Battery degradation diagnosis under normal usage without requiring regular calibration data

Abstract

Lithium-ion batteries undergo capacity loss and power fade over time. Despite indicating degradation, these changes lack internal insights. Degradation modes group various mechanisms but are challenging to quantify due to aging complexity. In this paper, a noninvasive and comprehensive diagnostic framework is proposed for the accurate estimation of the state of health (SOH) and degradation modes. A large amount of synthetic data is generated from the mechanistic model to cover many typical aging paths without using redundant experimental data. A data-driven multistep diagnosis method is developed by using incremental capacity sequences. This method analyses unique sensitivities to voltage responses from different degradation modes and identifies them sequentially to simplify complex interactions. In addition, overpotential correction is incorporated to estimate battery degradation modes under normal usage. This framework is validated through experimental data under various operating conditions, affirming the 2 % accuracy of SOH estimation and the effective automatic quantification of degradation modes. Furthermore, the method is applicable to common state-of-charge (SOC) windows ranging from 25% to 85 % (3.6 V–4.1 V for the voltage window) and a high current rate up to C/4. The diagnostic framework does not rely on any regular calibration data during model training and thus has high potential for practical application.