Early battery lifetime prediction based on statistical health features and box-cox transformation

Abstract

Early battery lifetime prediction is important for both safety reasons and battery development. It predicts battery lifetime before it degrades significantly and has the advantage of being low-cost, time-saving, and providing timely feedback. However, due to the nonlinear degradation behavior and limited data in the early stage, early battery lifetime prediction is difficult. In this paper, statistical health features are extracted from temperature and voltage data, and a statistical transformation method is proposed to enhance the feature's importance and help increase the prediction accuracy of battery lifetime. First, candidate health features are constructed by analyzing temperature and voltage data, and Box-Cox transformation (BCT) is used to enhance their linear correlation with battery lifetime to help with feature selection. The Pearson correlation coefficients of extracted temperature and voltage features with battery lifetime reach −0.73 and − 0.90 respectively. Early battery lifetime prediction based on the first 100 cycles of data is then conducted using Gaussian process regression with a root-mean-square-error (RMSE) of 112.14 cycles. Comparison experiments show that Gaussian process regression outperforms the other four common machine learning methods in both BCT and non-BCT situations. Moreover, adding temperature feature and BCT have a positive effect. When they are not used, RMSEs would increase by 13.8 % and at most 209 %, respectively. At last, prediction based on fewer data (50 cycles) is tested and gained acceptable result of an RMSE of 149.68 cycles.