AI-Driven Battery Degradation Modeling and Forecasting in EV’s

Abstract

The performance, reliability, and lifespan of electric vehicle (EV) batteries are critical factors influencing the efficiency and adoption of electric mobility. Over time, batteries experience degradation due to factors such as temperature variations, charge–discharge cycles, and usage patterns. This project presents an AI-driven battery degradation modeling and forecasting system that employs advanced machine learning algorithms to predict the state of health (SOH) and remaining useful life (R-UL) of lithium-ion batteries. By integrating real-time data from sensors, including voltage, current, and temperature, the system uses predictive analytics and neural network models to accurately estimate degradation trends.