Efficient Electrochemical State of Health Model for Lithium Ion Batteries under Storage Conditions

Abstract

Capacity degradation of batteries negatively impacts the lifetime of battery packs as well as the residual value of electric vehicles. Developing a degradation model for the prognosis of the state of health (SOH) under storage conditions is a critical aspect of developing algorithms to maximize the remaining useful lifetime of these systems. It is known that electrochemical degradation models have superior predictive ability compared to more empirical or data-driven models, but these still require improvement in terms of computational efficiency. In this work, we thus introduce a simple, reduced-order electrochemical degradation model for lithium-ion batteries. This model considers three key aging mechanisms with the ability to predict the SOH under various calendar aging conditions. Lumped model results are validated against a single particle-based degradation model and show close agreement, even as the simulation time is reduced by 2 orders of magnitude. This indicates significant potential in real-world applications to account and correct for the effects of storage on cell performance and lifetime.