Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling
This work addresses battery health assessment and recuperation for second-life Li-ion batteries, which is an incremental advancement in the domain of energy storage systems.
The paper tackled the problem of accurately estimating health and devising rejuvenation strategies for aged Li-ion batteries by conducting aging and reconditioning experiments on 62 commercial cells, achieving an average test error of 16.84% ± 1.87% for cycle life prediction using a gradient boosting regressor.
Battery health assessment and recuperation play a crucial role in the utilization of second-life Li-ion batteries. However, due to ambiguous aging mechanisms and lack of correlations between the recovery effects and operational states, it is challenging to accurately estimate battery health and devise a clear strategy for cell rejuvenation. This paper presents aging and reconditioning experiments of 62 commercial high-energy type lithium iron phosphate (LFP) cells, which supplement existing datasets of high-power LFP cells. The relatively large-scale data allow us to use machine learning models to predict cycle life and identify important indicators of recoverable capacity. Considering cell-to-cell inconsistencies, an average test error of $16.84\% \pm 1.87\%$ (mean absolute percentage error) for cycle life prediction is achieved by gradient boosting regressor given information from the first 80 cycles. In addition, it is found that some of the recoverable lost capacity is attributed to the lateral lithium non-uniformity within the electrodes. An equivalent circuit model is built and experimentally validated to demonstrate how such non-uniformity can be accumulated, and how it can give rise to recoverable capacity loss. SHapley Additive exPlanations (SHAP) analysis also reveals that battery operation history significantly affects the capacity recovery.