Optimization of Electric Vehicle Battery Performance Using Machine Learning Techniques

Abstract

Electric vehicles (EVs) have become among the foundations of green transportation because of the fast global shift towards sustainable transportation. Nevertheless, the problems of short battery life, extended charge durations, and unpredictable performance in different conditions are impediments to the mass adoption of EVs. Machine learning (ML) has therefore come in to overcome these shortcomings to become a groundbreaking application in maximizing the battery performance of electric vehicles. ML algorithms will be able to capture the nonlinear relationships that are present in battery systems to predict the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) with great accuracy. In this paper, I will discuss how the efficiency, reliability, and sustainability of EV batteries can be improved using machine learning techniques, specifically neural network, support vectors machine (SVM), and reinforcement learning. In the research, the secondary data will be based on the literature available to study the predictive models and optimization strategies that enhance battery management systems (BMS). Results point to the fact that implementing ML in EV battery management leads to alleviations in battery degradation, adaptive charging, and longer battery life. The paper concludes that the optimization of the electric mobility solutions towards energy efficiency, cost-effectiveness, and intelligent usage, is majorly driven by ML, and in line with global sustainability imperatives.