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.

Electric Vehicles, Predictive Maintenance, Edge AI, CAN Bus, Vehicle Diagnostics, Anomaly Detection

1. S. Patil, A. Mehta, and R. Kulkarni, “AI-driven predictive maintenance in electric vehicles using LSTM and federated learning,” in Proc. Int. Conf. Smart Mobility Syst., 2025, pp. 1–6. [Google Scholar] [Crossref]

2. H. Wang, Y. Li, and P. Zhang, “OBD-II based machine learning applications for vehicle monitoring,” IEEE Sensors Journal, vol. 25, no. 4, pp. 1123–1132, 2025. [Google Scholar] [Crossref]

3. M. Ahmed and D. Patel, “Quantum-enhanced artificial intelligence for fault diagnosis in electric vehicles,” IEEE Transactions on Vehicular Technology, vol. 74, no. 2, pp. 456–467, 2025. [Google Scholar] [Crossref]

4. A. Roy, K. Sharma, and P. Nair, “Privacy-preserving predictive maintenance using homomorphic encryption and edge AI,” in Proc. IEEE Int. Conf. Edge Computing, 2025, pp. 201–208. [Google Scholar] [Crossref]

5. Y. Zhang, T. Chen, and S. Huang, “A survey of anomaly detection in in-vehicle networks,” IEEE Access, vol. 12, pp. 54123–54139, 2024. [Google Scholar] [Crossref]

6. J. Lee, R. Park, and H. Kim, “AI for battery health monitoring using deep belief networks and recurrent neural networks,” Energies, vol. 17, no. 6, pp. 3112–3128, 2024. [Google Scholar] [Crossref]

7. R. Singh, P. Verma, and S. Gupta, “Reinforcement learning for energy management in hybrid vehicles,” Chinese Journal of Mechanical Engineering, vol. 37, no. 2, pp. 215–225, 2024. [Google Scholar] [Crossref]

8. M. Fernandez, L. Torres, and G. Romero, “Digital twin framework for EV powertrains,” World Electric Vehicle Journal, vol. 15, no. 5, pp. 234–245, 2024. [Google Scholar] [Crossref]

9. P. Gupta and A. Sharma, “Predictive maintenance in the automotive sector using machine learning and statistical models,” Journal of Industrial Engineering Research, vol. 62, no. 3, pp. 154–166, 2023. [Google Scholar] [Crossref]

10. T. Hossain, N. Alam, and M. Rahman, “AI applications in industrial equipment maintenance using CNN and SVM,” IEEE Transactions on Industrial Informatics, vol. 19, no. 7, pp. 8452–8464, 2023. [Google Scholar] [Crossref]

11. V. Kumar and S. Singh, “Cybersecurity in electric vehicles: A review of CAN bus protocols and intrusion detection systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, pp. 122–135, 2023. [Google Scholar] [Crossref]

12. H. Zhang, J. Liu, and F. Zhao, “AI integration in electric vehicle technology: A comprehensive review,” IEEE Access, vol. 11, pp. 78232–78249, 2023. [Google Scholar] [Crossref]

13. S. Das, A. Mondal, and K. Banerjee, “Remaining useful life estimation using DBN and LSTM models,” in Proc. Int. Conf. Prognostics and Health Management (PHM), 2023, pp. 211–219. [Google Scholar] [Crossref]

14. “Integrated vehicle health management: A conceptual framework,” Wikipedia, 2023. [Online]. Available: https://en.wikipedia.org/wiki/Integrated_vehicle_hea lth_management. [Accessed: Oct. 18, 2025]. [Google Scholar] [Crossref]

15. “Intelligent maintenance system (IMS),” Wikipedia, 2023. [Online]. Available: https://en.wikipedia.org/wiki/Intelligent_maintenanc e_system. [Accessed: Oct. 18, 2025]. [Google Scholar] [Crossref]

16. “Industrial Internet of Things (IIoT) for predictive maintenance,” Wikipedia, 2023. [Online]. Available: https://en.wikipedia.org/wiki/Industrial_Internet_of_ Things. [Accessed: Oct. 18, 2025]. [Google Scholar] [Crossref]

17. L. Zhao, Q. Wang, and J. Sun, “Vehicular edge computing and networking framework for AI deployment,” IEEE Internet of Things Journal, vol. 9, no. 8, pp. 7120–7134, 2022. [Google Scholar] [Crossref]

18. P. Mishra and K. Rao, “Distributed vehicular computing for predictive maintenance using fog AI,” in Proc. IEEE Int. Conf. Distributed Systems, 2022, pp. 134–140. [Google Scholar] [Crossref]

19. Y. Chen, L. Wu, and D. Xu, “CAN bus cyberattacks and countermeasures using intrusion detection systems,” in Proc. IEEE Vehicular Networking Conf., 2022, pp. 88–95. [Google Scholar] [Crossref]

20. R. Yadav, M. Sharma, and K. Singh, “Machine learning-based intrusion detection techniques for real-time CAN bus security,” Journal of Embedded Systems and Vehicular Security, vol. 14, no. 2, pp. 99–110, 2022. [Google Scholar] [Crossref]

• Internship Portals: A Systematic Review of Current Platforms and Future Directions
• Towards Better Urban Mobility: A Comprehensive Assessment of Pedestrian Infrastructure in Naval, Biliran Province, Philippines
• An Affordable and Sustainable Efficient Color Sorting System Using Arduino and TCS3200 Sensor
• Financial Stress and Mobility Patterns: Implication for Transportation Policy Among Jeepney Passengers
• Evaluating the Effectiveness of Marketing Strategies on Customer Visit Intentions: Evidence from Local Restaurants in Boac, Marinduque