Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework

Kaiyi Yang; Lisheng Zhang; Zhengjie Zhang; Hanqing Yu; Wentao Wang; Mengzheng Ouyang; Cheng Zhang; Qi Sun; Xiaoyu Yan; Shichun Yang; Xinhua Liu

doi:10.3390/batteries9070351

Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework

Kaiyi Yang, Lisheng Zhang, Zhengjie Zhang, Hanqing Yu, Wentao Wang, Mengzheng Ouyang, Cheng Zhang, Qi Sun, Xiaoyu Yan, Shichun Yang, Xinhua Liu

Centre for E-Mobility and Clean Growth

Research output: Contribution to journal › Review article › peer-review

18 Citations (Scopus)

237 Downloads (Pure)

Abstract

Lithium-ion batteries have become the primary electrical energy storage device in commercial and industrial applications due to their high energy/power density, high reliability, and long service life. It is essential to estimate the state of health (SOH) of batteries to ensure safety, optimize better energy efficiency and enhance the battery life-cycle management. This paper presents a comprehensive review of SOH estimation methods, including experimental approaches, model-based methods, and machine learning algorithms. A critical and in-depth analysis of the advantages and limitations of each method is presented. The various techniques are systematically classified and compared for the purpose of facilitating understanding and further research. Furthermore, the paper emphasizes the prospect of using a knowledge graph-based framework for battery data management, multi-model fusion, and cooperative edge-cloud platform for intelligent battery management systems (BMS).

Original language	English
Article number	351
Number of pages	37
Journal	Batteries
Volume	9
Issue number	7
DOIs	https://doi.org/10.3390/batteries9070351
Publication status	Published - 1 Jul 2023

Bibliographical note

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Funder

This research was funded by the National Natural Science Foundation of China (No. 52102470).

Keywords

SOH
artificial intelligence
multi-model fusion
end-cloud collaboration
CHAIN

UN SDGs

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

Access to Document

10.3390/batteries9070351Licence: CC BY

PublishedFinal published version, 3.5 MBLicence: CC BY

Cite this

@article{4b64dafc75504cdaa290b952aef8a818,

title = "Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework",

abstract = "Lithium-ion batteries have become the primary electrical energy storage device in commercial and industrial applications due to their high energy/power density, high reliability, and long service life. It is essential to estimate the state of health (SOH) of batteries to ensure safety, optimize better energy efficiency and enhance the battery life-cycle management. This paper presents a comprehensive review of SOH estimation methods, including experimental approaches, model-based methods, and machine learning algorithms. A critical and in-depth analysis of the advantages and limitations of each method is presented. The various techniques are systematically classified and compared for the purpose of facilitating understanding and further research. Furthermore, the paper emphasizes the prospect of using a knowledge graph-based framework for battery data management, multi-model fusion, and cooperative edge-cloud platform for intelligent battery management systems (BMS).",

keywords = "SOH, artificial intelligence, multi-model fusion, end-cloud collaboration, CHAIN",

author = "Kaiyi Yang and Lisheng Zhang and Zhengjie Zhang and Hanqing Yu and Wentao Wang and Mengzheng Ouyang and Cheng Zhang and Qi Sun and Xiaoyu Yan and Shichun Yang and Xinhua Liu",

note = "This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).",

year = "2023",

month = jul,

day = "1",

doi = "10.3390/batteries9070351",

language = "English",

volume = "9",

journal = "Batteries",

issn = "2313-0105",

publisher = "MDPI",

number = "7",

}

TY - JOUR

T1 - Battery State of Health Estimate Strategies

T2 - From Data Analysis to End-Cloud Collaborative Framework

AU - Yang, Kaiyi

AU - Zhang, Lisheng

AU - Zhang, Zhengjie

AU - Yu, Hanqing

AU - Wang, Wentao

AU - Ouyang, Mengzheng

AU - Zhang, Cheng

AU - Sun, Qi

AU - Yan, Xiaoyu

AU - Yang, Shichun

AU - Liu, Xinhua

N1 - This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

PY - 2023/7/1

Y1 - 2023/7/1

N2 - Lithium-ion batteries have become the primary electrical energy storage device in commercial and industrial applications due to their high energy/power density, high reliability, and long service life. It is essential to estimate the state of health (SOH) of batteries to ensure safety, optimize better energy efficiency and enhance the battery life-cycle management. This paper presents a comprehensive review of SOH estimation methods, including experimental approaches, model-based methods, and machine learning algorithms. A critical and in-depth analysis of the advantages and limitations of each method is presented. The various techniques are systematically classified and compared for the purpose of facilitating understanding and further research. Furthermore, the paper emphasizes the prospect of using a knowledge graph-based framework for battery data management, multi-model fusion, and cooperative edge-cloud platform for intelligent battery management systems (BMS).

AB - Lithium-ion batteries have become the primary electrical energy storage device in commercial and industrial applications due to their high energy/power density, high reliability, and long service life. It is essential to estimate the state of health (SOH) of batteries to ensure safety, optimize better energy efficiency and enhance the battery life-cycle management. This paper presents a comprehensive review of SOH estimation methods, including experimental approaches, model-based methods, and machine learning algorithms. A critical and in-depth analysis of the advantages and limitations of each method is presented. The various techniques are systematically classified and compared for the purpose of facilitating understanding and further research. Furthermore, the paper emphasizes the prospect of using a knowledge graph-based framework for battery data management, multi-model fusion, and cooperative edge-cloud platform for intelligent battery management systems (BMS).

KW - SOH

KW - artificial intelligence

KW - multi-model fusion

KW - end-cloud collaboration

KW - CHAIN

UR - http://www.scopus.com/inward/record.url?scp=85166380746&partnerID=8YFLogxK

U2 - 10.3390/batteries9070351

DO - 10.3390/batteries9070351

M3 - Review article

SN - 2313-0105

VL - 9

JO - Batteries

JF - Batteries

IS - 7

M1 - 351

ER -

Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework

Abstract

Bibliographical note

Funder

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this