FASTSim Paper
FASTSim Paper
FASTSim Paper
03
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Abstract. This paper calculates the amount of regenerative braking
energy when the vehicle is traveling in Hanoi's driving cycles by using
the FASTSim software. The study aims to evaluate the effectiveness of
regenerative braking in reducing energy consumption (E.C.) in different
driving cycles (D.C.s), such as urban and highway D.C.s. The results
demonstrate that regenerative braking can significantly reduce E.C.,
especially in traffic conditions with frequent stops. The study also
highlights the importance of driver behavior and road conditions in
determining the effectiveness of RBS. Overall, the analysis provides
valuable insights into the potential of RBS to improve the energy
efficiency of vehicles, which can contribute to reducing greenhouse gas
emissions and improving air quality.
Ключевые слова: управление движением автомобиля, динамика
рулевой системы, алгоритмы управления управляемыми колесами.
Keywords: regenerative braking, FASTSim, city, and highway driving.
ВВЕДЕНИЕ
INTRODUCTION
EVs are cleaner and kinder to the environment than their fossil fuel-
powered counterparts, since no exhaust fumes are produced, meaning
there are less dangerous greenhouse gases such as carbon dioxide
beingfewer dangerous greenhouse gases such as carbon dioxide are
expelled from the vehicles into the atmosphere. Promoting the
development of BEVs is considered as one of the promising solutions for
the treatment of severe air pollution in metropolises [1]. Moreover, the
drivetrains of E.V.s can operate at over 80% efficiency which shows that
t, which shows they have great potential in reducing the transportation
energy demand [2]. Many countries around the world offersworldwide
offer a series of incentive schemes (such as subsidies and tax credits) to
promote the adoption and use of Eadopting and using E.V.s, lowering
the cost to the consumer due to those aforementioned benefits [3].
However, low driving range, generally is the main reason that halts the
widespread use of E.V.s.
RBS is the great solution in enhancing the driving range of E.V.s.
Various studies have been conducted to calculate the recovered energy
by RBS. However, there is a few number of studies conducted in the
Vietna traffic scenarios and on E.V.s manufactured in Vietnam. Thus,
this paper focuses on calculating the regenerative energy generated by
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RBS using FASTSim (The Future Automotive Systems Technology
Simulator). Vinfast VF e34 specification and dynamics are the input
parameters of simulation. Four real-time driving cycles are chosen to
assess the recovered energy on V.F. e34. These driving cycles represent
different driving conditions, including the rush-hour and pre-peak hour
urban road, urban road during both peak and off-peak hour, and the
highway.
METHODOLOGY
FASTSim [4] (Future Automotive Systems Technology Simulator) is a
high-level advanced vehicle powertrain systems analysis tool supported
by the U.S. Department of Energy's Vehicle Technologies Office.
FASTSim have been validated for hundreds of different vehicles and
most existing powertrain options [ref].
The primary worksheet is the “VehicleIO” tab, as seen in Figure
1.26. It consists of three sections: inputs, simulation, and results. The
vehicle is defined in the inputs section at the top. The drive cycle
simulation is selected in the middle section. The results are displayed in
the lower section.
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Figure 1. FASTSim’s primary interface
The calculation modelling of vehicle in FASTSim is based on generic
physics-based model of longitudinal vehicle dynamics, the basic
automotive theory and operational characteristics of powertrain
components including battery.
The traction force, Ftrac which need to overcome resistances to propel
the vehicle forward (or for braking), is computed by,
Ftrac = Froll + Faero + Fgrad + Fa (1
)
in which the resistances of rolling F roll, aerodynamics drag Faero,
gradient Fgrad and acceleration Fa are defined as follows:
Froll = CRmgcos(α) (2)
(3)
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(4)
(5)
Thêm hình ảnh về giao diện Fastsim, phương pháp tính toán của phần
mềm (phương trình), đầu vào, đầu ra….
Vinfast VF e34 specification and vehicles models are imported into the
input data of FASTSim. Those parameters are presented in Table 1.
Table 1. Vinfast VF e34 specs
Parameter Value Unit Parameter Value Unit
Wheel friction of
Curb weight 1490 kg 0.7
coefficient
Rolling resistance
Wheelbase 2.611 m 0.008
coefficient
Mass centre height 0.58 m Frontal area 2.424 m2
Tire radius 0.32535 m Motor power 110 kW
Wheel's rotational 2
0.815 kg.m Motor efficiency 95 %
inertia
Transmission
95 % Battery energy 44.5 kWh
efficiency
Charger efficiency 86 % Battery efficiency 95 %
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The vehicle and its components are then simulated through four real-
time D.C.s. The simulated D.C.s can be shown in Figure 21.
1+ RA . e
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The regenerative braking power can also be calculated by using
these equations:
Pregen =Max( Min ( Pmoto r . ɳ trans , PW h eelReq .ɳ regen ) , 0)
¿
(83)
PW h eelReq PW h eelReq + P FricBrake (94)
Pmoto r =if ( P W h eelReq >0 , , )
¿
ɳ trans . ɳ bat ɳ trans . ɳ bat
PW h eelReq =P Roll + PInertiaW h eel + Pdrag + P slope + P accel (105
)
P FricBrake=−min(PW h eelReq + Pregen , 0) (116
)
Where: Pmoto r is the input power of the electric motor (W), PWheelReq is
¿
the wheel required power (W), P Roll is the rolling resistance power (W),
P InertiaWheel is the inertia system power (W), Pdrag is the aerodynamic
power (W), Pslope is the road gradient power (W), Paccel is the
acceleration power (W), ɳ trans is the transmission efficiency, ɳ bat is the
battery efficiency.
RESULTS
Table 2. E.V. component's E.C. and regenerative energy percentage
Driving cycle
Parameter
Urban cycle Highway Rush hour Off-Peak
Input power (Wh) 4170.15 11795.56 940.09 7478.5
Regenerative power
1225.7 1808.12 113.12 1409.21
(Wh)
Regenerative energy
29.93% 15.33% 12.03% 18.84%
percentage (%)
The accompanying data and information indicate that the energy
efficiency of a vehicle can vary significantly depending on the driving
cycle and the use of regenerative braking system (RBS). The highest
energy consumption was observed in the Highway driving cycle, while
the Rush hour and Off-peak driving cycles had lower energy
consumption compared to Highway but higher than Urban. This can be
explained by the frequency of stops and starts in congested traffic,
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although there are still other factors, such as road conditions and traffic
volume, that may affect energy consumption in these driving cycles. RBS
was found to collect and store less energy in the battery when driving on
highways, as indicated by the lower percentage of regenerated energy.
Meanwhile, the Urban driving cycle requires stricter acceleration and
deceleration, making RBS more effective in this case, as demonstrated
by the higher percentage of regenerated energy observed in this cycle.
Overall, these results highlight the importance of understanding the
energy consumption and recovery models of vehicles in different driving
scenarios, as well as the role that RBS can play in improving their
energy efficiency. To improve the efficiency of energy usage in electric
cars, it is necessary to research the optimization of renewable energy,
starting with optimizing the strategies and algorithms for controlling the
electric motor during the braking process.Để nâng cao hiệu quả sử dụng
năng lượng trên ô tô điện thì việc nghiên cứu tối ưu hóa lượng năng
lượng tái tạo là cần thiết, mà đầu tiên là tối ưu hóa các chiến lược và
thuật toán điều khiển mô tơ điện trong quá trình phanh[5].
CONCLUSION
This study introduces the E.C. calculation for a widely used E.V. in
Hanoi by using FASTSim. RBS recuperates more energy in urban cycles.
This research provides potential insights into E.V. energy use. It should
be expanded to include many different E.V. models and driving
conditions to indicate the feasibility of E.V.s in Vietnam traffic
conditions.
Reference
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vehicle energy consumption,» International Journal of Sustainable
Engineering, т. 4, pp. 1-10, 2011.
[3] Lu, Zhenbo and Zhang, Qi and Yuan, Yu and Tong, Weiping, «Optimal
Driving Range for Battery Electric Vehicles Based on Modeling Users'
Driving and Charging Behavior,» Journal of Advanced Transportation, pp.
1-10, 06 2020.
[4] Aaron Brooker, Jeffrey Gonder, Lijuan Wang, Eric Wood, Sean Lopp, and
Laurie Ramroth, «FASTSim: A Model to Estimate Vehicle Efficiency, Cost
and Performance,» SAE Technical Paper , p. 12, 2015.
[5] Le V.N., Dam H.P., Nguyen T.H., Kharitonchik S.V., Kusyak V.A. Research
of Regenerative Braking Strategy for Electric Vehicles. ENERGETIKA.
Proceedings of CIS higher education institutions and power engineering
associations. 2023;66(2):105-123. https://doi.org/10.21122/1029-7448-
2023-66-2-105-123