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Related Experiment Video

Updated: Oct 17, 2025

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Speed Management Strategy: Designing an IoT-Based Electric Vehicle Speed Control Monitoring System.

Gatera Antoine¹, Chomora Mikeka², Gaurav Bajpai³

¹African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali 3900, Rwanda.

Sensors (Basel, Switzerland)

|October 13, 2021

Summary

This summary is machine-generated.

This study developed an IoT-based system using pulse width modulation (PWM) and random forest (RF) modeling to control vehicle speed. The system effectively reduces motor voltage, enhancing road safety by mitigating speeding-related accidents.

Keywords:

Internet of Things electric vehicle road safety speed adaptation variable speed limit

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Area of Science:

Road safety engineering
Transportation technology
Data science in transportation

Background:

Road accidents pose a significant global public health challenge, with a notable increase in Rwanda.
Excessive speeding is a primary contributing factor to road traffic accidents, necessitating advanced safety measures.
Effective vehicle speed control is crucial for mitigating accident risks and improving road safety planning.

Purpose of the Study:

To develop an Internet of Things (IoT)-based system for continuous online monitoring of vehicle data.
To implement a structural speed control mechanism using pulse width modulation (PWM) and metal-oxide-semiconductor field-effect transistor (MOSFET) controllers.
To evaluate and compare multiple linear regression (MLR) and random forest (RF) models for estimating optimal motor voltage in specific zones.

Main Methods:

A novel system integrating PWM control and IoT for real-time vehicle data monitoring was designed.
Input and output PWM signals were utilized to command MOSFET controllers, regulating motor voltage.
Multiple linear regression (MLR) and random forest (RF) models were employed to predict required motor voltage, with performance evaluated using the coefficient of determination (R2).

Main Results:

The random forest (RF) model demonstrated superior performance over the multiple linear regression (MLR) model.
The RF model achieved a high coefficient of determination (R2) of 98.8%, indicating accurate voltage estimation.
The developed system successfully reduced the supplied voltage to the motors, directly contributing to increased vehicle safety.

Conclusions:

The proposed IoT-based speed control system effectively reduces motor voltage, enhancing road safety.
The random forest model provides a highly accurate method for estimating voltage requirements in vehicle speed control.
This research offers a significant advancement in leveraging technology for the prevention of road accidents caused by speeding.