Early Prediction of Battery Lifetime Using Centered Isotonic Regression with Quantile-Transformed Features

The rapid development of lithium-ion (Li-ion) batteries has raised requirements for cycle life prediction to ensure safety and reliability. However, the intricate and nonlinear behavior of the battery degradation process poses significant challenges in accurately predicting its cycle life at an early stage. This work addresses the battery lifetime prediction problem by leveraging machine learning (ML) methods. First, we apply quantile transformation (QT) to both features and battery cycle lives to improve their correlation. Next, we adopt a centered isotonic regression (CIR) method in our work, a variant of isotonic regression (IR) that centers the feature values and then reduces overfitting to improve model performance. Finally, we perform convex regression (CR) to capture specific patterns in the battery dataset where monotonicity is absent and achieve an overall prediction for battery cycle lives. To validate our proposed method, we have done a comprehensive comparison among several different benchmarks, including elastic net, gradient boosting regression tree, decision tree, support vector machine, random forest, and Gaussian process regression. In contrast to existing methods, our CIR model has shown the best performance, with an average percentage error of 9.8% and a root mean square error of 149 cycles. These experiment results demonstrate the capability and potential of our proposed CIR model in the problem of battery lifetime prediction.