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Immersion-cooled battery thermal management systems (BTMSs) are generally designed and analyzed using numerical simulations. These models must couple the electrochemical and thermal–fluid physics for accurate results. However, such a numerical approach is computationally expensive and may not be feasible, particularly for large systems. Here, we develop a computationally efficient approach to study immersion cooling-based BTMSs with the coupled physics. After validating the simplified immersion-cooled battery model for fixed convection coefficient, we then define two simplified immersion cooling models: one using existing heat transfer correlations and the other employing customized correlations trained from fully-coupled numerical models. The trained models are highly accurate (error <3%). Moreover, they are very flexible as they can be formulated to study different combinations of mass flow rates, fluids, and discharge rates using a single heat transfer correlation. Additionally, the trained models are data-frugal, requiring only data from two mass flow rates (for a given fluid and discharge rate) to predict the response for other mass flow rates. The significant reduction in computation cost [from hours or days for the fully-coupled numerical models to seconds for proposed models] makes the proposed approach more suitable for rapid analysis, optimization, and real-time implementation of the immersion-cooled BTMSs. 

Forced immersion cooling, where a dielectric fluid flows in contact with the cells, is an effective cooling approach for lithium-ion batteries. While previous models demonstrated effectiveness, they generally focused on thermal-fluid aspects and often neglected the coupling between temperature, cell potential, and heat generation (in other words, the electrochemistry remained unaffected by cooling conditions). Here, we use a fully coupled modeling approach that solves the detailed electrochemical model (with temperature-dependent properties) in conjunction with the thermal-fluid transport models at each time step. For an 18650 cell, we compare forced immersion cooling (water and mineral oil) to forced air cooling. Improved temperature control with immersion cooling leads to higher heat generation with increased capacity loss: a 3 K temperature rise corresponds to 10% loss, whereas 42 K temperature rise results in 0.4% loss at 5C discharge. Neglecting two-way coupling prohibits accurate analysis of the effectiveness of immersion cooling. Furthermore, the thermal conductivity and heat capacity of the fluid most significantly impact the electrochemical and thermal response. Finally, we define a new metric to compare performance with different flow parameters without computationally-expensive numerical simulations. Overall, this study provides insights that will be useful in understanding and design of immersion-cooled battery systems.