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

University of Bologna
Macao Polytechnic University
Technology Innovation Institute
University of California at Los Angeles

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

2 Citations (Scopus)

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.

Original language	English
Title of host publication	2022 IEEE Electrical Power and Energy Conference, EPEC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	206-211
Number of pages	6
ISBN (Electronic)	9781665463188
DOIs	https://doi.org/10.1109/EPEC56903.2022.9999742
Publication status	Published - 2022
Event	2022 IEEE Electrical Power and Energy Conference, EPEC 2022 - Virtual, Online, Canada Duration: 5 Dec 2022 → 7 Dec 2022

Publication series

Name	2022 IEEE Electrical Power and Energy Conference, EPEC 2022

Conference

Conference	2022 IEEE Electrical Power and Energy Conference, EPEC 2022
Country/Territory	Canada
City	Virtual, Online
Period	5/12/22 → 7/12/22

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

battery degradation
correlation analysis
electric vehicle
field testing data
lithium-ion battery
state of health

Access to Document

10.1109/EPEC56903.2022.9999742

Cite this

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. In 2022 IEEE Electrical Power and Energy Conference, EPEC 2022 (pp. 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. in 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., pp. 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 et al.
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).

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

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. In 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

Abstract

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UN SDGs

Keywords

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