Predictive Health Monitoring Systems for Electric Vehicle Powertrains Using Edge AI and CAN Bus Data

Electric vehicles (EVs) are becoming increasingly important in the shift toward sustainable mobility. While their adoption is accelerating, ensuring the health and reliability of EV powertrains remains a critical challenge. Failures in subsystems such as batteries, motors, and controllers may cause unexpected breakdowns, reduced efficiency, and safety issues. Predictive Health Monitoring (PHM) systems aim to prevent such failures by identifying anomalies before they escalate. Traditional PHM solutions often depend on cloud platforms, but these face drawbacks such as latency, high bandwidth requirements, and privacy risks. To address these challenges, edge-based PHM employs embedded devices to process Controller Area Network (CAN) bus data locally, enabling real-time diagnostics, enhanced privacy, and cost efficiency. This paper presents a structured survey of PHM techniques for EV powertrains using Edge Artificial Intelligence (Edge AI) and CAN bus data. The contributions include: (i) classification of PHM approaches into model-based, data-driven, security- focused, and edge-deployed methods, (ii) comparative analysis of recent works, (iii) a summary table of surveyed papers, (iv) discussion of hardware implementations reported in literature, and (v) identification of future research directions.