Reserving Charging Decision-Making Model and Route Plan for Electric Vehicles Considering Information of Traffic and Charging Station

Abstract

With the advance of battery energy technology, electric vehicles (EV) are catching more and more attention. One of the influencing factors of electric vehicles large-scale application is the availability of charging stations and convenience of charging. It is important to investigate how to make reserving charging strategies and ensure electric vehicles are charged with shorter time and lower charging expense whenever charging request is proposed. This paper proposes a reserving charging decision-making model for electric vehicles that move to certain destinations and need charging services in consideration of traffic conditions and available charging resources at the charging stations. Besides, the interactive mechanism is described to show how the reserving charging system works, as well as the rolling records-based credit mechanism where extra charges from EV is considered to hedge default behavior. With the objectives of minimizing driving time and minimizing charging expenses, an optimization model with two objective functions is formulated. Then the optimizations are solved by a K shortest paths algorithm based on a weighted directed graph, where the time and distance factors are respectively treated as weights of corresponding edges of transportation networks. Case studies show the effectiveness and validity of the proposed route plan and reserving charging decision-making model.

A Route Choice Model for Capturing Driver Preferences When Driving Electric and Conventional Vehicles

“…Additionally, a user equilibrium model where drivers could swap batteries at predetermined stations was formulated [56]. Lately, an optimization model was presented where drivers minimized travel time and charging costs to decide their route plan to charging stations [57].…”

Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles

“…In area D , the node list of the charging station is C end . C sta and C end are shown in Equation (22).…”

“…The data-driven methods have a particularly negative impact on the accuracy of users' travel demand analysis and the practical application of a charging guidance strategy. To avoid this negative impact, the formulation of charging electricity prices [16,19] and the calculation of guiding distance [20][21][22] have been conducted to select charging stations for users' travel demand. However, the analysis process of these studies was too complicated, and the established models had high complexity.…”

Path Planning Method for Electric Vehicles Based on Freeway Network

“…Taking a highway network as an example, the results show that the strategy can solve large-scale problems within the optimality gap of less than 5%. Liu et al [31] studied how to make an appointment charging strategy to ensure that electric vehicles can charge with shorter charging time and lower charging cost when charging requirements are put forward. Li et al [32] communicated vehicles with infrastructure components and real-time transmission of information, in order to find the best route.…”