Hybrid Powered IoT-Based BLDC Motor Monitoring System for Electric Vehicles

Abstract

This paper proposes a hybrid powered internet of things (IoT) system for monitoring the condition of brushless DC Motors (BLDC) in electric vehicles (EVs). This IoT system has been designed and developed using a hybrid energy storage system that helps to enhance the life span of motor and battery. This system employs a range of sensors, including load, vibration, temperature, voltage and current sensors integrated with an Arduino UNO and ESP8266 microcontroller. These components gather real time data from motors side and transmitted to the cloud server. The IoT system ensures that the system is capable of capturing and reporting vital motor parameters to the cloud server and an automatic notification is sent to operators when Motors abnormality is detected. An effective BLDC motor monitoring system can be achieved by early monitoring with instant notification to operators. The main benefits are cost reduction of maintenance, increased reliability, optimised motor performances and improved overall efficiency of the system.

Introduction

The BLDC motor drive system is vital for electric vehicles (EVs), providing precise speed and torque control. Early detection of motor faults—such as bearing and stator issues—is crucial to ensure reliability and efficiency. Key motor parameters for fault diagnosis include load, vibration, temperature, voltage, and current. Bearing failures cause the majority of motor breakdowns and lead to increased vibration and temperature. Battery management, especially for Lithium-ion batteries, is critical, with parameters like state of charge (SOC) closely monitored to ensure safe EV operation.

To improve remote monitoring, the study proposes a wireless IoT-based sensor system that collects real-time data from multiple sensors and transmits it securely to the cloud. Compared to traditional wired systems, IoT offers lower costs, easier installation, automated analysis, and instant fault notifications, enhancing maintenance, reliability, and failure prediction.

Literature Review:
Various studies address cloud-based battery management, SOC estimation, hybrid power sources for EVs, and advanced BLDC motor control techniques, emphasizing the integration of IoT and hybrid energy systems to optimize performance and extend battery life.

Proposed Framework:
The system integrates sensors for load, vibration, voltage, current, and temperature connected to Arduino UNO and ESP8266 microcontrollers. Data is sent to the cloud for real-time monitoring and automatic alerts on abnormalities. The power system combines Li-ion batteries with solar panels and includes dynamic switching between power sources based on availability.

Components & Functionality:

• Li-ion batteries for energy storage

• Solar panels to supplement charging

• Sensors for key parameters (voltage, current, temperature, vibration, load)

• Arduino UNO as control unit

• LCD for user display

• BLDC motor as propulsion system

Results:
Simulation and testing confirm the system's ability to accurately measure parameters, detect faults early (e.g., abnormal temperature rise due to bearing failure or increased vibration), and notify operators promptly via cloud platforms. The system effectively manages power supply switching between solar and battery to maintain stable voltage and protect battery life.

Conclusion

This paper briefly explains the BLDC motor monitoring system of electric vehicles (EVs), by combination of solar cells with Li-ion battery supply. The hardware designs are done by considering several parameters which are important for BLDC motor drive condition monitoring in electric vehicles (EVs). Temperature, current, voltage and vibration signal have been analyzed to predict any mechanical abnormality such as motor faulty ball bearing and roto imbalanced or cracked. Signals gathered by our IoT based sensors were fed into the cloud-base server for analysis and to determine the motor\'s health conditions. Moreover, the mechanical abnormality of the rotor can be predicted at the early stage. It also helps to increase the lifespan of the battery and make it reliable to use for electric vehicles (EVs). The proposed system is operated satisfactorily and possibly adopted in the future EVs due to its size, operational cost and flexibility of IoT based smart features.

References

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Copyright

Copyright © 2025 Arindam Ghorai, Avijit Chakraborty, Surajit Saha, Mintu Roy, Dipasri Saha. 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.