A Comparison of Machine Learning Algorithms on Lithium-ion Battery Cycle Life Prediction

With the increase of conventional vehicles and carbon emission from them boosted the need for electrical vehicles (E V). One of the major components of the E V s are their batteries and the commercialization of E V s are affected by their battery technology and performance. It is also obvious that the range of an EV is mainly affected by the lifetime of its battery. Estimation of the battery cycle life in the early cycles is one of the most important challenges for maximization of the E V s range. Charge-discharge cycles affect battery lifetime of the EV which also made the estimation of battery life cycle a matter of interest. In this study, different machine learning models are applied to predict the lifecycle of a battery at early stages of usage. Detailed experiments have been performed to analyze the prediction accuracy at early cycle numbers. Experimental results show that the error rate in cycle life estimation decreased from 9.2 to 2.4% using Adaptive Boosting method.