A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges

• Published in: Algorithms Vol. 13; no. 3; p. 62
• Main Authors: Tran, Manh-Kien, Fowler, Michael
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2020
• Subjects: Algorithms; battery faults; battery management system; battery safety; Diagnostic systems; Electric vehicles; Electrodes; Electrolytes; Failure; Fault detection; Fault diagnosis; fault diagnostic algorithms; Fault minimization; Flux density; Heat; Lithium; Lithium-ion batteries; lithium-ion battery; Parameter estimation; Phase transitions; Power management; Rechargeable batteries; Sensors; Thermal runaway
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a13030062

The usage of Lithium-ion (Li-ion) batteries has increased significantly in recent years due to their long lifespan, high energy density, high power density, and environmental benefits. However, various internal and external faults can occur during the battery operation, leading to performance issues and potentially serious consequences, such as thermal runaway, fires, or explosion. Fault diagnosis, hence, is an important function in the battery management system (BMS) and is responsible for detecting faults early and providing control actions to minimize fault effects, to ensure the safe and reliable operation of the battery system. This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and disadvantages of the reviewed algorithms, as well as some future challenges for Li-ion battery fault diagnosis, are also discussed in this paper.