Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data

Kei Long Wong; Ka Seng Chou; Davide Aguiari; Rita Tse; Su Kit Tang; Giovanni Pau

doi:10.1109/EPEC56903.2022.9999742

Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data

Kei Long Wong
, Ka Seng Chou
, Davide Aguiari
, Rita Tse
, Su Kit Tang
, Giovanni Pau

Faculty of Applied Sciences

研究成果: Conference contribution › 同行評審

2 引文斯高帕斯（Scopus）

摘要

State of health estimation of battery is crucial to ensure the safety and durability of electric vehicles. This paper presents six methods to extract the battery health indicator from electric vehicle field testing data. The methods for extracting health indicators from the discharge cycle show the ability to cope with the variable driving condition. In total, 157 health indicators are extracted from the collected data. Pearson correlation coefficient and Spearman's rank correlation coefficient are used to measure the correlation between the health indicators and the state of health. The results suggest that health indicators extracted by the presented methods have high correlations to the battery state of health.

原文	English
主出版物標題	2022 IEEE Electrical Power and Energy Conference, EPEC 2022
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	206-211
頁數	6
ISBN（電子）	9781665463188
DOIs	https://doi.org/10.1109/EPEC56903.2022.9999742
出版狀態	Published - 2022
事件	2022 IEEE Electrical Power and Energy Conference, EPEC 2022 - Virtual, Online, Canada 持續時間: 5 12月 2022 → 7 12月 2022

出版系列

名字	2022 IEEE Electrical Power and Energy Conference, EPEC 2022

Conference

Conference	2022 IEEE Electrical Power and Energy Conference, EPEC 2022
國家/地區	Canada
城市	Virtual, Online
期間	5/12/22 → 7/12/22

UN SDG

此研究成果有助於以下永續發展目標

Affordable and clean energy

存取文件

10.1109/EPEC56903.2022.9999742

其它文件與鏈接

Link to publication in Scopus

引用此

Wong, K. L., Chou, K. S., Aguiari, D., Tse, R., Tang, S. K., & Pau, G. (2022). Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data. 於 2022 IEEE Electrical Power and Energy Conference, EPEC 2022 (頁 206-211). (2022 IEEE Electrical Power and Energy Conference, EPEC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EPEC56903.2022.9999742

@inproceedings{6a6109894abe43cc9978d8395b1a0730,

title = "Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data",

abstract = "State of health estimation of battery is crucial to ensure the safety and durability of electric vehicles. This paper presents six methods to extract the battery health indicator from electric vehicle field testing data. The methods for extracting health indicators from the discharge cycle show the ability to cope with the variable driving condition. In total, 157 health indicators are extracted from the collected data. Pearson correlation coefficient and Spearman's rank correlation coefficient are used to measure the correlation between the health indicators and the state of health. The results suggest that health indicators extracted by the presented methods have high correlations to the battery state of health.",

keywords = "battery degradation, correlation analysis, electric vehicle, field testing data, lithium-ion battery, state of health",

author = "Wong, \{Kei Long\} and Chou, \{Ka Seng\} and Davide Aguiari and Rita Tse and Tang, \{Su Kit\} and Giovanni Pau",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Electrical Power and Energy Conference, EPEC 2022 ; Conference date: 05-12-2022 Through 07-12-2022",

year = "2022",

doi = "10.1109/EPEC56903.2022.9999742",

language = "English",

series = "2022 IEEE Electrical Power and Energy Conference, EPEC 2022",

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

pages = "206--211",

booktitle = "2022 IEEE Electrical Power and Energy Conference, EPEC 2022",

address = "United States",

}

Wong, KL, Chou, KS, Aguiari, D, Tse, R , Tang, SK & Pau, G 2022, Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data. 於 2022 IEEE Electrical Power and Energy Conference, EPEC 2022. 2022 IEEE Electrical Power and Energy Conference, EPEC 2022, Institute of Electrical and Electronics Engineers Inc., 頁 206-211, 2022 IEEE Electrical Power and Energy Conference, EPEC 2022, Virtual, Online, Canada, 5/12/22. https://doi.org/10.1109/EPEC56903.2022.9999742

Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data. / Wong, Kei Long; Chou, Ka Seng; Aguiari, Davide 等.
2022 IEEE Electrical Power and Energy Conference, EPEC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 206-211 (2022 IEEE Electrical Power and Energy Conference, EPEC 2022).

研究成果: Conference contribution › 同行評審

TY - GEN

T1 - Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data

AU - Wong, Kei Long

AU - Chou, Ka Seng

AU - Aguiari, Davide

AU - Tse, Rita

AU - Tang, Su Kit

AU - Pau, Giovanni

PY - 2022

Y1 - 2022

N2 - State of health estimation of battery is crucial to ensure the safety and durability of electric vehicles. This paper presents six methods to extract the battery health indicator from electric vehicle field testing data. The methods for extracting health indicators from the discharge cycle show the ability to cope with the variable driving condition. In total, 157 health indicators are extracted from the collected data. Pearson correlation coefficient and Spearman's rank correlation coefficient are used to measure the correlation between the health indicators and the state of health. The results suggest that health indicators extracted by the presented methods have high correlations to the battery state of health.

AB - State of health estimation of battery is crucial to ensure the safety and durability of electric vehicles. This paper presents six methods to extract the battery health indicator from electric vehicle field testing data. The methods for extracting health indicators from the discharge cycle show the ability to cope with the variable driving condition. In total, 157 health indicators are extracted from the collected data. Pearson correlation coefficient and Spearman's rank correlation coefficient are used to measure the correlation between the health indicators and the state of health. The results suggest that health indicators extracted by the presented methods have high correlations to the battery state of health.

KW - battery degradation

KW - correlation analysis

KW - electric vehicle

KW - field testing data

KW - lithium-ion battery

KW - state of health

UR - https://www.scopus.com/pages/publications/85146713366

U2 - 10.1109/EPEC56903.2022.9999742

DO - 10.1109/EPEC56903.2022.9999742

M3 - Conference contribution

AN - SCOPUS:85146713366

T3 - 2022 IEEE Electrical Power and Energy Conference, EPEC 2022

SP - 206

EP - 211

BT - 2022 IEEE Electrical Power and Energy Conference, EPEC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE Electrical Power and Energy Conference, EPEC 2022

Y2 - 5 December 2022 through 7 December 2022

ER -

Wong KL, Chou KS, Aguiari D, Tse R , Tang SK, Pau G. Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data. 於 2022 IEEE Electrical Power and Energy Conference, EPEC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 206-211. (2022 IEEE Electrical Power and Energy Conference, EPEC 2022). doi: 10.1109/EPEC56903.2022.9999742

Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data

摘要

出版系列

Conference

UN SDG

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