PID Control of Independent Drive Electric Vehicle Stability based on Neural Network
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摘要: 为提高独立驱动电动汽车在极限工况下的稳定性，提出了基于神经网络PID控制策略的直接横摆力矩决策算法，控制质心侧偏角和横摆角速度并进行转矩分配。基于2自由度车辆模型的线性化特征参数与实际车辆控制目标的偏差，引入动量优化项对神经网络权值进行在线更新，计算出跟踪理想质心侧偏角和横摆角速度所需的直接横摆力矩，通过车辆前后轴动态载荷估计，考虑驱动电机饱和输出力矩和路面限制条件的约束，对各驱动轮进行直接横摆力矩分配。将算法应用于CarSim/Simulink联合仿真模型进行工况仿真实验。结果表明，该方法能够保证车辆在中速情况下于光滑路面紧急转向和紧急移线换道操作稳定性，以及在路面湿滑情况下高速超车快速并线的稳定性。Abstract: To improve the stability of an independent drive electric vehicle under the extreme conditions, a direct yaw moment decision algorithm based on the neural network PID control strategy is proposed. The algorithm controls the side-slip angle and yaw rate and carries out the torque distribution. Based on the deviation between the linearized characteristic parameters of the 2-DOF (two degrees of freedom) vehicle model and the actual vehicle control target, the moment is optimized to update the weight of the neural network, and the direct yaw moment required to track the ideal side-slip angle and yaw rate is calculated. Through the dynamic load estimation of the front and rear axles of the electric vehicle, the direct yaw moment distribution of each driving wheel is taken into account by considering the saturation torque of the driving motor and the pavement constraints. The algorithm is applied to the CarSim/Simulink joint simulation model. The simulation results show that the algorithm can guarantee the stability of the emergency steering on a smooth road, the emergency lane transfer at the medium speed and the stability of high-speed overtaking under wet and slippery conditions.
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Key words:
- electric vehicle /
- neural network /
- control /
- stability
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表 1 独立驱动电动汽车仿真模型主要参数
参数名称 数值及单位 车辆质量m 1 110 kg 绕z轴转动惯量
Iz1 343.1 kg·m2 质心至前轴距离a 1.04 m 质心至后轴距离b 1.56 m 前后轴距l 2.60 m 平均轮距lw 1.48 m 质心高度hc 0.54 m 车轮半径r 0.31 m 参考模型前轮侧偏刚度kf -37 242.3 N/rad 参考模型后轮侧偏刚度kr -28 647.9 N/rad 
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