A Review on Online State of Charge and State of Health Estimation for Lithium-ion Batteries in Electric Vehicles

Zuolu Wang, Guojin Feng, Dong Zhen, Fengshou Gu, Andrew Ball

Research output: Contribution to journalReview articlepeer-review

Abstract

With electric vehicles (EVs) being widely widely accepted as a clean technology to solve carbon emissions in modern transportation, lithium-ion batteries (LIBs) have emerged as the dominant energy storage medium in EVs due to their superior properties, like high energy density, long lifespan, and low self-discharge. Performing real-time condition monitoring of LIBs, especially accurately estimating the state of charge (SOC) and state of health (SOH), is crucial to keep the LIBs work under safe state and maximize their performance. However, due to the non-linear dynamics caused by the electrochemical characteristics in LIBs, the accurate estimations of SOC and SOH are still challenging and many technologies have been developed to solve this challenge. This paper reviews and discusses the state-of-the-art online SOC and SOH evaluation technologies published within the recent five years in view of their advantages and limitations. As SOC and SOH are strongly correlated, the joint estimation methods are specifically reviewed and discussed. Based on the investigation, this study eventually summarizes the key issues and suggests future work in the real-time battery management technology. It is believed that this review will provide valuable support for future academic research and commercial applications.
Original languageEnglish
Pages (from-to)5141-5161
Number of pages21
JournalEnergy Reports
Volume7
Early online date25 Aug 2021
DOIs
Publication statusE-pub ahead of print - 25 Aug 2021

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