An Early Minor-Fault Diagnosis Method for Lithium-Ion Battery Packs Based on Unsupervised Learning Article Swipe
Xin Gu
,
Yunlong Shang
,
Yongzhe Kang
,
Jinglun Li
,
Ziheng Mao
,
Chenghui Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/jas.2023.123099
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/jas.2023.123099
Dear Editor, Any fault of a battery system that is not handled timely can cause catastrophic consequences. Therefore, it is significant to diagnose battery faults early and accurately. Due to the complex nonlinear features and inconsistency of lithium batteries, traditional fault diagnosis methods usually fail to detect battery minor faults in the early stages. Therefore, this letter proposes a real-time unsupervised learning diagnosis approach for early battery faults based on improved principal component analysis. The technique rotates the battery pack voltage sequence into a new coordinate space through linear combination, while the detection metrics of square prediction errors and modified contribution plots are employed to achieve minor fault traceability. In addition, the training sample relies on the voltage sequence of the battery health state instead of the fault data, which is difficult to collect. Moreover, this approach can not only locate the battery cell where the fault occurs but also diagnose battery open-circuit and short-circuit faults as well as the occurrence and duration of the fault in real-time. Furthermore, the feasibility and stability of the proposed method are verified by applying different experimental data. In summary, the presented approach provides an easy-to-implement option that does not require accurate mathematical modeling, expert understanding, and complex computational processes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jas.2023.123099
- https://ieeexplore.ieee.org/ielx7/6570654/10057126/10057196.pdf
- OA Status
- bronze
- Cited By
- 32
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4322730890
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4322730890Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jas.2023.123099Digital Object Identifier
- Title
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An Early Minor-Fault Diagnosis Method for Lithium-Ion Battery Packs Based on Unsupervised LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-01Full publication date if available
- Authors
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Xin Gu, Yunlong Shang, Yongzhe Kang, Jinglun Li, Ziheng Mao, Chenghui ZhangList of authors in order
- Landing page
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https://doi.org/10.1109/jas.2023.123099Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/6570654/10057126/10057196.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6570654/10057126/10057196.pdfDirect OA link when available
- Concepts
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Fault (geology), Battery (electricity), Computer science, Voltage, Fault detection and isolation, Sequence (biology), Reliability engineering, Artificial intelligence, Power (physics), Engineering, Electrical engineering, Actuator, Geology, Biology, Seismology, Genetics, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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32Total citation count in OpenAlex
- Citations by year (recent)
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2025: 11, 2024: 15, 2023: 6Per-year citation counts (last 5 years)
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10Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.is_oa | True |
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| primary_location.source.issn | 2329-9266, 2329-9274 |
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| primary_location.source.issn_l | 2329-9266 |
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| primary_location.source.display_name | IEEE/CAA Journal of Automatica Sinica |
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| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | |
| primary_location.pdf_url | https://ieeexplore.ieee.org/ielx7/6570654/10057126/10057196.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE/CAA Journal of Automatica Sinica |
| primary_location.landing_page_url | https://doi.org/10.1109/jas.2023.123099 |
| publication_date | 2023-03-01 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W4226105234, https://openalex.org/W3012264837, https://openalex.org/W2988649691, https://openalex.org/W2972437779, https://openalex.org/W2790327493, https://openalex.org/W1862394037, https://openalex.org/W2805771039, https://openalex.org/W2891965167, https://openalex.org/W3000488426, https://openalex.org/W2991630966 |
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