Multi-fidelity electrochemical modeling of thermally activated battery cells

Thermally activated batteries undergo a series of coupled physical changes during activation that influence battery performance. These processes include energetic material burning, heat transfer, electrolyte phase change, capillary-driven two-phase porous flow, ion transport, electrochemical reactions, and electrical transport. Several of these processes are strongly coupled and have a significant effect on battery performance, but others have minimal impact or may be suitably represented by reduced-order models. Assessing the relative importance of these phenomena must be based on comparisons to a high-fidelity model including all known processes. In this work, we first present and demonstrate a high-fidelity, multi-physics model of electrochemical performance. This novel multi-physics model enables predictions of how competing physical processes affect battery performance and provides unique insights into the difficult-to-measure processes that happen during battery activation. We introduce four categories of model fidelity that include different physical simplifications, assumptions, and reduced-order models to decouple or remove costly elements of the simulation. Using this approach, we show an order-of-magnitude reduction in computational cost while preserving all design relevant quantities of interest within 5 percent. The validity of this approach and these model reductions is demonstrated by comparison between results from the full fidelity model and the different reduced models.

Automated summary

This study introduces a high-fidelity multi-physics model that can predict how different physical processes affect battery performance and provide unique insights into difficult-to-measure processes during battery activation. By using four different categories of model fidelity, it effectively reduces computational costs while maintaining design-relevant quantities of interest within 5 percent. The validity of this approach and model reductions is demonstrated through comparisons between results from the full fidelity model and various reduced models.