**Abstract**

Electric vehicles have become a trend as a replacement to gasoline-powered vehicles and will be a sustainable substitution to conventional vehicles. As the number of electric vehicles in cities increases, the charging demand has surged. The optimal location of the charging station plays an important role in the electric vehicle transit system. This chapter discusses the planning of electric vehicle charging infrastructure for urban. The purpose of this work develops an electric vehicle fast-charging facility planning model by considering battery degradation and vehicle heterogeneity in driving range, and considering various influencing factors such as traffic conditions, user charging costs, daily travel, charging behavior, and distribution network constraints. This work identifies optimal fast-charging stations to minimize the total cost of the transit system for deploying fast-charging networks. Besides, this chapter also analyzes some optimization modeling approach for the fast charging location planning, and point out future research directions.

**Keywords:** Fast-charging station, charging network, charging station planning, electric vehicles, EVs traffic flow

### **1. Introduction**

Global environmental and energy problems are becoming more and more serious, and one of the main causes is fossil fuel-consuming transportation. Electric vehicles have obvious advantages in energy saving and emission reduction (such as reducing gas emissions, air pollution especially PM2.5 fine dust and noise, reducing dependence on fossil fuels, promoting industrial development and using renewable energy), so they are growing rapidly. Electric vehicles are the most promising solution for a green and clean environment when the world is more dependent on renewable energy sources. At the same time, they have also become an alternative to gasoline-powered vehicles and are promoted by policymakers worldwide as a solution to combat environmental problems and stimulate the economy. Electric vehicles are considered an extremely effective and urgent solution in the electrification of the transportation sector, and it will be an indispensable means of transportation in the future. Electric vehicles have been proven as a tool to reduce the negative effects of petroleum extraction, importation, refining and combustion. However, electric vehicles face many disadvantages compared to conventional gasoline and diesel-powered vehicles, including high initial investment costs, limited

driving range and especially a scarcity of available stations for recharging them. The popularity of electric vehicles in the future, more or less depends on the development of the infrastructure to serve this type of vehicle. Demand for electric vehicles is expected to increase over the next few years, it is still constrained by many factors especially battery cost and availability of charging station infrastructure. Investors are willing to invest in charging station infrastructure if and only if there is a sufficiently large number of electric vehicles in the network.

To attract consumers to purchase and use electric vehicles, charging station infrastructure must be deployed in convenient locations that are coordinated with each other. A power battery is one of the most important components of electric vehicles and the fundamental challenge for electric vehicles is to ensure a suitable energy storage device capable of supporting high range, fast charging and efficient driving. With an increasing number of electric vehicles on the road, the implementation of an efficient and well-planned charging infrastructure is highly desirable. In order to gradually replace traditional means of transport and put electric vehicles into use on a large scales, the construction of electric vehicle charging facilities has received strong support from governments around the world and has been focused on by scientists. As the number of electric vehicles in the city increases, the optimal location of the charging station plays an important role in ensuring the efficient operation of electric vehicles. To solve this problem, there are many design parameters related to charging stations available in the electric vehicle network that need to be considered. These parameters need to be involved to determine the optimal electric vehicle fast-charging station infrastructure. These parameters typically include: location, level, size and capacity of charging stations.

There are typically two different types of charging station configurations for electric vehicles: inter-city charging stations and intra-city charging stations. With inter-city charging stations required for electric vehicles to travel long distances, the electric vehicle will charge during the electric vehicle's journey. In contrast, for intra-city or urban charging stations with short distance travels, the electric vehicle's charging can be done when the electric vehicle finishes its journey. Different charging station locating approaches should be applied to the different charging demands.

### **2. Literature review**

Battery electric vehicles have enjoyed fast-growing adoption in recent year, however a number of factors are restricting the development of electric vehicles [1]. One of the typical limitations is that electric vehicles take a long time to charge. DC fast charging requires around half an hour to fill up to 80% of the battery capacity, whereas AC slow charging may take 6–8 h to fully recharge the battery [2]. In addition, electric vehicle charging piles are considered to be inconvenient and insuffïcient in number at present [3]. Fang He et al. [4] have proposed how to optimally locate public charging stations for electric vehicles on the road network, considering drivers'spontaneous adjustments and interactions of travel and recharging decisions. This paper adopts a tour-based approach to analyze the complete tour of the driver that may consist of several trips in a pre-determined order, and assume that their drivers simultaneously decide tour paths and recharging plans to minimize the travel and recharging times while ensuring not running out of charge before completing their tours.

The location model based on flow demand was first proposed by Hodgson, who developed a Flow-Capture Location Model (FCLM) based on the maximum coverage. On this basis, Kuby considered the driving range of the vehicle and proposed

#### *Fast-Charging Infrastructure Planning Model for Urban Electric Vehicles DOI: http://dx.doi.org/10.5772/intechopen.100011*

the Flow-Refueling Location Model (FRLM) [5, 6], Capacitated Fow Refueling Location Model (CFRLM) that considers capacity constraints [7] and Deviation-Fow Refueling Location Model (DFRLM) [8]. Patrick Jochem et al. [9] were extended the flow-refueling location model (FRLM) to the German autobahn, this model extension comprehends mainly the inclusion of the access distance for traffic participants to their closest network node. Traditionally, the FRLM has been formulated using a two-stage approach: the first stage generates combinations of locations capable of serving the round trip on each route, and then a mixed-integer programming is used to locate p facilities to maximize the flow refueled given the feasible combinations created in the first stage. Ismail Capar et al. [10] presented a Mixed-Binary-Integer Programming (MBIP) formula, which is an improvement of FRLM. The FRLM and flexible reformulation FRLM (FRFRLM) is used by Cheng Wang et al. [11] to solve the large-scale transportation network problem within a reasonable time.

Travel demand is the indispensable component to generate the travel routes of EVs, which provide the basic geographic information to locate charging stations. Several studies conducted the planning of EV charging stations with assumed traffic flow and network [12–14]. Jianmin Jia et al. [15] presents the approach to locate charging stations utilizing the reconstructed EVs trajectory derived from the Cellular Signaling Data, investigated the large-scale CSD and illustrated the method to generate the 24-hour travel demand for each EV. With the development of information technology, researchers started to explore the trajectory data in the locating problems of charging station on the basis of the floating vehicles, such as taxis, with Global Positioning System (GPS) devices [16]. The travel demand model can provide quick estimation of EV trips, while the trajectory data, such as the taxi GPS data, would better represent the real-world travel patterns of EVs. For locating fastcharging stations, in [17] Csaba Csiszár was presented an arc-based location optimisation method realized by using a geographic information system and greedy algorithm.
