A novel online model parameters identification method with anti‐interference characteristics for lithium‐ion batteries.

Summary: Model‐based state of charge (SOC) estimation method depends on the accuracy of the online identified battery model. However, when battery model parameters are identified by conventional recursive least squares (RLS), voltage and current noise will lead to the deviation of parameters and further affect the accuracy of SOC estimation. To analyze the difference influence of voltage and current noise, a simulation is carried out under the same noise variance of both voltage and current. Results indicate that voltage noise is the main cause of parameter deviation. Based on these results, a novel method, the auxiliary model recursive least square (AM‐RLS) which enjoys low computational complexity is proposed to compensate the parameter deviation caused by voltage noise. The AM‐RLS method is further combined with an extended Kalman filter (EKF) to estimate SOC in real time. Both simulation and experimental results show that AM‐RLS can effectively attenuate the parameter deviation and SOC estimation error under noise interference. Compared with RLS, the maximum SOC errors of the proposed method are reduced by 2.6% and 5.86% respectively under two current profiles. [ABSTRACT FROM AUTHOR]