RC Torque Vectoring for Autocross Performance

ISEF Category: Engineering Technology: Statics and Dynamics

Subcategory: Ground Vehicle Systems  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

A tiny car can lose speed in a corner for the same reason a full-size race car can, it wastes grip. If you can control how much torque each rear wheel gets, you can change how the car turns. That makes this project feel like race engineering, not just hobby tuning. You get to test whether smarter wheel control really beats a simple open diff.

What Is It?

Torque vectoring means sending different amounts of drive power to each wheel on purpose. Think of it like giving each foot a different push when you run through a turn. The outside wheel usually needs more help because it travels farther. The inside wheel often needs less, since it can spin too fast and waste traction.

An open differential splits power in a simple way, but it does not choose the best split for a turn. A torque-vectoring algorithm watches inputs, then changes motor commands to help the car rotate smoothly. In your project, you are not just asking whether the car goes faster. You are asking how wheel-level control changes cornering speed, tire slip, and lap-to-lap consistency.

Why This Is a Good Topic

This is a strong science fair topic because you can measure it, tune it, and compare it against a clear baseline. You can test one control algorithm against open-diff behavior on the same track, then use data to judge which setup handles corners better. The project connects to real problems in vehicle dynamics, traction control, and EV control systems. You can also learn signal logging, control logic, and fair experimental design.

Research Questions

• How does torque vectoring affect average lap time compared with an open-diff baseline?
• What is the effect of different left-right torque splits on corner exit speed?
• Does torque vectoring reduce lap-to-lap variation on the same autocross course?
• To what extent does torque vectoring change rear tire slip energy during tight turns?
• Which control rule gives the best balance between cornering speed and stability?
• How does battery voltage sag affect torque-vectoring performance over repeated runs?

Basic Materials

• 1:10 RC car chassis with independent rear brushless motors.
• Motor controller or ESC setup that supports separate rear motor control.
• Microcontroller with logging support, such as Arduino or Raspberry Pi.
• IMU or inertial sensor for yaw rate and acceleration.
• GPS or optical timing system for lap timing.
• Portable laptop for data download and analysis.
• Cones or markers for a parking-lot autocross course.
• Spare tires and wheels for repeatable testing.
• Digital multimeter for checking power and wiring.

Advanced Materials

• Dynamometer or wheel force test rig for motor characterization.
• High-rate data logger for motor current, wheel speed, and yaw response.
• Wheel speed sensors or encoders on each rear wheel.
• Surface temperature sensor or infrared thermometer for tire monitoring.
• Programmable motor controller with closed-loop torque control.
• High-precision current sensor for estimating electrical energy use.
• Motion capture system or overhead video setup for path tracking.
• CAD software for documenting chassis geometry and weight distribution.

Software & Tools

• Python: Cleans run data, computes lap metrics, and compares controller settings.
• ImageJ: Measures path traces from overhead video and estimates line quality.
• Tracker: Extracts motion data from video when GPS is too noisy.
• MATLAB or GNU Octave: Models vehicle response and tests control logic against logs.
• QGroundControl: Checks sensor streams and helps verify logging on the vehicle.

Experiment Steps

1. Define the exact cornering problem you want to improve, such as exit speed, slip, or consistency.
2. Choose one baseline control mode, then name the single torque-vectoring rule you will compare against it.
3. Design a measurement plan that records path, speed, yaw, and wheel-level behavior on the same course.
4. Build controls that keep driver input, track layout, and battery state as constant as possible.
5. Plan your analysis before testing, including how you will turn raw logs into lap metrics and slip metrics.
6. Set criteria for deciding whether the new controller really beats the baseline, not just once, but across repeated runs.

Common Pitfalls

• Comparing runs with different tire wear, which makes grip changes look like controller gains.
• Letting the throttle timing vary between trials, which hides the effect of torque split.
• Logging only lap time, which misses whether the car actually cornered better or just got lucky.
• Using low-rate or noisy sensor data, which makes yaw and slip estimates jump around.
• Testing on a course with changing pavement or debris, which adds track variation to your results.

What Makes This Competitive

A competitive version of this project goes beyond a simple faster-or-slower comparison. You would define clean metrics for cornering, slip, and consistency, then show which control law wins under specific conditions. Strong entries often include a fair baseline, repeated trials, uncertainty estimates, and a reasoned explanation of why one torque split works better. A deeper analysis of where the controller helps, and where it hurts, can make the project feel much more like real vehicle research.

Project Variations

• Test the same controller on wet pavement versus dry pavement to see how traction changes the best torque split.
• Compare a human driver with an autonomous throttle controller to see which one keeps lap times more consistent.
• Swap rear-only torque vectoring for front-rear torque biasing and measure how the balance changes in tight turns.

Learn More

• NASA Glenn Research Center: Search for vehicle dynamics, traction, and control system background articles and educational pages.
• MIT OpenCourseWare: Search for courses in vehicle dynamics, feedback control, and robotics that include lecture notes and problem sets.
• NHTSA: Use vehicle stability and handling resources to learn how engineers think about traction and cornering safety.
• SAE International Journals: Search for peer-reviewed papers on torque vectoring, electric vehicle control, and handling performance.
• PubMed: Search for articles on human factors and driving behavior if you want to study driver response alongside vehicle control.