Volume: 2 Issue: 2
Year: 2025, Page: 38-43, Doi: https://doi.org/10.70968/ijeaca.v2i2.E124
Received: July 24, 2025 Accepted: Nov. 20, 2025 Published: Dec. 12, 2025
Reliable operation of electric vehicle (EV) motors is fundamental to the advancement of sustainable mobility. This paper presents a multi-sensor IoT framework integrated with an intelligent fault classification engine for continuous health monitoring of EV motors. An ESP32 microcontroller acquires data from five heterogeneous transducers—vibration events, ambient gas concentration, temperature, current draw, and supply voltage—transmitting structured JSON payloads to Firebase Realtime Database over Wi-Fi. A Python-based analytical backend employs a hybrid classification pipeline combining Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) networks, augmented by a conditional generative AI data synthesis stage, to assign motor health into three states: Good, Average, and Needs Service. Concurrently, an on-device Exponential Moving Average (EMA) trend engine computes a five-dimensional weighted anomaly score, enabling alert generation independent of cloud connectivity. The system achieved 96.4% classification accuracy with average alert latency below 2 seconds and 99.5% cloud synchronization reliability. Experimental validation confirms early detection of bearing friction, winding thermal stress, and supply instability prior to critical failure thresholds.
Keywords: AI-Based Predictive Maintenance System Using IoT for EV Motor Fault Detection
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© 2025 Masalkar, et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Masalkar A, Bhapkar R, Wanave O, Kulal A, Shelar SD. AIBased Predictive Maintenance System Using IoT for EV Motor Fault
Detection. 2025; 2(2):38-43. https://doi.org/10.70968/ijeaca.v2i2.E124