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A framework for designing of electric vehicle charging infrastructure network.

机译：电动汽车充电基础设施网络设计的框架。

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摘要

The lack of sufficient public charging stations for electric vehicles has long been recognized as a major hinder for massive adoption of electric vehicles (EV). This dissertation aims to develop a framework for designing charging station infrastructure networks that electric vehicle with limited travel range can be recharge en-route to complete trips to destinations and then would facilitate the adoption of electric vehicles. The rest part of this dissertation is concerned with modeling travel range of electric vehicle and users behavior of deviating from their most preferred routes when siting charging stations. The proposed multi-path refueling location model provides the most cost effective deployment strategy of placing charging stations that are needed on the network to satisfy electric vehicle travel demand between all origin-destination (O-D) pairs. In the second part of the dissertation, heuristic based on greedy adding algorithms are developed to address the computational challenges of the multi-path refueling location model. The heuristics are tested on the Sioux Falls network and a real-life case study of South Carolina and compared with the exact solutions.;In reality, however, EV market matures gradually, in other words, not all the cities would become electric vehicle adopters at the current state. In third part of this dissertation, a multi-period multi-path refueling location model is developed to expand EV charging network to dynamically satisfy O-D trips with the growth of EV market. The model captures the dynamics in the topological structure of network and determines the cost effective station rollout scheme on both spatial and temporal dimensions. The multi-period location problem is formulated as a mixed integer linear program and solved by a heuristic based on genetic algorithm. The model and heuristic are justified using the benchmark Sioux Falls road network and implemented in a case study of South Carolina. The results indicate that the charging station rollout scheme is subject to a number of major factors, including geographic distributions of cities, vehicle range, and deviation choice, and is sensitive to the types of charging station sites.;The last part of this dissertation presents an extension of the multi-path refueling location model to integrate probabilities of cities becoming EV market into optimization of location decisions. This probability-based model differs from the multi-path model in two major aspects. First it maximizes the total weighted coverage of all cities with a given budget while the multi-path model minimizes the cost of covering all the O-D pairs. Second, instead of only consider one way trips as in the multi-path model, this model extends to also satisfy the round trips from destinations back to origins. A genetic algorithm based heuristic is adopted to solve this probability-based model. Numerical experiments are conducted to justify the incorporation of probability information in optimally siting charging station.
机译：长期以来，电动汽车缺乏足够的公共充电站一直被认为是大规模采用电动汽车（EV）的主要障碍。本文旨在为设计充电站基础设施网络开发一个框架，该网络可以将行程范围有限的电动汽车在途中进行充电，以完成前往目的地的行程，从而促进电动汽车的采用。本文的其余部分涉及到电动汽车行驶距离的建模以及用户在充电站设置时偏离其最喜欢路线的行为。提出的多路径加油位置模型提供了在网络上放置所需充电站以满足所有始发目的地（O-D）对之间的电动汽车行驶需求的最具成本效益的部署策略。在论文的第二部分中，开发了基于贪婪加法的启发式算法来解决多路径加油位置模型的计算难题。该启发式方法在Sioux Falls网络上进行了测试，并在南卡罗来纳州进行了实际案例研究，并与确切的解决方案进行了比较;然而，实际上，电动汽车市场逐渐成熟，换句话说，并非所有城市都会成为电动汽车的采用者在当前状态下。在论文的第三部分，建立了多周期多路径加油位置模型，以扩展电动汽车充电网络，以随着电动汽车市场的增长动态满足O-D出行。该模型捕获网络拓扑结构中的动态，并确定在空间和时间维度上具有成本效益的站点部署方案。将多周期位置问题公式化为混合整数线性程序，并通过基于遗传算法的启发式方法进行求解。该模型和启发式方法使用基准的苏福尔斯（Sioux Falls）道路网络进行了证明，并在南卡罗来纳州的案例研究中得以实现。结果表明，充电站的部署方案受城市地理分布，车辆行驶距离和偏差选择等诸多主要因素的影响，并且对充电站站点的类型很敏感。多路径加油位置模型的扩展，以将成为电动汽车市场的城市的概率整合到位置决策的优化中。这种基于概率的模型在两个主要方面不同于多径模型。首先，它以给定的预算最大化了所有城市的总加权覆盖范围，而多路径模型则最小化了覆盖所有O-D对的成本。其次，该模型不仅可以像多路径模型那样考虑单程旅行，还可以满足从目的地到起点的往返旅行。采用基于遗传算法的启发式算法来求解该基于概率的模型。进行数值实验以证明在最佳选址充电站中纳入概率信息是合理的。

著录项

  • 作者

    Li, Shengyin.;

  • 作者单位

    Clemson University.;

  • 授予单位 Clemson University.;
  • 学科 Transportation.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 124 p.
  • 总页数 124
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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