Data driven scalability and profitability analysis in free floating electric car sharing systems

Information Sciences(2023)

引用 1|浏览20
暂无评分
摘要
In this paper, we analyse the impact of system design options with different demand inten-sities for electric vehicle free-floating car sharing systems (EV-FFCS). We consider three dif-ferent cities for which we collected rental data from a car sharing system. Using these data, we build demand and supply models of an EV-FFCS. We evaluate the performance of dif-ferent design options from both the customers' and the operators' perspectives, i.e., quality of service and profitability. We study the number of chargers, their placement and the size of the fleet. We observe the impact on the system when demand is constant and then when demand increases. The results show that it is critical to scale the capacity of the charging infrastructure proportionally to the mobility demand. Conversely, the same fleet size can accommodate a 300% increase in demand, not satisfying less than 15% of it. Moreover, the observed demand and supply would likely not generate profits for the EV system. This is due to the high cost of electric vehicles and the need to manage the fleet for charging operations. The figure changes with at least a 5-fold increase in demand, with the current fleet size becoming profitable. (c) 2022 Elsevier Inc. All rights reserved.
更多
查看译文
关键词
Data-driven analytics,Car sharing,Scalability,Charging infrastructure,Profitability,Electric vehicles
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要