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.

中文翻译：

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基于多物理场建模和多目标优化的锂离子电池优化设计

电池设计变量的优化会严重影响电池容量，放电比功率和放电比能量。但是，需要考虑许多设计变量，这需要大量的计算和仿真。通过并行计算，在我们的优化研究中利用了我们先前开发的综合电池降级模型。开发了三电极电池，用于长期循环中的模型验证。优化的目的是使放电比功率和比能量最大化以及使容量损失最小化。通过修改的Elitist非支配排序遗传算法（NSGA-II），优化了几个设计变量（例如，厚度，粒度和孔隙率）。获得的帕累托最优解显示电极厚度，粒径，孔隙率，电导率和电导率是电池设计变量，会严重影响电池性能。另外，敏感性分析表明，较厚的电极和较小的粒径可以改善电池性能。经过优化的电池在750个周期的仿真过程中具有更好的性能：SOC摆动更少，容量减少更少。可以修改本文开发的设计优化框架，并将其应用于具有不同优化目标和电池设计变量的各种类型的电池。SOC摆动少，容量减少少。可以修改本文开发的设计优化框架，并将其应用于具有不同优化目标和电池设计变量的各种类型的电池。SOC摆动少，容量减少少。可以修改本文开发的设计优化框架，并将其应用于具有不同优化目标和电池设计变量的各种类型的电池。