Smart Regenerative Braking for Electric Skateboards

ISEF Category: Engineering Technology: Statics and Dynamics

Subcategory: Ground Vehicle Systems  ·  Difficulty: Intermediate  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

A skateboard going downhill can send energy back into the battery instead of wasting it as heat. The catch is timing, because braking too early or too late changes how much energy you recover. Your project can test whether a smart controller that predicts hills actually extends range on a real route. That makes this a real engineering problem, not just a ride-along experiment.

What Is It?

Regenerative braking is when a motor acts like a generator during slowing, and some of the skateboard’s motion energy flows back into the battery. Think of it like putting coins back into a jar every time you slow down. If your controller knows a hill is coming, it can decide when to coast, when to brake lightly, and when to let gravity do the work.

Your twist is prediction. A phone can read GPS location, and an elevation API can estimate the road’s height changes ahead of you. That lets you compare two strategies on the same route, one that reacts only when the board is already going downhill, and one that plans ahead based on the route profile.

Why This Is a Good Topic

This is a strong science fair topic because you can measure one clear outcome, battery range, and change one design choice, the control strategy. You also connect to real-world electric vehicles, where smart energy recovery affects efficiency and travel distance. You can learn route profiling, data logging, control logic, and basic statistics without needing a university lab.

Research Questions

• How does predictive braking change total battery range on the same route compared with naive regenerative braking?
• What is the effect of route steepness on the extra energy recovered by a predictive controller?
• Does using elevation data before a hill improve energy recovery more than reacting only to speed and slope sensors?
• To what extent does rider speed affect the benefit of predictive regenerative braking?
• Which route features, such as short hills or long descents, produce the largest gain in recovered energy?
• How does controller timing affect the tradeoff between range extension and ride smoothness?

Basic Materials

• Electric skateboard with regenerative braking capability or a comparable test platform
• Smartphone with GPS and offline map support
• Access to an open-elevation API or cached elevation data
• Battery voltage and current logger or a smart battery monitor
• Digital scale for checking rider and board mass
• Helmet and full safety gear
• Notebook or spreadsheet for route logs
• Measuring wheel or map app for route distance checks

Advanced Materials

• Programmable motor controller with regenerative braking support
• Inline current sensor with data logging
• Microcontroller or onboard computer for custom control logic
• GPS receiver with higher update rate
• IMU for slope and acceleration sensing
• Calibrated battery monitor with coulomb counting
• Test rider load system or repeatable weighted sled for controlled trials
• Laboratory power supply for bench validation of the controller

Software & Tools

• Google Earth Pro: Maps the route and helps you compare elevation changes before you ride it.
• Excel: Organizes ride logs and calculates energy recovery, range, and percent change.
• Python: Cleans GPS and battery data, then plots route profile versus recovered energy.
• PubMed: Finds review articles on regenerative braking, battery efficiency, and electric mobility.
• ImageJ: Helps if you need to analyze screenshots or exported graphs from phone apps.

Experiment Steps

1. Define the exact comparison between predictive braking and a baseline controller, so you know what counts as a fair test.
2. Map one fixed route and extract its elevation profile, then mark where hills start, peak, and end.
3. Choose the performance metric you will defend, such as range gained, recovered watt-hours, or efficiency per elevation drop.
4. Plan your controls for rider mass, tire pressure, battery state, and riding style, because each one can change the result.
5. Design a logging system that ties GPS position, elevation, speed, and battery data to the same trip timeline.
6. Set up your analysis before collecting data, so you know how you will compare uphill, flat, and downhill segments.

Common Pitfalls

• Trusting raw phone GPS without smoothing it, which makes the route elevation look noisier than it really is.
• Comparing rides with different rider weights or board loads, which changes energy use more than the braking strategy does.
• Mixing battery state of charge between trials, which makes later rides look worse even if the controller worked.
• Using a route with too few hills, which leaves you without enough downhill segments to test the prediction idea.
• Measuring only total trip distance and ignoring recovered energy, which hides whether the controller actually improved braking behavior.

What Makes This Competitive

A strong version of this project does more than compare two rides. You can model the route, estimate expected energy recovery, and check whether real data match the prediction. You can also test whether the controller helps more on some hill shapes than others, which makes the project feel like engineering instead of just logging rides. Strong controls and clean statistics will matter more than flashy hardware.

Project Variations

• Test the same braking idea on a scooter or one-wheel board, then compare how wheel size changes recovery.
• Swap GPS-based hill prediction for an IMU-based slope estimate, then compare which method predicts descents more accurately.
• Analyze separate route types, such as short urban hills versus long suburban grades, to see where predictive braking gives the biggest gain.

Learn More

• NASA Earthdata: Use terrain and elevation data to understand how route shape affects motion and energy, then search the site for elevation and geospatial tutorials.
• NOAA National Centers for Environmental Information: Explore map and terrain data methods that help you think about slope, distance, and local geography.
• USGS National Map: Find elevation and topographic data for route profiling and map-based analysis.
• MIT OpenCourseWare, Electric Vehicles or Control Systems courses: Review free lecture notes on motors, energy recovery, and feedback control.
• PubMed: Search for review articles on regenerative braking, lithium-ion battery behavior, and electric mobility efficiency.
• IEEE Xplore: Read abstracts and, when available, open-access papers on regenerative braking control and energy management in small electric vehicles.