A review of health estimation methods for Lithium-ion batteries in Electric Vehicles and their relevance for Battery Energy Storage Systems

Abstract

Modeling the performance and degradation of Battery Energy Storage Systems (BESS) has attracted much attention in recent years. BESS have the ability to support electric grid operation and stability as more Distributed and Renewable Energy Sources are added to the power mix. A battery’s ability to reliably deliver power during its life span is highly dependent on its operating and environmental conditions, including cycling rate, frequency of cycles, depth of discharge, average state of charge, and temperature. With the deployment of Lithium-ion batteries in Electric Vehicles (EV), extensive research, standards, and experimental data are available to analyze their performance and estimate their remaining service life. Comparatively, there is not much research available on modeling the performance and assessing the degradation of Lithium-ion batteries in grid storage applications. Relying on models constructed from experimental data from the analysis of lithium-ion batteries in EV applications can lead to incorrect conclusions about the operation and performance of batteries in grid-tied Energy Storage Systems. Moreover, degradation metrics such as capacity and power fade vary significantly based on the cycling profiles and main stress factors associated with BESS applications. This article reviews the main insights for modeling the performance and degradation of Li-ion batteries in EV applications and their relevance to BESS applications. Current state of health estimation methods, while technically proficient, rely heavily on analyzing metrics and trends in data collected from EV applications. The performance and health estimation techniques used for EVs can be extended to BESS applications; however, more tests and data collection for particular BESS applications and the development of standardized testing profiles are lacking and need to be developed.