Machine Learning Prediction of lithium-ion battery life with early cyclic data

Abstract

To accomplish the preliminary estimation of profit-oriented battery longevity, this research utilized machine learning techniques. We calculated the number of correct predictions to the battery dataset of various machine learning techniques. The classification tree (CT) approach showed the maximum and best precision of 98.2 percent among multiple algorithms to forecast whether the battery will sustain over 90 percent primary power after 660 cycles. Utilizing the preliminary two data periods, CT suggests that the primary function for calculating the durability of batteries is the difference in discharge power. Given the initial 200 cycles, the peak weight factor switches to the internal resistance for calculating battery’s durability.