A four parameter model for the solid-electrolyte interphase to predict battery aging during operation

TL;DR

This paper introduces a physics-based four-parameter model to predict lithium-ion battery aging due to SEI growth, validated across multiple protocols, highlighting the impact of operating conditions on capacity fade.

Contribution

The paper presents a novel four-parameter physical model for predicting battery aging, incorporating specific dependencies on time, current, temperature, and state of charge.

Findings

High predictive accuracy with 1.28% RMSE across protocols

Operating window significantly affects SEI growth

Applied current has minimal impact on aging

Abstract

Accurately predicting aging of lithium-ion batteries would help to prolong their lifespan, but remains a challenge owing to the complexity and interrelation of different aging mechanisms. As a result, aging prediction often relies on empirical or data-driven approaches, which obtain their performance from analyzing large datasets. However, these datasets are expensive to generate and the models are agnostic of the underlying physics and thus difficult to extrapolate to new conditions. In this article, a physical model is used to predict capacity fade caused by solid-electrolyte interphase (SEI) growth in 62 automotive cells, aged with 28 different protocols. Three protocols parametrize the time, current and temperature dependence of the model, the state of charge dependence results from the anode's open circuit voltage curve. The model validation with the remaining 25 protocols shows a high predictivity with a root-mean squared error of $1.28\%$. A case study with the so-validated model shows that the operating window, i.e. maximum and minimum state of charge, has the largest impact on SEI growth, while the influence of the applied current is almost negligible. Thereby the presented model is a promising approach to better understand, quantify and predict aging of lithium-ion batteries.