An improved mathematical model for State of Health estimation of lithium-ion batteries in electric vehicle under fast charging

Abstract

The Incremental Capacity (IC) analysis is a popular method to analyse battery State of Health (SOH). Existing IC curve-based approaches utilize a fixed number of peaks in the IC curve to determine SOH. However, this assumption does not hold well under fast charging where one or multiple peaks tend to disappear near the End of Life (EOL) of batteries. In this context, this paper presents an SOH estimation approach that overcomes these limitations by leveraging IC curve and Equivalent Circuit Model (ECM). Specifically, the proposed approach formulates a voltage-capacity model considering IC curve peak number as a function of SOH — ultimately enabling adaptive peak selection in IC curves and eliminating the need for any separate experimentation at a low C-rate. Subsequently, the proposed approach estimates SOH in terms of maximum capacity ($Q_{max}$) from the voltage capacity model, by employing a nonlinear least square optimization-based parameter estimation. The effectiveness of the proposed model and algorithm is examined using the open access data mimicking real scenario — which resulted in average Root Mean Square Error (RMSE) of 0.7793% in eight cells and a maximum RMSE of 0.9496% in one cell. Also, the robustness of rule-based peak selection strategy is studied and found satisfactory.