Tracking lithium-ion battery degradation with robust voltage–capacity signatures

The diagnostic and prognostic battery models are increasingly demanded by industry to extend battery lifetime, enhance control strategies, support second-life design, and meet emerging regulatory requirements such as battery passports. These models must be flexible in coping with practical challenges of real-world data, including frequent incomplete charge-discharge cycles, cycling at moderate-to-high C-rates, and overall data sparsity throughout the battery's lifetime. Here, we propose a feature-extraction method capable of deriving informative features from charge and discharge profiles without relying on specific physics-relevant signatures such as peak coordinates in derivative voltage or capacity curves. We develop and validate our methodology using a representative experimental aging dataset from a commercial 1.85 Ah lithium-ion cell subjected to partial charge and discharge. We identify a set of features encoding the relative change in charge exchange within a fixed voltage window as the most efficient indicator for the prediction of state-of-health, remaining-useful-life, and knee-point. Moreover, we perform a data sparsity analysis to demonstrate the high resilience of the framework against incomplete training sets from field operation. Our results demonstrate that the long-term battery degradation trends can be reliably tracked and predicted within the studied dataset using simple voltage–capacity descriptors and early-life baseline aging information.