Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life

TL;DR

This paper introduces a data-driven approach combining automated feature selection with Gaussian process regression to accurately predict battery capacity fade, knee point, and end of life, enhancing safety and economic use.

Contribution

It presents a novel automated feature selection method tailored for battery degradation modeling, improving prediction accuracy of capacity fade and end-of-life points.

Findings

Median capacity prediction error under 1%

Knee point prediction error of 2.6%

End of life prediction error of 1.3%

Abstract

Lithium-ion cells may experience rapid degradation in later life, especially with more extreme usage protocols. The onset of rapid degradation is called the `knee point', and forecasting it is important for the safe and economically viable use for batteries. We propose a data-driven method that uses automated feature selection to produce inputs for a Gaussian process regression model that estimates changes in battery health, from which the entire capacity fade trajectory, knee point and end of life may be predicted. The feature selection procedure flexibly adapts to varying inputs and prioritises those that impact degradation. For the datasets considered, it was found that calendar time and time spent in specific voltage regions had a strong impact on degradation rate. The approach produced median root mean square errors on capacity estimates under 1\%, and also produced median knee point and end of life prediction errors of 2.6\% and 1.3\% respectively.