Hybrid energy storage systems that combine lithium-ion batteries and supercapacitors are considered as an attractive solution to overcome the drawbacks of battery-only energy storage systems, such as high cost, low power density, and short cycle life, which hinder the popularity of electric vehicles. A properly sized hybrid energy storage system and an implementable real-time power management system are of great importance to achieve satisfactory driving mileage and battery cycle life. However, dimensioning and power management problems are quite complicated and challenging in practice. To address these challenges, this work proposes a Bi-level multi-objective design and control framework with the non-dominated sorting genetic algorithm-II and fuzzy logic control as key components, to obtain an optimal sized hybrid energy storage system and the corresponding optimal real-time power management system based on fuzzy logic control simultaneously. In particular, a vectorized fuzzy inference system is devised, which allows large-scale fuzzy logic controllers to run in parallel, thereby improving optimization efficiency. Pareto optimal solutions of different hybrid energy storage systems incorporating both optimal design and control parameters are obtained and compared to show the achieved enhancements of the proposed approach.

中文翻译：

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具有多重优化标准的电动汽车混合储能系统的尺寸和功率管理

结合锂离子电池和超级电容器的混合储能系统被认为是一种有吸引力的解决方案，以克服纯电池储能系统成本高、功率密度低、循环寿命短等阻碍电动汽车普及的缺点。车辆。大小合适的混合储能系统和可实施的实时电源管理系统对于实现令人满意的行驶里程和电池循环寿命非常重要。然而，尺寸和电源管理问题在实践中非常复杂和具有挑战性。为了应对这些挑战，这项工作提出了一种以非支配排序遗传算法-II和模糊逻辑控制为关键组成部分的双层多目标设计和控制框架，同时基于模糊逻辑控制获得最优规模的混合储能系统和相应的最优实时电力管理系统。特别是设计了矢量化模糊推理系统，允许大规模模糊逻辑控制器并行运行，从而提高优化效率。获得并比较结合了优化设计和控制参数的不同混合储能系统的帕累托最优解，以显示所提出方法的实现增强。