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Title: A correlation based fault detection method for short circuits in battery packs

Abstract

Here this paper presents a fault detection method for short circuits based on the correlation coefficient of voltage curves. The proposed method utilizes the direct voltage measurements from the battery cells, and does not require any additional hardware or effort in modeling during fault detection. Moreover, the inherent mathematical properties of the correlation coefficient ensure the robustness of this method as the battery pack ages or is imbalanced in real applications. In order to apply this method online, the recursive moving window correlation coefficient calculation is adopted to maintain the detection sensitivity to faults during operation. An additive square wave is designed to prevent false positive detections when the batteries are at rest. The fault isolation can be achieved by identifying the overlapped cell in the correlation coefficients with fault flags. Simulation and experimental results validated the feasibility and demonstrated the advantages of this method.

Authors:
 [1];  [2];  [3]; ORCiD logo [2]
+ Show Author Affiliations
  1. San Diego State University, CA (United States); University of California San Diego, La Jolla, CA (United States)
  2. San Diego State University, CA (United States)
  3. University of California San Diego, La Jolla, CA (United States)
Publication Date:
Research Org.:
Univ. of Michigan, Ann Arbor, MI (United States); San Diego State Univ., CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); Nanjing Golden Dragon Bus Co., Ltd
OSTI Identifier:
1534042
Alternate Identifier(s):
OSTI ID: 1410824
Grant/Contract Number:  
EE0005565; G00011032
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Power Sources
Additional Journal Information:
Journal Volume: 337; Journal Issue: C; Journal ID: ISSN 0378-7753
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; lithium-ion batteries; fault diagnosis; short circuit; correlation coefficient

Citation Formats

Xia, Bing, Shang, Yunlong, Nguyen, Truong, and Mi, Chris. A correlation based fault detection method for short circuits in battery packs. United States: N. p., 2016. Web. doi:10.1016/j.jpowsour.2016.11.007.
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Xia, Bing, Shang, Yunlong, Nguyen, Truong, & Mi, Chris. A correlation based fault detection method for short circuits in battery packs. United States. https://doi.org/10.1016/j.jpowsour.2016.11.007
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Xia, Bing, Shang, Yunlong, Nguyen, Truong, and Mi, Chris. Wed . "A correlation based fault detection method for short circuits in battery packs". United States. https://doi.org/10.1016/j.jpowsour.2016.11.007. https://www.osti.gov/servlets/purl/1534042.
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@article{osti_1534042,
title = {A correlation based fault detection method for short circuits in battery packs},
author = {Xia, Bing and Shang, Yunlong and Nguyen, Truong and Mi, Chris},
abstractNote = {Here this paper presents a fault detection method for short circuits based on the correlation coefficient of voltage curves. The proposed method utilizes the direct voltage measurements from the battery cells, and does not require any additional hardware or effort in modeling during fault detection. Moreover, the inherent mathematical properties of the correlation coefficient ensure the robustness of this method as the battery pack ages or is imbalanced in real applications. In order to apply this method online, the recursive moving window correlation coefficient calculation is adopted to maintain the detection sensitivity to faults during operation. An additive square wave is designed to prevent false positive detections when the batteries are at rest. The fault isolation can be achieved by identifying the overlapped cell in the correlation coefficients with fault flags. Simulation and experimental results validated the feasibility and demonstrated the advantages of this method.},
doi = {10.1016/j.jpowsour.2016.11.007},
journal = {Journal of Power Sources},
number = C,
volume = 337,
place = {United States},
year = {Wed Nov 09 00:00:00 EST 2016},
month = {Wed Nov 09 00:00:00 EST 2016}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.jpowsour.2016.11.007
Copyright Statement
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Cited by: 157 works
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    Works referencing / citing this record:

    Internal short circuit detection in Li-ion batteries using supervised machine learning
    journal, January 2020

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