Location Planning for Electric Charging Stations and Wireless Facilities in the Era of Autonomous Vehicle Operations

Amir Davatgari, Purdue University

Abstract

The emergence of Autonomous Vehicles (AVs) provides a valuable opportunity to reduce greenhouse gas emissions by improving traffic mobility. Due to AV-EV synergies, AVs will be likely introduced into the market when the Electric Vehicle (EV) market share is high. Hence, future AVs are expected to be electric, and it is anticipated that Autonomous Electric Vehicles (AEVs) will help address climate change and environmental pollution. This is the expectation particularly during the transition phase where mixed AV-HDV fleet will require lane management policies such as AV-exclusive lane. The possibility of installing wireless charging facility at AVexclusive lanes is expected to motivate great patronage of AVs. This thesis proposes a planning framework for AEV charging. The framework is intended to help transportation decision-makers determine EV charging facility locations and capacities for the mixed fleet of AV and HDV. The bi-level nature of the framework captures the decision-making processes of the transportation agency decision-makers and travelers, thereby providing solid theoretical and practical foundations for the EV charging network design. At the upper level, the decision-makers seek to determine the locations and operating capacities of the EV charging facilities, in a manner that minimizes total travel time and construction costs subject to budgetary limitations. In addition, the transportation decision-makers provide AV-exclusive lanes to encourage AV users to reduce travel time, particularly at wireless-charging lanes, as well as other reasons, including safety. At the lower level, the travelers seek to minimize their travel time by selecting their preferred vehicle type (AV vs. HDV) and route. In measuring the users delay costs, the thesis considered network user equilibrium because the framework is designed for urban networks where travelers route choice affects their travel time. The bi-level model is solved using the Non-Dominated Sorting Genetic Algorithm (NSGA-II) algorithm. The results of the numerical experiments suggest that for a higher weight ratio of user cost dollar to agency cost dollar, the optimal deployment plan will include a greater number of wireless-charging facilities. Furthermore, the results suggest that, compared to the scenario where the transport decision-makers construct charging stations and where construct wireless-charging facilities, the scenario where the transport decision-makers construct both of them, the total costs decrease by 49% and 11%, respectively. It is shown that enabling wireless-charging facilities at both AV-exclusive and general-purpose lanes can reduce total cost by 16% and 21% compared to plan where wireless-charging facilities are provided only at AV-exclusive and where are provided only at general-purpose lanes, respectively.

Degree

M.Sc.

Advisors

Labi, Purdue University.

Subject Area

Climate Change|Energy|Transportation

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