Battery fault diagnosis methods for electric vehicle lithium-ion batteries: Correlating codes and battery management system

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dc.authorscopusidEdwin Geo Varuvel / 25225283500
dc.authorwosidEdwin Geo Varuvel / AAE-5222-2022
dc.contributor.authorNaresh, G.
dc.contributor.authorPraveenkumar, T.
dc.contributor.authorMadheswaran, Dinesh Kumar
dc.contributor.authorVaruvel, Edwin Geo
dc.contributor.authorPugazhendhi, Arivalagan
dc.contributor.authorThangamuthu, Mohanraj
dc.contributor.authorMuthiya, S. Jenoris
dc.date.accessioned2025-04-17T08:16:07Z
dc.date.available2025-04-17T08:16:07Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Makine Mühendisliği Bölümü
dc.description.abstractLithium-ion batteries are the heart of modern electric vehicle technology. Operational stresses such as temperature changes, mechanical impacts, and electrochemical aging often subject them to faults, necessitating accurate fault diagnosis that adheres to international safety standards. Consequently, this review examines state-ofthe-art fault diagnosis methodologies, emphasizing their integration with global safety frameworks such as the International Organization for Standardization, International Electrotechnical Commission, Society of Automotive Engineers, etc. A thorough analysis of artificial fault induction techniques-such as overcharging and overheating-is presented to assess their effectiveness in validating diagnostic algorithms. Additionally, the role of machine learning in battery management systems is reviewed, where the Feature Fusion and Expert Knowledge Integration network emerged effective, achieving an anomaly detection rate of 98.5 %, outperforming conventional methods in accuracy and speed. Hybrid diagnostic frameworks integrating model-based and machine-learning techniques are also highlighted for their scalability and precision in addressing sub-extreme fault scenarios. Looking ahead, this study emphasizes the importance of interdisciplinary research to enhance fault detection, focusing on adaptive machine learning algorithms and real-world testing to ensure the long-term viability of contemporary battery technologies.
dc.identifier.citationNaresh, G., Praveenkumar, T., Madheswaran, D. K., Varuvel, E. G., Pugazhendhi, A., Thangamuthu, M., & Muthiya, S. J. (2025). Battery fault diagnosis methods for electric vehicle Lithium-ion batteries: correlating codes and battery management system. Process Safety and Environmental Protection, 106919.
dc.identifier.doi10.1016/j.psep.2025.106919
dc.identifier.endpage26
dc.identifier.issn0957-5820
dc.identifier.issn1744-3598
dc.identifier.scopus2-s2.0-85219091117
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.psep.2025.106919
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6153
dc.identifier.volume196
dc.identifier.wosWOS:001439451400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorVaruvel, Edwin Geo
dc.institutionauthoridEdwin Geo Varuvel / 0000-0002-7303-3984
dc.language.isoen
dc.publisherInstitution of chemical engineers
dc.relation.ispartofProcess safety and environmental protection
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBattery Fault Diagnostics
dc.subjectBattery Management System (BMS)
dc.subjectElectric Vehicles
dc.subjectLi-ion Batteries
dc.subjectMachine Learning
dc.subjectThermal Runaway
dc.titleBattery fault diagnosis methods for electric vehicle lithium-ion batteries: Correlating codes and battery management system
dc.typeArticle

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