Focusing on the driving force distribution control method of four-wheel independent drive electric vehicles, a driving force distribution strategy with a hierarchical structure is proposed in this paper. The upper layer used a neural network to optimize the fuzzy rules and membership function so as to adapt to changes in the process parameters and subjectivity owing to the limitations of the fuzzy control rules acquired by human experience. The lower layer used the optimal distribution method based on rules, focusing on the minimum tire load ratio. The proposed control strategy was verified by Carsim/Simulink co-simulation and a hardware-in-the-loop experiment. The results show that the proposed control strategy can improve the handling stability of vehicles on roads with different levels of adhesion.

针对四轮独立驱动电动汽车的驱动力分配控制方法，本文提出了一种分层结构的驱动力分配策略。上层利用神经网络优化模糊规则和隶属函数，以适应由于人类经验获得的模糊控制规则的局限性而引起的过程参数和主观性的变化。下层采用基于规则的最优分配方法，重点关注最小轮胎载荷比。所提出的控制策略通过 Carsim/Simulink 联合仿真和硬件在环实验进行了验证。结果表明，所提出的控制策略可以提高车辆在不同附着力水平道路上的操纵稳定性。