Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries

Jian Hu; Zhongbao Wei; Hongwen He

doi:10.1109/ICEI57064.2022.00024

Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries

School of Mechanical Engineering

Beijing Institute of Technology

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

4 Citations (Scopus)

Abstract

Heat generation rate is a significant safety indicator for lithium-ion battery thermal management which need to be monitored in real time. A distributed fiber optic sensor embedded smart battery configuration is proposed in this paper to acquire the multi-point temperature measurements inside and outside the battery. Hence, a machine learning-based heat generation rate estimation and diagnosis method for Lithium-ion batteries is proposed in this paper to estimate the heat generation rate leveraging the multi-point temperature measurements and detect the abnormal heat generation in real time. The proposed heat generation rate estimation method and smart configuration are experimentally validated to be effective and accurate, and the proposed abnormal heat generation diagnosis method is verified by simulation.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	108-112
Number of pages	5
ISBN (Electronic)	9781665493277
DOIs	https://doi.org/10.1109/ICEI57064.2022.00024
Publication status	Published - 2022
Event	6th IEEE International Conference on Energy Internet, ICEI 2022 - Virtual, Online, Norway Duration: 28 Dec 2022 → 29 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022

Conference

Conference	6th IEEE International Conference on Energy Internet, ICEI 2022
Country/Territory	Norway
City	Virtual, Online
Period	28/12/22 → 29/12/22

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Lithium-ion battery
battery thermal management
fault diagnosis
heat generation rate
smart battery

Access to Document

10.1109/ICEI57064.2022.00024

Cite this

Hu, J., Wei, Z., & He, H. (2022). Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries. In Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022 (pp. 108-112). (Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEI57064.2022.00024

Hu, Jian ; Wei, Zhongbao ; He, Hongwen. / Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries. Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 108-112 (Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022).

@inproceedings{6ea37def65ec456bb5addb88242d6bfc,

title = "Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries",

abstract = "Heat generation rate is a significant safety indicator for lithium-ion battery thermal management which need to be monitored in real time. A distributed fiber optic sensor embedded smart battery configuration is proposed in this paper to acquire the multi-point temperature measurements inside and outside the battery. Hence, a machine learning-based heat generation rate estimation and diagnosis method for Lithium-ion batteries is proposed in this paper to estimate the heat generation rate leveraging the multi-point temperature measurements and detect the abnormal heat generation in real time. The proposed heat generation rate estimation method and smart configuration are experimentally validated to be effective and accurate, and the proposed abnormal heat generation diagnosis method is verified by simulation.",

keywords = "Lithium-ion battery, battery thermal management, fault diagnosis, heat generation rate, smart battery",

author = "Jian Hu and Zhongbao Wei and Hongwen He",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th IEEE International Conference on Energy Internet, ICEI 2022 ; Conference date: 28-12-2022 Through 29-12-2022",

year = "2022",

doi = "10.1109/ICEI57064.2022.00024",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022",

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

pages = "108--112",

booktitle = "Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022",

address = "United States",

}

Hu, J, Wei, Z & He, H 2022, Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries. in Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022. Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022, Institute of Electrical and Electronics Engineers Inc., pp. 108-112, 6th IEEE International Conference on Energy Internet, ICEI 2022, Virtual, Online, Norway, 28/12/22. https://doi.org/10.1109/ICEI57064.2022.00024

Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries. / Hu, Jian; Wei, Zhongbao ; He, Hongwen.
Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 108-112 (Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022).

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

TY - GEN

T1 - Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries

AU - Hu, Jian

AU - Wei, Zhongbao

AU - He, Hongwen

PY - 2022

Y1 - 2022

N2 - Heat generation rate is a significant safety indicator for lithium-ion battery thermal management which need to be monitored in real time. A distributed fiber optic sensor embedded smart battery configuration is proposed in this paper to acquire the multi-point temperature measurements inside and outside the battery. Hence, a machine learning-based heat generation rate estimation and diagnosis method for Lithium-ion batteries is proposed in this paper to estimate the heat generation rate leveraging the multi-point temperature measurements and detect the abnormal heat generation in real time. The proposed heat generation rate estimation method and smart configuration are experimentally validated to be effective and accurate, and the proposed abnormal heat generation diagnosis method is verified by simulation.

AB - Heat generation rate is a significant safety indicator for lithium-ion battery thermal management which need to be monitored in real time. A distributed fiber optic sensor embedded smart battery configuration is proposed in this paper to acquire the multi-point temperature measurements inside and outside the battery. Hence, a machine learning-based heat generation rate estimation and diagnosis method for Lithium-ion batteries is proposed in this paper to estimate the heat generation rate leveraging the multi-point temperature measurements and detect the abnormal heat generation in real time. The proposed heat generation rate estimation method and smart configuration are experimentally validated to be effective and accurate, and the proposed abnormal heat generation diagnosis method is verified by simulation.

KW - Lithium-ion battery

KW - battery thermal management

KW - fault diagnosis

KW - heat generation rate

KW - smart battery

UR - https://www.scopus.com/pages/publications/85156090173

U2 - 10.1109/ICEI57064.2022.00024

DO - 10.1109/ICEI57064.2022.00024

M3 - Conference contribution

AN - SCOPUS:85156090173

T3 - Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022

SP - 108

EP - 112

BT - Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE International Conference on Energy Internet, ICEI 2022

Y2 - 28 December 2022 through 29 December 2022

ER -

Hu J, Wei Z , He H. Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries. In Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 108-112. (Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022). doi: 10.1109/ICEI57064.2022.00024

Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries

Abstract

Publication series

Conference

UN SDGs

Keywords

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