Research Article
An Intelligent Speed Control System for Electric Bike Using Sensor Feedback and Real-Time Display
@INPROCEEDINGS{10.1007/978-3-032-38705-9_10, author={A. Rosi and T. Suresh and R. Porselvi and C. Nirmala and N. D. Bobby and T. Vaishnavi}, title={An Intelligent Speed Control System for Electric Bike Using Sensor Feedback and Real-Time Display}, proceedings={Intelligent Transport Systems. 9th EAI International Conference, INTSYS 2025, Marietta, GA, USA, December 10, 2025, Proceedings}, proceedings_a={INTSYS}, year={2026}, month={10}, keywords={Electric bicycle Speed control Arduino microcontroller Sensor feedback Real-time display Motor control Embedded systems}, doi={10.1007/978-3-032-38705-9_10} }- A. Rosi
T. Suresh
R. Porselvi
C. Nirmala
N. D. Bobby
T. Vaishnavi
Year: 2026
An Intelligent Speed Control System for Electric Bike Using Sensor Feedback and Real-Time Display
INTSYS
Springer
DOI: 10.1007/978-3-032-38705-9_10
Abstract
The abrupt expansion of electric bicycles (E-bikes) as green transportation has unveiled the severe limitations in current speed control systems. Conventional E-bike controllers are based on human throttle inputs, relay motor drivers, and non-adaptive power supplies, which cause sluggish responses, excessive heating, battery waste, and reduced safety performance during dynamic traffic scenarios. This paper introduces a new intelligent speed control system for electric bicycles using an Arduino Uno microcontroller with sensor feedback and real-time display. The system incorporates a MOC sensor for accurate speed monitoring, a BTS7960 motor driver for effective torque control, and an OLED display for user-friendly interaction. The system is driven by a regulated 12 V lithium-ion battery via an LM2596 module with extensive safety features such as buzzers and LED signals for over-speed and low-battery indication. Comparative assessment against current methodologies reveals enhanced responsiveness, energy efficiency, and cost-effectiveness. The prototype achieved real-time speed regulation with sub-second response times, 25% improved energy efficiency compared to conventional relay-based systems, and 40% cost reduction over high-end fuzzy logic controllers while maintaining comparable accuracy levels. This modular and scalable solution addresses critical limitations of existing E-bike control systems, contributing to safer and more intelligent electric mobility infrastructure.