A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries

Abstract Aiming at the problem that it is difficult to accurately estimate the state of charge (SOC) of lithium‐ion batteries in the strongly nonlinear interval, a novel algorithm based on a fuzzy control strategy is proposed. It integrates extended Kalman filter (EKF) and ampere‐hour (Ah) integration accurately estimate the SOC of lithium‐ion batteries. First, the algorithm uses the advantage that the EKF algorithm has high estimation accuracy in the nonlinear interval and can solve the problem of the large error caused by the inaccurate initial value of the Ah integral algorithm. Then the fuzzy‐EKF‐Ah (F‐EKF‐Ah) is used to fuse the two algorithms of EKF and Ah integral. The fused algorithm can effectively solve the problems of the cumulative error caused by the sampling accuracy of the Ah integral algorithm and the large estimation error of the EKF algorithm in the strong nonlinear interval. Finally, the equivalent circuit model is used for analysis. The experimental results show that the improved algorithm can achieve high estimation accuracy under three experimental conditions.