📝 SummarXiv

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.