Adaptive Inertia Observer-Based Model-Free Predictive Current Control for PMSM Driving System of Electric Vehicles
Yao Wei, Dongliang Ke, Xinhong Yu, Fengxiang Wang, José Rodríguez
Abstract
Due to the harsh operating environment and complex operating conditions of electric vehicles (EVs), the physical parameters of the motor undergo nonlinear changes, leading to mismatches in both the physical model and system inertia. To address this issue, an adaptive inertia observer-based model-free predictive current control (MF-PCC) strategy is proposed in this paper, and applied to the EV's permanent magnet synchronous motor (PMSM) driving system to adjust the system inertia online. The current load of the EV is converted to mass and load inertia. An adaptive inertia method is designed to achieve inertia matching between system inertia and load inertia based on online estimated load torque, reducing the influence of inertia mismatch. The implementation of the proposed method requires no physical parameters to eliminate their effects. The control performance is analyzed in principle using Bode diagrams and zero-pole maps with different sampling periods and inertia ratios. The effectiveness and correctness of the proposed method are demonstrated through experimental results compared with the MF-PCC with fixed inertia and conventional PCC strategies, as well as the advantages of better dynamics and current quality with enhanced robustness through adaptive inertia.