Intuitive Degradation Mode Estimation Tool: ModEst

Abstract

Battery ageing is one of the main concerns in most battery applications. To reduce this degradation rate, it is key to understand how batteries age. However, the diagnosis of battery ageing is very challenging due to the little information that can be obtained without disassembling the battery cells. This paper presents a tool for battery degradation diagnosis. By a multi-objective optimisation algorithm, the tool estimates three degradation modes that lead to capacity fade: the loss of active material (LAM) of each electrode, and the loss of lithium inventory (LLI). The stepwise methodology tool is exposed and proved on three different degradation scenarios and battery cell chemistries. Two of the scenarios were simulated using a physics-based model (PBM), and the other scenario was obtained from an experimental degradation study. The results demonstrate accuracy and robustness of the tool while offering great simplicity and intuitiveness compared to other tools reported in the literature.