Strong tracking adaptive window multi‐innovation cubature Kalman filter algorithm for lithium‐ion battery state of energy estimation

Abstract

SummaryAccurate estimation of lithium‐ion battery state of energy (SOE) is an important prerequisite for prolonging battery life and ensuring battery safety. To achieve a high‐precision estimation of the SOE, while a ternary lithium‐ion battery being the specifically targeted in this study, a novel method for SOE estimation is proposed, which combines limited‐memory recursive least squares (LM‐RLS) with strong tracking adaptive window Multi‐innovation cubature Kalman filtering (STW‐MCKF). In the LM‐RLS algorithm, the model parameters at the current time are updated with a limited dataset to solve the data saturation problem and improve the recognition accuracy of the RLS algorithm. The CKF algorithm is optimized by the STW algorithm in the STW‐CKF algorithm to enhance its robustness under strong disturbances. Additionally, a self‐adaptive window multiple innovation strategy is proposed to improve the accuracy of SOE estimation and the stability of the CKF algorithm, while maintaining a balance between computational complexity and SOE estimation accuracy. To validate the effectiveness of the algorithm, experiments are conducted under DST and BBDST conditions. The results show that the STW‐MCKF algorithm has a maximum convergence time of 4 s and an SOE estimation error within 1.04% under DST conditions. Under BBDST conditions, the maximum convergence time is 3 s, and the SOE estimation error is within 2.34%. Furthermore, the STW‐MCKF algorithm demonstrates good stability under the two conditions, indicating the effectiveness of the proposed method for lithium‐ion battery SOE estimation.