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<p>Electric Vehicles (EVs) are emerging as the preferred mode of transportation owing to various advantages and to reach the Sustainable Development Goals (SDGs). However, the commercial usage of EVs is in its initial stage, due to multiple factors, among which faults caused by the operational environment and component failures are one. Furthermore, early diagnosis of such faults is necessary to protect from severe damage and catastrophic failures. This paper presents various fault modes in EV components, their impact on EV performance, and Fault Detection and Diagnosis (FDD) practices. Later sections of the paper focus on the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in FDD. Also, it provides a comparative analysis of model-based, signal-based, data-driven, and hybrid FDD approaches. Additionally, the paper explores component-specific FDD techniques, modelling of drive faults, current research trends, and suggestions on the selection of feature extraction techniques. Furthermore, the paper highlights the need to develop a hybrid FDD method to enhance the accuracy, reliability, and speed of FDD in EVs. The future scope of EV FDD techniques, considering various aspects, is also presented.</p>

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Advancements in fault detection and diagnosis methods for electric vehicles: a review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月17日

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  • Masadi Prashanth Kumar,
  • Srikanth Velpula,
  • Chidurala Saiprakash,
  • Buddhadeva Sahoo

摘要

Electric Vehicles (EVs) are emerging as the preferred mode of transportation owing to various advantages and to reach the Sustainable Development Goals (SDGs). However, the commercial usage of EVs is in its initial stage, due to multiple factors, among which faults caused by the operational environment and component failures are one. Furthermore, early diagnosis of such faults is necessary to protect from severe damage and catastrophic failures. This paper presents various fault modes in EV components, their impact on EV performance, and Fault Detection and Diagnosis (FDD) practices. Later sections of the paper focus on the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in FDD. Also, it provides a comparative analysis of model-based, signal-based, data-driven, and hybrid FDD approaches. Additionally, the paper explores component-specific FDD techniques, modelling of drive faults, current research trends, and suggestions on the selection of feature extraction techniques. Furthermore, the paper highlights the need to develop a hybrid FDD method to enhance the accuracy, reliability, and speed of FDD in EVs. The future scope of EV FDD techniques, considering various aspects, is also presented.