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Plug-in electric vehicles (PEVs) represent a substantial opportunity for governments to reduce emissions of both air pollutants and greenhouse gases. The Government of India has set a goal of deploying 6-7 million hybrid and PEVs on Indian roads by the year 2020. The uptake of PEVs will depend on, among other factors like high cost, how effectively range anxiety is mitigated through the deployment of adequate electric vehicle charging stations (EVCS) throughout a region. The Indian Government therefore views EVCS deployment as a central part of their electric mobility mission. The plug-in electric vehicle infrastructure (PEVI) model - an agent-based simulation modeling platform - was used to explore the cost-effective siting of EVCS throughout the National Capital Territory (NCT) of Delhi, India. At 1% penetration in the passenger car fleet, or ∼10 000 battery electric vehicles (BEVs), charging services can be provided to drivers for an investment of $4.4 M (or $440/BEV) by siting 2764 chargers throughout the NCT of Delhi with an emphasis on the more densely populated and frequented regions of the city. The majority of chargers sited by this analysis were low power, Level 1 chargers, which have the added benefit of being simpler to deploy than higher power alternatives. The amount of public infrastructure needed depends on the access that drivers have to EVCS at home, with 83% more charging capacity required to provide the same level of service to a population of drivers without home chargers compared to a scenario with home chargers. Results also depend on the battery capacity of the BEVs adopted, with approximately 60% more charging capacity needed to achieve the same level of service when vehicles are assumed to have 57 km versus 96 km of range.

© 2016 IOP Publishing Ltd. Plug-in electric vehicles (PEVs) represent a substantial opportunity for governments to reduce emissions of both air pollutants and greenhouse gases. The Government of India has set a goal of deploying 6-7 million hybrid and PEVs on Indian roads by the year 2020. The uptake of PEVs will depend on, among other factors like high cost, how effectively range anxiety is mitigated through the deployment of adequate electric vehicle charging stations (EVCS) throughout a region. The Indian Government therefore views EVCS deployment as a central part of their electric mobility mission. The plug-in electric vehicle infrastructure (PEVI) model - an agent-based simulation modeling platform - was used to explore the cost-effective siting of EVCS throughout the National Capital Territory (NCT) of Delhi, India. At 1% penetration in the passenger car fleet, or ∼10 000 battery electric vehicles (BEVs), charging services can be provided to drivers for an investment of $4.4 M (or $440/BEV) by siting 2764 chargers throughout the NCT of Delhi with an emphasis on the more densely populated and frequented regions of the city. The majority of chargers sited by this analysis were low power, Level 1 chargers, which have the added benefit of being simpler to deploy than higher power alternatives. The amount of public infrastructure needed depends on the access that drivers have to EVCS at home, with 83% more charging capacity required to provide the same level of service to a population of drivers without home chargers compared to a scenario with home chargers. Results also depend on the battery capacity of the BEVs adopted, with approximately 60% more charging capacity needed to achieve the same level of service when vehicles are assumed to have 57 km versus 96 km of range.

Government agencies, utilities, automakers, and charging network companies are increasingly investing in charging infrastructure to encourage the adoption of plug-in electric vehicles (PEVs), which include both battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). Public infrastructure is particularly important for those without access to home charging and for vehicles with driving range limitations. However, it is difficult to quantify the optimal number and location of public chargers needed for a growing number of PEVs. Finding the answer will depend on a mix of behavioral and economic factors that drive charging demand. Much is at stake. Too little infrastructure could cause congestion at the chargers and inhibit the adoption and use of PEVs, while developing more infrastructure than is needed would create unnecessary costs. For example, Level 2 public chargers can cost up to 15 times more than Level 2 at-home chargers. Researchers at UC Davis analyzed the choice of charging infrastructure of more than 3,000 PEV commuters who had access to home, work, and public locations to understand the importance of various factors driving demand for charging infrastructure at the three locations. Key factors include the cost of charging, driver characteristics, accessibility of charging infrastructure, and vehicle characteristics.

Electric vehicle (EV) adoption is growing worldwide with increasing market pull from consumers and market push from manufacturers of vehicles and charging equipment, as well as others in the supply chain. Governments have begun developing policies to support EV uptake and local governments, in particular, are examining what role they should play. In Australia, a large country with low population density, EV uptake has been slower in comparison to other similar economies. This paper discusses the status of EV charging infrastructure deployment in Australia with regards to local governments, by considering the extent to which they are relied upon for the deployment of such technology and what motivates them to act. It also covers the work undertaken by the authors with one local government in developing an EV charging infrastructure business model that will help the local community adopt and benefit from EVs. An applied use of the business canvas methodology adapted to suit local government interests is presented to assess the risks and benefits that different business models offer. The paper offers insights into the strategic and pragmatic responsibilities local governments balance in seeking to expand the EV charging infrastructure in their jurisdiction.

Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam University of Applied Sciences, in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht, and the Metropolitan Region of Amsterdam Electric, developed both the back- and front-end of a charging infrastructure assessment platform that processes and represents real-life charging data. Charging infrastructure planning and design methods described in the literature use geographic information system data, traffic flow data of non-EV vehicles, or geographical distributions of, for example, refueling stations for combustion engine vehicles. Only limited methods apply real-life charging data. Rolling out public charging infrastructure is a balancing act between stimulating the transition to zero-emission transport by enabling (candidate) EV drivers to charge, and limiting costly investments in public charging infrastructure. Five key performance indicators for charging infrastructure utilization are derived from literature, workshops, and discussions with practitioners. The paper describes the Data Warehouse architecture designed for processing large amounts of charging data, and the web-based assessment platform by which practitioners get access to relevant knowledge and information about the current performance of existing charging infrastructure represented by the key performance indicators developed. The platform allows stakeholders in the decision-making process of charging point installation to make informed decisions on where and how to expand the already existing charging infrastructure. The results are generalizable beyond the case study regions in the Netherlands and can serve the roll-out of charging infrastructure, both public and semi-public, all over the world.

Depending on the method of electricity generation, mass-market penetration of electric vehicles has the potential to reduce emissions of greenhouse gases and air pollutants considerably, and to reduce dependency on fossil fuels. This paper presents a novel methodology for Local Government Authorities (LGAs) to identify suitable locations for the initial provision of residential on-street Plug-in Electric Vehicle (PEV) charging infrastructure in urban areas to help remove barriers to PEV uptake. The methodology is practical for use by LGAs with limited financial resources as it is based on simple Geographic Information System (GIS) analysis of routinely available census and parking data to identify the spatial overlaps between areas where residents are most likely to be PEV users and areas where there is a high reliance on residential on-street parking. The methodology has been implemented in practice to determine a charging infrastructure installation strategy for Southampton, UK, where 128 streets (out of 1,924 in total) were recommended as suitable locations. These streets were reviewed by a group of experts during a workshop and confirmed as suitable locations for the initial installation of residential on-street charge points in the city.

Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam University of Applied Sciences, in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht, and the Metropolitan Region of Amsterdam Electric, developed both the back- and front-end of a charging infrastructure assessment platform that processes and represents real-life charging data. Charging infrastructure planning and design methods described in the literature use geographic information system data, traffic flow data of non-EV vehicles, or geographical distributions of, for example, refueling stations for combustion engine vehicles. Only limited methods apply real-life charging data. Rolling out public charging infrastructure is a balancing act between stimulating the transition to zero-emission transport by enabling (candidate) EV drivers to charge, and limiting costly investments in public charging infrastructure. Five key performance indicators for charging infrastructure utilization are derived from literature, workshops, and discussions with practitioners. The paper describes the Data Warehouse architecture designed for processing large amounts of charging data, and the web-based assessment platform by which practitioners get access to relevant knowledge and information about the current performance of the existing charging infrastructure represented by the key performance indicators developed. The platform allows stakeholders in the decision-making process of charging point installation to make informed decisions on where and how to expand the already existing charging infrastructure. The results are generalizable beyond the case study regions in the Netherlands and can serve the roll out of charging infrastructure, both public and semi-public, all over the world.

Low profit margins have become a significant barrier to investment in and the operation of electric vehicle-charging infrastructure, leading to an urgent need for new business models. Notwithstanding, nonmandatory policies and unclear responsibilities create a social dilemma in which it is difficult to promote charging facilities in urban residential areas. This study examines the feasibility of overcoming this dilemma by examining possible incentive mechanisms involving government, charging infrastructure operators, real estate agencies, and electric vehicle users. Leveraging evolutionary game theory, this study designs a theoretical model based on strategic interactions among different agents in promoting charging facilities in urban residential areas. Our results indicate that (1) the optimal scenario in one in which all participants work closely together to popularize charging facilities, and this scenario has theoretical possibilities in the real world; (2) government subsidies are necessary but not sufficient for promoting charging facilities in urban residential areas; (3) electric vehicle user participation in promotion is critical; and (4) the operation model in this study is more economically efficient than prevalent industrial operation models, and the role of real estate agencies cannot be ignored.

The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users' residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen's infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure.

Risk management is critical to the success of electric vehicle charging infrastructure public&ndash ; private partnership (EVCI-PPP) projects, as risks are present throughout the whole life cycle of projects. However, in EVCI-PPP projects, risk factors are often interdependent and, consequently, the interrelationships among factors affect the risk management, which is ignored in the existing studies. To identify the risk factors of EVCI-PPP projects and analyze their internal influence relations, this paper develops a risk identification and analysis model of EVCI-PPP projects based on the 2-tuple linguistic representation model and the decision-making trial and evaluation laboratory (DEMATEL) model. First, a risk factor set is established including 22 criteria involved in 5 dimensions of political/legal risk, economic/market risk, social/environment risk, project/technical risk, and managing risk. Next, the 2-tuple model is introduced to integrate the decision makers&rsquo ; evaluation information in a linguistic environment, and the direct relation matrix is calculated. Then, the cause&ndash ; effect relations and a significant degree of risk factors are interpreted using the extended DEMATEL technique. The results show that economic/market risk is the most significant factor of EVCI-PPP projects, and 22 criteria are classified into 14 cause factors and 8 effect factors. Finally, suggestions are provided for decision-makers to ensure the success of EVCI-PPP projects.