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arXiv · 2501.14573

A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction

Huang Zhang·Xixi Liu·Faisal Altaf·Torsten Wik
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Abstract

The techno-economic and safety concerns of battery capacity knee occurrence call for developing online knee detection and prediction methods as an advanced battery management system (BMS) function. To address this, a transferable physics-informed framework that consists of a histogram-based feature engineering method, a hybrid physics-informed model, and a fine-tuning strategy, is proposed for online battery degradation diagnosis and knee-onset detection. The hybrid model is first developed and evaluated using a scenario-aware pipeline in protocol cycling scenarios and then fine-tuned to create local models deployed in a dynamic cycling scenario. A 2D histogram-based 17-feature set is found to be the best choice in both source and target scenarios. The fine-tuning strategy is proven to be effective in improving battery degradation mode estimation and degradation phase detection performance in the target scenario. Again, a strong linear correlation was found between the identified knee-onset and knee points. As a result, advanced BMS functions, such as online degradation diagnosis and prognosis, online knee-onset detection and knee prediction, aging-aware battery classification, and second-life repurposing, can be enabled through a battery performance digital twin in the cloud.

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Huang Zhang, Xixi Liu, Faisal Altaf, Torsten Wik. 2025-08-15. A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction. https://doi.org/10.1016/j.jpowsour.2025.238028

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