Real-Time State-of-Health Estimation and Online Degradation Prognosis from Partial Battery Discharge Using Physics-Informed Neural Networks

2026-08-14

AI Summary

This study addresses the limitations of fixed discharge protocols and real-time prediction challenges in lithium-ion battery state-of-health (SOH) estimation by proposing a novel framework integrating physical mechanisms with deep learning. By constructing a physics-informed neural network that utilizes partial discharge curves from arbitrary voltage intervals, the method enables adaptive monitoring without requiring prior knowledge or extensive historical data. This approach overcomes traditional protocol constraints, achieving a mean absolute percentage error below 4% under complex operating conditions while accurately capturing critical aging transitions in real time. Consequently, the proposed framework significantly enhances the flexibility and reliability of battery lifecycle management, offering a robust solution for practical deployment where standardized testing is infeasible.

Abstract

With the increasing integration of renewable energy sources, energy storage systems have become essential, making the accurate estimation of their State of Health (SOH) and degradation behavior critical. In this work, we propose a physics-informed deep learning approach for lithium-ion battery SOH prediction using incomplete discharge curves extracted from arbitrary voltage ranges, thereby reflecting realistic and heterogeneous operating conditions. The proposed method combines data-driven learning with physically motivated degradation dynamics to ensure consistent and reliable SOH estimation from partial discharge information, achieving a MAPE below 4$\%$. In addition, a real-time degradation trend estimation strategy is introduced to detect key aging transitions without requiring prior knowledge or historical data, making it applicable to a wide range of batteries. Overall, our approach enables SOH estimation from arbitrary discharge segments and a real-time degradation forecast that continuously integrates all usage, overcoming previous methods that rely on fixed protocols or early, non-adaptive predictions.

Problem

• State-of-Health Estimation
• Degradation Prognosis
• Partial Discharge
• Lithium-ion Battery
• Real-time Monitoring

Innovation

• Physics-Informed Neural Networks
• Partial Discharge
• State-of-Health Estimation
• Real-Time Degradation Prognosis
• Arbitrary Voltage Ranges