Simulating Thermal Runaway Propagation with GT-SUITE

Learn how to use a model to evaluate battery pack safety during thermal runaway events.

Introduction

Designers of battery packs are tasked with a very challenging problem: to package a set of cells as tightly as possible and minimize the amount of non-cell weight in the battery while maintaining proper temperature levels of cells, protecting against premature cell degradation, and ensuring safe operation.  The last item on the list is, of course, the most important: ensuring that the battery pack is safe under any circumstance.

1C Discharge

The most common challenge to ensuring battery safety is thermal runaway.  Thermal runaway is a phenomenon that occasionally occurs in Lithium-ion (Li-ion) cells when extreme temperatures are reached.  During thermal runaway, undesired exothermic side reactions heat up the cell, and as the cell heats up, the rate at which the undesired reactions occur accelerates, eventually causing a catastrophic loop of events that concludes with a destroyed Li-ion cell and a lot of heat released.  This loop of events is summarized in the image below.

There are many potential causes of thermal runaway.  For example, if a cell is heated to extreme temperatures, thermal runaway can occur.  If a cell is pierced by a nail or crushed, this can cause an internal short which eventually leads to thermal runaway.  Other times, thermal runaway can occur for seemingly no reason at all – in these cases it is often manufacturing issues or even internal dendrite growth that lead to internal shorts inside the Li-ion cell.

With all these potential causes for thermal runaway, as a pack designer, how are you supposed to protect your cells from entering thermal runaway?  The unfortunate answer: you can’t.

In fact, this is the wrong question for a pack designer to be asking.  Because thermal runaway can occur for so many different reasons, and occasionally for no apparent reason, pack designers must assume that at some point a cell in a battery pack will enter thermal runaway.  The correct question to be asking is, “Is my pack designed well enough to withstand one cell entering thermal runaway without starting a chain reaction of neighboring cells entering thermal runaway?”

Thermal Runaway Propagation

Thermal Runaway Propagation is the key phenomenon to consider when designing a safe battery pack, this refers to the event of a single cell entering thermal runaway, releasing a large quantity of heat, and heating neighboring cells to the point of thermal runaway, essentially starting a chain reaction in which all cells in a battery pack are eventually destroyed.

There are various levels of success for this type of thermal runaway propagation scenario.  There are the intuitive “pass” or “fail” results where a “pass” would mean that after a cell enters thermal runaway, it does not cause a chain reaction and a “fail” would mean that after a cell enters thermal runaway, it does cause a chain reaction.  There is a less intuitive middle ground in these scenarios, too.  For instance, maybe a chain reaction is set off, but the time delay between the first cell entering thermal runaway and the entire battery pack being destroyed is a long period of time, this may also be a “passing” result, depending on the application.  If a cell is sensed to have entered thermal runaway while a vehicle is at highway speeds, does a family have enough time to stop and safely exit the vehicle before a fast-moving chain reaction is set off?  If a cell enters thermal runaway during an EVTOL flight, is a pilot able to land before the chain reaction becomes unstable?

Without Simulation

Testing the design of a battery pack against thermal runaway propagation is an expensive and dangerous endeavor.  First, expensive prototype versions of battery packs must be assembled, then a single cell is selected (either at random or with engineering discretion to determine which would be most likely to cause the undesired chain reaction), and finally thermal runaway is intentionally induced on the selected cell (either with a nail or by heating it to extreme temperatures).  After that, it is up to the design of the pack to determine whether or not neighboring cells enter thermal runaway, and if they do, how fast.

This experimental setup has two major downsides.  First, battery packs are expensive, and prototype versions of battery packs are even more expensive.  To build these and intentionally destroy them can result in a high cost for battery safety testing.  Second, this physical test is often done very late in the development cycle for the battery-powered product (e.g battery electric vehicle, EVTOL, electric bicycle).  If a battery pack fails this test, it can be a major setback for the release schedule of the product, which can be detrimental to businesses.

With Simulation

Using simulation to run virtual thermal runaway propagation tests for Li-ion battery packs is a great way to avoid the costs and risks associated with experimental testing.  In addition to that, single cells do not need to be picked out at random.  Instead, multiple tests can be setup testing the “what if” scenario for every cell in a battery pack.

GT-SUITE is the ideal platform to run virtual thermal runaway propagation tests.

Modeling Thermal Runaway Propagation in GT-SUITE

In a paper published with NASA, who has extensive experimental data on thermal runaway of Li-ion cells, GT-SUITE was used to model the propagation effect of thermal runaway in a small battery module.  The thermal runaway propagation model was built by converting CAD geometry and validated with experimental data.

Nominal Electrothermal Model of Battery Module

The study shows a number of test cases, including two of the battery modules during normal operation, which do not have cells entering thermal runaway.  The animation below shows one of these tests, a battery module discharging at a C-rate of 1C.  In the animation below, the blue – red contour animates local temperatures where blue is cool temperatures and red is hot temperatures.  From the animation below, we can see the battery slowly warms up while being discharged at 1C.

1C Discharge

 

To take this electrothermal battery model and setup the thermal runaway propagation model, a few extra steps were required.

Cell-Level Experimental Thermal Runaway Tests

NASA has created specialized bomb calorimeters that impose thermal runaway on a single cell through a variety of causes (internal short, nail penetration, excessive heating).  With this type of cell-level testing, NASA was able to measure the amount of energy released during a thermal runaway event.  Some example results from their testing of cylindrical cells are shown below.

Alterations to Battery Model

The nominal battery model that was setup for the previous electrothermal model was upgraded to include a model of thermal runaway.  This included the following changes:

  • The Trigger: If any jelly roll temperature rose above 180°C, the cell would immediately enter thermal runaway
  • The Heat Release: Once a cell entered thermal runaway, the cell would release energy in the form of heat (in this case 70 kJ)
    • 40% of the heat released would be absorbed by the jelly roll in 1.5 seconds
    • 60% of the heat released would be released as ejecta in 1.5 seconds
  • The Electrical Disconnection: Once a cell entered thermal runaway, it would no longer participate in the module, which means the neighboring cells, which are placed in parallel, would have more current flowing through them.

Module-Level Model of Thermal Runaway Propagation

Once these alterations were made to the battery model, any cell in the module can be selected as the “trigger” cell by applying an external heat until the trigger temperature of 180°C is reached.

In the first study, a cell in the corner of the module was selected to be the trigger cell.  It was artificially heated to its runaway temperature of 180°C and it immediately entered thermal runaway.  The animation below shows the results of the thermal runaway simulation with the corner cell (top of the image) selected as the trigger cell.  Once again, blue cells are relatively cool and red cells are hot.  From viewing the animation, we can see that the corner cell does not cause a chain reaction of neighboring cells entering thermal runaway.

Corner Cell Trigger

Since no real battery modules were destroyed in this simulation, this simulation can be repeated under different conditions.  The next study conducted was to test how the module behaves when a cell in the center of the module enters thermal runaway.  The image below shows how the module reacts when a cell in the center of the module is the trigger cell for thermal runaway.  Once again, we can see that the thermal runaway event does not propagate to neighboring cells.

Middle Cell Trigger

The virtual thermal runaway propagation tests shown above both show “passing” results.  The trigger cell self-heats to extremely high temperatures; however, the neighboring cells do not pass the 180°C threshold to enter thermal runaway.

In order to illustrate a “failing” test result, some changes were made to the module to make it more likely to propagate thermal runaway to neighboring cells.  The busbar in the module was included, which increased the amount of heat conducted between neighboring cells.  Additionally, the ratio of self-heat and heat released as ejecta was altered to be 30%-70% instead of the 40%-60% previously mentioned.

With these changes, the following results were observed.  In this case, the trigger cell very quickly causes a chain reaction among neighboring cells and causes a much more catastrophic event than the previous two test cases presented.

Failing Test

Time-to-Results

Because time is one of the most important resources of battery pack designers, one of the key considerations when faced with a modeling challenge such as thermal runaway propagation is the total time that it takes to get results.  The time that it takes to get results is the sum of the time it takes to build a model (“time-to-model”) and the time that it takes the model to run (“time-to-run”).  With GT-SUITE, both time-to-model and time-to-run are minimized.

Time-to-Model

In the examples given above, the CAD geometry was converted into a GT model using GT’s built-in CAD geometry pre-processing tool GEM3D and models were further setup using GT’s integrated simulation environment in roughly half of a day.

Time-to-Run

In the examples given above, the models run roughly 2-4 times faster than real-time on a laptop PC, resulting in a 30-minute simulation taking 7-15 minutes to run.  The finite element structure in this model consisted of 6,000 nodes and 13,000 elements.

This fast time-to run enables users to experiment with some of the uncertainty that comes with battery thermal runaway propagation.  Which cell initiates thermal runaway? How much heat does it release?  How is that heat released?  How much material is ejected from it?  All of these are sources of variability that can be explored with the help of fast-running models (look for a future blog on this specific topic!).  This type of variability analysis would not be possible when using extremely detailed 3D CFD models.

Conclusion

When designing a battery module or a battery pack, the battery’s response to a cell entering thermal runaway needs to be studied to analyze whether or not the cell causes a chain reaction of cells entering thermal runaway, known as thermal runaway propagation.  This can be done experimentally by building prototype modules and packs and imposing thermal runaway on a trigger cell; however, this can be extremely expensive and if the pack fails the test, can be a substantial setback in the development of the battery.

With GT-SUITE, these thermal runaway propagation tests can be done virtually.  This provides a number of advantages, including the large cost advantage and the ability to run any number of hypothetical thermal runaway propagation tests.

If you’d like to learn more or are interested in trying GT-SUITE to virtually test a battery pack for thermal runaway propagation, Contact us!

Written by Joe Wimmer and Jon Harrison