Abstract: The increasing adoption of electric vehicles (EVs) requires reliable and cost-effective solutions for fault detection to ensure safe operation. This paper presents a low-cost IoT-based prototype system for fault detection and basic AI-based condition analysis of electric vehicle motors. The proposed system continuously monitors key parameters such as current and temperature using sensors, and the collected data is transmitted to the ThingSpeak platform for real-time visualization and storage. A simple machine learning approach is implemented to analyze the data and classify the motor condition as normal or abnormal based on predefined patterns. Unlike complex predictive models, the proposed system focuses on practical implementation using limited data, making it suitable for low-cost applications. The integration of IoT enables remote monitoring, while the AI-based classification helps in early identification of potential faults. Experimental results from the prototype demonstrate effective detection of abnormal conditions, improving system safety and reducing the risk of unexpected failures. This work highlights a simple, scalable, and affordable approach for smart monitoring of electric vehicle motors.

Keywords: Electric Vehicles (EV), Internet of Things (IoT), Motor Fault Detection, ThingSpeak Cloud, Machine Learning, Real-Time Monitoring, Low-Cost Prototype.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14352