Lithium-ion Battery SOH Estimation with Varying Amount of Battery Operation Data

Xingjun Li; Dan Yu; Søren Byg Vilsen; Daniel-Ioan Store

doi:10.23919/EPE23ECCEEurope58414.2023.10264581

Lithium-ion Battery SOH Estimation with Varying Amount of Battery Operation Data

Xingjun Li, Dan Yu, Søren Byg Vilsen, Daniel-Ioan Store

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

Abstract

This work estimates SOH of lithium-ion batteries, aged by a forklift driving profile, based on multiple linear regression and compares the estimation accuracy at three levels. Unlike previous research, this work uses dynamic and field data rather than public datasets. The influence of data size and the position to extract features on the SOH estimation accuracy was researched. It is found that extracting features from smaller voltage segments contains more information. The estimation accuracy can be improved by 24.5% MAPE after the Box-Cox transformation.

Originalsprog	Engelsk
Titel	2023 25th European Conference on Power Electronics and Applications, EPE 2023 ECCE Europe
Antal sider	8
Forlag	IEEE
Publikationsdato	8 sep. 2023
Sider	1-8
Artikelnummer	10264581
ISBN (Trykt)	979-8-3503-1678-0
ISBN (Elektronisk)	9789075815412
DOI	https://doi.org/10.23919/EPE23ECCEEurope58414.2023.10264581
Status	Udgivet - 8 sep. 2023
Begivenhed	2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe) - Aalborg, Danmark Varighed: 4 sep. 2023 → 8 sep. 2023

Konference

Konference	2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe)
Land/Område	Danmark
By	Aalborg
Periode	04/09/2023 → 08/09/2023

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10.23919/EPE23ECCEEurope58414.2023.10264581

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10264581

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@inproceedings{9cc88f437b7c4f4491cb282e45675f2d,

title = "Lithium-ion Battery SOH Estimation with Varying Amount of Battery Operation Data",

abstract = "This work estimates SOH of lithium-ion batteries, aged by a forklift driving profile, based on multiple linear regression and compares the estimation accuracy at three levels. Unlike previous research, this work uses dynamic and field data rather than public datasets. The influence of data size and the position to extract features on the SOH estimation accuracy was researched. It is found that extracting features from smaller voltage segments contains more information. The estimation accuracy can be improved by 24.5% MAPE after the Box-Cox transformation.",

keywords = "Estimation, Europe, Linear regression, Lithium-ion batteries, Training, Transforms, Voltage, Lithium-ion battery, Battery Management Systems (BMS)",

author = "Xingjun Li and Dan Yu and Vilsen, {S{\o}ren Byg} and Daniel-Ioan Store",

year = "2023",

month = sep,

day = "8",

doi = "10.23919/EPE23ECCEEurope58414.2023.10264581",

language = "English",

isbn = "979-8-3503-1678-0",

pages = "1--8",

booktitle = "2023 25th European Conference on Power Electronics and Applications, EPE 2023 ECCE Europe",

publisher = "IEEE",

address = "United States",

note = "2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe) ; Conference date: 04-09-2023 Through 08-09-2023",

}

Li, X , Yu, D , Vilsen, SB & Store, D-I 2023, Lithium-ion Battery SOH Estimation with Varying Amount of Battery Operation Data. i 2023 25th European Conference on Power Electronics and Applications, EPE 2023 ECCE Europe., 10264581, IEEE, s. 1-8, 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe), Aalborg, Danmark, 04/09/2023. https://doi.org/10.23919/EPE23ECCEEurope58414.2023.10264581

Lithium-ion Battery SOH Estimation with Varying Amount of Battery Operation Data. / Li, Xingjun ; Yu, Dan ; Vilsen, Søren Byg et al.
2023 25th European Conference on Power Electronics and Applications, EPE 2023 ECCE Europe. IEEE, 2023. s. 1-8 10264581.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Lithium-ion Battery SOH Estimation with Varying Amount of Battery Operation Data

AU - Li, Xingjun

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PY - 2023/9/8

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N2 - This work estimates SOH of lithium-ion batteries, aged by a forklift driving profile, based on multiple linear regression and compares the estimation accuracy at three levels. Unlike previous research, this work uses dynamic and field data rather than public datasets. The influence of data size and the position to extract features on the SOH estimation accuracy was researched. It is found that extracting features from smaller voltage segments contains more information. The estimation accuracy can be improved by 24.5% MAPE after the Box-Cox transformation.

AB - This work estimates SOH of lithium-ion batteries, aged by a forklift driving profile, based on multiple linear regression and compares the estimation accuracy at three levels. Unlike previous research, this work uses dynamic and field data rather than public datasets. The influence of data size and the position to extract features on the SOH estimation accuracy was researched. It is found that extracting features from smaller voltage segments contains more information. The estimation accuracy can be improved by 24.5% MAPE after the Box-Cox transformation.

KW - Estimation

KW - Europe

KW - Linear regression

KW - Lithium-ion batteries

KW - Training

KW - Transforms

KW - Voltage

KW - Lithium-ion battery

KW - Battery Management Systems (BMS)

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DO - 10.23919/EPE23ECCEEurope58414.2023.10264581

M3 - Article in proceeding

SN - 979-8-3503-1678-0

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BT - 2023 25th European Conference on Power Electronics and Applications, EPE 2023 ECCE Europe

PB - IEEE

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Y2 - 4 September 2023 through 8 September 2023

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Lithium-ion Battery SOH Estimation with Varying Amount of Battery Operation Data

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