To address the problem of how to accurately and precisely estimate the state of charge(SOC)of lithium-ion batteries,a combined algorithm of adaptive extended Kalman filter(AEKF)and long short-term memory neural network(LSTM)is proposed for the optimal estimation of lithium-ion battery SOC.Using the hybrid pulse power characterization(HPPC)test method,the online parameter identification of the third-order equivalent circuit model(ECM)is performed by the forgetting factor recursive least squares method(FFRLS),and then the LSTM-AEKF algorithm is applied to the battery SOC real-time estimation experiment according to the equation,obtaining the algorithm to control the SOC estimation error within 1％.Finally,the comparative experimental results show that the algorithm improves the root mean square error by 1.25％and 0.81％compared with the EKF and LSTM algorithms,re-spectively,and has higher accuracy and precision.
关键词
锂电池/荷电状态/最小二乘法/自适应扩展卡尔曼算法/长短期记忆网络/均方根误差
Key words
state of charge/least squares method/adaptive extended kalman filter/long short-term memory network/root mean square error
维普
万方数据