Accurate 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 SOE, this study focuses on ternary lithium-ion batteries and proposes an SOE estimation method that combines limited-memory recursive least squares (LM-RLS) with strong tracking adaptive window Multi-innovation cubature Kalman filtering (STF-MCKF). A finite set of data is used for model parameter updates at the current time to solve the problem of data saturation and improve the identification accuracy of the RLS algorithm. By utilizing the STF algorithm, the CKF algorithm is optimized to enhance its robustness under strong disturbances. An adaptive window Multi-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 estimation accuracy. To validate the effectiveness of the algorithm, experiments are conducted under DST and BBDST conditions. The results show that the STF-MCKF algorithm has a maximum convergence time of 4s and an SOE estimation error within 1.04% under DST conditions. Under BBDST conditions, the STF-MCKF algorithm has a maximum convergence time of 3s and an SOE estimation error within 2.34%. Furthermore, the STF-MCKF algorithm demonstrates good stability under both the two conditions, indicating the effectiveness of the proposed improved algorithm for lithium battery SOE estimation.

The prediction of remaining useful life (RUL) of lithium-ion batteries takes a critical effect in the battery management system, and precise prediction of RUL guarantees the secure and reliable functioning of batteries. For the difficult problem of selecting the parameter kernel of the training data set of the RUL prediction model constructed based on the support vector regression model, an intelligent gray wolf optimization algorithm is introduced for optimization, and owing to the premature stagnation and multiple susceptibility to local optimum problems of the gray wolf algorithm, a differential evolution strategy is introduced to propose a hybrid gray wolf optimization algorithm based on differential evolution to enhance the original gray wolf optimization. The variance and choice operators of differential evolution are designed to sustaining the diversity of stocks, and then their crossover operations and selection operators are made to carry out global search to enhance the prediction of the model and realize exact forecast of the remaining lifetime. Experiments on the NASA lithium-ion battery dataset demonstrate the effectiveness of the proposed RUL prediction method. Empirical tests demonstrate that the maximum average absolute value error of the prediction of the fusion algorithm on the battery dataset is limited to within 1%, which reflects the high accuracy prediction capability and strong robustness.