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.