AI-Enhanced Multiphysics Framework for Predictive Battery Management System in EVs

Electric Vehicles (EVs) are central to the future of sustainable mobility, but battery degradation remains a critical challenge. This whitepaper introduces a multi-physics-informed AI framework that combines electrochemical, thermal, mechanical, and hardware-level models to predict battery degradation and optimize performance.

By leveraging real-time data from NASA datasets and advanced machine learning techniques—including LSTM, XGBoost, Random Forest, and Neural Networks—the framework enables accurate prediction of Remaining Useful Life (RUL), adaptive charging strategies, and proactive stress mitigation based on driver behavior, drive cycles and environmental conditions.

Battery degradation affects EV performance, safety, and cost. Traditional Battery Management Systems (BMS) are reactive and limited in scope. This AI-powered framework offers:

•    Predictive RUL estimation
•    Real-time health monitoring
•    Adaptive thermal and mechanical stress management
•    Fleet-wide learning and cloud integration

This technical whitepaper explores:

•    The Need for Predictive Battery Management
•    AI-Based Multiphysics Modeling for Battery Health
•     Real time health monitoring for Lithium-ion cell
•     RUL Prediction Using NASA Dataset 
•    Machine Learning Integration for Driver Behavior and Environmental Impact
•    Implementation Considerations for Scalable Deployment

Discover how intelligent systems are transforming EV battery management through physics-informed AI models, validated by real-world data.