Simulation-assisted exploration of charging infrastructure requirements for electric vehicles in urban environments

Viswanathan, V.; Zehe, Daniel; Ivanchev, Jordan; Pelzer, Dominik; Knoll, Alois; Aydt, Heiko

doi:10.1016/j.jocs.2015.10.012

Cited by 40 publications

(25 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A microscale analysis of parking and walking from CS to destinations is also a proposed approach [19]. Several studies have targeted more than one criterion by considering user-, destination-, and route-oriented approaches in a combined model [15,20,21].…”

Section: State Of the Art Of Cs Location Modelsmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

In recent years, with the increased focus on climate protection, electric vehicles (EVs) have become a relevant alternative to conventional motorized vehicles. Even though the market share of EVs is still comparatively low, there are ongoing considerations for integrating EVs in transportation systems. Along with pushing EV sales numbers, the installation of charging infrastructure is necessary. This paper presents a user- and destination-based approach for locating charging stations (CSs) for EVs—the electric charging demand location (ECDL) model. With regard to the daily activities of potential EV users, potential positions for CSs are derived on a micro-location level in public and semipublic spaces using geographic information systems (GIS). Depending on the vehicle users’ dwell times and visiting frequencies at potential points of interest (POIs), the charging demand at such locations is calculated. The model is mainly based on a survey analyzing the average time spent per daily activity, regional data about driver and vehicle ownership numbers, and the georeferenced localization of regularly visited POIs. Optimal sites for parking and charging EVs within the POIs neighborhood are selected based on walking distance calculations, including spatial neighborhood effects, such as the density of POIs. In a case study in southeastern Germany, the model identifies concrete places with the highest overall demand for CSs, resulting in an extensive coverage of the electric energy demand while considering as many destinations within the acceptable walking distance threshold as possible.

show abstract

Section: State Of the Art Of Cs Location Modelsmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

show abstract

“…However, most of the studies apply highly aggregated statistical data with a low spatial resolution (e.g. criterion demand density in Table 1, 26 out of 61 studies) (Spieker, Hagg, Gaier, Meilinger & Asteroth, 2016;Viswanathan et al, 2016).…”

Section: Data Sources For Different Modeling Approaches and Their Conmentioning

confidence: 99%

“…Dong et al (2014) or Hidalgo et al (2016), for example, adopt the approach not only to minimize the installation costs, but also to minimize the amount of failed trips. Only six studies present models that are user/destination-oriented and trafficoriented (Dong et al, 2014;Eisel et al, 2014;Huang et al, 2016;Spieker et al, 2016;Tu et al, 2016;Viswanathan et al, 2016).…”

Section: Target Criteria For Locating Csmentioning

confidence: 99%

A review of spatial localization methodologies for the electric vehicle charging infrastructure

Pagany

Camargo

Dorner

2018

International Journal of Sustainable Transportation

117

View full text Add to dashboard Cite

With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problem's spatial dimension, and derives perspectives for further research. ARTICLE HISTORY

show abstract

“…As existing optimization methods ignore traffic demands and infrastructure, Viswanathan et al took existing traffic, road network data and the dynamics of individual vehicle movement into consideration when determining charging infrastructure. They described a computational science approach to evaluate both the spatial and temporal aspects of charging station placement based on available real world traffic data [14]. Lam et al formulated the electric vehicle charging station placement problem (EVCSPP) and proved it to be NP-hard.…”

Section: Literature Reviewsmentioning

confidence: 99%

Study on Influence Factors of Electric Vehicles Charging Station Location Based on ISM and FMICMAC

Niu

2017

Sustainability

View full text Add to dashboard Cite

Abstract:Along with the rapid growth in the number of electric vehicles, there is urgent need to construct electric vehicles charging stations (EVCSs) to satisfy the charging demand. However, during the process of carrying out quantitative and qualitative analysis on location decisions, it is necessary to make clear the relationships and role between various factors which make impacts on charging station location. Studies are inadequate in analyzing the influence factors with regard to this respect. This study aims to identify the influence factors, as well as the driving and dependence power of these factors and to analyze the interactions among them. This work proposes to use interpretive structural modeling (ISM) and Matriced'Impacts Croisés Multiplication Appliquée á un Classement (fuzzy cross-impact matrix multiplication applied to classification) (FMICMAC) based approach which is a novel effort in this sector. Moreover, rankings of the identified factors have also been obtained. Based on review of literature and brainstorming among experts in the EVCS field and academia, this paper puts forward 12 factors that impact EVCS location in five aspects. After ISM and FMICMAC analysis, it is concluded that area attribute and geographical environment are defined as key factors while construction cost and annual operation and maintenance cost are the objective factors. The developed integrated structured model will be beneficial in understanding the interrelationship and dependency among the identified factors.

show abstract

Simulation-assisted exploration of charging infrastructure requirements for electric vehicles in urban environments

Cited by 40 publications

References 24 publications

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

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

A review of spatial localization methodologies for the electric vehicle charging infrastructure

Study on Influence Factors of Electric Vehicles Charging Station Location Based on ISM and FMICMAC

Contact Info

Product

Resources

About