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The surging demand for Li-ion batteries (LIBs) has started a quest for innovations in their design and technology. A notable improvement in this regard involves the use of Silicon (Si) as the active anode material in LIBs. However, a major challenge stopping its widespread adoption is the considerable volume change experienced by Si during the lithiation-delithiation process, leading to volumetric stress-induced capacity degradation. This study identifies three primary capacity fade mechanisms in these LIBs: volumetric-stress-induced cracking and delamination, along with the growth of the solid electrolyte interface (SEI) during charging and discharging cycles. These mechanisms are influenced by battery design and operating conditions, such as Si anode thickness, ambient working temperature, and charging rate, introducing uncertainty into the battery’s degradation rate. In this study, multiple finite element (FE) models are constructed to simulate capacity degradation resulting from these three capacity fade mechanisms and their predictions are validated against experimental data. To address the computational demands of multiple FE models simulating capacity degradation from these fade mechanisms, a Gaussian Process Regression (GPR) surrogate model is developed. This GPR model efficiently predicts capacity fade and is validated for accuracy. Subsequently, the GPR model is used in an uncertainty quantification study that is focused on the battery’s design and operating conditions. The objective is to pinpoint the factors that exert the most significant influence on capacity degradation in Si anode-based LIBs. 

Silicon is an emerging anode material due to its high lithium storage capacity. While some commercial batteries now include silicon particles, porous three-dimensional (3D) scaffolded silicon electrodes may enable higher silicon loading by accommodating the silicon volume expansion during lithiation without significant electrode swelling. However, the electrochemomechanical response of silicon films on metal scaffolds remains poorly understood due to the complex scaffold morphology. We explore the role of scaffold curvature in the cycling behavior of silicon films and show that different curvatures exhibit distinctive failure modes. Negative curvature leads to crack opening from tensile and compressive stresses. Positive curvature induces tensile stress-driven buckling. Zero curvature exhibits fragmentation. The electrode morphology and chemistry for these systems are evaluated via scanning transmission electron microscopy with energy-dispersive X-ray spectroscopy (STEM-EDS). COMSOL Multiphysics simulations support that the electrochemo-mechanics of silicon are curvature-dependent. These findings point toward design strategies for 3D architected silicon anodes with improved cycling integrity. 

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.