RISK PREDICTION AND EARLY WARNING SYSTEM FOR ELECTRIC VEHICLE BATTERIES USING MACHINE LEARNING

Аннотация:

The rapid growth of electric vehicles (EVs) has intensified the need for reliable battery management systems capable of ensuring safety, efficiency, and longevity. Lithium-ion batteries, widely used in EVs, are prone to degradation, thermal runaway, and unexpected failures under complex operating conditions. This study focuses on the development of a machine learning-based risk prediction and early warning system for EV batteries. Using real-world datasets obtained from battery management systems (BMS), the research applies supervised learning algorithms to predict failure risks and detect anomalies. Models such as Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks are evaluated for their predictive performance. Experimental results demonstrate that machine learning techniques significantly improve prediction accuracy and provide timely early warnings, contributing to enhanced safety and operational reliability of EV batteries.