Optimization and control of the brushless DC motor speed for a Battery Electric Vehicle (BEV) using Fuzzy-grasshopper optimization regenerative braking system

Abstract

Transportation electrification is at the core of the possible solutions to many challenges the world is currently facing. Efficient vehicle electrification has the potential to simultaneously reduce greenhouse gasses emissions and to tackle range anxiety issues. Among different strategies for advancements in Battery Electric Vehicle (BEV) efficiency, enhancing Regenerative Braking (RB) capabilities is an area with opportunities. As RB faces different impediments, upgrades in safety, efficiency, and/or battery quality of life are usually accompanied with further strain in energy management schemes, limiting RB performance. Power Electronics (PE) improvements are among the options that have the potential to benefit RB and overall efficiency. This work proposes a method to improves RB through brushless DC Motor. As the EV speed drops, the fuzzy-grasshopper optimization regulates the regenerative braking torque to maintain the braking system's dependability. The fuzzy-grasshopper optimization controller also provides real-time fulfillment of the brake distribution force between the front wheel, rear wheel, and subsection of the regenerative forces and mechanical. At the conclusion of the run, the estimated SOC for smooth roads was 23.72%, compared to the measured SOC for smooth roads of 20.71%, and the simulated FL-RB SOC is 21.25%. At the rough road's conclusion, the measured SOC was 14.8%, the SOC simulated with the FL-RB was 15.98%, whereas without the FL-RB, the predicted SOC was 20.72%.