Dynamic Operations of a Mobile Charging Crowdsourcing Platform

Yiming Yan (), Xi Lin (), Fang He () and David Z. W. Wang ()
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Yiming Yan: School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
Xi Lin: Department of Industrial Engineering and Department of Civil Engineering, Tsinghua University, Beijing 100084, P.R. China
Fang He: Department of Industrial Engineering, Tsinghua University, Beijing 100084, P.R. China
David Z. W. Wang: School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore

Transportation Science, 2024, vol. 58, issue 5, 995-1015

Abstract: This paper investigates the operation of a novel electric vehicles (EVs) charging service mode, that is, crowdsourced mobile charging service for EVs, whereby a crowdsourcing platform is established to arrange suppliers (crowdsourced chargers) to deliver charging service to customers’ electric vehicles (parked EVs) at low-battery levels. From the platform operator’s perspective, we aim to determine the optimal operation strategies for mobile charging crowdsourcing platforms to achieve specific objectives. A mathematical modeling framework is developed to capture the interactions among supply, demand, and service operations in the crowdsourced mobile charging market. To design an efficient solution method to solve the formulated model, we first analyze the model properties by rigorously proving that a crucial variable set for operating the mobile charging crowdsourcing system includes charging price, commission control, and period-specific aggregate demand control. Besides, we provide both an equivalent condition and a necessary condition for checking the feasibility of these crucial variables. On top of this, we construct a search tree according to the operation periods in a day to solve the optimal operation strategies, wherein a nondominated principle is adopted as an accelerating technique in the searching process. The solution obtained from the proposed solution algorithm is proved to be sufficiently close to the actual global optimal solutions of the formulated model up to the resolution of the discretization scheme adopted. Numerical examples provide evidence verifying the model’s validity and the solution method’s efficiency. Overall, the research outcome of this work can offer service operators structured and valuable guidelines for operating mobile charging crowdsourcing platforms.

Keywords: crowdsourced mobile charging; optimal operation strategies; charging service for electric vehicles; tree-based search (search for similar items in EconPapers)
Date: 2024
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