An Arduino-Based Framework for Early Electrical Fault Identification in Electric Vehicles

Author

Nikhil Meshram, Dharmendra Nimje, Jayesh Patode, Rohit Raut, Piyush Patil, Prof. C. S. Kamble

Abstract

  Electric vehicles (EVs) rely on interconnected electrical circuits and auxiliary systems, and any failure in these components may affect vehicle availability, user convenience, and operational safety. This paper proposes a practical approach for identifying selected low-voltage electrical faults using an Arduino UNO R3 as the primary monitoring and decision-making unit. The developed prototype is designed to monitor circuits associated with fuses, relays, the 12 V accessory/cigarette-lighter charging socket, communication connections, and charging-port wiring. The controller receives input signals indicating the operating condition of each circuit and evaluates them against predefined logic representing normal operation. If an abnormal condition or inconsistency is detected, the system displays the affected circuit, helping users identify the possible source of the fault and initiate troubleshooting more efficiently. Previous research has investigated data-driven fault analysis in EV charging stations and the application of machine-learning techniques for diagnosing faults in EV drive systems [1], [2]. In contrast, the proposed system emphasizes an affordable and easy-to-understand hardware prototype for monitoring selected low-voltage circuits rather than a fully validated predictive model for the entire high-voltage traction system. This prototype also offers a basis for further development, including sensor calibration, fault-event recording, severity classification, and predictive-maintenance techniques. However, its actual detection accuracy and predictive capabilities must be established through systematic experimental testing before any performance claims can be made.

Keywords

Electric vehicle, electrical fault detection, Arduino UNO, low-voltage circuit, relay, fuse, early warning, fault diagnosis.

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References

[1] A. Shufian, N. Hannan, M. M. H. Emon, S. Kabir, S. A. Fattah, and M. S. Hossain, “Automatic Fault Detection and Analysis of Electric Vehicle Charging Station by Machine Learning,” in 2024 IEEE Region 10 Symposium (TENSYMP), 2024. [Bibliographic details should be checked against the final IEEE record before submission.]

[2] M. P. Kumar, S. Velpula, and C. Saiprakash, “Fault Diagnosis and Prediction of Electric Vehicle Drive using Support Vector Machine Algorithm,” in 2025 IEEE 5th International Conference on Sustainable Energy and Future Electric Transportation (SEFET), 2025. [Bibliographic details should be checked against the final IEEE record before submission.]

[3] International Organization for Standardization, ISO 26262:2018, Road vehicles—Functional safety, 2018. This standard is cited as general safety context; the prototype described here is not claimed to be compliant with it.

[4] International Organization for Standardization, ISO 6469-3:2021, Electrically propelled road vehicles—Safety specifications—Part 3: Electrical safety, 2021. Consult the applicable edition and requirements before any vehicle-level implementation.

[5] M. Ehsani, Y. Gao, S. E. Gay, and A. Emadi, Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory, and Design, 2nd ed. Boca Raton, FL, USA: CRC Press, 2009.

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[7] Artificial Intelligence-Based Technique for Fault Detection and Diagnosis of EV Motors: A Review,” IEEE Transactions on Transportation Electrification, vol. 8, no. 1, pp. 384–406, 2022, doi: 10.1109/TTE.2021.3110318.

[8] R. Isermann, Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Berlin, Germany: Springer, 2006.

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