JACIII Vol.26 No.6 pp. 1022-1030
doi: 10.20965/jaciii.2022.p1022


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

Corresponding author

February 27, 2022
July 22, 2022
November 20, 2022
particle swarm optimization (PSO), electric vehicle (EV), supercapacitor (SC), energy management system (EMS), brushless DC motor (BLDC)

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.

Configuration of EV model

Configuration of EV model

Cite this article as:
W. Chai, M. bin Romli, S. Yaakob, L. Fang, and M. Aihsan, “Regenerative Braking Optimization Using Particle Swarm Algorithm for Electric Vehicle,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.6, pp. 1022-1030, 2022.
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Last updated on May. 19, 2024