Minimizing the Costs of Constructing All Electric Bus Transportation Systems

• Main Authors: CHUNG,CHEN-YUAN, 莊鎮源
• Other Authors: KE,BWO-REN
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/14102991465556171885

碩士 === 國立澎湖科技大學 === 電機工程系電資碩士班 === 104 === The impacts of climate change have continually worsened, causing countries around the world to foreground the topic of environmental protection and focus on developing energy-saving technologies that reduce carbon emissions. To solve traffic congestion, escalate transportation capacity, reduce air pollution, and mitigate energy consumption, mass transportation serves as a highly effective tool. Along with other technological advancements, buses that use alternative fuels other than fossil fuels have gradually emerged, such as hybrid electric buses, battery electric buses, and fuel cell buses. To comply with existing schedules and routes when introducing electric buses, a corresponding bus transportation system for them must be established. In addition, an appropriate number of vehicles are required to be in operational during each shift to enable calculating the overall establishment costs. This study investigated the effects of different charging methods (i.e., direct charging and cell exchange) on the establishment costs of an electric bus transportation system. The establishment costs comprised those of the buses, vehicle batteries, spare batteries, battery-charging-and-exchanging facilities, and power. After an operation and charging model of buses was established, optimization of power charging parameters was conducted using genetic and particle swarm algorithms. The optimized parameters consisted of the remaining power, charging time, and charging level of the batteries at each time slot during the operation period in a day. The results reveal that charging during the day and exchanging batteries enhanced the use efficiency of electric buses. Although the power costs were slightly increased, the establishment costs were reduced. Keywords: electric bus, genetic algorithm, particle swarm algorithm