As a response to the worldwide problems of global warming and environmental pollution, electric vehicles have become the main direction of development in the automobile industry. Taking the bus system of Penghu Islands as the subject, this study explores the switching of all the original diesel buses to electric buses, and it adjusts the departure time of all the buses, with the purpose of reducing the costs of the construction and electricity used in an electric bus system. Plug-in and battery-swapping buses are used as examples in the study, and the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and Simulate Anneal Arithmetic (SA) algorithms, as well as an algorithm that combines the above, is used to optimize the departure times, in order not to affect the volumes and passenger demands in units of five minutes, the shift starts within the range of 15 minutes before or after the scheduled time. After each new schedule is prepared, batteries are used to optimize the daytime charging schedule of electric buses, to ensure the lowest cost of each new schedule. The results show that, regardless of which algorithm is used to optimize the departure time, all the minimum costs are lower than the best results before the adjustment, especially for the PSO-GA algorithm. Hence, the proper adjustment of the departure time can really reduce the construction and electricity costs and carbon emissions of the electric bus system.

作为对全球变暖和环境污染的全球性问题的回应，电动汽车已经成为汽车工业的主要发展方向。本研究以澎湖列岛的客车系统为研究对象，探讨了将所有原始柴油客车转换为电动客车，并调整了所有客车的发车时间，以降低建设和用电成本的目的。在电动公交车系统中。这项研究以插入式和电池交换总线为例，遗传算法（GA），粒子群优化（PSO）和模拟退火算术（SA）算法以及结合了上述算法的算法，用于优化出发时间，以不影响五分钟为单位的交通量和乘客需求，轮班在计划时间之前或之后的15分钟范围内开始。在准备好每个新时间表后，将使用电池来优化电动公交车的白天充电时间表，以确保每个新时间表的成本最低。结果表明，无论使用哪种算法优化出发时间，所有最低成本均低于调整前的最佳结果，尤其是对于PSO-GA算法而言。因此，适当调整出发时间可以真正减少电动公交系统的建设，电力成本和碳排放。结果表明，无论使用哪种算法优化出发时间，所有最低成本均低于调整前的最佳结果，尤其是对于PSO-GA算法而言。因此，适当调整出发时间可以真正减少电动公交系统的建设，电力成本和碳排放。结果表明，无论使用哪种算法优化出发时间，所有最低成本均低于调整前的最佳结果，尤其是对于PSO-GA算法而言。因此，适当调整出发时间可以真正减少电动公交系统的建设，电力成本和碳排放。