Predictive Battery Management for Electric Vehicles Using Adaptive State Estimation Models

Abstract

Efficient battery management is critical for improving the performance, safety, and lifespan of electric vehicles (EVs). This paper presents a predictive battery management approach based on adaptive state estimation models to accurately monitor and forecast battery behavior under dynamic operating conditions. The proposed method integrates real-time data with advanced estimation techniques to determine key battery states such as state of charge (SOC), state of health (SOH), and remaining useful life (RUL). By employing adaptive algorithms, the system continuously updates model parameters to account for variations in temperature, load, and aging effects. This enhances prediction accuracy compared to conventional static models. The framework also incorporates predictive control strategies that optimize energy usage and prevent critical failures. Simulation and experimental results demonstrate improved estimation precision, robustness, and overall battery efficiency. The proposed approach contributes to the development of intelligent battery management systems, supporting reliable and sustainable EV operation.