Predicting Thermal Runaway Event in an Abused High Energy Li-Ion Cell

Abstract

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Also, in a battery pack, a thermal runaway event can propagate from a single "failed" cell to the pack, leading to a more significant event. Mitigating a thermal runaway event is important in the commercial and automotive sectors. However, preventing such events in an electric aircraft (or air taxis) is paramount due to the lack of alternatives in the event of a failure.Battery prognostics algorithms allow to predict state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle). For this presentation, we will extend this two-level battery prognostic algorithm to predict SOC, EOL, and maximum temperature during a simulated flight. The change in SOC and EOL is considered by decreasing the maximum stored charge and solid Li-ion diffusivity and increased internal resistance on cycling [1]. Cycling leads to an increase in the heat generated by an aged Li-ion cell with an NMC 811 cathode and a silicon-graphite anode. Aged cells lead to the increase in the thickness of the SEI layer, increase in the resistance of ion diffusion and reaction kinetics, and thermodynamic abuse due to fixed cycling voltages. Since SEI decomposition has the lowest onset temperature in the series of reactions leading to thermal runaway, the model considers the self-heating rate of the SEI decomposition. The parameters in the Arrhenius equation for the SEI heating rate depend on the number of cycles, the cell's operating temperature, and the cell's abuse history [2,3].Coupling the electrochemical, thermal, and aging model would allow the prognostic algorithm to predict the rise in the operating temperature for the same discharge profile for an electric aircraft. Which, in principle, could provide an early indication for a (possible) thermal runaway event.Predicting operating temperatures in addition to SOC, SOH, and EOL in battery management systems will allow better battery monitoring and strategies for mitigating catastrophic thermal runaway events.