The Temperature Effect on Electric Vehicle’s Lithium-Ion Battery Aging Using Machine Learning Algorithm

Abstract

This paper offers a brief insight into predicting the State of Health (SOH) of lithium-ion batteries in EVs using machine learning. Accurate SOH assessment is crucial for optimizing electric vehicles (EVs’) performance and longevity. Employing supervised machine learning on a diverse battery dataset, the research develops a robust SOH estimation method. Various algorithms are compared for efficacy, considering factors like temperature and charging patterns. Feature selection enhances model accuracy and efficiency. The proposed methodology offers promising real-world results, indicating high SOH prediction accuracy. This research contributes to EV battery management, applying machine learning for SOH estimation, which is vital for intelligent battery management systems, and enhancing EVs’ sustainability and efficiency.