A Lithium-Ion Battery Degradation Model Agnostic to Cell Chemistry with Integrated State-of-Charge and Temperature Dependence
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Abstract
Lithium-ion battery (LIB) lifetime prognosis remains a decisive enabler for safe, economical, and sustainable electrification across mobility and stationary energy sectors. Despite the maturity of this storage technology, the impact of capacity reduction due to battery aging remains a subject of interest to this day. This study presents a degradation model that computes the incremental loss of usable capacity for every equivalent cycle experienced by the battery. The model depends on the State of Charge values and the mean ambient temperature at which the cycle occurred. The methodology generalizes the results of a previous study where an initial degradation model was calibrated on long‑term cycling data at which multiple commercial cells were driven to their end of life under varied state of charge ranges in addition to a chemical analysis of cell's degradation due to different operating temperatures. Unlike traditional calendar-plus-cycling additives or chemistry-specific semi-empirical methods, the proposed model is formulated—and further validated—under the assumption that cells exposed to comparable operating conditions degrade at comparable rates, independent of their electrode chemistry. Consequently, only the data of a single degradation campaign at a given operational mode is needed, which is typically found in the cell's datasheet, eliminating the need for time-consuming aging tests. Validation is performed using a public experimental dataset provided by the University of Stanford dataset. The results show the model's ability to predict the end-of-life within one percent of the reported battery State of Health (SOH), even for an electric vehicle usage profile. By embedding the framework in battery management systems, fleet operators can estimate remaining useful life (RUL) based on historical operational data, optimize battery usage based on less degrading SoC values and cooling systems, schedule preventive maintenance before catastrophic capacity loss, and compare alternative uses without the need for degradation studies. These contributions align with conference themes on prognostics, health management, and data-driven energy system optimization, offering a practical pathway towards longer-lived and better-valued electrified assets.
How to Cite
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Remaining Useful Life (RUL), Data-driven degradation model, Temperature-dependent degradation, State of Charge-dependent degradation, Lithium-ion battery degradation, State of Health (SOH) prognostics
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