Improved PSO Algorithm for Distributed Drive Vehicle In-Wheel Motor Parameter Identification

Abstract

<div class="section abstract"><div class="htmlview paragraph">In the context of distributed-driven electric vehicles, the temperature of permanent magnet in-wheel motors tends to rise during prolonged and overload operating conditions. This temperature increase can lead to parameter drift in the motors, resulting in a decline in motor control performance, and in severe cases, motor failures. To address these issues, this paper establishes a motor parameter identification model based on the dq-axis stator current equation of the permanent magnet in-wheel motor. An improved Particle Swarm Optimization PSO algorithm is introduced to identify parameters such as motor resistance, inductance, and magnetic flux. In contrast to traditional parameter identification algorithms based on mathematical models, the improved PSO algorithm can simultaneously identify multiple parameters without encountering rank deficiency issues. Moreover, to overcome the slow convergence speed and low identification accuracy associated with traditional PSO algorithms, the improved PSO algorithm treats motor parameter identification as a time-series process. It incorporates the results of the previous parameter identification into the optimization process of particle velocities for the next iteration, providing guidance for PSO optimization. This accelerates the convergence speed of the population and enhances identification accuracy. Finally, through comparative simulations using Simulink, the results demonstrate that the improved PSO algorithm offers superior identification accuracy and faster convergence speed. It exhibits enhanced applicability in the control of permanent magnet in-wheel motors, effectively improving motor control precision and efficiency.</div></div>