EV Battery Thermal Runaway Model

ISEF Category: Engineering Technology: Statics and Dynamics

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

The Hook

A battery fire can spread faster than most people expect. In an EV pack, one overheated cell can push its neighbors into failure, like dominoes on a crowded table. You can model that spread with foamboard modules, nichrome wire, and temperature data. That gives you a real engineering problem you can test, measure, and improve.

What Is It?

Thermal runaway means a battery heats itself faster than it can cool down. Once one cell gets hot enough, it can vent gas, release more heat, and trigger nearby cells. In an EV pack, that chain reaction matters because the cells sit close together, often inside tight enclosures.

You can study the same idea with a smaller model. Think of each foamboard module as a simplified battery cell block. The nichrome wire stands in for the heat from venting, and the thermocouples act like tiny temperature reporters. A reduced-order Newton-cooling-network simulator uses a few equations to predict how heat moves from one module to the next. It is simpler than full computer fluid models, but still useful if you validate it against real test data.

Why This Is a Good Topic

This topic works well because you can vary one clear factor at a time, such as spacing, insulation, vent direction, or cooling path, and then measure how the temperature spread changes. It connects to EV safety, battery design, and fire prevention, so the real-world stakes are easy to explain. You can also build a strong project without a university lab if your school has basic sensors and a safe test space. The data gives you room to do real modeling, not just a simple demo.

Research Questions

• How does spacing between foamboard modules change peak temperature in neighboring modules during a venting event?
• What is the effect of added insulation between modules on the time for heat to reach a threshold in adjacent modules?
• Does a reduced-order Newton-cooling-network model predict measured temperatures better than a single-node cooling model?
• To what extent does vent direction change the temperature gradient across a module array?
• Which cooling boundary condition, open air, fan-assisted air flow, or heat sink contact, reduces propagation most in the mockup?
• How does module arrangement, straight line versus staggered layout, affect the number of modules that exceed a safety threshold?

Basic Materials

• ¼-inch foamboard sheets or foam core board.
• Nichrome wire and a low-voltage power supply.
• Type K thermocouples or digital temperature probes.
• Multichannel data logger or microcontroller with temperature input.
• Digital kitchen scale or ruler for consistent module sizing.
• Hot glue gun or low-heat adhesive.
• Wire cutters, needle-nose pliers, and electrical tape.
• Safety glasses and heat-resistant gloves.
• Laptop for logging and graphing data.
• Fire-safe test surface, such as ceramic tile or metal tray.

Advanced Materials

• High-channel-count thermocouple logger.
• Infrared camera or thermal imaging attachment.
• Bench power supply with current limiting.
• Variable-speed fan for controlled airflow tests.
• Material samples with known thermal properties for model fitting.
• Calibrated heat-flux sensor, if available.
• Data acquisition interface for synchronized logging.
• Fire blanket, Class ABC extinguisher, and lab heat shield.
• Enclosure materials for repeatable airflow control.
• MATLAB, Python, or similar analysis workstation.

Software & Tools

• Python: Fits the reduced-order cooling network, compares predictions to measurements, and plots residuals.
• ImageJ: Measures temperature map images or thermal camera frames if you capture them as images.
• LibreOffice Calc: Organizes trial data and builds first-pass graphs without paid software.
• Jupyter Notebook: Keeps your modeling, code, plots, and notes in one place.
• GeoGebra: Helps you sketch curve behavior and check simple model relationships before coding.

Experiment Steps

1. Define the failure chain you want to study, such as heat moving from one module to the next.
2. Choose the one geometry variable you will change first, such as spacing, vent direction, or insulation.
3. Build a simple thermal network on paper before you collect data, so your measurements match your model.
4. Plan your temperature checkpoints and decide where thermocouples will sit in each module.
5. Set controls that let you separate heat spread from room conditions, wiring heat, and airflow.
6. Fit the model to one trial set, then test whether it predicts a second set without retuning.

Common Pitfalls

• Letting the nichrome wire heat the foamboard unevenly, which turns your module into a shape test instead of a thermal spread test.
• Placing thermocouples too close to the heater, which makes peak readings look larger than the neighboring module response.
• Changing airflow between trials, which makes the cooling curve shift for reasons unrelated to your design variable.
• Comparing model output to raw temperature only, which hides whether the timing of heat transfer matches the experiment.
• Using too few repeated trials, which makes one noisy run look like a real propagation trend.

What Makes This Competitive

A strong version of this project does more than make a toy battery pack heat up. You can build a better model by comparing at least 2 or 3 network structures, then testing which one predicts spread patterns best. You can also add a harder metric, such as time-to-threshold, neighbor-to-neighbor lag, or error across different layouts. That turns your work into a real validation study, not just a classroom demo.

Project Variations

• Test how vent direction changes heat spread in a straight-line module array.
• Compare foamboard modules with different insulation layers to see which slows propagation most.
• Add a fan or passive heat sink and measure how airflow changes model accuracy and temperature rise.

Learn More

• NASA Technical Reports Server: Search for reports on battery thermal runaway, EV pack safety, and thermal management models.
• PubMed: Search review articles on lithium-ion battery thermal runaway and heat propagation.
• NOAA National Weather Service Heat Safety Materials: Useful for learning heat transfer, temperature logging, and safety basics in hot environments.
• MIT OpenCourseWare: Search heat transfer and differential equations course notes for Newton cooling and lumped models.
• USGS Fire Science resources: Find background on heat transfer, ignition, and fire spread concepts relevant to safety engineering.
• Journal of Power Sources: Search recent papers on thermal runaway propagation and validation methods.