The combinatorial optimization by Genetic Algorithm and Neural Network for energy storage system in Solar Energy Electric Vehicle
The combinatorial optimization by Genetic Algorithm and Neural Network for energy storage system in Solar Energy Electric Vehicle
复制标题
太阳能电动汽车储能系统的遗传算法和神经网络组合优化
DOI：
10.1109/wcica.2008.4593375
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Bing
中科院分区：
文献类型：
--
作者：
Shiqiong Zhou;L. Kang;Guifang Guo;Yanning Zhang;Bing
研究了太阳能电动汽车（SEEV）系统中储能系统的最佳尺寸。为此建立了一个模型系统，包括光伏电源、铅酸电池和飞轮。最优规模可以看作是一个约束优化问题：在以失电概率（LPSP）为主要约束条件下，使SEEV储能系统的总资金成本最小化。本文采用遗传算法或遗传算法与神经网络的组合优化。传统方法的决策变量除了电池的容量外，还包括飞轮的容量。研究证明，所采用的优化算法收敛性好，是可行的。遗传算法和神经网络组合优化可以减少计算时间，结果变化不大。
We investigated the optimal sizing of the energy storage system in a solar energy electric vehicle (SEEV) system. A model system was constructed for this that includes the photovoltaic power, the lead-acid battery and a flywheel.The optimal sizing 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). The genetic algorithm or combinatorial optimization by genetic algorithm and neural network were used in this paper. And the decision variables are not only the capacity of batteries in traditional methods, but also the capacity of flywheel. Studies have proved that the optimization algorithms used can converge well and they are feasible. Combinatorial optimization by genetic algorithm and neural network can lessen the calculation time, with the results change little.