Nevesdemelo 2015

Management of Environmental Quality: An International Journal
An outlook of electric vehicle daily use in the framework of an energy

management system
Hugo Neves de Melo João P. Trovão Carlos Henggeler Antunes Paulo G. Pereirinha Humberto M.
Jorge
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Hugo Neves de Melo João P. Trovão Carlos Henggeler Antunes Paulo G. Pereirinha Humberto M.
Jorge , (2015),"An outlook of electric vehicle daily use in the framework of an energy management
system", Management of Environmental Quality: An International Journal, Vol. 26 Iss 4 pp. 588 - 606
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MEQ
26,4
An outlook of electric vehicle
daily use in the framework of an
energy management system
588 Hugo Neves de Melo
R&D Unit INESC Coimbra, Institute for Systems and
Computers Engineering at Coimbra, Coimbra, Portugal

João P. Trovão
Department of Electrical Engineering and Computer Engineering,
Université de Sherbrooke, Sherbrooke, Canada and
Carlos Henggeler Antunes
Department of Electrical and Computer Engineering, FCTUC,
University of Coimbra, Coimbra, Portugal and
Paulo G. Pereirinha
Department of Electrical Engineering, ISEC,
Polytechnic Institute of Coimbra, Coimbra, Portugal;
Computers Engineering at Coimbra, Coimbra, Portugal and
APVE, Portuguese Electric Vehicle Association, Lisbosa, Portugal, and
Humberto M. Jorge
Department of Electrical and Computer Engineering, FCTUC,
University of Coimbra, Coimbra, Portugal and
Abstract
Purpose – The purpose of this paper is to present a prospective study of sustainable mobility in the
framework of a supporting energy management systems (EMS). Technological advances are still
required, namely electric vehicles (EV) endowed with improved EMS in order to increase their
performance by making the most of available energy storage technologies. As EVs may be seen as a
special domestic load, EMS are proposed based on demand-sensitive pricing strategies such as the
Energy Box discussed in this paper.
Design/methodology/approach – The study presents an overview of electric mobility and an urban
EV project, with special focus on the utilization of its energy sources and their relation with the energy
Management of Environmental This work has been developed under the Energy for Sustainability Initiative of the University of
Quality: An International Journal Coimbra and partially supported by the Energy and Mobility for Sustainable Regions Project
Vol. 26 No. 4, 2015
pp. 588-606 (CENTRO-07-0224-FEDER-002004) and by Fundação para a Ciência e a Tecnologia (FCT) under
© Emerald Group Publishing Limited
1477-7835
project Grants Nos MIT/SET/0018/2009, PTDC/EEA-EEL/121284/2010, FCOMP-01-0124-FEDER-
DOI 10.1108/MEQ-03-2014-0049 020391, and PEst-OE/EEI/UI0308/2014.
demand of a typical urban driving cycle. Results based on the ECE 15 standard driving cycle for Electric
different free market electricity tariffs are presented.
Findings – The analysis based on present Portuguese power and energy tariffs reveals that it is vehicle daily
highly questionable whether the resulting profit will be enough to justify the potential inconveniences use
to the vehicle user, as well as those resulting from the increased use of batteries.
Practical implications – The conclusions indicate that more studies on the trade-offs between
grid to vehicle and vehicle to grid schemes and electricity pricing mechanisms are needed in order
to understand how the utilization of EVs can become more attractive in the end-users’ and utilities’ 589
perspectives.
Originality/value – The paper proposes an approach for future electricity tariff behavior that could
be applied to EVs in order to understand whether or not their grid integration in charge and discharge
situations would be beneficial for end-users and utilities, in the framework of smart energy
management technologies.
Keywords Electric vehicles, Energy management systems, Energy sources, Sustainable mobility,
V2G
Paper type Research paper
1. Introduction
Energy availability and cost are at the heart of today’s political and scientific agenda
involving several economic, ecological, and geopolitical aspects. The European Union
has established the objectives of reducing greenhouse gas (GHG) emissions by 20
percent, increasing the share of renewable energy by 20 percent and improving energy
efficiency by 20 percent by 2020[1]. The emissions from thermoelectric plants and the
massive utilization of internal combustion engine (ICE) vehicles in the transportation
sector substantially increase the global GHG emissions. ICE vehicle emissions are one
of the major urban pollution sources, especially in medium and large cities, leading to
public health issues, as air pollution contributes to mortality and morbidity (Trovão
et al., 2009). Thus, pollutant emissions should be drastically reduced to improve life
quality and planet sustainability. The reduction of GHG emissions is achievable
through the increase of efficiency in all branches of the electricity industry, including
generation, transmission, distribution, and consumption. Smart grids are expected
to foster further overall energy efficiency, by using advanced information and
communication technologies to manage and control electricity flows from all (large
scale and distributed) generation sources to satisfy a diversified demand, enabling to
make an optimized use of integrated generation, storage and demand-side resources
(Molderink et al., 2010). EVs are expected to offer a significant contribution to overall
energy efficiency (Byeon et al., 2013). While looking forward to the ideal solution of zero
emission vehicles, a transition step is low emissions vehicles as hybrid electric vehicles
(HEV), and specially plug-in HEV (PHEV). Several projects and models of EV, HEV,
and PHEV, including buses, vans and cars, have been developed in the last few years,
resulting in cleaner, more economic and less noisy vehicles, some of them already
commercially available[2]. Therefore, the future of sustainable mobility surely includes
Battery Electric Vehicles (BEV) supplied from wind, hydro and photovoltaic generated
electricity, or other clean renewable energy sources, and expectedly involving the
hybridization of multiple energy sources (Pereirinha and Trovão, 2012). The intensive
use of BEV and PHEV is expected to be a reality in a near future. This introduces
significant changes in power systems planning and operation, mainly in the distribution
network (Raghavan and Khaligh, 2012). Additionally, other distributed energy resources
such as (local) generation and demand response will increase their penetration. Therefore,
new approaches and methodologies for management and operation of electricity
MEQ distribution are required to ensure the security and reliability of future power
26,4 systems.
The evolution toward smart grids in the distribution network provides the
technological basis for a more efficient use of the electric power infrastructure at a
global scale (Adika and Lingfeng, 2014). In the residential sector there will be a growing
need for technologies that make a sustainable, intelligent and optimized management
590 of all energy resources. Technologies based on demand-sensitive pricing strategies
have already proved to be effective in changing the patterns of electricity usage.
Demand-sensitive pricing of electricity is expected to become the standard pricing
mechanism in smart grids, contributing to maintain the electric system security and
reliability at least cost. Energy management systems (EMS), such as the Energy Box
(EB), using as inputs grid signals, including dynamic tariffs, end-users’ preferences and
comfort requirements regarding the operation of loads, and forecasts for local generation,
and endowed with adequate algorithms will provide automatic optimized energy
decisions in households (Livengood and Larson, 2009). Due to its magnitude, in
comparison with other residential loads, and its ability to offer the stored energy locally
or sell it back to the grid, the EV plays an important role in this integrated optimization
process of energy resources. Therefore, both charging (grid to vehicle – G2V) and
delivering stored energy back to the grid (vehicle to grid – V2G) should be the target
of optimization processes to derive economic and/or operational benefits for EV owners
and utilities, also with potential environmental benefits. Accordingly, technologies
like the EB connected to EV bidirectional and power adaptive chargers, as mentioned
in this paper, will allow exploiting those benefits associated with the management
of EVs as important players (both loads or suppliers) in the end-users’ and utilities’
perspectives (Lopes et al., 2012; Soares et al., 2012).
The main contribution of this work is the EB technology including the specification
of the EV charger characteristics. An overview of electric mobility and an urban
EV project is also presented, with special focus on its energy sources and their relation
with the energy demand in a typical urban driving cycle. Results based on the ECE 15
standard driving cycle for different market electricity tariffs (as of August 2012 in
Portugal) are presented. Based on these results, some conclusions are drawn about the
conditions needed to make V2G attractive for EV owners.
2. Urban electric mobility

In Europe, on average each person covers a distance of 35 km per day, distributed
by 5.5 journeys and 52 percent of the journeys do not exceed 3 km (Pereirinha and
Trovão, 2012). In urban cycle driving, a small car is easier to move and park.
2.1 The VEIL project

An on-going project at the Department of Electrical Engineering of Coimbra Institute of
Engineering (DEE-ISEC) has converted a small vehicle, initially equipped with an ICE,
into an EV. With focus on urban mobility, an ICE license-free car has been chosen,
a LIGIER 162 GL (see Plate 1), which is ideal for urban traffic, having two seats and a
luggage volume of 400 dm3, and weighing only nearly 350 kg. This EV is named VEIL,
meaning license free electric vehicle (EV). It constitutes a platform that is being used in
several projects as a test-bed for diverse EV technological aspects such as motor and
drive powertrain, batteries and other storage/energy sources, speed and acceleration
control, improved autonomy, x-by-wire systems, and Controller Area Network (CAN) and
Electric
vehicle daily
use
591
Plate 1.
VEIL’s project
on-road tests at ISEC
campus, and its
powertrain motor,
electronic converter
and the LiFePO4
batteries
Note: Authors’ own photographs
Flexible Time-Triggered communication on CAN (FTT-CAN) protocol (Santos et al.,

2006). The characteristics of the original and electric version are listed in Table I.
In the conversion process, LiFePO4 batteries, supercapacitors (SCs), photovoltaic
panels (PVs), motor and power electronic devices have been included (see Plate 1).
In order to complement the energy storage systems (ESS), namely to extend
autonomy and optimize the energy power flow, two other energy sources, SC and PV
are planned: a series of five SCs modules (40-81 V) to optimize the energy storage in
Ligier 162 GL characteristics Original version Full electric version
Engine 4 stroke diesel (Lombardini) −

505 cm3, 5.4 hp
Max. rotation: 3,100 rpm
Max. torque: 15.1 Nm at 2,340 rpm
Electric motor − 4 kW, 5.4 hp
Max. torque: 55 Nm at 2,800 rpm
Range (km) ~400 68.6 (at NEDC driving cycle)
LiFePO4 batteries − 30 cells (TS-LFP90AHA)
Supercapacitors − 37 × 2 cells (PC2500)
PV array − 5 panels (BP MSX 30) Table I.
Curb weight (kg) 450 465 Vehicle
Note: Authors’ own elaboration characteristics
MEQ regenerative braking mode and in rapid acceleration mode, and PV panels to generate
26,4 energy to charge the batteries. Presently, five PV panels in series (84-105 V) are
planned to be installed at the EV rooftop.
2.2 Multiple source hybridization in VEIL

A “full” EV requires significant progresses in battery technology and the use of
592 different energy sources with optimized management of the energy flows, as none
of the available energy sources can easily fulfill alone the global demand of EVs to
enable them to compete with gasoline/diesel powered vehicles. The concept of using
and coordinating multiple energy sources to power the EV is generally called

hybridization. Hence, the specific advantages of the various energy sources can be fully
utilized, leading to optimized energy economy while satisfying the expected driving
range and maintaining other EV performances (Bossel, 2007).
The power scheme of the VEIL hybridization project is presented in Figure 1(a)
where a variable frequency driver and an induction motor are considered. The
configuration of the PV array is presented in Figure 1(b) where the usable space on the
rooftop and in the hood consists of two surfaces with the following dimensions:
1,300 × 1,100 and 550 × 1,100 mm2, respectively. In this surface it is possible to install
five selected PVs, four in the rooftop, and one in the hood. Figure 1(c) presents the solar
energy study, which uses the average hourly statistics for direct normal solar radiation
(Wh/m2) for the last 30 years at the project location, Coimbra – central region of
Portugal (Trovão et al., 2009).
The basic operations of this hybrid system are:
• for high power operation (hard acceleration or traveling up slopes), the two
energy sources (high specific energy and power) provide power to the powertrain
system;
• for low power operation (travel at constant speed, i.e. cruising), the source with
high specific energy provides power to the drive system while simultaneously
recharges the second source that has high specific power to prepare it for new
high power demand situations; and
• for braking operations, the regenerative energy is essentially stored in the source
with high specific power, particularly the peaks, and only a small fraction, limited
to its maximum power value, is absorbed by the source with high specific energy.
Therefore, this hybridization concept uses complementary feeding systems combining
the advantages of each source and better responding to the driving requests. For this
purpose, any work related to hybridization should start with an optimized sizing of the
on-board vehicle energy sources, meeting the minimum requirements aimed for the EV,
as presented in Trovão et al. (2009). Other more specific aspects of VEIL’s multiple
source hybridization system are further described in Mierlo et al. (2006); and Pereirinha
et al. (2009).
2.3 Daily urban EV usage

The selected daily urban scenario corresponds to a typical routine for mobility in
big European cities, with low average speed and very frequent stop and start. It is
considered that the VEIL is daily out of home from 7:30 a.m. until 6:30 p.m. Along this
daily time slot, the first drive time period (to get to work) goes from 7:30 a.m. to
9:00 a.m. Then the EV stays parked from 9 a.m. until 5 p.m. The EV is equipped with
(a) (b)
Power Flow
Direction
Mechanical Transmission
PV 01 PV 02
HSE Energy Source PV 05
90V
VFD IM
PV 03 PV 04
HSP Energy Source
SC Multiple Inputs Power Convert

with Power Flow Controller
(c)
800
700
600
500
400
(Wh/m²)
300
200
Normal Solar Radiation

100
0
20
Dec
15 Oct Nov
Hou 10 Aug Sep
r of May Jun Jul
Day 5 Apr a r
of Ye
Jan Feb Mar Month
0
Source: Based on Pereirinha and Trovão (2012)
radiation
implementation;
use
vehicle daily
VEIL PV array
power scheme; (b)
(a) VEIL project
normal solar
average hourly
Figure 1.
593
(c) Coimbra’s
Electric
MEQ approximately 135 W PV panels for battery charging (Pereirinha and Trovão, 2012).
26,4 The daily trip back home takes one-and-half hour, arriving at home around 6:30 p.m.
In the driving periods, the ECE 15 driving cycle was considered representative of
this mobility pattern (see Figure 2(a)). This cycle was used for the tests presented in
Pereirinha and Trovão (2012) for the VEIL prototype with only the battery pack, with
regenerative braking capacity. Thus, each one of the 1.5 hour driving periods were
594 formed by 27 consecutive ECE 15 driving cycles corresponding to approximately
27.35 km, being the daily total journey distance of 54.7 km (Pereirinha and Trovão,
2012).
Figure 2(b) presents the results for the proposed scenario using LiFePO4 batteries
(96 V pack, using Thunder Sky, Ah cells), PVs (BP Solar, 84-105 V, 135 W), and SCs
(Maxwell, 81 V, 100 F) (Pereirinha and Trovão, 2012).
As presented in Figure 2(b), a very important part of the regenerative braking
energy can be recovered using SCs. Although their present cost is high, this is expected
to decrease in a near future. Regenerative braking is particularly important for urban
traffic. The expected energy generated by PV is approximately 1.35 kWh per day,
supposing that the driver can find a sunny and adequately oriented parking place,
and it is helpful as an extra source to deal with unforeseen requests and minimize the
recharge of the ESS through the grid. This setting requires the hybridization of
multiple energy sources and a comprehensive EMS (Pereirinha and Trovão, 2012).
3. The EV in a household perspective

EVs offer a relevant potential for overall household energy management, and
technologies have been presented to exploit it. EVs differ from other residential loads
because they allow storing and providing significant amounts of electrical energy given
the increasing storage capacity of their energy sources (different battery technologies and
SCs). Therefore, in the next sections a household EMS, designated as EB is presented
(Lopes et al., 2012; Donnelly and Livengood, 2008; Melo et al., 2011), as well as associated
technologies to deal with EVs in a perspective of global optimization of all energy
resources in a residential setting.
3.1 The EB system

The EB has been proposed as a 24/7 operating system for optimally managing the
usage of electrical energy in a home or small business. The EB is designed to operate in
a demand-sensitive real-time pricing environment, which is expected to become a standard
price mechanism through smart grids technology, see Figure 3 (Donnelly and Livengood,
2008; Melo et al., 2011).
The on-going evolution of the electrical grid toward smart grids generates
opportunities for implementation of demand-side EMS based on different pricing
strategies allowing a more efficient use of the electric power infrastructure. Engaging
in alternative electricity consumption patterns means benefits for the end-users (namely
decreasing their electricity bill without degrading comfort levels) and for utilities as well
(e.g. enabling a better management of power peaks, flattening the aggregate demand
curve, and making demand “to follow” supply), leading also to environmental benefits
(Lopes et al., 2012; Soares et al., 2012). Concerning the EB system, the most relevant aspect
in this paper is the capability to manage the energy of EVs as a specific residential
load. The aim is making decisions of storing, using or selling electricity back to the grid,
given dynamic variables such as electricity prices, weather conditions, comfort and
(a) (b)
60 Daily Average Energy
9,000
50 8,000
7,000
40
6,000
30 5,000
Speed (Km/h)
Energy (W.h)
4,000
20
3,000
10
2,000 Available energy without PV Energy (Bat.+Reg.)
Available energy with Regenerative and August’s PV Energy
Available energy without Regenerative and PV Energy (Bat. only)
0 1,000
0 20 40 60 80 100 120 140 160 180 200 0 5 10 15 20
Time (s) Hour of Day
Source: Based on Pereirinha and Trovão (2012)

use
vehicle daily
the selected urban

ECE 15 urban
mobility scenario
average energy for
vs time; daily
driving cycle speed
Figure 2.
595
Electric
MEQ
26,4
External weather
Home controls: ex. conditions
thermostast, AC...
596
Distributed generaton
Current state of the
home
Grid conditions
including real-time
Plug-in vehicle price of electricity and
storage level and load shedding request
Energy Box
constraints
Electric load due

to storage Local storage
controls level and
controls
Figure 3.
Energy box concept
illustration
Source: Created by authors based on Livengood and Larson (2009)
service requirements, and electricity availability from decentralized renewable sources,

without requiring constant human intervention (Soares et al., 2014).
A bidirectional communication system will be needed to send and receive
information signals about energy prices, peak demand, requests to reduce the power
demand, and unused renewable energy. Decisions about storing, using or selling
electricity to the grid will be based on the specific user profile and other information
(e.g. preferred schedule for the operation of appliances including the EV). This system
will provide autonomous support and an optimized use of all energy resources based on
adequate algorithms.
3.2 Controllable charger

To explore the advantages offered by the EB also controlling an EV, some hardware
requirements should be fulfilled. To accomplish the desired interaction between the EV
and the EB, the EV requires a charger designed according to some specifications. Those
are bidirectionality in power flow for charge (G2V) and discharge (V2G) operation modes,
capability to establish unit power factor and low total harmonic distortion (THD) in order
to not degrading power quality. The EV charger should also have low construction
complexity, perform power control, possibly being an on-board charger, and compatible
with a regular 16 A plug. In order to fulfill these requirements the charger topology
presented in Figure 4 has been developed, which is discussed in (Melo et al., 2012).
The battery technology is LiFePO4, which has been selected based on recent studies on
the applicability of ESSs in EVs showing this is one of the most commercially appropriate
batteries for EV powertrain (Pereirinha and Trovão, 2012).
4. Energy pricing scenario analysis

As mentioned above, in the near future demand-sensitive electricity pricing is expected
to become the operative standard for energy tariffs. In this section a high-level diagram
Grid AC Filter Rectifier / Inverter DCBus Chopper(C Class) Batteries Electric
vehicle daily
use
L AC
AC
L Chopper
Cbus 597
BAT
Power Signal PWM (Pulse With Modulation)
PWM
CONTROLLER
Energy Box Figure 4.
SoC (State of Charge) Proposed EV
charger topology
Source: Created by authors based on Melo et al. (2012)
(Figure 5) is presented to illustrate the strategy to cope with electricity dynamic pricing
for the case study of the VEIL project equipped with a LiFePO4 battery pack,
regenerative braking capacity and PV panels for battery recharge (Pereirinha and
Trovão, 2012). In order to exploit the maximum PV generation, the time period is
August 2012, in Portugal, in which the PV modules present the higher value of energy
generated in a typical year[3] (Pereirinha et al., 2009). The “access to grid” tariff value
was €0.0654/kWh for end-users with power contracted under 20.7 kVA, according to
the energy services regulatory authority 2012 data[4].
The diagram in Figure 5 illustrates the strategy underlying the construction of the
dynamic price curves used in the case study. The data regarding the average final price
in the free electricity market in Portugal in August 2012 were extracted from the OMIE
web site (see footnote 3). The data regarding each hour of each day were collected and
analyzed to understand the differences in the electricity price for the working days and
weekends. The market price and the access to grid tariff were combined according to
the pricing architecture used by the regulator. The final dynamic price curves were
then obtained as an average of all data collected for working days and weekends.
4.1 Scenario description

This section characterizes the scenario of the case study: a single EV is considered and
therefore the impact on the grid of several EVs simultaneously connected for charge
or discharge is not addressed.
It should be noted that the expected lifetime conservation and safety-level
preservation require a maximum of 80 percent of the battery state of charge (SoC)
utilization for LiFePO4 technology [5]. The battery operation temperature is considered
constant (around 25 °C), and the proposed charger for this application ensures that the
permitted voltage and current limits are not exceeded (see footnote 5) (Pereirinha et al.,
2013; Melo et al., 2013a, b). The maximum SoC level (100 percent) corresponds to 8.640
kWh while the minimum allowed SoC (20 percent) is equal to 1.728 kWh. It is also
considered that the EV is the family’s second car, which is used for traveling only during
working days (and performing energy exchange with the grid), and during weekends
26,4
598
MEQ
Figure 5.
Dynamic tariff
construction diagram
Average for the
/MWh 1,000 /kWh working and weekend
days
Note: Authors’ own elaboration

essentially for energy exchange with the grid (Pereirinha et al., 2013). The strategy for Electric
buying or selling electricity is described in the flowchart presented in Figure 6. vehicle daily
The strategy for buying or selling electricity is based on the knowledge of the
maximum and minimum electricity price for the next day (24 hours period) for each day
use
of the month (upper graphs of Figure 7(a) and Figure 8(a)). Energy is bought and the
battery is recharged, when and while the battery SoC allows it, if the energy price is
below the daily average price; if the energy price is above the daily average price, 599
EV Pluged to the
Wait to plug...
grid?
N
Buy energy
Y Y
Soc Value - Soc Energy price ˂ daily (Power level has a
Min (1.728 kWh) average price? linear relation to
the energy price)
N
Waits for suitable
energy price to buy
N
Y Sell energy
Soc Value . Soc Energy price . daily Y (Power level has a
Max (8.640 kWh) average price? linear relation to
the energy price)
N Waits for suitable

N energy price to sell
Buy energy
Soc Min ˂ Soc Value Energy price ˂ daily Y (Power level has a
< Soc Max average price? linear relation to
the energy price)
Sell energy
N (Power level has a
linear relation to
the energy price)
Figure 6.
Decisions for buying
or selling energy
26,4
600
MEQ
Figure 7.
Working days
(a) (b)
8
0.13 Week Day Purchase Amount ( )
Week Day Sale Amount ( )
0.125 Week Day Purchase and Sale Difference ( )
7
August Week Days Purchase and Sale Difference ( )
0.12
/kWh
6
0.115
0.11 Week Electricity Price Evolution

5
0 5 10 15 20
Parking Period Time (h)
4
9,000
EV Week Energy Evolution
8,000
3
7,000
6,000
5,000 2
Wh
4,000
3,000
Morning Driving Period 1
2,000
1,000
0 5 10 15 20
0
Time (h) Afternoon Driving Period
Note: (a) Electricity price and EV stored energy; (b) energy cost per day and per month, considering dynamic energy pricing.
Authors' own elaboration
(a) (b)
0.16 2
0.14 1.5
0.12
/kWh
1
0.1 0.5
Weekend Electricity Price Evolution
0.08 0
0 5 10 15 20
Time (h)
–0.5
9,000
EV Weekend Energy Evolution
8,000 –1
7,000
6,000 –1.5
5,000
Wh
4,000 –2
3,000 Weekend Day Purchase Amount ( )
Weekend Day Sale Amount ( )
2,000 –2.5
Weekend Day Purchase and Sale Difference ( )
1,000 August Weekend Days Purchase and Sale Difference ( )
0 5 10 15 20 –3
Time (h)
Notes: (a) Electricity price and EV stored energy; (b) energy cost per day and per month, considering dynamic energy pricing.
Authors’ own elaboration
use
vehicle daily
Weekend days
Figure 8.
601
Electric
MEQ energy is sold and the battery is discharged. The power level used for energy buy/sale
26,4 linearly depends on the difference between the lowest/highest price and the daily
average price. It is important to remark that the charger’s maximum power level
(2.3 kW) is used when the electricity price is maximum or minimum, and the
instantaneous power depends on the difference between the hourly price and
the average price (around €0.12 for working days, in Figure 7(a)). Thus, the slope of the
602 stored energy curve is higher when this difference is bigger, as can be seen on the lower
graph of Figure 7(a).
When the EV is out from home, from 7:30 a.m. to 6:30 p.m., two driving periods exist
in which there is mainly energy consumption due to traveling, and one parking period
of eight hours where the EV is recharged through the utilization of PV, as presented
in Figure 7(a). In Figure 7(b) the energy cost per day and the total monthly energy
utilization costs are presented, for working days (“Week day”). The total cost for
driving during the 23 working days in August is approximately €7.44, which is much
less than what would be needed for an ICE vehicle.
In Figure 8 the corresponding results for the weekend days are presented. Using the
EV to store and sell energy to the grid during the weekend, the total energy cost for the
eight August weekend days is €−2.63, which means a profit in the user’s perspective.
Therefore, in these scenarios all the EV user needs were fulfilled being still possible
obtaining some profit from the energy exchanges with the grid.
In Figure 9 the utilization costs for this case study are presented. The weekend days
allow a revenue of €2.63 and the working days driving cost is reduced from
€7.44 to €4.81, i.e. a reduction of 35.35 percent. Therefore, the total cost to perform the
considered daily routine (using these data for August 2012 in Portugal), i.e. 23 working
days driving 54.7 km/working day, a total of 1,258.1 km per month, is only €4.81.
Moreover, by analyzing Figure 10 it is possible to determine that the energy
required for traveling the total distance in the whole month is approximately
equivalent to 50 kg of CO2. The total involved for working and weekend days is
8
August Week Days Amount ( )
August Weekend Days Amount ( )
August Week and Weekend Days Difference Amount ( )
Figure 9. –2
Final utilization
costs for the
considered
variable tariff –4
g /kWh Electric
350
vehicle daily
300 use
250
200
150 603
100
50
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
CO2 (g/kWh) Radioactive Residues (g/kWh)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 10.
Hydro Coal Fossil Cogeneration Wind Natural Gas 2012 generation mix
for Portugal and
Other Renewable Other Nuclear respective CO2
(g/kWh) emissions
Source: www.edpsu.pt/pt/origemdaenergia/Folhetos%20de%20Rotulagem/Rotulagem% (based on)
20de%20energia%20el%C3%A9trica%20EDP%20SU%202012.pdf
approximately 64 kg of CO2. These values are calculated using the previously

presented battery SoC information and the values available on the 2012 EDP report,
referred in [6]. The calculation process is presented in (1) to (6).The analysis of Figure 10
enables to understand that in August of 2012 each kWh equals approximately 260 g,
which means 0.26 kg/kWh:

100% - 8:640 kWh
Battery Pack SoC )
20% - 1:728 kWh
Solar energy gathered in working days ≈ 1:35 kWh (1)
Daily energy available from the battery pack ¼ 8:640 1:728 ¼ 6:912 kWh (2)
Total energy available at working days ¼ 6:912 þ 1:35 ¼ 8:263 kWh (3)
From (2) and (3), the CO2 emission are computed:
CO2 amount for the eight weekend days ¼ 8 6:912 0:26 ¼ 14:38 kg (4)
CO2 amount for the 23 working days ¼ 23 8:263 0:26 ¼ 49:41kg (5)
MEQ And the total CO2 emissions for August are obtained:
26,4
CO2 Augusttotal amount ¼ 49:41 þ 14:38 ¼ 63:79kg (6)
5. Conclusions
In this paper a small EV platform for research dealing with hybridization of multiple
604 energy sources was presented. An EB EMS designed to operate in a demand-sensitive
real-time pricing environment, which will expectedly become feasible through the
evolution to smart grids, will allow taking advantage of EVs as a specific domestic
load, with benefits for end-users, utilities and the environment. Results of a case study
based on the ECE 15 driving cycle for different market electricity tariffs have been
presented, for possible EB decisions. The results consider battery recharging using PV
solar panels on the vehicle. Due to the complexity of this solution, including different
voltage levels of the PV panels and the other energy sources, as well as a relatively
small amount of energy achievable, it is not being followed by the manufacturers.
Indeed, the EVs that have PV panels normally use them to air-conditioning the vehicle
or feed auxiliary circuits. However, these PV panels could be applied in parking areas
and therefore there is no need to apply this technology directly in the EVs. The battery
capacity of the EV considered, about 8.9 kWh, is very small. The battery capacity of a
current EV is almost three times this value. Thus, results could be significantly altered.
Despite these limitations and with the current electricity tariffs, it is possible to
conclude that selling energy to the grid with higher revenue than the purchase cost
being controlled automatically by a device like the EB can reduce the energy costs by
about 35 percent for the same mobility patterns. While this saving is very significant in
relative terms, it amounts to about €2.63 in absolute value. The energy demand for
traveling the total distance (in August 2012) is approximately equivalent to 50 kg of
CO2, being approximately 64 kg of CO2 the total amount for the considered working
and weekend days. These values are considerable smaller than for ICE CO2 emissions.
Assuming that a vehicle with higher capacity could save higher values, the main
conclusion is that with the current tariff variations, V2G is of limited interest to the EV
owner: the reduction in the monthly costs of energy hardly justifies the complexity and
the costs of associated systems. For this system to be interesting for the EV user, the
electricity tariff should display a much larger difference between maximum and
minimum values. In the utility’s perspective, this would be interesting depending on the
amount of aggregated EVs, for instance company fleets, where a high number of EVs
controlled by a given entity would allow achieving a significant amount of “dispatchable”
power, capable of providing ancillary services such as secondary reserve mechanisms.
Notes
1. http://ec.europa.eu/archives/european-council/index_en.htm
2. www.thechargingpoint.com/buying-guide.html
3. www.omie.es/files/flash/ResultadosMercado.swf
4. www.erse.pt/pt/electricidade/tarifaseprecos/Paginas/default.aspx
5. www.thunderstruck-ev.com/Manuals/Thundersky%20Product%20Manual.pdf
6. www.edpsu.pt/pt/origemdaenergia/Folhetos%20de%20Rotulagem/Rotulagem%20de%
20energia%20el%C3%A9trica%20EDP%20SU%202012.pdf
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paper, MIT-Portugal Program, Engineering Systems Division, Massachusetts Institute of
Technology, Cambridge, MA.
About the authors

Hugo Neves de Melo received the MSc Degree on Electrical Engineering (Automation) in 2012,
from the University of Coimbra, Portugal. He is a Full Time Researcher since 2012, with the
Institute for Systems Engineering and Computers at Coimbra (INESC-Coimbra), Portugal.
His research interests cover the areas of electric vehicles, control systems, electronics,
microcontrollers, and batteries technology.
João P. Trovão was born in Coimbra, Portugal, in 1975. He received the MSc Degree and the PhD
Degree in Electrical Engineering from the University of Coimbra, Coimbra, Portugal, in 2004 and 2013,
respectively. From 2000 to 2014, he was a Teaching Assistant and an Assistant Professor with the
Polytechnic Institute of Coimbra-Coimbra Institute of Engineering (IPC-ISEC), Portugal. Since 2014,
he has been a Professor with the Department of Electrical Engineering and Computer Engineering,
Université de Sherbrooke, Sherbrooke, QC, Canada. His research interests cover the areas of electric
vehicles, renewable energy, energy management, power quality, and rotating electrical machines.
João P. Trovão is the corresponding author and can be contacted at: jtrovao@isec.pt
Professor Carlos Henggeler Antunes received his PhD Degree in Electrical Engineering
(Optimization and Systems Theory) from the University of Coimbra, Coimbra, Portugal, in 1992.
He is a Full Professor at the Department of Electrical and Computer Engineering, University of
Coimbra, Coimbra, Portugal. His research areas include multiple objective optimization and
energy planning.
Professor Paulo G. Pereirinha received the PhD Degree in Electrical Engineering from the
University of Coimbra, Coimbra, Portugal. Since 1995, he has been with the Polytechnic Institute
of Coimbra-Coimbra Institute of Engineering (IPC-ISEC), where he is currently a Coordinator
Professor with the Department of Electrical Engineering and the President of the Scientific
Committee. His classes and research interests include electrical machines, electric vehicles,
electromechanical drives, finite elements, and renewable energies.
Professor Humberto M. Jorge received his Electrical Engineering Degree in 1985 and his PhD
Degree in 1999, both from the University of Coimbra, Coimbra, Portugal. He is an Auxiliary
Professor at the Department of Electrical and Computer Engineering of University of Coimbra,
Coimbra, Portugal. His teaching and research interests include power systems, load research, load
forecast, load profile, power quality, power distribution, energy efficiency, and energy storage.
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Management of Environmental Quality: An International Journal

An outlook of electric vehicle daily use in the framework of an energy

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Computers Engineering at Coimbra, Coimbra, Portugal

2. Urban electric mobility

2.1 The VEIL project

Flexible Time-Triggered communication on CAN (FTT-CAN) protocol (Santos et al.,

Ligier 162 GL characteristics Original version Full electric version

Engine 4 stroke diesel (Lombardini) −

2.2 Multiple source hybridization in VEIL

and coordinating multiple energy sources to power the EV is generally called

2.3 Daily urban EV usage

HSP Energy Source

SC Multiple Inputs Power Convert

Normal Solar Radiation

Source: Based on Pereirinha and Trovão (2012)

3. The EV in a household perspective

3.1 The EB system

Source: Based on Pereirinha and Trovão (2012)

the selected urban

Electric load due

service requirements, and electricity availability from decentralized renewable sources,

3.2 Controllable charger

4. Energy pricing scenario analysis

Power Signal PWM (Pulse With Modulation)

4.1 Scenario description

Note: Authors’ own elaboration

N Waits for suitable

0.11 Week Electricity Price Evolution

approximately 64 kg of CO2. These values are calculated using the previously

About the authors

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