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