On-board state of health estimation of lithium ion batteries with incremental capacity analysis based on gaussian function

Tongyi Liang; Lingjun Song

doi:10.1115/IMECE2018-86902

On-board state of health estimation of lithium ion batteries with incremental capacity analysis based on gaussian function

Tongyi Liang
, Lingjun Song^*

^*Corresponding author for this work

School of Transportation Science and Engineering

Beihang University

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

2 Scopus citations

Abstract

Accurately online state of health estimation is one of the key issues in battery management system (BMS), which enable the batteries function safely and efficiently. With excellent performance in in-situ degradation modes detection and precise state of health estimation modeling ability, incremental capacity (IC) analysis is widely used to analyze the situation of aged batteries. This paper discussed the difficulties in IC analysis application at first, and a robust method is then proposed, which parameterize the IC curve with Gaussian function. Battery cycle life experiment is conduced to validate the feasibility and accuracy of the proposed method. A capacity is constructed based the parameters of Gaussian function in each peak. The results show that the model estimation error is less than 3% of normalized capacity in each aging state, promising to be implemented in real BMS.

Original language	English
Title of host publication	Energy
Publisher	American Society of Mechanical Engineers (ASME)
ISBN (Electronic)	9780791852071
DOIs	https://doi.org/10.1115/IMECE2018-86902
State	Published - 2018
Event	ASME 2018 International Mechanical Engineering Congress and Exposition, IMECE 2018 - Pittsburgh, United States Duration: 9 Nov 2018 → 15 Nov 2018

Publication series

Name	ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
Volume	6A-144113

Conference

Conference	ASME 2018 International Mechanical Engineering Congress and Exposition, IMECE 2018
Country/Territory	United States
City	Pittsburgh
Period	9/11/18 → 15/11/18

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Electrical Vehicle
Gaussian Function
Incremental Capacity
Lithium-ion Battery
State of Health

Access to Document

10.1115/IMECE2018-86902

Cite this

@inproceedings{28858459bb0842768720a42d0555272e,

title = "On-board state of health estimation of lithium ion batteries with incremental capacity analysis based on gaussian function",

abstract = "Accurately online state of health estimation is one of the key issues in battery management system (BMS), which enable the batteries function safely and efficiently. With excellent performance in in-situ degradation modes detection and precise state of health estimation modeling ability, incremental capacity (IC) analysis is widely used to analyze the situation of aged batteries. This paper discussed the difficulties in IC analysis application at first, and a robust method is then proposed, which parameterize the IC curve with Gaussian function. Battery cycle life experiment is conduced to validate the feasibility and accuracy of the proposed method. A capacity is constructed based the parameters of Gaussian function in each peak. The results show that the model estimation error is less than 3\% of normalized capacity in each aging state, promising to be implemented in real BMS.",

keywords = "Electrical Vehicle, Gaussian Function, Incremental Capacity, Lithium-ion Battery, State of Health",

author = "Tongyi Liang and Lingjun Song",

note = "Publisher Copyright: Copyright {\textcopyright} 2018 ASME.; ASME 2018 International Mechanical Engineering Congress and Exposition, IMECE 2018 ; Conference date: 09-11-2018 Through 15-11-2018",

year = "2018",

doi = "10.1115/IMECE2018-86902",

language = "英语",

series = "ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)",

publisher = "American Society of Mechanical Engineers (ASME)",

booktitle = "Energy",

address = "美国",

}

Liang, T & Song, L 2018, On-board state of health estimation of lithium ion batteries with incremental capacity analysis based on gaussian function. in Energy. ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), vol. 6A-144113, American Society of Mechanical Engineers (ASME), ASME 2018 International Mechanical Engineering Congress and Exposition, IMECE 2018, Pittsburgh, United States, 9/11/18. https://doi.org/10.1115/IMECE2018-86902

On-board state of health estimation of lithium ion batteries with incremental capacity analysis based on gaussian function. / Liang, Tongyi; Song, Lingjun.
Energy. American Society of Mechanical Engineers (ASME), 2018. (ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE); Vol. 6A-144113).

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

TY - GEN

T1 - On-board state of health estimation of lithium ion batteries with incremental capacity analysis based on gaussian function

AU - Liang, Tongyi

AU - Song, Lingjun

PY - 2018

Y1 - 2018

N2 - Accurately online state of health estimation is one of the key issues in battery management system (BMS), which enable the batteries function safely and efficiently. With excellent performance in in-situ degradation modes detection and precise state of health estimation modeling ability, incremental capacity (IC) analysis is widely used to analyze the situation of aged batteries. This paper discussed the difficulties in IC analysis application at first, and a robust method is then proposed, which parameterize the IC curve with Gaussian function. Battery cycle life experiment is conduced to validate the feasibility and accuracy of the proposed method. A capacity is constructed based the parameters of Gaussian function in each peak. The results show that the model estimation error is less than 3% of normalized capacity in each aging state, promising to be implemented in real BMS.

AB - Accurately online state of health estimation is one of the key issues in battery management system (BMS), which enable the batteries function safely and efficiently. With excellent performance in in-situ degradation modes detection and precise state of health estimation modeling ability, incremental capacity (IC) analysis is widely used to analyze the situation of aged batteries. This paper discussed the difficulties in IC analysis application at first, and a robust method is then proposed, which parameterize the IC curve with Gaussian function. Battery cycle life experiment is conduced to validate the feasibility and accuracy of the proposed method. A capacity is constructed based the parameters of Gaussian function in each peak. The results show that the model estimation error is less than 3% of normalized capacity in each aging state, promising to be implemented in real BMS.

KW - Electrical Vehicle

KW - Gaussian Function

KW - Incremental Capacity

KW - Lithium-ion Battery

KW - State of Health

UR - https://www.scopus.com/pages/publications/85063790090

U2 - 10.1115/IMECE2018-86902

DO - 10.1115/IMECE2018-86902

M3 - 会议稿件

AN - SCOPUS:85063790090

T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)

BT - Energy

PB - American Society of Mechanical Engineers (ASME)

T2 - ASME 2018 International Mechanical Engineering Congress and Exposition, IMECE 2018

Y2 - 9 November 2018 through 15 November 2018

ER -

On-board state of health estimation of lithium ion batteries with incremental capacity analysis based on gaussian function

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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