Machine learning algorithm based battery modeling and management method: A Cyber-Physical System perspective

Shuangqi Li; Hongwen He; Jianwei Li; Peng Yin; Hanxiao Wang

doi:10.1109/CVCI47823.2019.8951635

Machine learning algorithm based battery modeling and management method: A Cyber-Physical System perspective

Shuangqi Li
, Hongwen He^*
, Jianwei Li
, Peng Yin
, Hanxiao Wang

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

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

7 Citations (Scopus)

Abstract

In recent years, in order to realize the accurate state monitoring and management of battery, the development of a flexible, self-reconfigurable and reliable model has become one of the most crucial technologies for electric vehicles. This paper mainly focuses on the battery management issues in new energy vehicles, in which the concept of artificial intelligence and grid-connected vehicle is introduced. Firstly, the concept of Cyber-Physical system (CPS) is applied in battery management issues in our work for a better use of battery data. To establish a precise battery model in cloud, the Support vector regression (SVR) algorithm, a classical artificial intelligence algorithm, is used in our work to model the battery. Finally, a rain-flow cycle counting algorithm-based battery degradation quantification method is proposed to deal with the influence of battery aging phenomenon during modeling the battery.

Original language	English
Title of host publication	3rd Conference on Vehicle Control and Intelligence, CVCI 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728126845
DOIs	https://doi.org/10.1109/CVCI47823.2019.8951635
Publication status	Published - Sept 2019
Event	3rd Conference on Vehicle Control and Intelligence, CVCI 2019 - Hefei, China Duration: 21 Sept 2019 → 22 Sept 2019

Publication series

Name	3rd Conference on Vehicle Control and Intelligence, CVCI 2019

Conference

Conference	3rd Conference on Vehicle Control and Intelligence, CVCI 2019
Country/Territory	China
City	Hefei
Period	21/09/19 → 22/09/19

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

big data
cyber-physical system
electric vehicle
grid-connected vehicle
lithium-ion battery
machine learning

Access to Document

10.1109/CVCI47823.2019.8951635

Cite this

Li, S., He, H., Li, J., Yin, P., & Wang, H. (2019). Machine learning algorithm based battery modeling and management method: A Cyber-Physical System perspective. In 3rd Conference on Vehicle Control and Intelligence, CVCI 2019 Article 8951635 (3rd Conference on Vehicle Control and Intelligence, CVCI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CVCI47823.2019.8951635

@inproceedings{e8b9d81e37604b2c989f6fd4c5da9fdb,

title = "Machine learning algorithm based battery modeling and management method: A Cyber-Physical System perspective",

abstract = "In recent years, in order to realize the accurate state monitoring and management of battery, the development of a flexible, self-reconfigurable and reliable model has become one of the most crucial technologies for electric vehicles. This paper mainly focuses on the battery management issues in new energy vehicles, in which the concept of artificial intelligence and grid-connected vehicle is introduced. Firstly, the concept of Cyber-Physical system (CPS) is applied in battery management issues in our work for a better use of battery data. To establish a precise battery model in cloud, the Support vector regression (SVR) algorithm, a classical artificial intelligence algorithm, is used in our work to model the battery. Finally, a rain-flow cycle counting algorithm-based battery degradation quantification method is proposed to deal with the influence of battery aging phenomenon during modeling the battery.",

keywords = "big data, cyber-physical system, electric vehicle, grid-connected vehicle, lithium-ion battery, machine learning",

author = "Shuangqi Li and Hongwen He and Jianwei Li and Peng Yin and Hanxiao Wang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 3rd Conference on Vehicle Control and Intelligence, CVCI 2019 ; Conference date: 21-09-2019 Through 22-09-2019",

year = "2019",

month = sep,

doi = "10.1109/CVCI47823.2019.8951635",

language = "English",

series = "3rd Conference on Vehicle Control and Intelligence, CVCI 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "3rd Conference on Vehicle Control and Intelligence, CVCI 2019",

address = "United States",

}

Li, S, He, H , Li, J, Yin, P & Wang, H 2019, Machine learning algorithm based battery modeling and management method: A Cyber-Physical System perspective. in 3rd Conference on Vehicle Control and Intelligence, CVCI 2019., 8951635, 3rd Conference on Vehicle Control and Intelligence, CVCI 2019, Institute of Electrical and Electronics Engineers Inc., 3rd Conference on Vehicle Control and Intelligence, CVCI 2019, Hefei, China, 21/09/19. https://doi.org/10.1109/CVCI47823.2019.8951635

Machine learning algorithm based battery modeling and management method: A Cyber-Physical System perspective. / Li, Shuangqi; He, Hongwen ; Li, Jianwei et al.
3rd Conference on Vehicle Control and Intelligence, CVCI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8951635 (3rd Conference on Vehicle Control and Intelligence, CVCI 2019).

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

TY - GEN

T1 - Machine learning algorithm based battery modeling and management method

T2 - 3rd Conference on Vehicle Control and Intelligence, CVCI 2019

AU - Li, Shuangqi

AU - He, Hongwen

AU - Li, Jianwei

AU - Yin, Peng

AU - Wang, Hanxiao

PY - 2019/9

Y1 - 2019/9

N2 - In recent years, in order to realize the accurate state monitoring and management of battery, the development of a flexible, self-reconfigurable and reliable model has become one of the most crucial technologies for electric vehicles. This paper mainly focuses on the battery management issues in new energy vehicles, in which the concept of artificial intelligence and grid-connected vehicle is introduced. Firstly, the concept of Cyber-Physical system (CPS) is applied in battery management issues in our work for a better use of battery data. To establish a precise battery model in cloud, the Support vector regression (SVR) algorithm, a classical artificial intelligence algorithm, is used in our work to model the battery. Finally, a rain-flow cycle counting algorithm-based battery degradation quantification method is proposed to deal with the influence of battery aging phenomenon during modeling the battery.

AB - In recent years, in order to realize the accurate state monitoring and management of battery, the development of a flexible, self-reconfigurable and reliable model has become one of the most crucial technologies for electric vehicles. This paper mainly focuses on the battery management issues in new energy vehicles, in which the concept of artificial intelligence and grid-connected vehicle is introduced. Firstly, the concept of Cyber-Physical system (CPS) is applied in battery management issues in our work for a better use of battery data. To establish a precise battery model in cloud, the Support vector regression (SVR) algorithm, a classical artificial intelligence algorithm, is used in our work to model the battery. Finally, a rain-flow cycle counting algorithm-based battery degradation quantification method is proposed to deal with the influence of battery aging phenomenon during modeling the battery.

KW - big data

KW - cyber-physical system

KW - electric vehicle

KW - grid-connected vehicle

KW - lithium-ion battery

KW - machine learning

UR - https://www.scopus.com/pages/publications/85078756186

U2 - 10.1109/CVCI47823.2019.8951635

DO - 10.1109/CVCI47823.2019.8951635

M3 - Conference contribution

AN - SCOPUS:85078756186

T3 - 3rd Conference on Vehicle Control and Intelligence, CVCI 2019

BT - 3rd Conference on Vehicle Control and Intelligence, CVCI 2019

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 21 September 2019 through 22 September 2019

ER -

Li S, He H , Li J, Yin P, Wang H. Machine learning algorithm based battery modeling and management method: A Cyber-Physical System perspective. In 3rd Conference on Vehicle Control and Intelligence, CVCI 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8951635. (3rd Conference on Vehicle Control and Intelligence, CVCI 2019). doi: 10.1109/CVCI47823.2019.8951635

Machine learning algorithm based battery modeling and management method: A Cyber-Physical System perspective

Abstract

Publication series

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UN SDGs

Keywords

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