A data-driven bias correction method based lithium-ion battery modeling approach for electric vehicles application

Xianzhi Gong; Rui Xiong; Chunting Chris Mi

doi:10.1109/itec.2014.6861807

A data-driven bias correction method based lithium-ion battery modeling approach for electric vehicles application

Xianzhi Gong, Rui Xiong, Chunting Chris Mi^*

^*Corresponding author for this work

School of Mechanical Engineering

University of Michigan

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

5 Citations (Scopus)

Abstract

Due to the inconsistency and varied characteristics of lithium-ion battery cells, the battery pack modeling remains a challenging problem. To model the operation behaviors of each cell in the battery pack, considerable work effort and computation time is needed. This paper proposes a data-driven bias correction based lithium-ion battery modeling method, which can significantly reduce the computation work and remain good model accuracy.

Original language	English
Title of host publication	2014 IEEE Transportation Electrification Conference and Expo
Subtitle of host publication	Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479922628
DOIs	https://doi.org/10.1109/itec.2014.6861807
Publication status	Published - 21 Jul 2014
Event	2014 IEEE Transportation Electrification Conference and Expo, ITEC 2014 - Dearborn, United States Duration: 15 Jun 2014 → 18 Jun 2014

Publication series

Name	2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014

Conference

Conference	2014 IEEE Transportation Electrification Conference and Expo, ITEC 2014
Country/Territory	United States
City	Dearborn
Period	15/06/14 → 18/06/14

UN SDGs

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

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10.1109/itec.2014.6861807

Cite this

Gong, X., Xiong, R., & Mi, C. C. (2014). A data-driven bias correction method based lithium-ion battery modeling approach for electric vehicles application. In 2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014 Article 6861807 (2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/itec.2014.6861807

Gong, Xianzhi ; Xiong, Rui ; Mi, Chunting Chris. / A data-driven bias correction method based lithium-ion battery modeling approach for electric vehicles application. 2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. (2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014).

@inproceedings{b494acd9dad24df6920d367c0faa8cbe,

title = "A data-driven bias correction method based lithium-ion battery modeling approach for electric vehicles application",

abstract = "Due to the inconsistency and varied characteristics of lithium-ion battery cells, the battery pack modeling remains a challenging problem. To model the operation behaviors of each cell in the battery pack, considerable work effort and computation time is needed. This paper proposes a data-driven bias correction based lithium-ion battery modeling method, which can significantly reduce the computation work and remain good model accuracy.",

author = "Xianzhi Gong and Rui Xiong and Mi, {Chunting Chris}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Transportation Electrification Conference and Expo, ITEC 2014 ; Conference date: 15-06-2014 Through 18-06-2014",

year = "2014",

month = jul,

day = "21",

doi = "10.1109/itec.2014.6861807",

language = "English",

series = "2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014",

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

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address = "United States",

}

Gong, X, Xiong, R & Mi, CC 2014, A data-driven bias correction method based lithium-ion battery modeling approach for electric vehicles application. in 2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014., 6861807, 2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014, Institute of Electrical and Electronics Engineers Inc., 2014 IEEE Transportation Electrification Conference and Expo, ITEC 2014, Dearborn, United States, 15/06/14. https://doi.org/10.1109/itec.2014.6861807

A data-driven bias correction method based lithium-ion battery modeling approach for electric vehicles application. / Gong, Xianzhi; Xiong, Rui; Mi, Chunting Chris.
2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 6861807 (2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014).

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

TY - GEN

T1 - A data-driven bias correction method based lithium-ion battery modeling approach for electric vehicles application

AU - Gong, Xianzhi

AU - Xiong, Rui

AU - Mi, Chunting Chris

PY - 2014/7/21

Y1 - 2014/7/21

N2 - Due to the inconsistency and varied characteristics of lithium-ion battery cells, the battery pack modeling remains a challenging problem. To model the operation behaviors of each cell in the battery pack, considerable work effort and computation time is needed. This paper proposes a data-driven bias correction based lithium-ion battery modeling method, which can significantly reduce the computation work and remain good model accuracy.

AB - Due to the inconsistency and varied characteristics of lithium-ion battery cells, the battery pack modeling remains a challenging problem. To model the operation behaviors of each cell in the battery pack, considerable work effort and computation time is needed. This paper proposes a data-driven bias correction based lithium-ion battery modeling method, which can significantly reduce the computation work and remain good model accuracy.

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U2 - 10.1109/itec.2014.6861807

DO - 10.1109/itec.2014.6861807

M3 - Conference contribution

AN - SCOPUS:84912103095

T3 - 2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014

BT - 2014 IEEE Transportation Electrification Conference and Expo

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE Transportation Electrification Conference and Expo, ITEC 2014

Y2 - 15 June 2014 through 18 June 2014

ER -

Gong X, Xiong R, Mi CC. A data-driven bias correction method based lithium-ion battery modeling approach for electric vehicles application. In 2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 6861807. (2014 IEEE Transportation Electrification Conference and Expo: Components, Systems, and Power Electronics - From Technology to Business and Public Policy, ITEC 2014). doi: 10.1109/itec.2014.6861807

A data-driven bias correction method based lithium-ion battery modeling approach for electric vehicles application

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