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Lithium-ion batteries (LIBs) from electrified vehicles (EVs) that have reached the automotive end of life (EoL) may provide a low-cost, highly available energy storage solution for grid-connected systems, such as peak shaving and ancillary services. There are several issues related to the integration of second life batteries (SLBs) in power systems, such as the variability of the pack design and cell chemistry, in-field assessments of the state of health (SoH), and estimations of the expected lifetimes of SLBs in different power system applications. Model-based approaches are commonly used in the automotive industry for estimating/predicting the capacity and power fade trajectories of LIBs during their life. However, a large variety of models are available with different fidelities, complexities, and computational costs. The accuracy of these estimations is critical for the derivation of business models for SLB applications. This paper presents a qualitative and quantitative assessment of the performance of two well-accepted, state-of-the-art aging models, initially developed for automotive applications and here applied to different SLB applications to predict both the capacity and power fade. These models are evaluated with respect to several performance metrics, such as fidelity of estimation and capability of extrapolation outside the calibration data range. The considered models are classified as semi-empirical physics-based and empirical models, respectively. Three different SLB power profiles, bulk energy for DC fast charge stations and two frequency regulation profiles, are considered, corresponding to different ranges of the SoC, C-rates, and battery temperatures, with the aim of exciting different aging mechanisms. The numerical results provide insight for the selection of aging models for SLB applications based on their performances and limitations. |
