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The city of Berlin has significantly expanded public charging infrastructure for electric vehicles. As a result of this investment, real-world charging data for the city of Berlin are available for the first time. In addition to other metrics, this dataset contains specific information about carsharing vehicles. This research letter offers numerous...
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... Felix Schulz and al. encountered areas where the first public charging stations were installed and based their results on annual information of the EVSE [18]. Hardinghaus et al. studied the use of the charging infrastructures in the city of Berlin [19]. Albert Y. S. Lam et al. claimed the importance of the location of the charging station and studied the EV charging station placement problem [20]. ...
... As a result, further research across the world is focused on evaluating and understanding any disparity that exist in spatial accessibility to charging stations. Hardinghaus et al., (2020) studied the distribution of EV charging infrastructures in the city of Berlina European city, to understand the demands for public charging. Findings showed that charging station distribution across the city is unequal and that they are more likely to be in or around the downtown core therefore a higher density of stations are likely to be found in the city center. ...
... The study showed that the travel and average waiting time for charging points to be available could be significantly reduced through spatial optimization. Thus, the research by Hardinghaus et al., (2020) and Li et al., (2015) showed that locating EV stations is a combination of evaluation of spatial distribution (understanding of the demand and supply for charging station) and optimization through operation research. However, obtaining trajectory information of EV as suggested by Li et al., (2015) to conduct spatial optimization analysis is a daunting task and most researchers opts for the study of EV stations accessibility at areal -city, Ward, and census level. ...
The paper evaluates the impact of the new additional electric vehicle (EV) charging stations on accessibility to charging in the city of Windsor Ontario. It uses locations and coordinates information of electric vehicle charging stations extracted from the Environmental Canada database combined with details of the new additions by the city, manually abstracted from publications and google. These were spatially aggregated into the city’s Ward along with demographic and stations-per-Ward determined.
Point-in-polygon, floating catchment area - FCA, and network level (travel-time) methods were used to understand variations in accessibility across Wards of the city, considering the existing and a combination of existing and new stations. The study found that the point-in-polygon method tends to overestimate the accessibility of Wards with a low population where kinetic (affluents) resides and could lead to improper identification of Wards highly accessible to charging. Findings from FCA and network accessibility methods highlights caution with the use of the point-in-polygon method which favors low-populated areas. Accessibility analysis using FCA, and network level (travel-time) technique revealed that the addition of new stations in the city does not significantly change accessibility levels. The study found that only a few Wards in the city of Windsor will be benefited from the addition of new stations as the median travel times from Wards to stations did not reduce significantly and larger variations exist around the median times suggesting that the new stations are randomly introduced rather than planned. Multicriteria analysis however aided in ranking of Wards based on available charging infrastructure, EV owned, travel time to stations and population density per Ward. The paper recommends data-driven machine learning approach in measuring accessibility and locating charging infrastructure in the city.
... As the electric vehicle sector has become a research hotspot, many studies in the EV sector have been conducted to tackle various issues related to transportation electrification. These studies span a range of subjects, including strategic charging infrastructure placement (Dong et al., 2014;Micari et al., 2017), E-mobility recommendation (Lee and Wood, 2020), transportation equity (Hardinghaus et al., 2020), emergency response Feng et al., 2020), and many others (Namdeo et al., 2014;Chen et al., 2013). Due to the interconnected nature of the EV sector with other systems, many of these studies require interdisciplinary strategies and the integration of multiple, cross-domain data sources. ...
Over the past decade, the electric vehicle industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV-centric knowledge graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV-related knowledge, including EV adoption, electric vehicle supply equipment, and electricity transmission network, to support decision-making related to EV technology development, infrastructure planning, and policy-making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV-relevant ontology modules from existing well-known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV-related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV-related knowledge into a single, easily accessible resource, the EVKG supports decision-makers in making informed choices about EV technology development, infrastructure planning, and policy-making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape.
... Apart from grid-impact observations concurring with other studies, they observe unintended consequences such as daytime parking policies encouraging drivers to start charging earlier to secure parking spots and therefore interfere with grid management. Hardinghaus, Locher, and Anderson (2020) also observed unintended consequences such as an increase in parking spots blocking when allowed by pricing structures. Bi et al. (2017) noted that policies favoring uniform CS distribution could lead to underutilization of over 50% of residential CS due to low demand in certain areas, corroborating observations by Hardinghaus et al. (2020). ...
... Hardinghaus, Locher, and Anderson (2020) also observed unintended consequences such as an increase in parking spots blocking when allowed by pricing structures. Bi et al. (2017) noted that policies favoring uniform CS distribution could lead to underutilization of over 50% of residential CS due to low demand in certain areas, corroborating observations by Hardinghaus et al. (2020). In summary, while many studies (Jahangir et al., 2019;Su et al., 2019;Wolbertus et al., 2018b;Zhao et al., 2018) noted that accurate modeling and a better understanding of charging behavior/profiles could help with grid load management significantly, some studies (Bi et al., 2017;Hardinghaus et al., 2020;Wolbertus et al., 2018b) highlighted the need to carefully evaluate unintended consequences of pro-EV policies. ...
... Bi et al. (2017) noted that policies favoring uniform CS distribution could lead to underutilization of over 50% of residential CS due to low demand in certain areas, corroborating observations by Hardinghaus et al. (2020). In summary, while many studies (Jahangir et al., 2019;Su et al., 2019;Wolbertus et al., 2018b;Zhao et al., 2018) noted that accurate modeling and a better understanding of charging behavior/profiles could help with grid load management significantly, some studies (Bi et al., 2017;Hardinghaus et al., 2020;Wolbertus et al., 2018b) highlighted the need to carefully evaluate unintended consequences of pro-EV policies. ...
In the wake of the COVID-19 pandemic, scholars mobilized their efforts to address its far-reaching societal problems. With mobility restrictions being front and center of the pandemic, a new cohort of transportation science was developed within a short period of time. Here, we examine more than 400 studies related to COVID-19 published across transportation journals during 2020 and 2021. The aim is (i) to scope this newly developed segment of transportation research, (ii) outline the diversity of pandemic-related issues across various divisions of the transportation field and (iii) provide a roadmap for the future of this line of research. Common themes are identified and existing congruence and discrepancies across findings are discussed. Results show that although conventional methods of transportation research were adopted in virtually all COVID-19 studies, no pre-pandemic study was particularly instrumental in the development of this segment of transportation literature. The COVID-19 segment appears to have developed its own independent knowledge foundation, in that, it does not systemically and frequently look back at any particular pre-pandemic reference. Potential impacts of this newly developed segment on the metrics of transportation journals are quantified and discussed.
... The additional energy demand is acquired on a spatial distribution for the Netherlands. In [13], real-world charging data from the city of Berlin is used to show the spatial utilization within the city. It is found that despite an uneven distribution of charging stations, the utilization is relatively equal. ...
... Surprisingly, very few vehicles are registered in the three states (Hamburg, Berlin, and Bremen) with the highest annual average utilization. One possible explanation could be free-flow car-sharing services operating in these cities, which use EVs registered in other states, as reported in [13] for the city of Berlin. Figure 6 shows the average annual annuities of the charging parks per district areas. ...
The current increase in the number of electric vehicles in Germany requires an adequately developed charging infrastructure. Large numbers of public and semi-public charging stations are necessary to ensure sufficient coverage of charging options. In order to make the installation worthwhile for the mostly private operators as well as public ones, a sufficient utilization is decisive. This paper gives an overview of the differences in the utilization across the public charging infrastructure in Germany. To this end, a dataset on the utilization of 21164 public and semi-public charging stations in Germany is evaluated. The installation and operating costs of various charging stations are modeled and economically evaluated in combination with the utilization data. It is shown that in 2019–2020, the average utilization in Germany was rather low, albeit with striking regional differences. We consider future scenarios allowing the regional development forecasting of economic viability. It is demonstrated that a growth in electric mobility of 20%-30% per year leads to a large number of economically feasible charging parks in urban agglomeration areas.
... Apart from grid-impact observations concurring with other studies, they observe unintended consequences such as daytime parking policies encouraging drivers to start charging earlier to secure parking spots and therefore interfere with grid management. Hardinghaus, Locher, and Anderson (2020) also observed unintended consequences such as an increase in parking spots blocking when allowed by pricing structures. Bi et al. (2017) noted that policies favoring uniform CS distribution could lead to underutilization of over 50% of residential CS due to low demand in certain areas, corroborating observations by Hardinghaus et al. (2020). ...
... Hardinghaus, Locher, and Anderson (2020) also observed unintended consequences such as an increase in parking spots blocking when allowed by pricing structures. Bi et al. (2017) noted that policies favoring uniform CS distribution could lead to underutilization of over 50% of residential CS due to low demand in certain areas, corroborating observations by Hardinghaus et al. (2020). In summary, while many studies (Jahangir et al., 2019;Su et al., 2019;Wolbertus et al., 2018b;Zhao et al., 2018) noted that accurate modeling and a better understanding of charging behavior/profiles could help with grid load management significantly, some studies (Bi et al., 2017;Hardinghaus et al., 2020;Wolbertus et al., 2018b) highlighted the need to carefully evaluate unintended consequences of pro-EV policies. ...
... Bi et al. (2017) noted that policies favoring uniform CS distribution could lead to underutilization of over 50% of residential CS due to low demand in certain areas, corroborating observations by Hardinghaus et al. (2020). In summary, while many studies (Jahangir et al., 2019;Su et al., 2019;Wolbertus et al., 2018b;Zhao et al., 2018) noted that accurate modeling and a better understanding of charging behavior/profiles could help with grid load management significantly, some studies (Bi et al., 2017;Hardinghaus et al., 2020;Wolbertus et al., 2018b) highlighted the need to carefully evaluate unintended consequences of pro-EV policies. ...
Increasing electric vehicle (EV) sales have shifted the focus of researchers from EV adoption to new operational challenges such as charging infrastructure deployment and management. These challenges require an accurate characterization of EV user charging behavior, especially with evolving battery technology. This study critically reviews approaches and data sources used to elicit EV charging behavior and patterns from a demand-side perspective and investigates how supply-side studies on charging infrastructure deployment and management incorporate charging behavior. We observe a noticeable disconnect between both strands of the literature, as supply-side studies still rely on simplistic assumptions about charging behavior and focus on a handful of aspects in isolation. More specifically, several studies either consider personal EVs or ride-hailing services with only public fast-charging infrastructure while ignoring available home/work charging infrastructure. We recommend shifting from this silo approach to a system-level dynamic planning framework where future charging demand is forecasted by combining charging behavior models with the models to forecast travel demand and EV adoption, followed by an integration of demand information into supply-side optimization. The framework can thus capture complex supply–demand interactions and inform the charging infrastructure planning policies, laying out a roadmap for emerging and mature EV markets.
... Based on this analysis, station utilization rate (i.e. the share of time that a charging station is operating at nominal power, see Eq. 4 in methods) emerges as a key determinant of LCOC, as it interacts with all project cost parameters and can thus lessen or exacerbate costs of equipment and installationespecially with increased capital costs at higher power levels. Apart from theoretical model sensitivities, real-world charging infrastructure features great variations concerning its utilization rate, in particular at commercial charging stations 20,[39][40][41][42][43][44][45][46][47] . With more expensive equipment and no fixed user base, there is a higher potential risk of underutilization negatively affecting the LCOC. ...
... For commercial charging infrastructure we estimate average yearly charging amounts of relatively well-utilized stations. We use real-world measurements of charging behavior at privately accessible workplace or fleet charging infrastructure [39][40][41] , publicly accessible stations 42-46 as well as specifically for DC fast charging points 20,[42][43][44]47 . For consistency, the amount of charging energy at commercial charging sites is not differentiated between countries. ...
... In reality it seldom runs at fully capacity and thus the share of time the station is occupied is higher. Typical utilization rates of utilized infrastructure today are roughly 10-20% for medium AC charging, roughly 5-10% for high AC charging and roughly 1-5% for DC fast charging20,[39][40][41][42][43][44][45][46][47]83 . The yearly charging energies assumed in the model base case correspond to utilization rates of 10%, 5% and 4%, respectively. ...
With rapidly decreasing purchase prices of electric vehicles, charging costs are becoming ever more important for the diffusion of electric vehicles as required to decarbonize transport. However, the costs of charging electric vehicles in Europe are largely unknown. Here we develop a systematic classification of charging options, gather extensive market data on equipment cost, and employ a levelized cost approach to model charging costs in 30 European countries (European Union 27, Great Britain, Norway, Switzerland) and for 13 different charging options for private passenger transport. The findings demonstrate a large variance of charging costs across countries and charging options, suggesting different policy options to reduce charging costs. A specific analysis on the impacts and relevance of publicly accessible charging station utilization is performed. The results reveal charging costs at these stations to be competitive with fuel costs at typical utilization rates exhibited already today.
... At a more local scale, Hardinghaus et al. presented the approach of implementing public charging infrastructure in Berlin, Germany [9,10]. Subsequently, the authors analyzed real-world charging derived from this infrastructure to provide recommendations for future locations of charging stations [11]. The authors also examined the role of carsharing vehicles on demand for public infrastructure. ...
... The parking situation is considered very problematic. Accordingly, guaranteed parking and charging facilities are very attractive to the residents, which is in accordance with previous research [11]. Hence, in inner-city neighborhoods where many vehicles are parked in public spaces, concepts such as neighborhood charging can be of great importance for the acceptance of EVs. ...
... Electronics 2022, 11, 2476 ...
Electric vehicles offer a means to reduce greenhouse gas emissions in passenger transport. The availability of reliable charging infrastructure is crucial for the successful uptake of electric vehicles in dense urban areas. In a pilot project in the city of Hamburg, Germany, public charging infrastructure was equipped with a reservation option providing exclusive access for local residents and businesses. The present paper combines quantitative and qualitative methods to investigate the effects of the newly introduced neighborhood charging concept. We use a methodology combining a quantitative questionnaire survey and qualitative focus group discussions as well as analyses of charging infrastructure utilization data. Results show that inner-city charging and parking options are of key importance for (potential) users of electric vehicles. Hence, the neighborhood concept is rated very positively. Providing guaranteed charging and parking facilities is therefore likely to increase the stock of EVs. On the other hand, this could to a large extent lead to additional cars with consequential disadvantages. The study shows that openly accessible infrastructure is presently utilized much more intensely than the exclusive option. Consequentially, the concept evaluated should be part of an integrated approach managing parking and supporting efficient concepts like car sharing.
... However, even though such a revolutionary direction is accepted by the automakers, the overall realization of the stated goal can only be possible in practice on the condition that an appropriate infrastructure is created, specifically a CS network, without which EV production is either irrational or even unjustified. At the same time, studies of СS infrastructure development indicate significant problems in this area, such as the СS network being insufficient or scarce even in developed countries, CS chaotic territorial distribution, as well as the need for architectural changes being introduced by the building owners, utilities and designers (Efthymiou et al. 2017;Goswami et al. 2020;Hardinghaus et al. 2020;Metais et al. 2022;Petratos et al. 2021;Shanti et al. 2020). The above-mentioned issues evoke drivers' concern related to stable access to facilities to charge the EV's battery during its active operation. ...
... The authors analyse the existing methods of building CS networks in a certain area, which differ in the degree of response to certain factors of influence and emphasize the necessity to consider the time factor. Using CS coverage in Berlin as an example, Hardinghaus et al. (2020) demonstrate approaches to the optimal creation and exploitation of charging infrastructure both in the city centre and in the outskirts. Infrastructure efficiency, the cost of services and user demand were chosen as the criteria for the survey. ...
This paper proves that the trend of development of modern transport in the world is to maximize the level of providing the personal use of electric vehicles. This mechanism would also partially solve the environmental problems of mankind. To implement this idea, some global automakers have announced the decision of the full transition of production to electric vehicles. At the same time, for effective functioning of the electric-vehicle market, adequate infrastructure needs to be created. There is a positive trend in the annual growth of the charging-station network in developed countries, that characterizes the charging-station market as dynamic and promising, but mostly chaotic and imbalanced at the regional level. The main hypothesis of the research is about the independence between the level of electric-vehicle market development and networks of charging stations. The object of the study is the Washington (USA) electric-vehicle market, as it is the market segment with the highest development characteristics. To test the hypothesis, the authors provided a multifactor analysis of the local electric-vehicle market and the existing charging infrastructure. A comprehensive analysis of the electric-vehicle market and the charging-station network in Washington (USA) was performed, and the market characteristics were defined accordingly: the degree of electric-vehicle spread in the regional localities; the level of charging-station-network coverage and concentration; the ratio of electric vehicles to charging stations. Authors identified the tendency of the state location to innovations connected with electric vehicles. Clusterization and recommendations according to the level of development of the electric-vehicle market aimed to balance and grow the total electric-vehicle market and connected infrastructure.
... Apart from grid-impact observations concurring with other studies, they observe unintended consequences such as daytime parking policies encouraging drivers to start charging earlier to secure parking spots and therefore interfere with grid management. Hardinghaus et al. [80] also observed unintended consequences such as an increase in parking spots blocking when allowed by pricing structures. Bi et al. [78] noted that policies favoring uniform CS distribution can lead to underutilization of over 50% residential CS due to low demand in certain areas, corroborating observations by Hardinghaus et al. [80]. ...
... Hardinghaus et al. [80] also observed unintended consequences such as an increase in parking spots blocking when allowed by pricing structures. Bi et al. [78] noted that policies favoring uniform CS distribution can lead to underutilization of over 50% residential CS due to low demand in certain areas, corroborating observations by Hardinghaus et al. [80]. In summary, while many studies [75,76,79,81]) noted that accurate modeling and a better understanding of charging behavior/profiles could help with grid load management significantly, some studies [78,79,80] highlighted the need to carefully evaluate unintended consequences of pro-EV policies. ...
... Bi et al. [78] noted that policies favoring uniform CS distribution can lead to underutilization of over 50% residential CS due to low demand in certain areas, corroborating observations by Hardinghaus et al. [80]. In summary, while many studies [75,76,79,81]) noted that accurate modeling and a better understanding of charging behavior/profiles could help with grid load management significantly, some studies [78,79,80] highlighted the need to carefully evaluate unintended consequences of pro-EV policies. ...
Electric vehicle (EV) market growth is critical to achieving sustainable development goals, governing aspirations to achieve full-scale electrification targets across the globe. Increasing EV sales have shifted the focus of researchers from EV adoption to new operational challenges such as the optimal deployment of charging stations and grid load management, which in turn also affects EV adoption. These challenges require an accurate characterization of EV user charging behavior, especially with evolving battery technology and driving ranges. This study critically reviews approaches and data sources used to elicit EV charging behavior and patterns from a demand-side perspective and investigates how supply-side studies on charging infrastructure deployment and management incorporate charging behavior. We observe a noticeable disconnect between both strands of the literature, as supply-side studies still rely on simplistic assumptions about charging behavior and focus on a handful of aspects in isolation. More specifically, several studies either consider personal EVs or ride-hailing services with only public fast-charging infrastructure while ignoring available home/work charging infrastructure. We recommend shifting from this silo approach to a system-level dynamic planning framework where future charging demand is forecasted by combining charging behavior models with the models to forecast travel demand and EV adoption, followed by an integration of demand information into supply-side optimization. The framework can thus capture complex supply-demand interactions and inform the charging infrastructure planning policies, laying out a roadmap for emerging and mature EV markets.
