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

TL;DR

This paper introduces a transferable physics-informed framework for real-time battery degradation diagnosis, knee-onset detection, and prediction, enhancing battery management systems with improved accuracy and adaptability across different cycling scenarios.

Contribution

It proposes a novel hybrid physics-informed model with a histogram-based feature engineering and fine-tuning strategy for online battery degradation analysis.

Findings

Effective fine-tuning improves degradation mode estimation.

Strong correlation between knee-onset and knee points.

Histogram-based features outperform other sets.

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.