摘要：Accurately estimating the state of health (SOH) of lithium-ion batteries is crucial for ensuring the availability and efficiency of the energy storage systems based on lithium-ion batteries. This paper proposes a method based on a temporal pattern attention (TPA) mechanism and a CNN-LSTM model to improve the SOH estimation accuracy. Firstly, the health factors (HFs) related to capacity degradation are selected based on the charge–discharge curves of lithium-ion batteries, and the local outliers in the health factor data are detected through the local outlier factor (LOF) method. The Lagrange interpolation is then applied to correct these outliers to ensure the continuity of the health factor data. Secondly, the effectiveness of the health factor is evaluated by using the Pearson and Spearman correlation analyses, with the valid health factor forming the HFs dataset. Thirdly, the TPA mechanism is integrated into the CNN-LSTM model to form a CNN-LSTM-TPA model to enhance its ability to capture key information. The whale optimization algorithm (WOA) is employed to optimize the model's hyperparameters. Finally, the proposed method is tested on the NASA and CALCE lithium-ion battery datasets. The experimental results show that on the NASA dataset, the proposed method improves the SOH estimation accuracy by approximately 26.39% to 46.05% compared to the LSTM method; on the CALCE dataset, the accuracy improves by approximately 56.05% to 73.32% compared to the LSTM method, respectively. These experimental results indicate that the proposed method achieves higher accuracy. ? 2024 Elsevier Ltd

ISSN号：0045-7906

卷、期、页：卷122

发表日期：2025-03-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：三区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Computers and Electrical Engineering

参与作者：Huang, Jie,何霆,朱文龙,Liao, Yongxin,曾建华

第一作者：徐泉,牛迎春

论文类型：期刊论文

论文概要：Huang, Jie,何霆,朱文龙,Liao, Yongxin,曾建华,徐泉,牛迎春，A lithium-ion battery SOH estimation method based on temporal pattern attention mechanism and CNN-LSTM model，Computers and Electrical Engineering，2025，卷122

论文题目：A lithium-ion battery SOH estimation method based on temporal pattern attention mechanism and CNN-LSTM model