Battery Cooling Testing

The Summary

Decision: 6 W server fan operating point. Delivers , holding cells under 60 °C up to 39 °C ambient, which covers nearly all FSAE competition conditions. Enough margin to downgrade from a 10k to a 7k RPM fan without risk.

What it eliminated: Whether our pack needed active cooling at all, how much fan power, and which fan topology. Server fans outperforming blowers at equal power was the unexpected result that changed the hardware selection.

Evidence boundary: The h-correlation itself is experimentally validated across four power levels. Max safe ambient estimates depend on mass flow rate, which was back-calculated from h rather than measured directly.

Motivation

Over the course of endurance (22 km, ~25 minutes) our battery pack has to maintain temperatures under 60 °C (FSAE rules for maximum cell temperature). My cell and race profile modeling tools showed either cooling or power limiting must be done to keep our pack within temperatures.

Action

It was determined that doing physical airflow testing on our battery would produce higher quality results faster than CFD. The result of this experiment was creating a correlation between fan power and the heat transfer coefficient (cooling ability).

Result

The correlation allows us to predict cooling performance at any fan power setting. At 10 W fan power, we achieve . However, the h-coefficient alone doesn't tell the whole story. As air flows past each row of cells, it absorbs heat and warms up, reducing cooling effectiveness for downstream rows. Our analysis accounts for this cumulative air heating effect to determine realistic “max safe ambient” temperatures, giving us high confidence that our cooling system can maintain cell temperatures below 60 °C even in unusually hot conditions.

Why Physical Testing?

The irregular geometry of Enepaq modules makes analytical heat transfer calculations (from array cooling textbook examples) unreliable. Physical testing was the fastest path to valid results.

Understanding

The fundamental energy balance for cell cooling:

During cooldown testing, we simplify by eliminating heat generation and assuming convection dominates:

Which gives us Newton's law of cooling with thermal mass:

CFD vs Experimentation

This project originally started because I set out to create realistic cooling estimates for our battery. I had a brief stint of trying to set up thermal CFD but quickly realized that I was neither confident in heat transfer fundamentals (started this in 2024), nor understood CFD solvers. I ended up asking the FSAE discord and Ethan Perrin (Tesla Battery Engineer, personal communication) pointed me toward IRL physical testing being much more likely to give valid results much faster.

Heat Transfer First Principles and MATLAB Files

The start of this project was in the semester before I had taken my university's heat transfer course. Before I designed any test setups or plans I worked on a couple thermal models in MATLAB.

Remaining Unknowns

The MATLAB models gave me fundamental understanding, but several parameters remained uncertain. I have ideas to look deeper into these, but more pressing priorities take up my time for the amount of benefit these would give.

• Airflow velocity— fan specs don't translate directly to air speed over cells
• Turbulence effects— how does fan-induced turbulence affect heat transfer?
• Pressure drop— what's the system impedance across the module?
• Cumulative error— how do uncertainties compound in detailed models?

These unknowns motivated physical testing over analytical approaches.

Designing

The goal: create a mock Enepaq brick that could be heated and have air blown past it. Success criteria:

• Build quickly
• Approximate thermal characteristics of real batteries
• Test quickly and repeatably

Aluminum turned out to be ideal. It has similar density and heat capacity to lithium-ion cells at room temperature. The problem was I could only source imperial stock in Canada (5/8" vs 18 mm cells).

I wrote a dynamic similarity script (see MATLAB dropdown above) to check if imperial stock would skew results. Verdict: close enough for valid data, but 18 mm stock would make the mock modules dimensionally identical to real ones. Worth the wait.

The AliExpress Saga

University purchasing delays pushed this project back over a month. I ended up buying the aluminum myself from AliExpress. I designed two mock brick versions:

• 3D printed: Cheap, fast. Mainly for validating system airflow impedance
• Laser cut: Wood + aluminum rods. Enables oven preheating to test progressive air heating

The designs worked surprisingly well. The downside was by the time everything arrived and laser cutting was done, finals season was approaching.

Mock Brick Design

Testing

The physical testing can be broken up into two categories:

• Aluminum mock Enepaq brick (heated externally by heat gun)
• Real Enepaq brick (heated by electrical discharge)

Scrappy Test Setup

Temperature measurement used K-type thermocouples with thermal paste, taped to the aluminum surface. Data was logged via a Chinese TC logger to CSV files.

Real Enepaq Brick Testing

The Enepaq brick's built-in thermistors provided comparison data for the live cell tests. These internal sensors are shielded by the plastic casing, which means they lag behind actual cell surface temperature. Important context when comparing to thermocouple readings.

Test Parameters

*h-coefficient calculated from the hottest cell temperature — this is the conservative approach since we care about the worst-case cell.

Representative Test Analysis

The h-coefficient calculation uses Newton's law of cooling:

Where:

• = thermal mass of cells (5 cells × 49 g × 900 J/kg·K)
• = exposed surface area (90% of cylindrical surface)
• = temperature difference between cell and ambient

Comparison Across Runs

Creating Takeaways

The Correlation

This correlation is the primary deliverable. It allows predicting heat transfer coefficient for any fan power setting without additional physical testing.

Golden Table (with High Safety Margins)

Max Safe Ambient = 60 °C limit − Cell ΔT − Air Heating offset.
Air heating estimates depend heavily on mass flow rate. Mass flow wasn't measured directly — it was back-calculated from the h-coefficient correlation to airspeed. This table will be updated once I measure actual volumetric flow rate with my “low-backpressure trash bag air catcher” (yes, it's exactly what it sounds like).

Air Temperature Gradient

Design Implications

1. Fan selection: Server fans actually outperform blowers at equivalent power — this was unexpected. The 10k server fan handles both static pressure and high airflow well.
2. Operating point: 6 W fan power provides , sufficient for almost all race conditions (ambient < 39 °C).
3. Blowers inadequate: Same power input yields ~30% lower h-coefficient, unexpected result.
4. Room to optimize: Very confident we can downgrade to a 7k RPM fan and still meet thermal requirements with margin.

Analysis Code

Processing pipeline:

• Smoothing for noise reduction
• Newton's law of cooling for h-coefficient extraction
• Power law curve fitting for correlation
• Row-by-row air temperature gradient simulation

Future Work

• Weather statistics to make decisions on ambient conditions at competition venues
• Effect of additional heating from sun exposure and hot pavement
• Pre-cooling strategies (ice intake air) for extreme conditions
• Trash-bag air measurement test