Abstract:

Conventional engine-powered vehicles gradually decline in sales due to their emission effects as well as the unavailability of fuels in 2030. Alternatively, Electric Vehicles (EVs) which is substantially growing in the automobile sector due to their zero-emission and sustainable power. Electric Vehicles utilize lithium-ion batteries for their significant properties such as high specific power, long lifespan, high efficiency, moderate energy density, and minimum loading effect. A Battery Management System (BMS) is primarily used to monitor the battery operating conditions and its health in real-time. Another primary role of a battery management system is to detect the fault that arises in the battery during its operation. This paper consolidates various internal and external battery faults and their detection techniques executed on the battery management system. The fault detection techniques are classified into model-based, Knowledge-based and data-driven methods programmed on BMS, which analyses the fault data acquired from the battery and stores the diagnostic trouble code (DTC) in the fault memory. Effective fault detection algorithms and appropriate sensors fixed around batteries help to detect battery faults in advance and alert the user to avoid catastrophic failure in EVs are discussed.<

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