Utility Maximization Framework for Opportunistic Wireless Electric Vehicle Charging
Utility Maximization Framework for Opportunistic Wireless Electric Vehicle Charging
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2017-08
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通讯作者：
M. Z. Khan;M. Chowdhury;S. Khan;Ilya Safro;Hayato Ushijima-Mwesigwa
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作者：
M. Z. Khan;M. Chowdhury;S. Khan;Ilya Safro;Hayato Ushijima-Mwesigwa
Khan, Chowdhury, Khan, Safro， Ushijima-Mwesigwa摘要电动汽车（EV）无线充电技术的进步已经启动了针对动态充电或边驾驶边充电（CWD）应用的无线充电单元（WCU）优化部署的大量研究。本研究提出了一个新的框架，称为基于仿真的无线充电效用最大化（SUMWC），该框架旨在通过在信号交叉口使用机会性CWD的概念，通过优化WCU部署，最大化WCU对电动汽车充电的效用。首先，该框架需要一个校准的感兴趣区域的交通微观模拟网络。利用标定后的交通网络，在路网的选定路口为选定的每条车道创建效用函数和控制延迟函数。这些车道是根据它们为电动汽车充电的潜力来选择的。利用效用和控制延迟函数构建优化问题，以wcu的效用最大化为目标，决策变量为wcu的位置和长度以及交通信号配时。优化公式的约束条件是预算、位置、最小绿灯时间和可接受服务水平（LOS）。利用遗传算法对该优化问题进行了全局求解。将优化后的效用与其他部署方案进行了比较，例如遵循中间中心性部署和在交通量最大的车道上部署。SUM-WC框架每小时的效用至少是其他部署方案的1.5倍。随着电动汽车（EV）无线充电技术的进步，针对动态充电或边走边充电（CWD）应用的无线充电单元（WCU）的优化配置展开了大量研究。本研究提出了一个新的框架，称为基于仿真的无线充电效用最大化（SUMWC），该框架旨在通过在信号交叉口使用机会性CWD的概念，通过优化WCU部署，最大化WCU对电动汽车充电的效用。首先，该框架需要一个校准的感兴趣区域的交通微观模拟网络。利用标定后的交通网络，在路网的选定路口为选定的每条车道创建效用函数和控制延迟函数。这些车道是根据它们为电动汽车充电的潜力来选择的。利用效用和控制延迟函数构建优化问题，以wcu的效用最大化为目标，决策变量为wcu的位置和长度以及交通信号配时。优化公式的约束条件是预算、位置、最小绿灯时间和可接受服务水平（LOS）。利用遗传算法对该优化问题进行了全局求解。将优化后的效用与其他部署方案进行了比较，例如遵循中间中心性部署和在交通量最大的车道上部署。SUM-WC框架每小时的效用至少是其他部署方案的1.5倍。
236 + Text: 5432 + References: 737 + 4 tables/figures: 1000 = 7405 words Submission date: August 1, 2017 Khan, Chowdhury, Khan, Safro, Ushijima-Mwesigwa ABSTRACT The advancements in Electric Vehicle (EV) wireless charging technology have initiated substantial research on the optimal deployment of Wireless Charging Units (WCU) for dynamic charging or Charging While Driving (CWD) applications. This study presents a novel framework, named as the Simulation-based Utility Maximization of Wireless Charging (SUMWC), which aims to maximize the utility of WCUs for EV charging through the optimal WCU deployment using the concept of opportunistic CWD at signalized intersections. At first, a calibrated traffic micro-simulation network of the area of interest is required for this framework. The calibrated traffic network is used to create the utility function and control delay function for each selected lane at the selected intersections of the road network. The lanes are selected based on their potential to charge EVs. An optimization problem is formulated using the utility and control delay functions, where the objective is to maximize the utility of WCUs, and the decision variables are location and length of WCUs and traffic signal timing. The constraints of the optimization formulation are budget, locations, minimum green signal times and acceptable Level of Service (LOS). A global solution is achieved for this optimization problem using the Genetic Algorithm. The optimized utility is compared with other deployment schemes, such as deployment following betweenness centrality and placement at lane with the highest traffic volume. SUM-WC framework achieves at least 1.5 times more utility per hour than these other deployment schemes.The advancements in Electric Vehicle (EV) wireless charging technology have initiated substantial research on the optimal deployment of Wireless Charging Units (WCU) for dynamic charging or Charging While Driving (CWD) applications. This study presents a novel framework, named as the Simulation-based Utility Maximization of Wireless Charging (SUMWC), which aims to maximize the utility of WCUs for EV charging through the optimal WCU deployment using the concept of opportunistic CWD at signalized intersections. At first, a calibrated traffic micro-simulation network of the area of interest is required for this framework. The calibrated traffic network is used to create the utility function and control delay function for each selected lane at the selected intersections of the road network. The lanes are selected based on their potential to charge EVs. An optimization problem is formulated using the utility and control delay functions, where the objective is to maximize the utility of WCUs, and the decision variables are location and length of WCUs and traffic signal timing. The constraints of the optimization formulation are budget, locations, minimum green signal times and acceptable Level of Service (LOS). A global solution is achieved for this optimization problem using the Genetic Algorithm. The optimized utility is compared with other deployment schemes, such as deployment following betweenness centrality and placement at lane with the highest traffic volume. SUM-WC framework achieves at least 1.5 times more utility per hour than these other deployment schemes.