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The development of lithium-ion battery technology has ensured that battery thermal management systems are an essential component of the battery pack for next-generation energy storage systems. Using dielectric immersion cooling, researchers have demonstrated the ability to attain high heat transfer rates due to the direct contact between cells and the coolant. However, feedback control has not been widely applied to immersion cooling schemes. Furthermore, current research has not considered battery pack plant design when optimizing feedback control. Uncertainties are inherent in the cooling equipment, resulting in temperature and flow rate fluctuations. Hence, it is crucial to systematically consider these uncertainties during cooling system design to improve the performance and reliability of the battery pack. To fill this gap, we established a reliability-based control co-design optimization framework using machine learning for immersion cooled battery packs. We first developed an experimental setup for 21700 battery immersion cooling, and the experiment data were used to build a high-fidelity multiphysics finite element model. The model can precisely represent the electrical and thermal profile of the battery. We then developed surrogate models based on the finite element simulations in order to reduce computational cost. The reliability-based control co-design optimization was employed to find the best plant and control design for the cooling system, in which an outer optimization loop minimized the cooling system cost while an inner loop ensured battery pack reliability. Finally, an optimal cooling system design was obtained and validated, which showed a 90% saving in cooling system energy consumption. [ABSTRACT FROM AUTHOR]

Micro- and nanostructured surfaces offer the potential to enhance two-phase heat transfer. However, the mechanisms behind these enhancements are not well-understood due to insufficient diagnostic methods, leading to reliance on trial-and-error surface development. We introduce in situ boroscopy to investigate microscale bubble dynamics during flow boiling nucleation and subsequent flow regime development. This method was applied in saturated flow boiling experiments within chemically etched aluminum and copper tubes. Although the surfaces have self-similar surface structures, our findings revealed varied heat transfer coefficient enhancements, with increases of up to 391% on aluminum and 41% on copper. Using boroscopy, we identified key mechanisms of heat transfer enhancement. We further used mercury porosimetry to determine the impact of pore size distribution on thermal performance. The boroscopy technique introduced here not only elucidates the underlying processes of flow boiling heat transfer enhancement but also has potential applications for the study of other two-phase phenomena.