Regenerative Braking Optimization Using Particle Swarm Algorithm for Electric Vehicle

Abstract

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.