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"Soft costs" such as permitting costs, siting costs, utility interconnection application costs, opportunity costs, the cost of delays, and so on create significant overhead for EV charging station network operators. Those costs are ultimately passed on to EV drivers, making it more difficult for EVs to reach cost parity with ICE equivalents. But the soft costs of deploying charging infrastructure are poorly understood, unpredictable, very hard to quantify, and almost entirely undocumented in the literature. Our original research, based on 24 interviews with a variety of stakeholders as well as existing literature and publicly available information on utility procurements, identifies some major categories of soft costs that need to be investigated further. This research should inform future legislation and harmonization of regulations and processes in order to reduce the total system cost of charging infrastructure.

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.

Planning the charging infrastructure for electric vehicles (EVs) is a new challenging task. This book treats all involved aspects: charging technologies and norms, interactions with the electricity system, electrical installation, demand for charging infrastructure, economics of public infrastructure provision, policies in Germany and the EU, external effects, stakeholder cooperation, spatial planning on the regional and street level, operation and maintenance, and long term spatial ...

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.

From the end user perspective, the main barriers for widespread electric vehicle (EV) adoption are high purchase cost and range anxiety, both regarding battery capacity and availability of accessible EV charging infrastructure. Governments and public bodies in general are taking steps towards overcoming these barriers by, among others, setting up regulatory requirements regarding standardisation, customer information and recommending objectives of publicly accessible charging infrastructure. However, the economic performance of publicly accessible charging infrastructure is unknown and any deployment plan should be backed up by a rigorous cost-benefit analysis, to check the efficiency of the plan in economic terms.This paper presents the results of the economic assessment performed within the FP7 EU-funded Green eMotion project, where relevant conclusions for helping industry strategic approach and decision makers have been taken.

From the end user perspective, the main barriers for widespread electric vehicle (EV) adoption are high purchase cost and range anxiety, both regarding battery capacity and availability of accessible EV charging infrastructure. Governments and public bodies in general are taking steps towards overcoming these barriers by, among others, setting up regulatory requirements regarding standardisation, customer information and recommending objectives of publicly accessible charging infrastructure. However, the economic performance of publicly accessible charging infrastructure is unknown and any deployment plan should be backed up by a rigorous cost-benefit analysis, to check the efficiency of the plan in economic terms. This paper presents the results of the economic assessment performed within the FP7 EU-funded Green eMotion project, where relevant conclusions for helping industry strategic approach and decision makers have been taken. ; European Commission's FP7

Facing climate change, The European Union has set ambitious greenhouse gas (GHG) reduction targets. Within Europe, heavy-duty vehicles (HDV) account for a quarter of greenhouse gas emissions in the transport sector and therefore plays a central role in achieving the climate targets. A potential solution to reduce GHG emissions is the use of battery electric vehicles (BEV). However, the limited range of BEV requires a European public fast-charging network to ensure widespread deployment of BEV. Here, European road freight transport flows are modelled based on the publicly available European Transport policy Information System (ETISplus) dataset. The resulting truck flows serve as input for a charging infrastructure network model. Potential charging stations are located using a coverage-oriented approach and sized according to a queuing model such that an average waiting time of five minutes is guaranteed at each location. Our results show that for a share of 15% BEV in HDV stock and a dense network with charging locations every 50 km, a total of 4,067 charging points at 1,640 locations are required by 2030. In contrast, with a share of 5% BEV and charging locations every 100 km, 1,715 charging points are needed at 812 locations. Our findings provide insights for the design of a public fast-charging network in Europe and thus supports the planning of future infrastructure projects.

Facing climate change, The European Union has set ambitious greenhouse gas (GHG) reduction targets. Within Europe, heavy-duty vehicles (HDV) account for a quarter of greenhouse gas emissions in the transport sector and therefore plays a central role in achieving the climate targets. A potential solution to reduce GHG emissions is the use of battery electric vehicles (BEV). However, the limited range of BEV requires a European public fast-charging network to ensure widespread deployment of BEV. Here, European road freight transport flows are modelled based on the publicly available European Transport policy Information System (ETISplus) dataset. The resulting truck flows serve as input for a charging infrastructure network model. Potential charging stations are located using a coverage-oriented approach and sized according to a queuing model such that an average waiting time of five minutes is guaranteed at each location. Our results show that for a share of 15% BEV in HDV stock and a dense network with charging locations every 50 km, a total of 4,067 charging points at 1,640 locations are required by 2030. In contrast, with a share of 5% BEV and charging locations every 100 km, 1,715 charging points are needed at 812 locations. Our findings provide insights for the design of a public fastcharging network in Europe and thus supports the planning of future infrastructure projects.

© 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.

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.