Abstract
Battery pack designers must ensure that all battery packs are designed to withstand a single cell entering thermal runaway without the heat from this cell causing thermal runaway to occur in neighboring cells, leading to thermal runaway propagation. Pack designers must be confident that no matter which cell enters thermal runaway, and no matter how the cell enters thermal runaway, that thermal runaway will not propagate into a pack-level fire.
Studying thermal runaway events at the pack-level has historically relied heavily on expensive pack-level experimental tests using prototype batteries or computationally-expensive 3D CAE analyses. Because the cost of a single test in either scenario (real cost or computational cost) is very high, both options allow for only a handful of scenarios to be realistically studied.
Additionally, when reduced-order models of pack-level thermal runaway are employed, they have historically relied on empirically-driven models for the heat generation of cells entering thermal runaway. These models often rely on knowing the heat ejected from a cell ahead of time and often ignores the history of cells leading up to their thermal runaway events.
This presentation will demonstrate the benefits of a physics-driven approach to reduced-order modelling of pack-level thermal runaway propagation. By combining 1D/3D thermal structure modelling of cell temperature, 1D flow simulation of flow paths between cells, pseudo-2D electrochemical modelling of cells leading up to thermal runaway, and generalized chemical modelling for thermal runaway reactions inside the cell, this presentation demonstrates a computationally efficient approach to modeling thermal runaway propagation that also has high model fidelity and accuracy.
With an accurate and fast-running model, more virtual experiments can be setup to enable more what-if scenarios to be tested. This includes robust case sweeps and even multi-factor design of experiments (DoE) tests to be done.