文献类型：期刊文献

英文题名：Attention-based bidirectional LSTM model construction and application for lithium-ion battery state-of-health prediction

作者：An, Zhoujian Ma, Jin Du, Xiaoze Ding, Yong Zhang, Dong Fu, Jian

第一作者：An, Zhoujian

通信作者：An, ZJ[1];Du, XZ[1];An, ZJ[2];An, ZJ[3]

机构：[1]Lanzhou Univ Technol, Sch Energy & Power Engn, Lanzhou 730050, Peoples R China;[2]Guizhou Inst Technol, Sch Aerosp Engn, Guiyang 550025, Peoples R China;[3]Shouhang Hitech Energy Technol Co Ltd, Jiuquan 735000, Peoples R China

第一机构：Lanzhou Univ Technol, Sch Energy & Power Engn, Lanzhou 730050, Peoples R China

通信机构：corresponding author), Lanzhou Univ Technol, Sch Energy & Power Engn, Lanzhou 730050, Peoples R China;corresponding author), Guizhou Inst Technol, Sch Aerosp Engn, Guiyang 550025, Peoples R China;corresponding author), Shouhang Hitech Energy Technol Co Ltd, Jiuquan 735000, Peoples R China.|贵州理工学院;

年份：2025

外文期刊名：IONICS

收录：;Scopus(收录号：2-s2.0-105015431307);WOS:【SCI-EXPANDED(收录号:WOS:001566238300001)】；

基金：This work was financially supported by the National Natural Science Foundation of China (52206087; 52130607), the Youth Doctor Foundation Project of Gansu Provincial Education Department (2025QB-027), the Lanzhou Youth Science and Technology Talent Innovation Project (2024-QN-18), the Science and Technology Project of Gansu province (25CXGA058), the Industrial Support Plan Project of Gansu Provincial Education Department (2025CYZC-034), the Guizhou Provincial Basic Research Program (Natural Science) (MS[2025]189), the Doctoral Research Funds of Lanzhou University of Technology (061907), and the Red Willow Excellent Youth Project of Lanzhou University of Technology.

语种：英文

外文关键词：Lithium-ion battery; State of health; Bi LSTM memory; Attention mechanism

摘要：Precisely forecasting the State of Health (SOH) of lithium-ion batteries is essential to enhance vehicle safety and refine battery management systems. This study seeks to address the shortcomings of conventional approaches in feature representation, temporal dependency modeling, and forecasting precision by introducing an attention-enhanced bidirectional long short-term memory (Bi LSTM) model for battery SOH estimation. First, the method selects five key features highly correlated with aging from the battery's charge-discharge cycles as model inputs and validates their effectiveness using Pearson correlation coefficients to ensure the reliability and representativeness of the input data. During model construction, Bi LSTM is employed to fully exploit the temporal dependencies between features, and the attention mechanism dynamically adjusts feature weights, enabling the model to concentrate on more predictive information. Finally, evaluation and validation using NASA's publicly accessible lithium-ion battery dataset demonstrate the method's superior effectiveness in SOH prediction for both individual and similar batteries. Compared to a standard LSTM model, it achieves an average decrease of approximately 1.27% in root mean square error (RMSE) and around 1.26% in mean absolute error (MAE). This improvement enhances the model's ability to generalize while effectively capturing the battery capacity degradation trend and reducing prediction errors.