ADVANCED BATTERY HEALTH PREDICTION IN ELECTRIC VEHICLES USING OPERATIONAL PARAMETER ANALYSIS

Abstract

Electric vehicles (EVs) are playing a vital role in the transition toward sustainable and eco-friendly transportation systems. However, battery degradation significantly affects the performance, efficiency, and lifespan of EV batteries. Accurate prediction of battery health is essential for improving vehicle reliability and optimizing battery management strategies. This study proposes an advanced battery health prediction framework for electric vehicles based on operational parameter analysis. The proposed system analyzes various operational factors such as temperature conditions, charging and discharging cycles, driving behavior, and load variations to understand their impact on battery degradation. Using datadriven techniques and machine learning algorithms, the system predicts the battery’s state of health and identifies patterns associated with degradation over time. By integrating intelligent predictive models, the framework enables early detection of battery performance decline, allowing timely maintenance and improved battery management. The proposed approach enhances the accuracy of battery degradation prediction and contributes to the development of more reliable and efficient electric vehicle systems. This framework ultimately helps extend battery life, reduce operational costs, and support the advancement of intelligent transportation technologies.