Regenerative Braking Optimization Using Particle Swarm Algorithm for Electric Vehicle
Wong Siu Chai*, Muhammad Izuan Fahmi bin Romli*,**,, Shamshul Bahar Yaakob*, Liew Hui Fang**, and Muhammad Zaid Aihsan***
*Faculty of Electrical Engineering Technology, University Malaysia Perlis (UniMAP)
Pauh Putra, Arau, Perlis 02600, Malaysia
**Electric Vehicle Energy Storage System (eVess) Research Group, Centre of Excellence Renewable Energy (CERE), Universiti Malaysia Perlis (UniMAP)
Pauh Putra, Arau, Perlis 02600, Malaysia
***Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka
Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia
In recent years, the topic of reducing fuel consumption and greenhouse gas emission has become one of the major focuses on the automotive industry leading toward the development of electric vehicles to create awareness of environmental protection. Thus, the development of hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), and fully electrical vehicle (EV) has started growing up to replace the gasoline car, which is fully depends on fuel to operate, to help fight against the world climate change issues. This research is mainly focused on solving the problem of charging period of traditional used batteries pack, energy storage system of EV, and the limitation on travel distance for EV with the use of batteries pack as an energy source. The proportional-integral (PI) controller based on particle swarm optimization (PSO) algorithm is implemented in this simulation to optimize the speed of BLDC motor by obtaining an optimized parameter of Kp and Ki. The MATLAB/Simulink software is used for graphical modelling, simulating, and analyzing the behavior of supercapacitor in various condition. The simulation results represent the proposed PSO-based energy management method can achieve greater energy efficiency as compared to the traditional method. All in all, moving forward in developing a fully electric buses or vehicles can bring society into a new generation of zero greenhouse gas emissions. In this paper, the optimization of the PI controller based on PSO algorithm is applied and the results show that there is an increment of 6% in total distance traveled by the EV. Besides, there is the 3.69% of improvement for maximum speed and peak to peak speed of the EV and 14.57% of improvement in terms of average speed of EV within the total travel duration of 1300 s.
-  M. Montazeri-Gh and M. Mahmoodi-k, “Development a new power management strategy for power split hybrid electric vehicles,” Transp. Res. Part D: Transp. Environ., Vol.37, pp. 79-96, doi: 10.1016/j.trd.2015.04.024, 2015.
-  E. Kamal and L. Adouane, “Intelligent Energy Management Strategy Based on Artificial Neural Fuzzy for Hybrid Vehicle,” IEEE Trans. Intell. Veh., Vol.3, No.1, pp. 112-115, doi: 10.1109/tiv.2017.2788185, 2018.
-  J. Wu et al., “The role of environmental concern in the public acceptance of autonomous electric vehicles: A survey from China,” Transp. Res. Part F, Traffic Psychol. Behav., Vol.60, pp. 37-46, doi: 10.1016/j.trf.2018.09.029, 2019.
-  I. E. Agency, “Global EV Outlook 2018: Towards Cross-Modal Electrification,” 2018. https://www.iea.org/reports/global-ev-outlook-2018 [accessed December 18, 2021]
-  G. Hiermann et al., “Routing a mix of conventional, plug-in hybrid, and electric vehicles,” Eur. J. Oper. Res., Vol.272, No.1, pp. 235-248, doi: 10.1016/j.ejor.2018.06.025, 2019.
-  J. Li et al., “SMES/Battery Hybrid Energy Storage System for Electric Buses,” IEEE Trans. Appl. Supercond., Vol.26, No.4, pp. 1-5, doi: 10.1109/TASC.2016.2527730, 2016.
-  Q. Zhang and G. Li, “Experimental Study on A Semi-Active Battery-Supercapacitor Hybrid Energy Storage System for Electric Vehicle Application,” IEEE Trans. Power Electron., Vol.35, No.1, pp. 1014-1021, doi: 10.1109/tpel.2019.2912425, 2019.
-  L. Kouchachvili, W. Yaïci, and E. Entchev, “Hybrid battery/supercapacitor energy storage system for the electric vehicles,” J. Power Sources, Vol.374, pp. 237-248, doi: 10.1016/j.jpowsour.2017.11.040, 2018.
-  Z. Chen et al., “Multimode Energy Management for Plug-In Hybrid Electric Buses Based on Driving Cycles Prediction,” IEEE Trans. Intell. Transp. Syst., Vol.17, No.10, pp. 2811-2821, doi: 10.1109/TITS.2016.2527244, 2016.
-  J. Du et al., “Battery degradation minimization oriented energy management strategy for plug-in hybrid electric bus with multi-energy storage system,” Energy, Vol.165, Part A, pp. 153-163, doi: 10.1016/j.energy.2018.09.084, 2018.
-  Z. Chen et al., “Optimal energy management strategy of a plug-in hybrid electric vehicle based on a particle swarm optimization algorithm,” Energies, Vol.8, No.5, pp. 3661-3678, doi: 10.3390/en8053661, 2015.
-  T. Mesbahi et al., “Optimal Energy Management for a Li-Ion Battery/Supercapacitor Hybrid Energy Storage System Based on Particle Swarm Optimization Incorporating Nelder-Mead Simplex Approach,” IEEE Trans. Intell. Veh., Vol.2, No.2, pp. 99-110, doi: 10.1109/tiv.2017.2720464, 2017.
-  B. Chen et al., “An Improved TL Buck Converter for Fast-Charging Energy Storage System Using UCs,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.7, pp. 1086-1093, doi: 10.20965/jaciii.2016.p1086, 2016.
-  Z. Zheng et al., “Adaptive Energy Control Strategy for a Hybrid Energy Storage System in a DC Micro-Grid of an Unmanned Surface Vehicle,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.2, pp. 287-292, doi: 10.20965/jaciii.2019.p0287, 2019.
-  F. Naseri, E. Farjah, and T. Ghanbari, “An efficient regenerative braking system based on battery/supercapacitor for electric, hybrid, and plug-in hybrid electric vehicles with BLDC motor,” IEEE Trans. Veh. Technol., Vol.66, No.5, pp. 3724-3738, doi: 10.1109/TVT.2016.2611655, 2017.
-  M. Muhammad, Z. Rasin, and A. Jidin, “Bidirectional Quasi-Z-Source Inverter with Hybrid Energy Storage for IM Drive System,” IEEE 9th Symp. Comput. Appl. Ind. Electron., pp. 75-80, doi: 10.1109/iscaie.2019.8743869, 2019.
-  S. S. Bhurse and A. A. Bhole, “A Review of Regenerative Braking in Electric Vehicles,” 7th IEEE Int. Conf. Comput. Power, Energy, Inf. Commun. (ICCPEIC), pp. 363-367, doi: 10.1109/ICCPEIC.2018.8525157, 2018.
-  Y. Bai et al., “Battery anti-aging control for a plug-in hybrid electric vehicle with a hierarchical optimization energy management strategy,” J. Clean. Prod., Vol.237, Article No.117841, doi: 10.1016/j.jclepro.2019.117841, 2019.
-  S. Fan et al., “A modularized discharge-type balancing topology for series-connected super capacitor string,” Energies, Vol.11, No.6, Article No.1438, doi: 10.3390/en11061438, 2018.
-  Y. Wang, Z. Sun, and Z. Chen, “Rule-based energy management strategy of a lithium-ion battery, supercapacitor and PEM fuel cell system,” Energy Procedia, Vol.158, pp. 2555-2560, doi: 10.1016/j.egypro.2019.02.003, 2019.
-  O. Smiai et al., “Exploring particle swarm optimization to build a dynamic charging electric vehicle routing algorithm,” A. D. Gloria (Ed.), “Applications in Electronics Pervading Industry, Environment and Society,” Vol.512, Springer Cham, 2017.
-  H. Marefat, “New Energy Management Systems for Battery Electric Vehicles with Supercapacitor,” Master’s thesis, University of Waterloo, 2019.
-  A. C. Shekar and S. Anwar, “Real-time state-of-charge estimation via particle swarm optimization on a lithium-ion electrochemical cell model,” Batteries, Vol.5, No.1, Article No.4, doi: 10.3390/batteries5010004, 2019.
-  K. Mathew and D. M. Abraham, “Particle swarm optimization based sliding mode controllers for electric vehicle onboard charger,” Comput. Electr. Eng., Vol.96, Part A, Article No.107502, doi: 10.1016/j.compeleceng.2021.107502, 2021.
-  B. Tifour et al., “An optimal fuzzy logic control for a fuel cell hybrid electric vehicle based on particle swarm and advisor,” IEEE Green Technol. Conf., pp. 148-154, doi: 10.1109/GREENTECH48523.2021.00033, 2021.
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