Zheng Tuo

Wuhan Business University

Abstract：

The performance of new energy vehicles (NEVs) is fundamentally determined by the efficiency and robustness of their motor control systems. Conventional control strategies, such as field-oriented control, are typically optimized for single performance objectives and therefore fail to adequately address the multi-objective nature of real-world operating conditions. This paper proposes a multi-objective optimization framework for NEV motor control based on a genetic algorithm. The control problem is formulated by jointly considering efficiency maximization, torque ripple minimization, and energy loss reduction. A permanent magnet synchronous motor (PMSM) model is established in the d–q reference frame, and key control parameters are encoded for evolutionary optimization. A non-dominated sorting genetic algorithm is employed to obtain Pareto-optimal solutions. Simulation results demonstrate that the proposed method achieves significant improvements in efficiency and dynamic performance compared with conventional approaches. The study provides a practical pathway toward intelligent and adaptive motor control in electric vehicles.

Key Words：

new energy vehicle; PMSM; motor control; multi-objective genetic algorithm; Pareto optimization