Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives
Hazards in electric vehicles (EVs) often stem from lithium-ion battery (LIB) packs during operation, aging, or charging. Robust early fault diagnosis algorithms are essential for enhancing safety, efficiency, and reliability. LIB fault types involve internal batteries, sensors, actuators, and system...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Review |
| Published: |
Elsevier B.V.
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833351798148038656 |
|---|---|
| author | Abdolrasol M.G.M. Ayob A. Lipu M.S.H. Ansari S. Kiong T.S. Saad M.H.M. Ustun T.S. Kalam A. |
| author2 | 35796848700 |
| author_facet | 35796848700 Abdolrasol M.G.M. Ayob A. Lipu M.S.H. Ansari S. Kiong T.S. Saad M.H.M. Ustun T.S. Kalam A. |
| author_sort | Abdolrasol M.G.M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Hazards in electric vehicles (EVs) often stem from lithium-ion battery (LIB) packs during operation, aging, or charging. Robust early fault diagnosis algorithms are essential for enhancing safety, efficiency, and reliability. LIB fault types involve internal batteries, sensors, actuators, and system faults, managed by the battery management system (BMS), which handles state estimation, cell balancing, thermal management, and fault diagnosis. Prompt identification and isolation of defective cells, coupled with early warning measures, are critical for safety. This review explores data-driven methods for fault diagnosis in LIB management systems, covering implementation, classification, fault types, and feature extraction. It also discusses BMS roles, sensor types, challenges, and future trends. The findings aim to guide researchers and the automotive industry in advancing fault diagnosis methods to support sustainable EV transportation. ? 2024 Elsevier B.V. |
| format | Review |
| id | my.uniten.dspace-36126 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | my.uniten.dspace-361262025-03-03T15:41:25Z Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives Abdolrasol M.G.M. Ayob A. Lipu M.S.H. Ansari S. Kiong T.S. Saad M.H.M. Ustun T.S. Kalam A. 35796848700 26666566900 58562396100 57218906707 57216824752 7202075525 43761679200 55543249600 Battery management systems Battery Management Data driven Data-driven fault diagnosis Fault types Faults diagnosis Future perspectives Ion batteries Lithium ions Machine-learning Management systems Battery Pack Hazards in electric vehicles (EVs) often stem from lithium-ion battery (LIB) packs during operation, aging, or charging. Robust early fault diagnosis algorithms are essential for enhancing safety, efficiency, and reliability. LIB fault types involve internal batteries, sensors, actuators, and system faults, managed by the battery management system (BMS), which handles state estimation, cell balancing, thermal management, and fault diagnosis. Prompt identification and isolation of defective cells, coupled with early warning measures, are critical for safety. This review explores data-driven methods for fault diagnosis in LIB management systems, covering implementation, classification, fault types, and feature extraction. It also discusses BMS roles, sensor types, challenges, and future trends. The findings aim to guide researchers and the automotive industry in advancing fault diagnosis methods to support sustainable EV transportation. ? 2024 Elsevier B.V. Final 2025-03-03T07:41:25Z 2025-03-03T07:41:25Z 2024 Review 10.1016/j.etran.2024.100374 2-s2.0-85207692948 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207692948&doi=10.1016%2fj.etran.2024.100374&partnerID=40&md5=69c07496ee53e519d8a3ec94b2329de6 https://irepository.uniten.edu.my/handle/123456789/36126 22 100374 Elsevier B.V. Scopus |
| spellingShingle | Battery management systems Battery Management Data driven Data-driven fault diagnosis Fault types Faults diagnosis Future perspectives Ion batteries Lithium ions Machine-learning Management systems Battery Pack Abdolrasol M.G.M. Ayob A. Lipu M.S.H. Ansari S. Kiong T.S. Saad M.H.M. Ustun T.S. Kalam A. Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives |
| title | Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives |
| title_full | Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives |
| title_fullStr | Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives |
| title_full_unstemmed | Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives |
| title_short | Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives |
| title_sort | advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: progress, challenges, and future perspectives |
| topic | Battery management systems Battery Management Data driven Data-driven fault diagnosis Fault types Faults diagnosis Future perspectives Ion batteries Lithium ions Machine-learning Management systems Battery Pack |
| url_provider | http://dspace.uniten.edu.my/ |