skip to main content
10.1145/3366194.3366255acmotherconferencesArticle/Chapter ViewAbstractPublication PagesricaiConference Proceedingsconference-collections
research-article

Design of Rapid Battery Pre-test Diagnostic Process

Authors Info & Claims
Published:20 September 2019Publication History

ABSTRACT

WIith the increasing aging of electric vehicle batteries in large quantities, rapid pre-test diagnosis of power battery modules and screening of defective batteries have become market demand. This paper was based on the portable fast battery diagnostic instrument, which quickly checked the lithium iron phosphate battery packs with different degrees of aging to determine whether the battery management system and the battery modules were connected properly. Furthermore, the constant current discharge curve of the battery was analyzed, and the internal resistance of each single cell in the battery module was obtained, thereby characterizing the difference in the aging degree of different single cells. Finally, the same battery module was tested using a portable fast battery diagnostic device. The verification found that after the battery management system and the single battery connection problem were eliminated, the portable device obtained the same result under different detection conditions, and the battery module was aged. The single cell was judged to be a problematic single cell because the internal resistance was too large and the open circuit voltage was too low. The portable device has low cost, simple operation, convenient and fast, and is suitable for rapid troubleshooting and internal resistance detection of the battery module in the field, and does not affect the life of the battery pack itself. The results of the fast battery diagnostic process are reliable and can provide a reference for the promotion of the rapid diagnosis of power batteries.

References

  1. Wang Xuechao, Wang Cheng, Zhang Changling. Analysis of the status quo and trend of China's new energy automobile industry development [J]. Automotive Industry Research, 2016 (06): 4--9.Google ScholarGoogle Scholar
  2. Wang Zhenpo, Sun Fengchun, Lin Cheng. Analysis of the Influence of Inconsistency on the Service Life of Power Battery Packs[J]. Journal of Beijing Institute of Technology, 2006(07):577--580.Google ScholarGoogle Scholar
  3. MA Youliang, CHEN Quanshi, QI Zhanning. Definition and detection method of battery SOC for electric vehicles[J]. Journal of Tsinghua University(Science and Technology), 2001(11): 95--97+105.Google ScholarGoogle Scholar
  4. Li Xiaoyu. Development of test platform for electric vehicle battery management system [D]. Harbin Institute of Technology, 2013.Google ScholarGoogle Scholar
  5. Guo Jipeng, Zhong Guobin, Xu Kaiqi, Su Wei, Xiang Hongfa. Comparison of constant current and constant power test characteristics of lithium iron phosphate battery[J], 2017, 54(03):109--115.Google ScholarGoogle Scholar
  6. WANG Tao, LIU Shao-peng, LI Chuan, LIU Jia-lai, YANG Dan, LI Yourong. Study on bolt loosening detection based on piezoelectric time inversion method[J]. Journal of Transduction Technology, 2015, 28(12):1795--1799.Google ScholarGoogle Scholar
  7. Xu Min, Liu Zhongcai, Yan Xiao, Huang Bixiong, Wang Ying, Wang Wei. Online detection method for capacity increment internal resistance consistency [J/OL]. Energy storage science and technology: 1--11[2019-08-22].Google ScholarGoogle Scholar
  8. Xuebing Han, Minggao Ouyang, Languang Lu, Jianqiu Li, Yuejiu Zheng, Zhe Li. A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification[J]. Journal of Power Sources, 2014, 251.Google ScholarGoogle Scholar

Index Terms

  1. Design of Rapid Battery Pre-test Diagnostic Process

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence
      September 2019
      803 pages
      ISBN:9781450372985
      DOI:10.1145/3366194

      Copyright © 2019 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 20 September 2019

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

      Acceptance Rates

      RICAI '19 Paper Acceptance Rate140of294submissions,48%Overall Acceptance Rate140of294submissions,48%
    • Article Metrics

      • Downloads (Last 12 months)4
      • Downloads (Last 6 weeks)0

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader