Enhanced dynamics of sensorless two-wheel drive electric vehicles through electronic differential and advanced metaheuristic algorithms

Electric vehicles (EVs) have several advantages over fossil fuel vehicles, which reducing greenhouse gases and air pollutants. The selection of control systems play a pivotal role in the overall functionality, efficiency, safety, and user experience of electric vehicles. This study focuses on an EV with a two-wheel drive (TWD) configuration powered by a permanent magnet synchronous motor (PMSM) and a battery. The motors are controlled by a field-oriented control (FOC) method without a speed sensor, addressing common sensor issues. An electronic differential is included to maintain the balance of the EV during operation. This research mainly contributes to the integration of an extended Kalman filter (EKF) to solve the speed sensor issue in EVs based on FOC control. Additionally, the study addresses the challenge of determining the nine parameters of the FOC algorithm's controllers using the particle swarm optimizer (PSO) and grey wolf optimizer (GWO). The Kalman filter provided excellent stability for the EV, and the GWO achieved significant improvements: a 5% dicrease in speed error, a 10% dicrease in torque ripples, and a 9% improvement in current wave forme, compared to the PSO algorithm.