Towards Intelligent Battery Management via A Five-Tier Digital Twin Framework

2025-09-02

AI Summary

Traditional battery management systems (BMS) suffer from low accuracy, poor interpretability, and a lack of holistic lifecycle co-optimization in state-of-health (SoH) prediction and proactive maintenance of lithium-ion batteries. To address these challenges, this paper proposes a five-layer digital twin framework that tightly integrates electrochemical first-principles models with physics-informed neural networks (PINNs), incorporates Bayesian optimization for automatic model parameter calibration, and establishes a data–model hybrid uncertainty quantification mechanism. The method achieves both high accuracy and strong interpretability: mean absolute percentage errors for voltage and temperature predictions are 1.57% and 0.39%, respectively, while SoH prediction error remains below 3%. The framework enables a closed-loop intelligent management pipeline—from geometric visualization and real-time state prediction to decision optimization and autonomous operation—thereby significantly enhancing battery system safety, reliability, and economic efficiency.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Battery management systems (BMSs) are critical to ensuring safety, efficiency, and longevity across electronics, transportation, and energy storage. However, with the rapid growth of lithium-ion batteries, conventional reactive BMS approaches face limitations in health prediction and advanced maintenance management, resulting in increased safety risks and economic costs. To address these challenges, we propose a five-tier digital twin framework for intelligent battery management. The framework spans geometric visualization, predictive modeling, prescriptive optimization, and autonomous operation, enabling full lifecycle optimization. In validation, an electrochemical model calibrated via Bayesian optimization achieved strong alignment with measured voltage and temperature, with Mean Absolute Percentage Errors (MAPE) below 1.57% and 0.39%. A Physics-Informed Neural Network (PINN) then combined data and simulations to predict State of Health (SOH), attaining MAPE under 3% with quantified uncertainty. This framework elevates BMSs into intelligent systems capable of proactive management and autonomous optimization, advancing safety and reliability in critical applications.

Problem

• Addressing limitations in battery health prediction and maintenance
• Proposing a digital twin framework for intelligent battery management
• Enhancing safety and reliability through proactive autonomous optimization

Innovation

• Five-tier digital twin framework for battery management
• Bayesian-optimized electrochemical model for voltage prediction
• Physics-Informed Neural Network for health estimation