Empirical Li-ion aging model derived from single particle model

Abstract

Abstract Battery lifetime simulation and optimization still suffers from the need of accurate but not too computationally intensive models. Electrical equivalent models (EEMs) are often used in automotive industry due to their efficiency. However, aging extensions for EEMs are typically based on empirical relations and fail outside their design scope. Single particle models (SPMs) on the other hand, are one of the simplest forms of electrochemical models which might still be suitable for automotive applications. Although EEM and SPM are differently motivated and developed, cross connections between both model types are discussed. Based on a simplification of SPM aging relations, a more comprehensive empirical aging model for EEMs denoted by SPM–EEM is described and compared to already existing models. As an example, both models are adjusted to fit a LiMn2O4 (LMO) cell chemistry. The derived SPM–EEM shows first promising results to improve the accuracy. Even though SPMs are slightly superior in predictability than EEMs, they are often too complex to run on control devices. Improving the predictability of EEMs is, therefore, an important step to improve aging estimation and optimization for automotive applications.