All projects
TU/e Automotive Design-Based Learning · group project · 2022

Small-Scale EV – Speed Sensing & Rolling Resistance

First-year automotive project. A Hall-sensor speedometer for our team's electric car, and coast-down tests to measure its rolling resistance for the driving strategy.

  • Arduino
  • C++
  • Interrupts
  • Vehicle dynamics
  • Data analysis
Source code

The challenge

In the first-year automotive Design-Based Learning project, each team built and tuned a small electric car (Group 8) to complete a driving challenge as efficiently as possible. That meant knowing the car’s speed and the forces slowing it down.

Speed measurement system

Written in Arduino C++ with a teammate:

  • A Hall-effect sensor on the drivetrain triggers a rising-edge interrupt. The ISR only counts pulses, so timing stays accurate.
  • Once a second the count is converted to motor RPM, then wheel RPM through the gear ratio, then km/h.
  • Speed is shown on a 16×2 LCD, and four RGB LED drivers (bit-banged clock, data and latch) change colour by speed band, so the driver can see at a glance whether they are on target.

Vehicle optimisation

The team used CFD and MATLAB simulations to optimise the gearing, powertrain, drivetrain and body of the RC-scale car, following the V-model from requirements to verification.

Rolling-resistance measurement

Rolling resistance and air drag make up about 80% of the resistive forces on a vehicle, so the team needed an accurate value for the energy budget and the driving strategy.

  • Method: coast-down tests in the Markthal on campus, with the wheels decoupled from the motor, while an Arduino Due logged speed over time.
  • Analysis: from the three cleanest runs, deceleration → total resistive force (F = m·a with m = 11.95 kg) → rolling-resistance force → coefficient c_r as a function of speed.
  • Result: a mean c_r ≈ 0.0252, which fed into the tank-to-wheel efficiency analysis and the most efficient driving strategy around the lap.