Electric Vehicle Charging Facility Planning Based on Flow Demand—A Case Study

The location of electric vehicle charging facilities is of great significance in promoting the use of electric vehicles. Most existing electric vehicle location models, including the flow refueling location model (FRLM) and its flexible reformulation (FRFRLM), are based on flow demand. At present, these models cannot effectively deal with large-scale traffic networks within a limited time, and there has been little comparison of their relative benefits and limitations. Additionally, there have been few evaluations of the actual construction and location of charging facilities in cities. This paper describes an algorithm that can solve the large-scale transportation network problem within a reasonable time. Using this algorithm, the FRLM and FRFRLM models are compared in a case study focused on Jiading District, Shanghai, China, which provides some direction for the future development of flow demand models. Finally, to evaluate the actual construction of urban charging facilities, this paper presents an algorithm that can map the actual charging facilities to the transportation network, and compares the actual construction situation with the model output. This enables a comprehensive evaluation of the actual construction of charging facilities and provides guidance for future construction.

Location Optimization of Electric Vehicle Mobile Charging Stations Considering Multi-Period Stochastic User Equilibrium

This study researches the dynamical location optimization problem of a mobile charging station (MCS) powered by a LiFePO 4 battery to meet charging demand of electric vehicles (EVs). In city suburbs, a large public charging tower is deployed to provide recharging services for MCS. The EV’s driver can reserve a real-time off-street charging service on the MCS through a vehicular communication network. This study formulates a multi-period nonlinear flow-refueling location model (MNFRLM) to optimize the location of the MCS based on a network designed by Nguyen and Dupuis (1984). The study transforms the MNFRLM model into a linear integer programming model using a linearization algorithm, and obtains global solution via the NEOS cloud CPLEX solver. Numerical experiments are presented to demonstrate the model and its solution algorithm.

Optimizing Number and Locations of Alternative-Fuel Stations Using a Multi-Criteria Approach

The transition to alternative fuels is obligatory due to the finite amount of available fossil fuels and their rising prices. However, the transition cannot be done unless enough infrastructure exists. A very important infrastructure is the fueling station. As establishing alternative-fuel stations is expensive, the problem of finding the optimal number and locations of initial alternative-fuel stations emerges and it is investigated in this paper. A mixed-integer linear programming (MILP) formulation is proposed to minimize the costs using net present value (NPV) technique. The proposed formulation considers the criteria of the two most common models in the literature for such a problem, namely P-median model and flow refueling location model (FRLM). A decision support system is developed for the users to be able to control the parameter values and run different scenarios. For case study purposes, the method is used to find the optimal number and locations of the alternative-fuel stations in the city of Chicago. Some data wrangling techniques are used to overcome the inability of the method to solve very large-scale problems.