太阳能电动汽车储能系统的优化配置  

OPTIMIZATION OF ENERGY STORAGE SYSTEM IN SOLAR ENERGY ELECTRIC VEHICLE

作　　者：周世琼[1] 康龙云[1,2] 曹秉刚[1] 程苗苗[1] 

机构地区：[1]西安交通大学机械工程学院机电系,西安710049 [2]华南理工大学汽车工程学院,广州510640

出　　处：《太阳能学报》2008年第10期1278-1282,共5页Acta Energiae Solaris Sinica

基　　金：科技部星火计划(2004EA105003)

摘　　要：在太阳能电动汽车(SEEV)系统中,储能系统的优化配置是一个重要且具挑战性任务。太阳能电动汽车储能系统的优化配置可以看成一个具有约束的优化问题:以储能系统的成本最小为优化目标,以表达系统可靠性指标的负载失电率为约束。决策变量不仅包含传统方法中的蓄电池充电电流而且还包含储能飞轮的质量。优化算法是采用基于遗传算法和神经网络的组合优化方法,即把机会约束遗传算法中比较耗时的个体检验部分交给神经网络处理。研究结果表明,基于遗传算法和神经网络的组合优化算法在被应用于太阳能电动汽车储能系统的优化配置时,算法收敛良好,计算时间少且可行。In the design of solar energy electric vehicle （SEEV）, the optimizing of energy storage system in SEEV is an important and challenging task. The coordination between energy storages and load is very complicated. The optimizing of energy storage system in SEEV can be considered as a constrained optimization problem： minimization the total capital cost of energy storage system in SEEV, subject to the main constraint of the loss of power supply probability （LPSP）. And the decision variables were not only the charge current of batteries in traditional methods, but also the mass of flywheel. The combinatorial optimization by genetic algorithm and neural network, namely the individual test section that spent more time was transferred to neural network to deal with, was used to optimize the system. Studies prove that the combinatorial optimization converges well, lessen calculation time and therefore it is feasible.

关 键 词：太阳能电动汽车 蓄电池 飞轮 遗传算法 人工神经网络 优化 

分 类 号：TK8[动力工程及工程热物理—流体机械及工程]