为了降低人类生活对石油资源的依赖程度，改善人类生存的环境质量，世界各国都在提倡大力发展新能源汽车。而四轮独立驱动电动汽车作为新能源汽车的一种新型布置形式，简化了整车传动系统，提高了传动效率和整车的可控自由度。通过对各轮毂电机输出转矩进行精确控制，以此改善车辆行驶过程中的操纵稳定性是四轮独立驱动电动汽车亟需解决的一项关键技术问题。本文针对该关键技术问题，通过以下具体工作完成驱动力分配策略设计和实验验证。

首先，对四轮独立驱动电动汽车多自由度非线性系统进行简化，在Simulink中搭建七自由度车辆动力学模型，与Carsim中成熟的车辆模型进行结构和性能参数匹配，通过相同工况下的仿真，验证所搭建车辆仿真平台的准确性和有效性。

    其次，采用分层结构对驱动力分配策略进行设计，其中上层利用神经网络的学习能力，对模糊控制器的模糊规则和隶属度函数等相关参数进行优化，计算出车辆稳定行驶所需的附加横摆力矩和需求纵向力，下层根据行驶工况的不同分别采用基于规则分配方法和以轮胎负荷率最小为目标的优化分配方法进行驱动力矩分配。

然后，依据GB/T6362-2014汽车操纵稳定性试验方法，通过Carsim/Simulink联合仿真，对所设计的驱动力分配策略进行高附着路面上的方向盘阶跃工况、双移线工况以及低附着路面上的蛇形工况仿真验证。

最后，通过实时仿真平台dSPACE和控制器DSP28335搭建硬件在环测试平台，控制器采集驾驶员的操纵信息以及dSPACE车辆实时模型中输出的车辆状态信息，进行方向盘阶跃输入工况下的硬件在环测试。

In order to reduce the dependence on petroleum resource and improve the environmental quality of human existence, countries all over the world are advocating the development of new energy vehicles strongly. As a new layout of new energy vehicle, the four-wheel independent drive electric vehicle simplifies the vehicle transmission system, improves the transmission efficiency and the controllability of the vehicle. It is a key technical problem that four-wheel independent drive electric vehicles need to solve urgently by controlling the output torque of each in-wheel motor accurately to improve the handling stability of the vehicle during the driving process. Aiming at this key technical issue, this paper completes the driving force distribution strategy design and experimental verification through the following specific work.

Firstly, the multi-degree-of-freedom nonlinear system of four-wheel independent drive electric vehicle is simplified and a seven-degree-of-freedom vehicle dynamics model is built in Simulink. The structure and performance parameters are matched with the mature vehicle model in Carsim. The accuracy and effectiveness of the vehicle simulation platform are verified by simulation under the same working conditions.

Secondly, the driving force distribution strategy is designed with the hierarchical structure. By leveraging the learning ability of the neural network in upper layer, the parameters of the fuzzy rules and membership function in the fuzzy controller are optimized, and the additional yaw moment and longitudinal force required for the stable driving of the vehicle are calculated. In the lower layer, the distribution method and the optimal distribution method targeted for the minimum tire load rate are adopted in specific driving conditions to distribute the driving torque.

Then, according to the GB/T6362-2014 automobile handling stability test method, the designed driving force distribution strategy is simulated and vertified through Carsim/Simulink co-simulation in the steering wheel step condition, double-line shifting condition on the high-adhesion road and snake-shape conditions on the low-adhesion road.

Finally, a hardware-in-the-loop test platform is built through the real-time simulation platform dSPACE and the controller DSP28335. The controller collects the driver's manipulation information and the vehicle status information that outputs from the dSPACE vehicle real-time model, and performs hardware-in-the-loop tests under steering wheel step conditions.

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