Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

A Comprehensive Review of Lithium-ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems

Version 1 : Received: 5 July 2021 / Approved: 6 July 2021 / Online: 6 July 2021 (17:34:49 CEST)

How to cite: Samanta, A.; Williamson, S.S. A Comprehensive Review of Lithium-ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems. Preprints 2021, 2021070164 (doi: 10.20944/preprints202107.0164.v1). Samanta, A.; Williamson, S.S. A Comprehensive Review of Lithium-ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems. Preprints 2021, 2021070164 (doi: 10.20944/preprints202107.0164.v1).

Abstract

Highly nonlinear characteristics of lithium-ion batteries (LIBs) are significantly influenced by the external and internal temperature of the LIB cell. Moreover, cell temperature beyond the manufacturer’s specified safe operating limit could lead to thermal runaway and even fire hazards and safety concerns to operating personnel. Therefore, accurate information of cell internal and surface temperature of LIB is highly crucial for effective thermal management and proper operation of a battery management system (BMS). Accurate temperature information is also essential to BMS for the accurate estimation of various important states of LIB such as state of charge, state of health and so on. High capacity LIB pack, used in electric vehicles and grid-tied stationary energy storage system essentially consists of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell especially at the cell core is not practically feasible from the solution cost, space and weight point of view. A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to the researchers and industries towards developing a highly accurate, intelligent, adaptive, easy to implement and compute efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS.

Subject Areas

electric vehicles; machine learning; Kalman filter; thermal modelling; online prediction; electromagnetic impedance spectroscopy; computational cost

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