Optimal Design of Li-Ion Batteries through Multi-Physics Modeling and Multi-Objective Optimization

Battery design variable optimization can significantly affect battery capacity, discharge specific power, and discharge specific energy. However, many design variables need to be taken into consideration, which requires intensive computation and simulation. Our previously developed comprehensive battery degradation model is utilized in this optimization study via parallel computing. A three-electrode cell is developed for model validation over long term cycling. The objectives of optimization are maximizing discharge specific power and specific energy as well as minimizing capacity loss. Several design variables (e.g., thickness, particle size, and porosity) are optimized through a modified Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II). The obtained Pareto-optimal solutions that show electrode thickness, particle sizes, porosity, and conductivity are the battery design variables that can significantly affect battery performance. In addition, a sensitivity analysis suggests that a thicker electrode and a smaller particle size can improve battery performance. The optimized batteries have a better performance over 750 cycle's simulation: less SOC swing and less reduction of capacity. The design optimization framework developed herein can be modified and applied to various type of batteries with different optimization objectives and battery design variables.