State of health prediction of lithium-ion batteries based on SSA optimized hybrid neural network model.

Zhou, Jiani; Wang, Shunli; Cao, Wen; Xie, Yanxin; Fernandez, Carlos

Abstract

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) is crucial for the operation and maintenance of new energy electric vehicles. To address this current problem, an improved hybrid neural network model for SOH prediction based on a sparrow search algorithm (SSA) optimized convolutional bi-directional long short-term memory neural network (CNN-Bi-LSTM) is proposed. Firstly, by analyzing the battery aging data, several feature indicators with highly correlated battery life degradation are constructed. Secondly, the CNN-Bi-LSTM model is used to extract the battery aging data features and the latent timing laws. Finally, the SSA optimizes the parameters to improve the model accuracy. Experimental results based on the NASA-Pcoe battery dataset show that the SSA-CNN-Bi-LSTM model outperforms other models, and the root-mean-square errors of the SOH prediction results are all less than 0.6%. It indicates that the proposed SSA-CNN-Bi-LSTM model is capable of predicting SOH accurately and with high precision.

Citation

ZHOU, J., WANG, S., CAO, W., XIE, Y. and FERNANDEZ, C. 2024. State of health prediction of lithium-ion batteries based on SSA optimized hybrid neural network model. Electrochimica acta [online], 487, article number 144146. Available from: https://doi.org/10.1016/j.electacta.2024.144146

• Journal Article Type: Article
• Acceptance Date: Mar 19, 2024
• Online Publication Date: Mar 29, 2024
• Publication Date: May 20, 2024
• Deposit Date: Apr 8, 2024
• Publicly Available Date: Mar 30, 2025
• Journal: Electrochimica acta
• Print ISSN: 0013-4686
• Electronic ISSN: 1873-3859
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 487
• Article Number: 144146
• DOI: https://doi.org/10.1016/j.electacta.2024.144146
• Keywords: Lithium-ion batteries; State of health; Convolutional neural network; Bi-directional long short-term memory; Sparrow search algorithm
• Public URL: https://rgu-repository.worktribe.com/output/2294380