基于软测量技术的电动汽车行驶状态估计

Estimation of Electric Vehicle Running States Based on Soft Computing

为了实现对电动汽车运行信息的低成本估计,通过建立了以3自由度电动汽车动力学模型为基础的EKF软测量算法。利用低成本传感器测得的纵向、侧向加速度、横摆角速度和转向盘转角信号,实现了对电动汽车横、纵向车速和质心侧偏角运动状态信号进行精确估计;同时引入了HSRI(highway safety research institute)轮胎模型;在动态特性下轮胎的侧向力可得到有效的估计。最后通过Car Sim与Matlab/Simulink联合仿真对EKF算法进行了验证,从而验证了EKF软测量技术能够准确、实时地估计汽车行驶动态参数信息。

The extended Kalman filter soft computing algorithm can be established based on three degrees of freedom electric vehicle dynamic model in order to realize the low cost estimation of the electric vehicle running information. And the extended Kalman filter applies low-cost sensor signals including the longitudinal acceleration,lateral acceleration,yaw rate and steering wheel angle in order to achieve the accurate estimates of the electric vehicle running states. And simple HSRI tire model is introduced to estimate tire lateral force under the dynamic characteristics. Finally co-simulation is carried out based on Car Sim and Matlab/Simulink. The results prove that EKF can accurately and real-time estimate the dynamic vehicle parameters.