ALSSA-PID control-based wheel-side motor control system for four-wheel-drive electric vehicles

To enhance the response speed and accuracy of the motors in a four-wheel motor-driven electric vehicle, precise control of the wheel side motor speed is essential to improve vehicle handling stability. This paper proposes a chaotic adaptive sparrow-optimized PID control algorithm. Firstly, the structural principles of the four-wheel side motor-driven electric vehicle are analyzed. A mathematical model and a simulation model of the electric vehicle are established, and the accuracy of the tram model is verified under ECE urban working conditions. Subsequently, the traditional sparrow algorithm is improved using Logistic-Tent chaotic mapping, adaptive search parameters, and the Levy flight strategy. These modifications enable the algorithm to escape local optima and achieve better convergence speed and accuracy. Based on these improvements, the ALSSA-PID control algorithm is developed, and the motor model is simulated and tested. Finally, a physical control platform for the wheel-edge motor is constructed to test the control algorithm. The results indicate an average reduction of 48.83% in rise time and 11.27% in rise time, as well as a 27.31% reduction in stabilization time and 7.17% reduction in stabilization time for the ALSSA-PID control algorithm when compared to the open-loop control and SSA-PID control, respectively, at the required speed under the ECE condition. These findings demonstrate that the proposed control algorithm significantly improves the control performance of the wheel-edge motor control system.