Work

Real-time state-of-health estimation and online degradation prognosis from partial battery discharge using Physics-Informed Neural Networks

Year: 2026

Type: article

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 arbitrary 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. In addition, a real-time degradation trend estimation strategy is introduced to detect key aging transitions without requiring battery-specific information, such as chemistry, nominal capacity, or cycling protocol. This makes the approach agnostic to the battery type by design, although its validation is limited to the datasets considered in this work. 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.

Source: Journal of Power Sources

DOI: 10.1016/j.jpowsour.2026.241725

Authors: Begoña Ispizua, Sergio Gil-López, Leire Arrizabalaga, Ibai Laña

Institutions: Tecnalia, University of the Basque Country, Mondragon Unibertsitatea

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,230