Predictive Health Monitoring Systems for Electric Vehicle Powertrains Using Edge AI and CAN Bus Data
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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.
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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.
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.
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.
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.
Y. Zhang, T. Chen, and S. Huang, “A survey of anomaly detection in in-vehicle networks,” IEEE Access, vol. 12, pp. 54123–54139, 2024.
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.
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.
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.
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.
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.
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.
H. Zhang, J. Liu, and F. Zhao, “AI integration in electric vehicle technology: A comprehensive review,” IEEE Access, vol. 11, pp. 78232–78249, 2023.
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.
“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].
“Intelligent maintenance system (IMS),” Wikipedia, 2023. [Online]. Available: https://en.wikipedia.org/wiki/Intelligent_maintenanc e_system. [Accessed: Oct. 18, 2025].
“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].
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.
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.
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.
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.
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