A novel technique for optimization of BLDC-based dual-motor electric vehicles using adaptive BFO-based PID controller

This study addresses the imperative for electric vehicle (EV) propulsion systems to operate at higher speeds with effective motor control, given the rapid advancement of EV technology. Specifically focusing on electric 2-wheelers, we aim to enhance their maximum speed range from 45 km/hr to 110 km/hr by optimizing the control strategy of a widely used commercial e-bike from Vespa. Our approach explores the feasibility of employing a dual motor system instead of a single motor, coupled with optimization techniques for a proportional-integral-derivative (PID) controller governing a linear brushless DC (BLDC) motor. Implemented in MATLAB/Simulink, our method offers advantages such as consistent convergence, ease of implementation, and high computational efficiency. By employing bacterial foraging optimization (BFO) along with an adaptive BFO (ABFO) technique to optimize the PID controller, we achieve significantly faster response times compared to conventional BFO methods. These findings underscore the efficacy of our approach in enhancing the speed control and acceleration characteristics of EV propulsion systems, contributing to the ongoing evolution of electric mobility solutions.