In our previous post, we explored internal resistance and how it affects power output, efficiency, and, critically, heat generation. When a battery operates, not all of the energy goes into powering devices; some is inevitably lost as heat due to internal resistance and other electrochemical processes. Managing this heat is essential, especially in large-format cells used in electric vehicles or grid storage, where temperature fluctuations can impact performance, lifespan, and safety. This is where thermal models come into play.
Where the heat comes from
Every battery generates heat during operation. The main sources of heat include Ohmic losses, reaction overpotentials, and concentration gradients within the electrodes and electrolyte. In small cells or at low currents, the generated heat may dissipate naturally. However, in high-power or large-capacity applications, heat can accumulate, leading to temperature rises that affect battery performance and accelerate degradation. In extreme cases, excessive heat can trigger thermal runaway, a dangerous, self-reinforcing cycle of overheating.
Coupling thermal and electrochemical models
It is essential to accurately model how battery temperature evolves over time, and that’s precisely what thermal models do. These models are coupled with electrochemical models, such as the Doyle-Fuller-Newman (DFN) model: the electrochemical processes dictate how much heat is generated, while the battery temperature affects transport properties inside the cell. This coupling makes the models significantly more complex and essential for accurate predictions.
Lumped vs spatially distributed thermal models
At the core of thermal modeling is the heat equation, which governs the conservation of energy in the system. However, there are different versions we can use in thermal modelling of batteries:
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Lumped Thermal Models: These treat the entire battery as having a uniform temperature. While simpler and computationally efficient, they are most suitable for systems where temperature gradients are minimal or for real-time battery management systems (BMS) that require fast calculations.
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Spatially Distributed Models: These account for temperature variations within the battery. Depending on the desired level of resolution, some models capture temperature differences across the current collector or cell thickness, while others provide detailed variations within each battery layer. This approach is essential for large-format cells or high-power applications, where internal temperature gradients can significantly impact performance and safety.
Choosing between these approaches depends on the specific application and the required level of accuracy. In practice, many battery management systems use simplified models for real-time control, while more detailed models are employed for design and analysis.
Temperature control isn’t just about efficiency, it directly influences battery aging. Elevated temperatures accelerate chemical degradation processes, leading to capacity fade and increased internal resistance.
Thermal models are essential for predicting performance across temperature ranges, and validating them requires running coupled simulations. The Predict stage in Ionworks Studio lets teams build and validate thermal-electrochemical models from cycler data, closing the loop between test and simulation. Book a demo to see thermal coupling in practice.
That brings us to our next topic: battery degradation and State of Health (SoH).
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