To reduce the energy consumption and improve the stability of distributed drive electric vehicles, a torque allocation strategy based on an economy and stability optimisation function (ESOF) and a fuzzy proportional-integral-derivative rule control (FPRC) strategy are proposed while considering motor efficiency, braking energy recovery and motor failure. First, the vehicle dynamics and motor equivalent models are established. Subsequently, a torque prediction model and fuzzy controller for the vehicle are designed to calculate the total desired torque and yaw moment, respectively. A torque optimisation function is established to minimise power losses in the electric motor and maximise braking energy recovery, and it is solved using an improved genetic algorithm. While satisfying vehicle driving constraints, the ESOF-based controller can effectively coordinate the operation of each motor in the high-efficiency range under driving and braking conditions. After one motor fault is detected, the ESOF-based controller is replaced with an FPRC-based controller to distribute the vehicle demand torque. A co-simulation platform integrating MATLAB/Simulink and CarSim is developed to verify the effectiveness of the proposed ESOF-based controller in the New European Driving Cycle (NEDC) and Federal Test Procedure 75 (FTP75) driving cycles. The effectiveness of the FPRC-based controller in step steering condition is verified using the co-simulation platform. The simulation results indicate that the vehicle economy and driving range of the ESOF-based controller improved compared with the results afforded by the typical torque distribution strategy based on the front–rear axle dynamic load ratio. The average efficiencies of the motors in the NEDC and FTP75 driving cycles increased by 2.94% and 2.4%, respectively. More importantly, the FPRC-based controller can more significantly improve the steering stability of a vehicle with motor failure compared with the ESOF-based controller.

为降低能耗，提高分布式驱动电动汽车的稳定性，在考虑电机效率的情况下，提出了基于经济稳定优化函数（ESOF）和模糊比例-积分-微分规则控制（FPRC）策略的转矩分配策略。 ，制动能量回收和电机故障。首先，建立车辆动力学和电机等效模型。随后，设计了车辆的扭矩预测模型和模糊控制器，分别计算总期望扭矩和横摆力矩。建立转矩优化函数以最小化电动机的功率损失并最大化制动能量回收，并使用改进的遗传算法求解。在满足车辆行驶约束的同时，基于ESOF的控制器可以在驱动和制动条件下有效协调各电机在高效范围内的运行。在检测到一个电机故障后，将基于 ESOF 的控制器替换为基于 FPRC 的控制器来分配车辆需求扭矩。开发了一个集成 MATLAB/Simulink 和 CarSim 的联合仿真平台，以验证所提出的基于 ESOF 的控制器在新欧洲驾驶循环 (NEDC) 和联邦测试程序 75 (FTP75) 驾驶循环中的有效性。使用联合仿真平台验证了基于 FPRC 的控制器在步进转向条件下的有效性。仿真结果表明，与基于前后轴动载荷比的典型扭矩分配策略相比，基于 ESOF 控制器的车辆经济性和续驶里程有所提高。NEDC 和 FTP75 驱动循环中电机的平均效率分别提高了 2.94% 和 2.4%。更重要的是，与基于 ESOF 的控制器相比，基于 FPRC 的控制器可以更显着地提高电机故障车辆的转向稳定性。