Method for estimating state of health of Li-ion batteries based on interval information fusion

Chengao Wu; Zhiduan Cai; Wuzhe Zhang; Chenwei Qin; Jiahao Shen

doi:10.1117/12.3037498

27 September 2024 Method for estimating state of health of Li-ion batteries based on interval information fusion

Chengao Wu, Zhiduan Cai, Wuzhe Zhang, Chenwei Qin, Jiahao Shen

Author Affiliations +

Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 132750T (2024) https://doi.org/10.1117/12.3037498
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China

Abstract

To ensure the safety, efficiency, and stability of batteries, accurate estimation of battery health status is crucial. To address this, this paper proposes a method for estimating battery state of health by integrating interval local information. Firstly, the full-cycle discharge data of the battery are divided into voltage intervals, and the time differences within each voltage interval segment are extracted as features to characterize the battery's health status. Then, the features within each voltage interval segment are fused. Finally, a Long Short-Term Memory neural network algorithm is employed to construct a battery health status prediction model, achieving accurate estimation of the battery health status. Experimental results demonstrate that the proposed method can accurately estimate the health status of individual cells and battery modules, with R-squared values (coefficient of determination) exceeding 0.99 and root mean square errors within 1%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chengao Wu, Zhiduan Cai, Wuzhe Zhang, Chenwei Qin, and Jiahao Shen "Method for estimating state of health of Li-ion batteries based on interval information fusion", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 132750T (27 September 2024); https://doi.org/10.1117/12.3037498

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KEYWORDS

Batteries

Data modeling

Feature extraction

Feature fusion

Education and training

Model based design

Information fusion

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