Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

Pagany, Raphaela; Marquardt, Anna; Zink, Roland

doi:10.3390/su11082301

Cited by 48 publications

(34 citation statements)

References 26 publications

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“…By 2018, the number had increased to attain 239,000, accounting for 6% of the total vehicle ownership in Shanghai [7], in which percentages of BEV and PHEV were around 30% and 70%, respectively.One of the most pressing challenges is to deploy charging infrastructures in public locations considering the travel characteristics of EV drivers, so as to promote EVs and maximize the overall travel electrification [8][9][10]. Mismatch and disparity of charging demand and charging facility supply may lead to underutilized charging stations and points [11][12][13]. To this end, the EV industry falls into a kind of "egg-chicken" paradox in promoting EVs and charging infrastructures [14,15].…”

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confidence: 99%

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

Yun

Sun

Zhang³

et al. 2019

Sustainability

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Electric vehicles (EVs) are promising alternatives to replace traditional gasoline vehicles. The relationship between available charging stations and electric vehicles has to be precisely coordinated to facilitate the increasing promotion and usage of EVs. This paper aims to investigate the choice of the charging location with global positioning system (GPS) trajectories of 700 Plug-in Hybrid Electric Vehicle (PHEV) users as well as the charging facility data in Shanghai. First, the recharge accessibility of each PHEV user was investigated, and 9% rely solely on public charging networks. Then, we explored the relationship between fuel consumption and the average distance between charging to analyze the environmental benefits of PHEVs. It was found that 16% PHEVs are similar to EVs, and 9% whose drivers rely solely on public charging stations are similar to internal combustion engine (ICE) vehicles. PHEV users were divided into four types based on the actual recharge access: home and workplace-based user (private + workplace + public), the home-based user (private + public), the workplace-based user (workplace + public), and the public-based user (public). Models were developed to identify and compare the factors that influence PHEV user's charging location choices (home, workplace, and public stations). The modeling and results interpretation were carried out for all PHEV users, home and workplace-based users, home-based users, and workplace-based users, respectively. The estimation results demonstrated that PHEV users tended to charge at home or workplace rather than public charging stations. Charging price, charging price tariff, the initial state of charge (SOC), dwell time, charging power, the density and size of public charging stations, the total number of public charging, vehicle kilometer travel (VKT) of the current trip and current day are the main predictors when choosing the charging location. Findings of this study may provide new insights into the operational strategies of the public charging station as well as the deployment of public charging facilities in urban cities.Sustainability 2019, 11, 5761 2 of 23 electrification through considerable investments in consumer incentives, charging infrastructures as well as technology research and development [4,5]. In order to promote the usage and adoption of EVs, Shanghai had made great investments to subsidize EV consumers and manufacturers, offered free and specific license plate, built public charging stations, and offered purchasing tax deductions and exemptions and other non-monetary incentives (e.g., free usage restriction policy during peak hours) [6]. In 2016, the EV ownership in Shanghai has surpassed the 100,000, ranking the first in the world. By 2018, the number had increased to attain 239,000, accounting for 6% of the total vehicle ownership in Shanghai [7], in which percentages of BEV and PHEV were around 30% and 70%, respectively.One of the most pressing challenges is to deploy charging infrastructures in public locations considering the tra...

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confidence: 99%

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

Yun

Sun

Zhang³

et al. 2019

Sustainability

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“…As mentioned in Gauglitz et al (2020), studies range from higher-level distributions using simple allocation variables such as population or vehicle density (Braun et al, 2018;Vopava et al, 2017;50Hertz Transmission GmbH, Amprion gmbH, Tennet TSO GmbH, TransnetBW GmbH, 2018) to specific studies of individual application areas considering detailed local conditions such as the American state roads (Xu and Meng, 2020) or individual cities like Hamburg (Rothfuchs et al, 2018). A comprehensive overview, analysis and categorization of spatial localization methodologies for charging infrastructere is provided by Pagany et al (2019b). A connection to traditional vehicle fleets and driving profiles is established in Bundesministerium für Verkehr und digitale Infrastruktur (2021).…”

Section: Introductionmentioning

confidence: 99%

Modeling public charging infrastructure considering points of interest and parking potentials

et al. 2021

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Abstract. Considering climate change, it is essential to reduce CO2 emissions. The provision of charging infrastructure in public spaces for electromobility – along with the substitution of conventional power generation by renewable energies – can contribute to the energy transition in the transport sector. Scenarios for the spatial distribution of this charging infrastructure can help to exemplify the need for charging points and their impact, for example on power grids. We model two kinds of demand for public charging infrastructure. First, we model the demand for public charging points to compensate for the lack of home charging points, which is derived from a previously developed and published model addressing electric-vehicle ownership (with and without home charging options) in households. Second, and in the focus of the work presented here, is the demand for public charging infrastructure at points of interest (POIs). Their locations are derived from OpenStreetMap (OSM) data and weighted based on an evaluation of movement profiles from the Mobilität in Deutschland survey (MiD, German for “Mobility in Germany”). We combine those two demands with the available parking spaces and generate distributions for possible future charging points. We use a raster-based approach in which all vector data are rasterized and computations are performed on a municipality's full grid. The presented application area is Wiesbaden, and the methodology is generally applicable to municipalities in Germany. The model is compared with three other models or model variants in a correlation comparison in order to determine the influence of certain model assumptions and input data. The identification of potential charging points in public spaces plays an important role in modeling the future energy system – especially the power grid – as the rapid adoption of electric vehicles will shift locations of electrical demand. With our investigation, we would like to present a new method to simulate future public charging point locations and show the influences of different modeling methods.

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“…For instance, the Electric Vehicle Infrastructure Projection Tool (EVI-Pro) by NREL Laboratory exploits a vast dataset of travel information and vehicle and charging station features to calculate the number and kind of CPs for each scenario [16,17]. Alternatively, drivers' habits and needs can be used to estimate the geographic distribution of charging demand resorting to suitable models, based on which the locations characterised by the highest demand are selected for CPs installation [18]. In this regard, commercial databases are available and can ease the retrieval of relevant data about drivers, although they eventually refer to conventional vehicles [19].…”

Section: Introductionmentioning

confidence: 99%

A Multistage Design Procedure for Planning and Implementing Public Charging Infrastructures for Electric Vehicles

et al. 2020

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Presented in this paper is a Multistage Design Procedure (MSDP) for planning and implementing Public Charging Infrastructures (PCIs) to satisfy intracity charging demand of Electric Vehicles (EVs). The proposed MSDP splits planning and design processes into multiple stages, from macroscale to fine-scale levels. Consequently, the preliminary results achieved at each stage can be refined at the subsequent stages, leading to determine the accurate number and precise geographical location of each charging point. The main advantage of the proposed approach is that it splits a very complicated procedure into multiple and simpler stages, at each of which appropriate goals, targets and constraints can be included. As a result, the iterative interactions among all the stakeholders involved in the PCI design process are significantly simplified. The proposed MSDP has been employed in the planning and design of the PCI of the Italian island of Sardinia, accordingly to all the public bodies.

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Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

Cited by 48 publications

References 26 publications

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

Modeling public charging infrastructure considering points of interest and parking potentials

A Multistage Design Procedure for Planning and Implementing Public Charging Infrastructures for Electric Vehicles

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