’s and Park’s Transform

Clarke’s Transform- Here Three-phase quantities either voltages or currents which changes
in time along the axes a, b, and c can be mathematically transformed into two-phase voltages
or currents which are varying in time along the axes α and β by the following transformation
matrix-

 1 −1 
1 2 2 
 
2  3 − 3
T  O =   0 (4.3)
3 2 2 
 
1 1 1 
2 2 2 


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Assuming that the axis a and the axis α are along same direction and β is orthogonal to them,
we have the following phasor diagram –

Fig. 4.3 Vector diagram of Clarke transformation

Park’s Transform - This is one of the widely used transformation in the Filed oriented
control. This projection will transform the two-phase fixed orthogonal system (α, β) into d, q
synchronously rotating reference system. It is basically converting stationery reference frame
to synchronously rotating frame. The transformation matrix is explained below:

 cos  sin  
Tdq =   (4.4)
 − sin  cos  

If ‘𝜃’ is the angle between stationary reference frame and rotating reference frame then
phasor diagram for Park’s transform is given below –

Fig. 4.4 Vector diagram of Park's Transformation

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A 3-phase parameter such as voltage and current can be reduced to 2-phase parameter set by
using above approach. With the magnetic axis is formed in quadrature in nature. The stator
and rotor variable (voltage, current, and flux linkages) of an induction motor may rotate at an
angular velocity or remains at stationary, when changed to a reference frame. This reference is
generally termed as arbitrary reference frame.

Fig. 4.5 a,b,c to dq0 transformation

Fig. 4.6 Equivalent circuit of induction motor in dq0 frame

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Under balanced condition the stator voltage of an Induction motor can be expressed as-

Va = Vm sin(t ) (4.5)

2
Vb = Vm sin(t − ) (4.6)
3

4
Vc = Vm sin(t − ) (4.7)
3

With the help of Clarke’s transformation we can convert the above equations into two phase
system which are quadrature in nature-

𝑉𝛼 2 1 1/2 −1/2 𝑉𝑎
[ ]=3∗[ ] [𝑉𝑏 ] (4.8)
𝑉𝛽 0 √3/2 −√3/2
𝑉𝑐

Now with the help of Park’s transformation equation (2.8) can be converted into synchronous
rotating frame-

 Vd   cos  sin    Va 
 =   (4.9)
 Vq   − sin  cos   Vb 

In similar way stator current and rotor currents are also converted into synchronous rotating
frame and voltage equation of equivalent circuit of induction motor in synchronous rotating
frame can be written with the help of fig.(4.6) as –

d  qs
Vqs = Rs I qs + + e *  ds (4.10)
dt

d  ds
Vds = Rs I ds + − e  qs (4.11)
dt

d  qr
Vqr = Rr * I qr + + (e − r ) dr (4.12)
dt

d  dr
Vdr = Rr I dr + + (e − r ) qr (4.13)
dt

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Where Ѱds, Ѱqs, Ѱdr and Ѱqr are the stator and rotor flux linkages in synchronously
rotating frame, Ꞷe is synchronous speed, Ꞷr is rotor speed, Ꞷe -Ꞷr is the slip speed. As the
motor is squirrel cage induction motor and it’s rotor windings are short circuited by end rings
there fore the voltages Vdr and Vqr become zero. These stator and rotor flux linkages can be
calculated as follow-

 d r = Llr I dr + Lm (Ids + Idr ) (4.14)

 ds = Lls I ds + Lm (Ids + Idr ) (4.15)

 qr (Lls + Lm ) − Lm  qs
I qr = (4.16)
(Lls Llr + Lls Lm + Llr Lm )

 qr = Llr I qr + Lm (Ids + Iqr ) (4.17)

With the help of equations we can calculate stator current and rotor current ids, iqs, idr, and
iqr in synchronously rotating reference frame

 ds (Llr + Lm ) − L m  dr
I ds = (4.18)
(Lls Llr + Lls Llm + Llr Lm )

 qs (Llr + L m ) − L m  qr
I qs = (4.19)
(Lls Llr + Lls Lm + Llr Lm )

 dr (Llr + Lm ) − L m  ds
I dr = (4.20)
(Lls Llr + Lls Lm + Llr Lm )

 qr (Llr + L m ) − L m  qs
I qr = (4.21)
(Lls Llr + Llr Lm + Llr Lm )

4.2 Summery
This chapter explains about basics of vector control, it also explains about Clarke’s and Park’s
transform and also explains about the various reference frame. It explain about how to
calculate Park’s and Clark’s transformation from the given three phase quantities.

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 Chapter 5
Field Oriented Control for Induction motor
we can control the speed of an induction motor by scalar control with constant flux operation.
But the problem with this method is that torque and flux are coupled each other. if we control
flux to control the speed it will affect torque also effected, that is both are coupled each other
so we well get good steady state response but transient response would be poor. To improve
transient response field oriented control are proposed in which machine is first converted into
dq reference variable. Conventional torque equation of and induction machine is seventh
order equation. So, analysis not that easy. If we convert to dq reference frames than it will
become fourth order system. If we align resultant flux into d axis component of the flux than q
axis component of the flux will be zero and we will get torque is a function of q component of
current alone so we well get dc machine like performance as in induction machine.

5.1 Principles of Vector Control

The vector control implementation can be explained with following figure given below.
where the machine model is transformed in a synchronously rotating reference frame.

Fig.5.1 Block diagram of vector control for AC drive

Here the current ia, ib and ic are representing the stator current of Induction motor, which are

converted into stationary reference frame. (idss and iqss) by Clarke’s transformation after that

idss and iqss are converted into synchronously rotating frame. (i dse and i qse) by their unit

vectors. Sinθ e and cosθe before applying to machine de-qe model. In FOC there are are two

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stage of transformation. In 1st stage we convert ia, iband ic to i dse and iqse using Clarke and
Park’s transformation and in 2nd stage (control stage) we apply inverse Clarke & Park’s

transformation to the reference currents idsand iqs to get reference stator currents ia * , ib *

ic*). In last inverter switching is controlled from the error generated by the difference of
actual stator currents and reference stator currents.

There are basically two methods of vector control. One is called the ‘direct or feedback
method’ and the other is called as the ‘indirect or feed forward method'. The methods are

different by how the unit vector (sinθe and cosθe) is calculated.

5.2 Direct or Feedback Vector Control

In direct vector control the first step is sensing of stator current and voltage. After sensing we
have to convert them into stationary reference frame. After converting we give them to the
voltage or current model to produce rotor flux and unit vector. Along this we have to sense
rotor speed also. After this we have to select reference value of rotor flux and speed.

The difference in actual speed and reference speed is given to PI controller which produce

reference torque component of stator current in synchronously rotating frame (iqse), similarly
the difference in actual flux and reference flux is given to PI controller which produce

reference flux component of stator current in synchronously rotating frame (idse).

Now these currents are converted into reference current for inverter (ia*, ib*, ic*) by using
unit vector signal generated from the voltage model or current model. After this voltage fed
inverter switching is controlled by comparing actual stator currents with reference stator
currents.

5.2.1 Voltage Model for Flux Estimation

From the Clarke’s transformation we can write-
2 1 1
iqs = ia − ib − ic (5.1)
3 3 3
1 1
ids = − ib + ic (5.2)
3 3
Similarly for voltage,
2 1 1
Vqs = Va − Vb − Vc (5.3)
3 3 3

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 1 1
Vds = − Vb + Vc (5.4)
3 3
 ds =  (Vds − Rs ids )dt (5.5)

 qs =  (Vqs − Rs iqs )dt (5.6)

 s =  ds 2 +  qs 2 (5.7)

 dm s = Lm (ids + idr ) (5.8)

 qm s = Lm (iqs + iqr ) (5.9)

 dr s = Lmids + Lr idr (5.10)

 qr s = Lmiqs + Lr iqr (5.11)

On eliminating idr and iqr

from equation (5.10) and (5.11) with the help of (5.8) and(5.9) we get
Lm
 dr s =  dm − Llr ids (5.12)
Lr
Lm
 qr s =  qm − Llr iqs (5.13)
Lr
5.2.2 Current Model for Flux Estimation

Fig 5.2 ds qs Model of Induction Motor

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From ds-qs model of machine we can write
d  dr
+ Rr idr + r  qr = 0 (5.14)
dt
d  qr
+ Rr iqr − r  dr = 0 (5.15)
dt
On adding (LmRr/ Lr)ids and (LmRr/ Lr)iqs respectively on both side of equations
d  dr Rr L R
+ ( Lmids + Lr idr ) + r  qr = m r iqs (5.16)
dt Lr Lr
d  qr Rr L R
+ ( Lmiqs + Lr iqr ) − r  dr = m r iqs (5.17)
dt Lr Lr
The direct method of vector control which is discussed above is difficult to operate especially
at very low frequency including zero speed of operation because of the following problems:

•At frequency which are low in magnitude, The magnitude of the voltage signals Vds and Vqs
are quite low. In addition, to that ideal integration becomes difficult because ofh the presence
of dc offset.

•The parameter variation such as the value of Rs inductance Lls,Llrand Lm will limit the
accuracy of the estimated variables. Out of which temperature variation of Rs becomes more
dominant. But this compensation is (Variation of resistance) is comparatively easy. At high
voltage we can neglect this effect..

5.3 Simulation of Direct Vector Control

Here in the Fig.(5.2) motor which is used in simulation is of 5hp, 230V, 1500 rpm, 50 Hz
squirrel cage induction motor fed through 3-phase full bridge inverter which is connected to
400V DC battery. There are 4 blocks in simulation, subsystem1 has inverter module inside it,
subsystem block has gate pulse generation through hysteresis band inside it, control block has
torque and speed control loop inside it.

5.3.1 Machine Rating and Parameters

Following are the rating of the machine under control. Here induction motor with bellow
rating is used. The machine is delta connected and operating at a frequency of 50Hz and no of
poles are 4.

Machine : 3-Φ squirrel cage induction motor (Delta connected)

Power : 5 hp

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Voltage : 230 V
Current : 12 Amp
Frequency : 50 Hz
No. of poles : 4
Stator inductance :0.009 H
Stator resistance : 1.78 ohm
Rotor inductance : 0.009 H
Rotor resistance : 1.78 ohm Magnetising inductance: 0.2363 H Inverter switching
frequency : 5000 Hz
5.3.2 Simulation Diagram: Direct Vector Control ( Current Based)
Fig 5.3 shows the simulation diagram of current based direct vector control. Here electric
vehicle is modelled and applied to the motor. Vector control block will generate abc reference
current and in gate controlled block it is compared with actual current and it is used to control
the inverter. Here theta is calculated by using system voltage and current equation. Here load
is modelled as E rickshaw and that load is applied to induction motor.

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 Fig. 5.3 DVC simulation

Fig 5.4 shows the theta generation circuit of current based vector control drive. This theta is
used to make reference current in abc reference frame. Here theta is derived from system
voltage and current equation. This method gives accurate result in low speed region of
operation of the drive under control.

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 Fig. 5.4 Subsystem of DVC (Current model simulation

Fig 5.5 shows the current based direct vector control drive, here first part will generate
reference current. In gate control block it will compare with actual current and inverter is

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controlled.

Fig. 5.5 Subsystem of DVC ( Control block simulation)

5.3.3. Simulation Result
Fig.(5.6) shows the current drawn by Induction motor in abc frame is almost sinusoidal which
means pulsation in torque is very small. And from this figure we can also conclude that stator

current in d-q-0 reference frame is divided into two components torque component (Iq) and

flux component (Id) which are completely decoupled and DC in nature.

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Fig 5.6 shows the Performance of current based direct vector control. Here actual flux and
reference flux is compared and flux component of the current id derived. Actual speed and
reference speed is compared and torque component of the current is derived. Here third fig
represents the current in rotating reference frame and fourth fig represents theta and sixth fig
represents the abc reference currents. By using this method, we can control torque component
and flux component of the current individually. That is here torque component and flux
component are decoupled in nature. The reference current generated here are compare with
actual currents and gate signals are generated. This gives good performance in low speed
region of operation of the drive under control.

Fig. 5.6. Performance of current based direct vector control

Fig5.7 shows the performance of current based vector control. Here torque input is applied to
motor in such a way corresponds to vehicle torque. At different time zone such as starting
accelerating free rung interval torque required is calculated an vehicle model will apply that
torque to the motor. In current based model the angle theta is calculate based on current based
equations. The main problem with this method is that the accuracy of this method is closely
related with parameters of the motor parameter such as resistance of the motor is a function of
motor temperature. When motor temperature changes resistance of the motor will change so
this will affect the estimated value of theta will change. Here rotor flux is used as reference
flux in upcoming method it is explained about the stator flux oriented control in which
estimation of the stator flux is somewhat easy but in that case we need to add one decoupler

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circuit to avoid the coupling effect. This method is effective at lower speed Voltage based
method is usually sued in high speed range of operation. The set of equation used for
estimation of there in this case is called backlashe equations. Here there is no need to worry
about instability problem as in normal scalar based control. Because here hysteresis controller
will always limit the current so current will not go beyond that limit. Transient response of
this method is also good as par with dc motor due control operation Here torque and current
are decoupled so we will get d machine like performance.

Fig 5.7 represents Performance of current based model. Here first fig represents the voltage
across the motor and second fig represent current drawn by motor and third fig represents and
the dc link voltage and fourth fig represent the load torque and fifth fig represent the vehicle
speed. It should be note that here vehicle speed is represented in (rad/s).Here the torque
represented in fourth fig is vehicle torque. Here load is modelled as E rickshaw and that load
is applied to the motor input. This method can be used especially in the low speed region of
the e rickshaw. because this model gives good response in low speed region of operation of
the drive. This method gives good response in staring region also.

Fig. 5.7 Performance of current based direct vector control

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 Table 8 Performance of Current Based Direct Vector Control

Current Based model

Reference Speed(rad/s) 152

Actual Speed(rad/s) 151

Torque Ripple (%) 1.02

5.3.4 Simulation Result of Voltage Based Model

Fig 5.8 shows the voltage based vector control Here vector control block will derive reference
current .Load is applied as e rickshaw .in gate control block hysterics controller is used to
compare actual current and reference current.

Fig. 5.8 Voltage based vector control

Fig 5.9shows the theta generation circuit for Voltage based model. Here Theta is generate by
using system equations. Voltage based model is also a direct vector control but the difference
is that how we are calculating the theta for vector control here set of voltage equation is used
to calculate theta of the motor. That is the main difference between voltage based model and
current based model. This model will give accurate value of theta estimation at high speed

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region. At low frequency this method is not accurate because of the presence of integrator.
Since it is not considering DC drift. Here parameter variation such as Resistance and
inductance will effect the accuracy of this estimation. This can be used for high speed
application.

Fig. 5.9Theta Generation of Voltage Based Model

5.3.5 Simulation Result

Fig 5.10 shows the performance of voltage based vector control fed drive.. Here actual flux is
compared with reference flux and flux component of the current id derived and actual speed
and reference speed is compared and torque component of the current is derived. Here first fig
represents the actual flux and reference flux, second fig represents the actual speed in(rad/s)
and reference speed in(rad/s), Third fig represents the Ids and Iqs in synchronously rotating

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reference frame. Fourth fig represents the theta in rad fifth fig represents Ids and Iqs in
Stationary reference plane and sixth fig represents the reference current in abc reference plane.
It should be noted that here load is applied in such a way that e rickshaw are modelled based
on torque equation of rickshaw and that is appalled to motor input. The reference current in
sixth fig is compared with actual current an that is how gate pulse are generated. Here torque
component and flux component of the current are decoupled in nature.

Fig. 5.10. Performance of Voltage based direct vector control

Fig 5.11 represent the Performance of the voltage based model. Here first fig represents the
voltage across the motor and second fig represents the current drawn by the motor and third
fig represents the dc link voltage and fourth fig represents the load torque and fifth fig
represents the speed of the motor. This method gives better performance in high speed region
of operation. Because theta estimated by this method gives good results in high speed region
of operation of the drive. This method also the output is effected by parameter variation such
as resistance and leakage reactance of the motor.

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 Fig. 5.11Performance of Voltage Based Direct Vector Control

Table9Performance of Voltage Based Direct Vector Control

Voltage Based Model
Speed reference(rad/s) 152
Actual Speed(rad/s) 153
Torque ripple(%) 2.04

So we can conclude that voltage based vector control can be used for high speed application
and and current based vector control can be used for low speed application and in practice we
have to go for hybrid one in which we can use both type of vector control and in high speed it
should work as a current based model and in low speed it should worked as voltage based

model. The parameter variation such as Rs Lls Llr are affect the accuracy of the model but
more dominant is the resistance variation with respect to temperature. Here theta is calculated
by using stator voltage equation that is why this method is called voltage-based model. Here
also it has inherent current stability feature is present, In normal scalar control approach
current sometimes may go beyond rated value but in this case hysterics controller is used
which will limit current between two set values so it will limit the current .since it is a vector
control decupling effect is also inherently present so we will get good transient response as in
dc machine. The output represent above corresponds s to torque produced by 400kg machine
vehicle load supplied by and induction motor various instant. In upcoming chapters it is

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explained about the indirect vector control and slip gain turning approach which will give
somewhat good accuracy with parameter variation of the machine under control.

5.4 Indirect or Feed forward Vector control

Indirect vector control is very commonly used. The indirect vector control method is almost
the same as direct vector control, the difference here is that unit vector signals (cosθ and sinθ )
are generated in feed forward manner.in industrial application. It is less dependent on
parameter of the machine so there is less effect on estimated flux and unit vectors due to
parameter variation.

Fig. 5.12 Phasor diagram for Indirect vector control

Figure 5.12 revels the basic idea of indirect vector control with the aid of a phasor diagram.

The axes ds-qs are fixed on the stator and the axis dr qr which are fixed on the rotor, and they
are moving at a speed of ωr. Synchronously rotating axis de qe .are rotating infront of the rotor
axis by slip angle which is correspond to speed of the slip or slip speed ωsl.

 e =  r +  sl (5.18)

e =  e dt (5.19)

e =  (r + sl )dt (5.20)

As we know in Induction motor the pole position of the rotor is not absolute, it is slipping with
respect to the rotor at frequency of wsl. The phasor diagram shows that for decoupling
control, the stator flux component of current ids should be aligned on the deaxis, and the

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torque component of current iqs should be on the qe axis(both are perpendicular) as shown in
above phasor diagram. The rotor circuit equation can be written as –
d  dr
+ Rr iqr − (e − r ) qr = 0 (5.21)
dt
d  qr
+ Rr iqr + (e − r ) dr = 0 (5.22)
dt
The rotor flux linkage can be expressed as-
 dr = Lr idr + Lmids (5.23)

 qr = Lr iqr + Lmiqs (5.24)

From above equations we can write,

 dr Lmids
idr = − (5.25)
Lr Lr
 qr Lmiqs
iqr = − (5.26)
Lr Lr

on putting value of equation (5.25) & (5.26) in equation (5.21) & (5.22) we get-

d  qr Rr  qr Lm Rr iqs
+ − + sl dt = 0 (5.27)
dt Lr Lr

Where, sl = e − r

For decoupling control, it is desirable that

Ѱ𝑞𝑟 = 0 (5.28)
d  qr
=0 (5.29)
dt
Because of this the total rotor flux is directed on de axis. By putting this in equation (5.27) and
(5.28) we get

Lr d  r
+  r = Lmiqs (5.30)
Rr dt
Lm Rr
sl = iqs (5.31)
Lr  r

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If rotor flux Ψr = constant, we can say that the rotor flux is directly proportional to current ids
in steady state.
 r = Lm * ids (5.32)

To make use of the indirect vector control strategy, it is required to take Equations (5.30),
(5.31) into consideration. Fig. (5.13) shows Induction motor drive using Indirect vector

control method. The flux component of current idsfor the desired rotor flux lift is determined
from Equation (5.32), and is maintained constant here in the open loop manner for the sake of
simplicity. The power circuit consists of a diode rectifier at input side followed by PWM
inverter fed with the dc link. The speed control loop produce the torque component of

current iqs..The slip frequency is generated from iqs in feed forward manner. Signal wsl is

added with speed signal wr to generate synchronous speed signal ωe. The variation of
magnetizing inductance will cause some drift in the flux. The unit vector signals cosθ and sinθ
are generated from ωe by the process of integration. The speed signal generated from an
incremental-position encoder is must in indirect vector control as the slip signal locates the

pole with respect to the rotor dr axis in feed forward manner, which is moving at speed wr..
The range of speed control in indirect vector control can easily extended from starting to the
flux-reducing region.

Fig. 5.13 Block diagram of indirect vector control.

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5.5Simulation of Indirect Vector Control
Here in the Fig. (5.14) motor which is used in simulation is of 5hp, 230V, 1500 rpm, 50 Hz
squirrel cage induction motor fed through 3-phase full bridge inverter which is connected to
400V DC battery.

5.5.1. Machine Rating and Parameters

Following are the rating of the machine under control. Here induction motor with bellow
rating is used. The machine is delta connected and operating at a frequency of 50Hz and no of
poles are 4
Machine : 3-Φ squirrel cage induction motor (Delta connected)
Power : 5 hp
Voltage : 230V
Current : 12 Amp
Frequency : 50 Hz
No. of poles : 4
Stator inductance : 0.009 H
Stator resistance : 1.78 ohm
Rotor inductance : 0.009 H
Rotor resistance : 1.78 ohm
Magnetising inductance : 0.2363 H
5.5.2. Simulation Diagram of Indirect Vector Control
Fig 5.14 shows IDVC simulation diagram. Here indirect vector control block will generate the
reference currents in abc domain and hysterics controller in gate control block will control the
inverter. Here load electric vehicle is modelled and that’s how load is applied to the motor.

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 Fig. 5.14 IDVC simulation

Here in above Fig.(5.14) speed error is fed to PID which produces reference torque
component of current (iq* ) which is used to calculate slip frequency which is added to rotor
frequency and then integrated to get rotor angle theta (θ).

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 Fig. 5.15 Subsystem of IDVC (Control)

5.5.3. Simulation Result of IDVC

Fig 5.16 shows performance of indirect vector control based drive.. Here flux component of
the current is derived by comparing actual flux and reference flux. Torque component of the
current is derived by comparing actual speed by reference speed and is converted onto abc
reference plane. Here first fig represents the actual flux and reference flux, second fig
represents the actual speed in(rad/s) and reference speed in(rad/s), Third fig represents the Ids
and Iqs in synchronously rotating reference frame. Fourth fig represents the theta in rad fifth

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fig represents Ids and Iqs in Stationary reference plane and sixth fig represents the reference
current in abc reference plane. Here also load is modelled as electric vehicle and is applied to
the induction motor under control. Torque component of the current and flux component of
the current are decoupled in nature. So we can control them separately. The reference current
generated here is compare with actual currents and fed to the inverter gate circuit. The
problem with this methods is moderate transient response this can be improved by type 2
based fuzzy or ANN based controller

Fig. 5.16Performance of Indirect Vector Control

Fig 5.17 shows the performance of indirect vector controlled drive. Here first figure
represents the voltage applied across the motor and second fig represents the current drawn by
the motor. Here third fig represents the dc bus voltage and fourth fig represents the load
torque and fifth fig represents the speed of the motor. In this case transient response in not so
good due to normal PI based controller. This can be reduced by means of type 2 fuzzy based
controller or ANN based controller. Here also E rickshaw are modelled based on torque
equation and that is applied across the load of the induction motor.

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 Fig. 5.17. Performance of Indirect Vector Control

Table 10Performance of Indirect Vector Control

Indirect Vector control
Speed reference (rad/s) 152
Actual Speed(rad/s) 149
Torque ripple (%) 2.06

5.6 Indirect Vector Control with Slip Gain Tuning

The slip gain Ks in Indirect field oriented control which is used to calculate slip frequency so
that we can find accurate rotor position, is a function of machine parameters because of that it
is very important that these calculated parameters must matched with actual parameters of
machine for completely decoupled control of machine. We know at steady state the roto flux
Ѱr =LmIds because of that Ks becomes function of rotor time constant Tr=Lr/Rr. Here rotor
resistance play an important role, it may lead to wrong calculation of slip gain. It’s effect on
slip frequency can be explained with the help of Fig.(5.18).

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 Fig. 5.18 Detuning of Ks due to rotor resistance

Here Rr is the actual value of rotor resistance and R^ is the calculated value of rotor resistance
used in Ks. If R^ is lower than R’ r then estimated slip speed will be lower than actual.

we increase torque the de-axis component of Iqs’ will leads to over fluxing of machine.

And if R^ is higher than the Rr then estimated slip will be higher than the actual one causes

Ids’’ & Iqs’’ will lead from it’s actual position and now if we increase torque then it lead to
under fluxing of machine this can be explained with the help of Fig.(5.19)

Fig. 5.19 Effect of detuning on response

A Model adaptive reference system (MRAS) based technique can be used to reduce the effect
of rotor resistance and proper tuning of slip gain. In MRAS based technique we use two
models namely ‘Reference Model’ and ‘Adaptive Model’. Reference model produces output

X* which is function of Ids, Iqs , Lm and Lr while Adaptive model produce output X which is

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function of stator voltage, current and rotor resistance. The reference model output is
compared with adaptive model output and resulting error is fed to PID controller which give
slip gain (Ks). When X exactly matches with X* we say that tuning of slip gain done
accurately. Here-

3* PLm 2 I ds* I q s*
X =*
(5.33)
2*2* Lr

3* P( dss Iqss −  qs s Ids s )

X= (5.34)
2*2

 s ds =  (Vs ds − i s dsRs )dt (5.35)

 s qs =  (V s qs − i s qs Rs )dt (5.36)

Fig. 5.20 Block diagram of MRAS

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5.6.1. Simulink Model and Result
Fig 5.21 shows simulation diagram for model reference adaptive control. Here vector control
block will calculate the reference current in abc reference frame. Here theta is calculated using
reference model and adaptive model.

Fig. 5.21simulation diagram for model reference adaptive control

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Fig 5.22 shows the theta generation circuit in MRAS. Here reference model and adaptive
model is compared and theta is generated. By using this method variation of rotor resistance is
almost negligible as compared to normal indirect vector control.

Fig. 5.22Theta generation in MRAS

Fig. 5.23Reference model

Reference current ide and iqe are used to produced output of reference model shown in

Fig.(5.23)Here reference model and adaptive model is used to find the theta. Now output of
reference model (X1) and adaptive model (X2) are compared and fed to PID controller whose

output is multiplied to iqe to produce slip frequency.

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 Fig.5.24 Adaptive Model

Fig 5.25 shows performance of model reference adaptive control. Here flux component of the
current is derived by comparing actual flux by the reference flux. Torque component of the
current is derived by comparing the actual speed by reference speed and reference current in
abc reference frame is calculated with the help of theta. The main advantage of this method as
compared to conventional vector control is that by using this approach for calculating theta the
variation in the resistance of the rotor circuit is almost negligible this is because in this case
the method used to calculate theta is not considering the resistance of the rotor circuit. The
first fig represents actual and and reference flux, second fig represents actual speed and the
reference speed of the motor. Third fig represents the Ids and Iqs in rotating reference frame,
fourth fig represents the theta fifth fig represents the Ids and Iqs in stationery reference frame
and sixth fig represents the reference current in abc domain. Here also transient response is
moderate.

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 Fig. 5.25Performance of Model Reference Adaptive Control

Fig 5.26 represents the performance of model reference adaptive control. Here first fig
represents the voltage across the motor and second fig represents the current through the
motor and third fig represents the dc link voltage, fourth fig represents the load torque of the
motor and fifth fig represents the speed of the motor. In this case transient responses not so
good because of conventional pi controller. This problem can be eliminated by using type 2
fuzzy or other ANN based controller. The main difference between this method and
convention vector control is how theta is calculated. Here adaptive model and reference is
used to calculate the theta. Here also gate signal is generated by comparing the actual current
and reference current generated by using model reference adaptive control and is used for
controlling the gate.

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 Fig. 5.26Performance of Model Reference Adaptive Control

Table 11Performance of Model Reference Adaptive Control

Model Reference Adaptive Control

Speed reference(rad/s) 152

Actual Speed(rad/s) 151

Torque Ripple(%) 3.04

Fig(5.21),(5.25)and (5.26) shows the model reference adaptive control method with slip gain
tuning. In previous we have explained about voltage based method, current based method and
indirect method the main problem with above method is that the theta calculated by above
method is not that accurate Theta calculation accuracy depends upon parameter variation of
the machine under control. This problem can be somewhat rectified by using slip gain Turing.
Here one reference model and adaptive model is used in this equation resistance of the
machine is absent .As far as a parameter variation is concerned resistance of the machine is
varied with respect to temperature. So in this case this effect is nullified since the term
resistance us absent in the adaptive and reference model so this model is somewhat sensitive
to parameter variation. Here also vehicle torque at various instant is calculated and applied to

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motor at respective instant which is shown in fig. Here also inherent current limiting is present
in this control that is in the case of normal scalar based control sometimes current will go
beyond rated value. In this case hysteresis controller are present so that will limit the current
with set value. Here also tuning has to be proper if estimated value of resistance is greater than
actual value it will leads to Over fluxing and if estimated value is less than actual value that
will leads to under fluxing at that time it can’t produce rated torque of the machine. Torque at
different instant is calculated and applied to the motor.

5.7 Stator Flux Oriented Control

Fig. 5.27Block Diagram of Stator Flux Oriented Control

In this fig we can seed the block diagram of stator flux oriented control. In this case main
difference is that instead of sensing rotor flux here stator flux is sensed. So sensing becomes
simple but the problem with this method is that if we write torque equation in dq variable it is
interrelated that is decoupling is absent in this case. That torque is not function of iqs alone as
in normal vector control. Here torque depends on ids as well, To nullify that effect decoupling
block is added. By using decoupling block we well get torque as a function of iqs alone as in
dc motor. Decupling component will nullify effect of ids in torque. So we will get decoupling
effect. So far it is discussed about rotor flux-oriented control only. I we are aligning stator flux
we have to take care about decupling effect. But it has an advantage that accuracy is higher as
compared to other methods. Because accuracy is limited by only stator resistance.

 dr = Lr idr + Lmids (5.37)

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 qr = Lr iqr + Lmiqs (5.38)

(1 + STr ) dr − Lmids − Trsl  qr = 0 (5.39)

(1 + STr ) qr − Lmiqs + Trsl  dr = 0 (5.40)

In this equation Ψdr and Ψqr to be eliminated and replace Ψds and Ψqs. This can be written as

 ds = Ls ids + Lmidr (5.41)

 qs = Ls iqs + Lmiqr (5.42)

 ds Ls
idr = − ids (5.43)
Lm Lm

 qs Ls
iqr = − iqs (5.44)
Lm Lm

Substituting eqn(5.43) and eqn (5.44) in (5.37) and (5.38) we get

Lr LL
 dr =  ds + ( Lm − r s )ids (5.45)
Lm Lm

Lr LL
 qr =  qs + ( Lm − r s )iqs (5.46)
Lm Lm

Where σ = 1- Lm2/Ls Lr

This equation relate stator and rotor flux with stator current substituting 5.45 and 5,46 in
equation 5.39 and 5.40 and multiplying both side by Lm/Lr .we get

(1 + STr ) ds = (1 +  STr ) Ls ids + slTr [ qs −  Ls iqs ] (5.47)

(1 + STr ) qs = (1 +  STr ) Ls iqs − slTr [ ds −  Ls ids ] (5.48)

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5.7.1.Vector Diagram of Stator Flux Oriented Control

Fig. 5.28. Vector Diagram of Stator Flux Oriented Control

Fig 5.28 shows the vector diagram of stator flux oriented vector control, In normal case
resultant flux is aligned to direct axis rotor flux by making quadrature component of rotor flux
is zero. In this case resultant flux is aligned to stator flux more accurately direct component of
stator flux. Here also quadrature component of stator flux is made zero. In this case from
above equations we can easily understand that stator flux is a function of both direct and
quadrature axis current. To nullify effect of quadrature axis current decoupler is used.

If we makeΨqs =0 than Ψs=Ψds. Therefore eqn (5.47)and(5.48) becomes

(1 + STr ) ds = (1 +  STr ) Ls ids −  LsTrsl iqs (5.49)

(1 +  STr ) Ls iqs = slTr [ ds −  Ls ids ] (5.50)

In other words coupling effect is present. That is if we change torque by change in iqs which
will indirectly changes flux so both are inter related.

5.7.2. Decoupling Scheme of Stator Flux Oriented Control

Fig. 5.29 Decoupler

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Fig 5.28 shows the decoupling method in stator flux oriented vector control. From the above
equations it is clear that stator flux is the function of both ids and iqs. So coupling effect is
present. In order to avoid coupling effect decoupling block is used. From fig we can write that

ids* = G ( ds* −  ds ) + idq (5.51)

Where G represents PI controller. Substituting equations (5.49) in (5.47) we get

(1 + STr ) ds = Ls (1 +  STr )G ( ds* −  ds ) + (1 +  STr )idq −  Trsl iqs  (5.52)

For decoupling control pf yds with the help of idq

(1 +  STr )idq −  Trsl iqs = 0 (5.53)

From this Idq

 Trsl iqs
idq = (5.54)
(1 +  STr )

From eqn (5.50) wsl

(1 +  STr ) Ls iqs
sl = (5.55)
Tr ( ds −  Ls ids )

Combining equation (5.54)and (5.55) we get

 Ls iqs 2
idq = (5.56)
( ds −  Ls ids )

5.7.3. Simulink Model and Result for Stator Flux Oriented Vector Control
Fig 5.30 shows the simulation diagram of stator flux oriented control. Here stator flux oriented
control block will compare create the reference current in abc domain and gate signal
generator block is hysterics controller block and it will control the gate of inverter block.

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 Fig. 5.30 Simulation Diagram of Stator Flux Oriented Control

Fig5.31 shows the stator flux oriented vector control. Here decoupler block is used to decuple
the coupling effect. Here stator flux is oriented so coupling effect is inherently present. So to
avoid that decupling block is used

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 Fig. 5.31. Simulation Diagram of Stator Flux Oriented Vector Control
Fig 5.32 shows the decoupler block of stator flux oriented control. This block is used to avoid
coupling effect. Here above block will calculate the idq based on leakage inductance Ls and it
will inject in such a way to avoid coupling effect present in stator flux oriented control

Fig. 5.32 Decoupler

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Fig 5.33 show the performance of stator flux oriented control. First fig compares actual flux
with reference flux and based on that flux component of the current is derived. similarly by
sensing speed it will derive the torque component of the current and based on that it will
create reference current in abc domain. Here First fig represents actual flux and reference flux
of the motor, second fig represents the actual speed and reference speed of the motor third fig
represents the Ids and Iqs in rotating reference frame Fourth fig represents the theta Fifth fig
represents the Ids and Iqs in stationery reference frame and sixth fig represents the reference
current in abc domain.

Fig. 5.33Performance of stator Flux Oriented Control

Fig 5.34 shows the performance of stator flux oriented control. Here first fig represents the
voltage applies across the motor and second fig represents the current drawn by the motor. In
stator flux oriented method advantage is that accuracy is not limited but rotor parameter .since
it is sensing stator flux. But the problem is that it will create coupling effect default by using
decoupling circuit we can reduce that coupling into some extend .Here flux chattering may
occur which is due to presence of normal integrator in decoupling circuit due to dc drift .This
can be improved by adding an advance integrator which will consider the dc drift as well. Or
we have to limit Ls as well by using some control.

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 Fig. 5.34. Performance of Stator Flux Oriented Control

Table12Performance of Stator Flux Oriented Control

Stator Flux Oriented Control

Speed reference(rad/s) 152

Actual Speed(rad/s) 132

Torque ripple (%) 4.76

5.8 Summery
After Simulating FOC of induction motor drive it can be say that it gives very good result as
compare to scalar control, and it can be also concluded that IDVC technique is more reliable
as compare to DVC because it is less dependent on machine parameters and also effect of
rotor resistance in case of IDVC can be reduce by slip gain tuning. If stator flux oriented
control are used than sensing of stator flux becomes simple. But if merely go for stator flux-
oriented control decoupling effect of torque and flux will not be present in final equation. So it
has to to add a decoupling circuit to nullify the effect of flux in torque and by that way we can
accomplish vector control by sensing stator flux as well. If direct vector control approach are
used , we can go for either current based model or voltage based model depending upon the
speed of operation if the drive is operating in the low speed region we can go for current based
model this can be used for starting region as well as slow speed region If we are using high
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speed region of the operation we need to go for voltage based model. But those to model
accuracy will be limited especially if machine parameter is varying this can be reduced if we
are using model reference approach. In actual practice we can also go for hybrid model as
well in which at low speed it will work as current based model and high speed it will work as
voltage based model. As a future expansion instead of using speed sensor we can estimate the
speed as well which is called sensor less vector control. But especially for vehicle application
speed sensor is inherently present in the vehicle so it sensors control for vehicle application is
rarely used

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 Chapter 6
Conclusion and Future Work
1.Conclusion
Renewable energy is considered to be the prime energy source of future. Solar panels are used
to charge the Batteries in E rickshaw. Different MPPT based methods Such as Perturb and
Observe, Incremental Conductance and adaptive MPPT methods are proposed for battery
charging of E rickshaw. The proposed charger will indicate DTE (Distance to empty) of E
rickshaw in the vehicle cluster and battery status. Induction motors are widely available and
they have good static and dynamic performance. By using vector control methods, it can be
controlled just like separately exited DC motor. It doesn’t have any permanent magnet
material so there is no need of rare earth material as in BLDC and PMSM motors. In direct
vector control both in voltage based model and current based model theta is calculated by
system voltage and current equation. In indirect Vector Control theta is calculated based on
slip speed (ωSl). Here also accuracy is limited by variation in resistance of the Induction
Motor. This problem can be eliminated by using Model Reference Adaptive Control. Here
theta calculation is based on reference model and actual model. Here also accuracy is limited
by rotor parameter variation. The variation of rotor parameter can be eliminated if stator flux
orientation methods are used. The proposed method is giving good transient and dynamics
performance.

2. Future Work
In this report different solar energy based battery chargers for rickshaws such as Perturb and
Observe, Incremental Conductance and Adaptive MPPT are proposed. Induction motor
control like Direct vector control, Indirect vector control, Model Reference Adaptive Control
and Stator flux oriented control are discussed. Following are the future work in this area.

• More Advanced Direct MPPT Without no oscillation at all.

• Fast chargers for electric vehicles
• New chargers for electric vehicle.
• Application of above methods in BLDC and PMSM motors

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Contact Address
Name: Arjun T C
Address: Ananda Sadanam
Palayad P.O
Kannur, Kerala
670661
Email:tcarjun2@gmail.com

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