自适应扩展卡尔曼滤波电池荷电状态估算方法

To more accurately estimate the state of charge of the power source of EVs, Thevenin equivalent circuit model is optimized, and the charge of state is estimated by adaptive extended Kalman filter. Firstly, the external characteristic data of the experimental battery and the open circuit voltage curves under charge and discharge state are obtained. The factors of charge and discharge state change are added to the corresponding curve of open circuit voltage-charge of state. Secondly, in the aspect of parameter identification, the off-line identification is optimized. The charge-discharge state and charge of state are considered on the basis of the off-line identification of fixed parameters. The estimation of terminal voltage is compared with on-line identification. Finally, based on the optimized battery model, the charge of state is estimated by adaptive extended Kalman filter and its comparison algorithm. And the estimation accuracy of terminal voltage and charge of state is compared under complex pulse current conditions. Experimental results show that the accuracy of terminal voltage estimation for off-line identification of optimized battery model is less than 0.01 V, which is higher than that for on-line identification. Based on the optimization model and off-line identification, adaptive extended Kalman and extended Kalman and interactive multi-model algorithm are constructed to estimate the charged state of the battery. The experimental results show that the estimation accuracy of charged state based on optimization model adaptive algorithm is 0.05, which is higher than that of the two contrast algorithms. The accuracy of adaptive extended Kalman filter based on optimization model is higher than that of interactive multi-model extended Kalman filter and extended Kalman filter.