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Research of Battery Optimal Distribution in Charging Station Network Based on Ant Colony Algorithm

机译:基于蚁群算法的充电站网络电池优化分配研究

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Battery optimal distribution in charging station network is vital to normal operation of large-scale new energy vehicles, and is urged to be resolved. It is essentially a typical NP problem of finding the optimal path process. Ant Colony Algorithm (ACA), as a novel swarm intelligence global optimization algorithm, is adopted to solve the problem. First, a mathematical description of battery distribution problem is analyzed. And then, the basic principle of ACA is introduced followed by steps for battery optimal distribution using ACA. Finally, taking a city charging station network for example, compared to simulated annealing algorithm, simulation experiments are carried out using an ACA procedure which is developed by matlab m-language to solve battery optimal distribution. The results show that it is feasible to solve battery optimal distribution problem using ACA.
机译:充电站网络中的电池优化分配对于大型新能源汽车的正常运行至关重要,因此亟待解决。本质上,这是找到最佳路径过程的典型NP问题。蚁群算法是一种新型的群体智能全局优化算法。首先,分析电池分配问题的数学描述。然后,介绍了ACA的基本原理,然后介绍了使用ACA进行电池最佳分配的步骤。最后,以城市充电站网络为例,与模拟退火算法相比,使用由matlab m语言开发的ACA程序进行模拟实验,以解决电池的最佳分配问题。结果表明,使用ACA解决电池最优分配问题是可行的。

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