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To enhance straight driving stability of distributed drive electric vehicle (DDEV) actuated by switched reluctance motors (SRMs), a stability control strategy for the vehicle confronting wheel torque discordance caused by differences of phase angle (DPA) coming from motors on two sides is proposed in this paper. The potential influence of the DPA between coaxial motors on vehicle stability is studied based on the established SRM and DDEV dynamics models. Then, the stability control strategy eliminating DPA is proposed by introducing feedback control for vehicle yaw rate, after DPA calculator and control selector are designed. Finally, the stability control system including DPA control system, vehicle dynamics model, driver model, and SRM drive system is founded and carried out under conditions of two-wheel and four-wheel driving modes. The co-simulation results prove the stability control strategy can effectively eliminate DPA such that the straight driving stability is greatly enhanced on both driving modes.

To improve the comprehensive performance of the switched reluctance motor (SRM) for electric vehicles (EVs) under actual driving conditions, a novel geometrical optimization method based on the time-varying phase current characteristics and the comprehensive indicator with justified weight coefficients is proposed in this paper. First, based on the established dynamic models of EV and SRM, the actual operating phase currents of the motor working under the New European Driving Cycle (NEDC) are extracted and classified to develop the representative optimization conditions. Then, considering the requirements of EV, five indicators are defined to evaluate the comprehensive performance of SRM, and a new multi-objective synchronization optimization function is proposed with five weight coefficients obtained by the statistical analysis method for the sample data of the indicators. Furthermore, the geometrical optimization for a four-phase 8/6 SRM is carried out by the combination of Particle Swarm Optimization (PSO) and BP neural network algorithms, after analyzing the effects of key geometrical parameters on the indicators. Finally, the work performance of the optimized SRM is evaluated and compared with that of the initial prototype, and the results show that the optimized SRM with the proposed multi-objective synchronization optimization method can improve the comprehensive indicator by 59.59%, and can significantly enhance the vehicle actual working economy by 50.74% under NEDC cycle.