关键词: 锂离子电池；健康状态预测；混合模型；参数优化

Abstract: Lithium-ion battery health estimation is critical for ensuring their safe use and optimizing the energy management of electric vehicle battery packs. To overcome the shortcomings of existing methods in prediction accuracy, a hybrid model combining improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), osprey-cauchy-sparrow search algorithm (OCSSA), and deep extreme learning machine (DELM) is proposed. ICEEMDAN algorithm is used to decompose the battery capacity decay data and separate the high-frequency and low-frequency components. The sparrow search algorithm (SSA) is improved by chaotic mapping, osprey optimization and cauchy variation to improve the parameter optimization accuracy. OCSSA optimizes DELM model parameters to enhance prediction accuracy. In order to verify the effectiveness of the proposed method, this paper tests the data set of the national aeronautics and space administration (NASA) and the center for advanced life cycle engineering (CALCE). The experimental results show that the model has high prediction accuracy and stability, and the average absolute error and root mean square error of the model prediction are within 1%. Moreover, high prediction accuracy is still maintained when predicting multiple steps in the future.

Key words: lithium-ion battery; state of health prediction; hybrid model; parameter optimization