Learning Li-ion battery health and degradation modes from data with aging-aware circuit models

TL;DR

This paper introduces an aging-aware circuit model combined with Gaussian process regression to accurately estimate Li-ion battery health and degradation modes from operational data, addressing limitations of purely model-based or data-driven methods.

Contribution

It presents a novel hybrid approach that integrates an equivalent circuit model with machine learning to improve battery health estimation and degradation analysis.

Findings

Achieved less than 1% relative RMSE in capacity estimation.

Attained less than 2% MAPE in resistance prediction.

Highlighted the importance of accurate open circuit voltage characterization.

Abstract

Non-invasive estimation of Li-ion battery state-of-health from operational data is valuable for battery applications, but remains challenging. Pure model-based methods may suffer from inaccuracy and long-term instability of parameter estimates, whereas pure data-driven methods rely heavily on training data quality and quantity, causing lack of generality when extrapolating to unseen cases. We apply an aging-aware equivalent circuit model for health estimation, combining the flexibility of data-driven techniques within a model-based approach. A simplified electrical model with voltage source and resistor incorporates Gaussian process regression to learn capacity fade over time and also the dependence of resistance on operating conditions and time. The approach was validated against two datasets and shown to give accurate performance with less than 1% relative root mean square error (RMSE) in capacity and less than 2% mean absolute percentage error (MAPE). Critically, we show that the open circuit voltage versus state-of-charge function must be accurately known, and any inaccuracies or changes in this over time strongly influence the inferred resistance. However, this feature (or bug) may also be used to estimate in operando differential voltage curves from operational data.