Investigation into Rapid State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles

Zuolu Wang; Xiaoyu Zhao; Eric Li; Henry Brunskill; Dong Zhen; Fengshou Gu; Andrew Ball

doi:10.1007/978-3-031-49413-0_82

Investigation into Rapid State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles

Zuolu Wang, Xiaoyu Zhao, Eric Li, Henry Brunskill, Dong Zhen, Fengshou Gu, Andrew Ball

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The state of health (SOH) is an important indicator of lithium-ion batteries in electric vehicles (EVs), and therefore accurate SOH estimation plays a fundamental role in ensuring the reliable operation and safety of EVs. Currently, a variety of methods have been developed for online/offline battery SOH estimation, while most of them present high complexity due to complex tests, modelling, and algorithm implementation. Developing a rapid and accurate SOH estimation approach based on the simple test is a research hotspot to reduce the estimation cost. This paper summarizes and reclassifies the existing estimation methods considering the estimation complexity in practical applications. First, the rapid estimation methods are categorized into electrical parameters-based estimation and material properties-based estimation. Second, the working principles and performance of the rapid estimation methods are introduced combined with the review and experimental studies. Finally, this paper compares them in view of advantages and disadvantages and suggests the future trend for further improvement when used in practice. It is believed that this investigation can provide valuable guidance for academic research and engineering applications.

Original language	English
Title of host publication	Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1
Editors	Andrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang
Publisher	Springer, Cham
Pages	1063-1080
Number of pages	18
Volume	151
ISBN (Electronic)	9783031494130
ISBN (Print)	9783031494123, 9783031494154
DOIs	https://doi.org/10.1007/978-3-031-49413-0_82
Publication status	Published - 30 May 2024
Event	The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom Duration: 29 Aug 2023 → 1 Sep 2023 https://unified2023.org/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer
Volume	151 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences
Abbreviated title	UNIfied 2023
Country/Territory	United Kingdom
City	Huddersfield
Period	29/08/23 → 1/09/23
Internet address	https://unified2023.org/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

Wang, Z., Zhao, X., Li, E., Brunskill, H., Zhen, D., Gu, F., & Ball, A. (2024). Investigation into Rapid State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles. In A. D. Ball, H. Ouyang, J. K. Sinha, & Z. Wang (Eds.), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1 (Vol. 151, pp. 1063-1080). (Mechanisms and Machine Science; Vol. 151 MMS). Springer, Cham. https://doi.org/10.1007/978-3-031-49413-0_82

Wang, Zuolu ; Zhao, Xiaoyu ; Li, Eric et al. / Investigation into Rapid State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles. Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1. editor / Andrew D. Ball ; Huajiang Ouyang ; Jyoti K. Sinha ; Zuolu Wang. Vol. 151 Springer, Cham, 2024. pp. 1063-1080 (Mechanisms and Machine Science).

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title = "Investigation into Rapid State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles",

abstract = "The state of health (SOH) is an important indicator of lithium-ion batteries in electric vehicles (EVs), and therefore accurate SOH estimation plays a fundamental role in ensuring the reliable operation and safety of EVs. Currently, a variety of methods have been developed for online/offline battery SOH estimation, while most of them present high complexity due to complex tests, modelling, and algorithm implementation. Developing a rapid and accurate SOH estimation approach based on the simple test is a research hotspot to reduce the estimation cost. This paper summarizes and reclassifies the existing estimation methods considering the estimation complexity in practical applications. First, the rapid estimation methods are categorized into electrical parameters-based estimation and material properties-based estimation. Second, the working principles and performance of the rapid estimation methods are introduced combined with the review and experimental studies. Finally, this paper compares them in view of advantages and disadvantages and suggests the future trend for further improvement when used in practice. It is believed that this investigation can provide valuable guidance for academic research and engineering applications.",

keywords = "Electric vehicles, Lithium-ion battery, Rapid estimation, State of health",

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Wang, Z, Zhao, X, Li, E, Brunskill, H, Zhen, D, Gu, F & Ball, A 2024, Investigation into Rapid State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles. in AD Ball, H Ouyang, JK Sinha & Z Wang (eds), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1. vol. 151, Mechanisms and Machine Science, vol. 151 MMS, Springer, Cham, pp. 1063-1080, The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences, Huddersfield, United Kingdom, 29/08/23. https://doi.org/10.1007/978-3-031-49413-0_82

Investigation into Rapid State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles. / Wang, Zuolu; Zhao, Xiaoyu; Li, Eric et al.

Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1. ed. / Andrew D. Ball; Huajiang Ouyang; Jyoti K. Sinha; Zuolu Wang. Vol. 151 Springer, Cham, 2024. p. 1063-1080 (Mechanisms and Machine Science; Vol. 151 MMS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Investigation into Rapid State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles

AU - Wang, Zuolu

AU - Zhao, Xiaoyu

AU - Li, Eric

AU - Brunskill, Henry

AU - Zhen, Dong

AU - Gu, Fengshou

AU - Ball, Andrew

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024/5/30

Y1 - 2024/5/30

N2 - The state of health (SOH) is an important indicator of lithium-ion batteries in electric vehicles (EVs), and therefore accurate SOH estimation plays a fundamental role in ensuring the reliable operation and safety of EVs. Currently, a variety of methods have been developed for online/offline battery SOH estimation, while most of them present high complexity due to complex tests, modelling, and algorithm implementation. Developing a rapid and accurate SOH estimation approach based on the simple test is a research hotspot to reduce the estimation cost. This paper summarizes and reclassifies the existing estimation methods considering the estimation complexity in practical applications. First, the rapid estimation methods are categorized into electrical parameters-based estimation and material properties-based estimation. Second, the working principles and performance of the rapid estimation methods are introduced combined with the review and experimental studies. Finally, this paper compares them in view of advantages and disadvantages and suggests the future trend for further improvement when used in practice. It is believed that this investigation can provide valuable guidance for academic research and engineering applications.

AB - The state of health (SOH) is an important indicator of lithium-ion batteries in electric vehicles (EVs), and therefore accurate SOH estimation plays a fundamental role in ensuring the reliable operation and safety of EVs. Currently, a variety of methods have been developed for online/offline battery SOH estimation, while most of them present high complexity due to complex tests, modelling, and algorithm implementation. Developing a rapid and accurate SOH estimation approach based on the simple test is a research hotspot to reduce the estimation cost. This paper summarizes and reclassifies the existing estimation methods considering the estimation complexity in practical applications. First, the rapid estimation methods are categorized into electrical parameters-based estimation and material properties-based estimation. Second, the working principles and performance of the rapid estimation methods are introduced combined with the review and experimental studies. Finally, this paper compares them in view of advantages and disadvantages and suggests the future trend for further improvement when used in practice. It is believed that this investigation can provide valuable guidance for academic research and engineering applications.

KW - Electric vehicles

KW - Lithium-ion battery

KW - Rapid estimation

KW - State of health

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BT - Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1

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Wang Z, Zhao X, Li E, Brunskill H, Zhen D, Gu F et al. Investigation into Rapid State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles. In Ball AD, Ouyang H, Sinha JK, Wang Z, editors, Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1. Vol. 151. Springer, Cham. 2024. p. 1063-1080. (Mechanisms and Machine Science). Epub 2024 May 29. doi: 10.1007/978-3-031-49413-0_82

Investigation into Rapid State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles

Abstract

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UN SDGs

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