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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. 

Immersion cooling, where cooling fluid flows in direct contact with the Li-ion cell, provides superior temperature control compared to other battery thermal management systems (BTMSs). Although the temperature rise during charging/discharging is low for immersion cooling, the inherent complexity of the cooling approach makes it difficult to predict and control performance. In general, numerical approaches are required to design and analyze immersion cooled BTMS configurations. For accurate results, models must fully couple the electrochemical and thermal-fluid physics solvers. However, such a numerical approach is computationally expensive and may not be feasible, in particular, for large BTMS systems. In the present study, we develop a computationally-efficient approach with coupled electrochemical and thermo-fluid physics to study immersion cooling based BTMSs. The core strategy is to use either analytical expressions or combination of analytical and numerical solutions for all the governing physics. Depending on the discharge rate and on the final design objective, different levels of simplification are leveraged to analyze the thermal and electrochemical response of the system. For the present analysis, we consider discharging of a cylindrical nickel manganese cobalt oxide (NCM) 18650 cell that is immersed in a cooling fluid stream. Different combinations of mass flow rates and fluids (deionized water, mineral oil, and air) are considered to evaluate performance. For every configuration, we first analyze the system with a fully-coupled fully numerical model and this data serves as the reference to judge the accuracy of the newly developed models. Note that the parameters used in the electrochemical models (such as electrolyte and electrode properties, as well as the reaction rates) are the same for the fully-numerical and the quasi-analytical models allowing direct comparison of the results. To isolate the impact of using the simplified models for the electrochemical aspects, a second set of comparison data is generated using the analytical electrochemical model in conjunction with the full-scale numerical thermo-fluid model. The analytical models rely on calculating a heat transfer coefficient to include the impact of the fluid flow and the heat transfer coefficient can be estimated from correlations or from the fully-coupled fully-numerical models. In general, for air-cooled configuration, the thermal and electrochemical performance (i.e., trend and magnitude of temperature rise and cell potential) of the quasi-analytical models matches the fully-coupled full-scale numerical model. But for other fluids, the results deviate from the baseline fully-coupled fully-numerical models unless the numerical fluid models are used to estimate the evolution of heat transfer coefficient throughout the discharging process. Specifically, a small number of fully-coupled fully-numerical simulations are leveraged to train the quasi-analytical model (based on a particular geometry) for rapid analysis and optimization of the BTMS. In summary, the newly developed models including the numerical data-driven learning provide an efficient trade-off between computation cost and accuracy. 

As the demand for utilizing lithium-ion batteries (LIB) has increased due to the entering of the informational era and the efforts of carbon neutrality, battery thermal management has become an important topic. Previous work has focused on removing heat using forced air and indirect liquid cooling. Due to the cost-effective cooling performance, single-phase static direct immersive cooling has recently gained attention. This paper aims to understand single cylindrical LIB’s thermal behavior under different surroundings when implementing single-phase static immersive cooling. This paper also aimed to explore how different methods of attaching thermocouples affect the precision of the measurement. By attaching thermocouples with various orientations and attachment methods on the different regions of the LIB, the temperature measurement by thermocouples is obtained by varying two battery brands, four fluid types, two submerging percentages, and four discharge rates. The expected result is that Sony battery reaches the maximum temperature of 34 °C when under 3C discharge rate and 90% under AmpCool-110 engineering fluid, which is 25 °C cooler relative to the same setup when cooling by 23 °C ambient air. The experiment also expected horizontal-orientated thermocouples with additional TIM between the cell’s wall and the probe to provide the most accurate measurement of temperature. The present study will serve as the foundation for validating and tuning the computational model of the battery, which will be utilized to research the optimal configuration of the battery pack for cooling performance per energy density. 

While it is well known that the electrochemical performance of lithium-ion batteries degrades with repeated cycling, the impact of aging on thermal properties is less well understood. Degradation of thermal transport within the cell can lead to increased or even excessive temperatures that in turn lead to accelerated ageing or even thermal runaway. Thus, understanding how aging impacts thermal properties is critical to ensuring safe and reliable operation of batteries. In this presentation, we evaluate the evolution of the thermal diffusivity, heat capacity, and density of the electrodes of lithium-ion battery cells which were aged at different thermal conditions. From the measured properties, we estimate the thermal conductivity of the electrodes and the active materials in the electrodes as well. Overall, the transport properties approximately follow a trend with the time-averaged temperature during the aging process. The changes in the thermal properties are correlated with observations of changes to the microstructure of the electrodes during cycling. These results can impact the design of battery systems for improved performance and stability throughout their lifetime.