基于NSGA2算法的燃料电池系统净功率优化  

Net power optimization of fuel cell system based on NSGA2 algorithm

作　　者：李先允[1] 冯瀚飞 LI Xianyun;FENG Hanfei(Nanjing Institute of Technology,Nanjing Jiangsu 211167,China)

机构地区：[1]南京工程学院,江苏南京211167

出　　处：《电源技术》2023年第1期83-87,共5页Chinese Journal of Power Sources

摘　　要：在质子交换膜燃料电池系统中,寄生功率占据燃料电池系统输出功率的相当一部分,系统的净输出功率是燃料电池系统的重要指标之一。目前对于燃料电池本身性能的改善主要集中在凭经验对单一控制参数的调整。提出了在稳态下,结合质、能量守恒公式推导,将空气过量系数、温度、阴极压力和阳极压力协同控制,将控制参数作为决策变量,燃料电池功率、空压机功率和散热器功率作为目标函数,采用一种非支配排序遗传算法(NSGA2)进行优化,在提高燃料电池输出功率的同时降低空压机和散热器的功率,并和传统的遗传算法(GA)作比较,验证了NSGA2的适用性,并探究了稳态条件对优化效果的影响。In the proton exchange membrane fuel cell(PEMFC) system, parasitic power occupies a considerable part of the output power of the fuel cell system, and the net output power of the system is one of the important indicators of the fuel cell system. At present, the improvement of the performance of the fuel cell itself is mainly focused on the adjustment of a single control parameter based on experience. This paper proposes that in the steady state, combined with the derivation of the conservation of mass and energy, the air excess coefficient, temperature, cathode pressure and anode pressure are controlled together. Taking control parameters as decision variables and fuel cell power, air compressor power and radiator power as objective functions, a non dominated sequencing genetic algorithm(NSGA2)is adopted for optimization. The power of air compressor and radiator is reduced while the output power of fuel cell is increased. Compared with the traditional genetic algorithm(GA), the applicability of NSGA2 is verified, and the influence of steady-state conditions on the optimization effect is explored.

关 键 词：质子交换膜燃料电池 能量守恒定律 参数优化 寄生功率 NSGA2算法 

分 类 号：TM911[电气工程—电力电子与电力传动]