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

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  • Dhage Abhishek Yuvraj

    (Department of Electronics and Telecommunication Engineering, Ajeenkya DY Patil School of Engineering, Pune, India)

  • Aniket Atresh

    (Department of Electronics and Telecommunication Engineering, Ajeenkya DY Patil School of Engineering, Pune, India)

  • Prof. Urmila Burde

    (Asst. Professor, Department of Electronics and Telecommunication Engineering, Ajeenkya DY Patil School of Engineering, Pune, India)

Abstract

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.

Suggested Citation

  • Dhage Abhishek Yuvraj & Aniket Atresh & Prof. Urmila Burde, 2026. "Predictive Health Monitoring Systems for Electric Vehicle Powertrains Using Edge AI and CAN Bus Data," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 15(1), pages 01-08, January.
    • Handle: RePEc:bjb:journl:v:15:y:2026:i:1:p:01-08
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