Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling

Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling

iScience

10.1016/j.isci.2024.109416

http://dx.doi.org/10.1016/j.isci.2024.109416

Riko I Made, Jing Lin, Jintao Zhang, Yu Zhang, Lionel C.H. Moh, Zhaolin Liu, Ning Ding, Sing Yang Chiam, Edwin Khoo, Xuesong Yin, Guangyuan Wesley Zheng

Electrochemical energy storage Machine learning Materials science;, Machine Learning, Materials Sciences and Engineering, Batteries, battery modeling, SOH, batteries cycle

05 March 2024

I Made, R., Lin, J., Zhang, J., Zhang, Y., Moh, L. C. H., Liu, Z., Ding, N., Chiam, S. Y., Khoo, E., Yin, X., & Zheng, G. W. (2024). Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling. IScience, 27(4), 109416. https://doi.org/10.1016/j.isci.2024.109416

Battery health assessment and recuperation play crucial roles in the utilization of second-life Li-ion batteries. However, due to ambiguous aging mechanisms, it is challenging to estimate battery health and devise an effective strategy for cell rejuvenation. This paper presents aging and reconditioning experiments of 62 commercial lithium iron phosphate cells, which allow us to use machine learning models to predict cycle life and identify important indicators of recoverable capacity. An average test error of 16.84% ± 1.87% (mean absolute percentage error) for cycle life prediction is achieved by gradient boosting regressor. Some of the recoverable lost capacity is found to be attributed to the non-uniformity in electrodes. An experimentally validated equivalent circuit model is built to demonstrate how such non-uniformity can be accumulated, and how it can give rise to recoverable capacity loss. Furthermore, Shapley additive explanations (SHAP) analysis also reveals that battery operation history significantly affects the capacity recovery.

Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Career Development Fund
Grant Reference no. : C210812038

This research / project is supported by the Energy Market Authority - EMA-EP011-SLEP-001
Grant Reference no. : EMA-EP011-SLEP-001 (SC25/21-708712))

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Advanced Manufacturing and Engineering Programmatic Fund
Grant Reference no. : A1898b0043

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Industry Alignment Fund – Pre-Positioning
Grant Reference no. : M22K8a0048 (SC25/23–833412)

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Career Development Fund
Grant Reference no. : C210112037

https://oar.a-star.edu.sg/communities-collections/articles/21216

2589-0042

Institute of Materials Research and Engineering