Fault Detection and Monitoring of BLDC Motors used in EV

Abstract

This research develops an IoT-based fault detection and predictive health monitoring system for a 48V, 750W BLDC geared e-bike motor. Sensors monitor current, voltage, temperature, vibration, and RPM through an ESP32. An ESP32 controller collects and processes the sensor data to identify abnormal operating conditions through IoT based learning techniques. Real-time monitoring, and fault alerts are provided through an IoT dashboard, enabling early fault detection and predictive maintenance. The proposed system helps improve motor reliability, reduce unexpected failures, and extend the operational life of the e-bike motor.

Introduction

The IoT-Based Fault Detection and Condition Monitoring System for BLDC Motors in Electric Vehicles is designed to improve motor reliability, safety, and service life through continuous monitoring and early fault detection. Brushless DC (BLDC) motors are widely used in electric vehicles because of their high efficiency, compact design, and favourable speed and torque characteristics. However, faults such as overcurrent, overheating, bearing wear, excessive vibration, gear degradation, and sensor failures can reduce performance and cause unexpected breakdowns.

The proposed system is developed for a 48 V, 750 W BLDC geared motor and uses multiple sensors to monitor its electrical, thermal, mechanical, and rotational conditions. An ACS712 current sensor measures motor current, a DS18B20 temperature sensor monitors temperature, an MPU6050 or SW-420 sensor detects abnormal vibration, and an optical encoder measures rotational speed in RPM.

An ESP32 microcontroller collects, filters, and processes the sensor data. Fault detection is performed using predefined thresholds and learning-based techniques to identify abnormal operating conditions. For example, current exceeding 20 A for a predefined duration indicates a potential overload, while a motor temperature above 80°C triggers an overheating warning. Abnormal vibration and missing rotational feedback can also indicate developing mechanical or sensor-related faults.

The system incorporates Wi-Fi communication and an IoT dashboard to display motor parameters in real time, record operating data, and generate alerts when faults occur. A buzzer provides local warnings, while a relay-controlled protective mechanism can disconnect the motor during critical fault conditions to prevent further damage.

The main advantages of the proposed system include continuous condition monitoring, early fault identification, reduced dependence on periodic manual inspections, improved operational safety, and support for predictive maintenance. However, reliable fault classification requires appropriate sensor calibration, validated detection thresholds, and sufficient data to distinguish genuine faults from normal variations in motor load and operating conditions.

References

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Copyright

Copyright © 2026 Dr. S. P. Ashtankar, Astha Bhaware, Sanika Umak, Suhani Alone, Aditya Ghavat, Subodh Wadaskar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.