Artificial Intelligence Technique-Based EV Powertrain Condition Monitoring and Fault Diagnosis: A Review
Xiaotian Zhang, Yihua Hu, Chao Gong, Jiamei Deng, Gaolin Wang
Abstract
Electric powertrain used in electric vehicles (EVs), which is constituted of a motor, transmission unit, inverter, battery packs, and so on, is a highly integrated system. Its reliability and safety are not only related to industrial costs but more importantly to the safety of human life. This review contributes to comprehensively summarizing artificial intelligence (AI)-based/AI-supported approaches in EV powertrain condition monitoring and fault diagnosis that can be used in EV applications. The application of AI on PE in EV is a new attempt, which can solve many issues with better performance than traditional methods and even achieve functions that the conventional methods cannot achieve. This article thoroughly discusses the motivation, advantages, limitations, and challenges associated with AI-supported methods through case summaries, classification, comparisons, and quantitative analyses between conventional and AI-based approaches. Furthermore, the review concludes by proposing forward-looking future trends in this field.