Advancing Federal Infrastructure Through Innovation: Cost of Charging Station Installation
This presentation covers the variation in installation costs and EVI-LOCATE.
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This presentation covers the variation in installation costs and EVI-LOCATE.
EVI-LOCATE (Electric Vehicle Infrastructure-Locally Optimized Cost Assessment Tool and Estimator) is a site assessment tool to estimate costs to install EV charging stations. EVI-LOCATE enables users to generate site-specific, user-specific, and location-specific EV charging station installation designs and cost estimates. The tool integrates National Electrical Code, deep-learning pixel classification algorithm, and component-level costs to estimate the costs.
One of the longest stages in the deployment of electric vehicle supply equipment (EVSE) is the initial planning of the infrastructure itself. Engineers and fleet experts from the National Renewable Energy Laboratory (NREL) have supported dozens of charging infrastructure site plans over the past couple decades, including the generation of site schematics, determinations of electric capacity, and estimates for likely costs. As the market for electric vehicles (EVs) has matured, this approach should no longer require a time and personnel intensive process. In order to shorten the time taken to develop site plans and cost estimates, NREL developed a tool that fleet managers, facility managers, electricians, EVSE installers, and members of the public can use to develop initial schematics and ballpark pricing for charging station installations. The Electric Vehicle Infrastructure - Locally Optimized Charger Assessment Tool and Estimator (EVI-LOCATE) provides a structured and consistent way for users to enter information about their planned EVSE project in a relatively simple web-based format. EVI-LOCATE then calculates electrical equipment capacity, wiring runs, and project costs. It produces a site diagram optimized around surface characteristics with differential trenching costs for softscape such as grass compared to hardscape such as asphalt that can be adjusted by users in the tool. It also stores the resulting site plans and costs in a dashboard for access at a later date, including plan revisions if necessary. This document guides users through the EVI-LOCATE screens and associated questions. It contains tip text boxes throughout on how best to interface with the tool and find additional information or context. The appendices contain the assumptions and calculations underpinning the tool. Much of the information for EVI-LOCATE was gathered through industry engagements with EVSE installers, invoices from completed EVSE installations, Gordian's RS Means construction data, and the General Services Administration blanket purchase agreement for EVSE. For a visual tutorial of the tool, users can watch EVI-LOCATE Step-by-Step Video. The tool itself is available at https://evi-locate.nrel.gov.
This report presents an analysis of the requirements for charging station installation and electric vehicle operation at the US Army Corps of Engineers - Chena Site, located in a cold weather climate in the Fairbanks North Star Borough, AK. The report includes findings from a site visit, and a detailed electric vehicle (EV) charging site plan with cost estimates. Cost for three 50-ampere pedestal chargers located on the edge of the existing parking lot is estimated at $\$$53,100, and the cost of three 80-ampere chargers is estimated at $\$$89,400. The authors did not assess the cost of a heated garage. The USACE Chena site reaches extreme cold temperatures of -40 Degrees Celsius (-40 Degrees Fahrenheit) and below in a typical winter, often for days on end. Considerations of operating EVs as well as electrical vehicle supply equipment (EVSE) at this site can be applicable to other cold or extremely cold locations. Interviews with EV users in cold climates and a literature review indicated that EVs operate well but have significantly decreased range compared to 21 Degrees Celsius (70 Degrees Fahrenheit) operations. Some strategies such as prewarming the vehicle while it is plugged in and using heated seats and steering wheel instead of cabin heat, can improve cold weather performance. Storing the EV in a garage would mean the battery and cabin are automatically preheated, the battery would not age as rapidly as when the vehicle is stored outside, and problems with charging the vehicle are less likely. Lowest temperate-rated Electric Vehicle Supply Equipment (EVSE), as electric vehicle chargers are known as, are rated to -40 Degrees Celsius (-40 Degrees Fahrenheit), and sometimes malfunction. No EVSE is rated to the temperatures that USACE Chena site experienced for more than a week in winter 2023-4, of -50 Degrees Celsius (-45 Degrees Fahrenheit) and which are typical for the area. If reliability is a must, entities may want to consider a heated garage to minimize potential problems with charging equipment. There is a companion technical report to this titled "Electric Vehicle and Charging Infrastructure Assessment in Cold-Weather Climates: A Case Study of Fairbanks, Alaska" that examines the data on EV and EVSE cold-weather functionality in more detail. (Esparza, Truffer Moudra, and Hodge 2024).
This presentation is an overview of EVI-LOCATE, an NREL tool developed to estimate the costs of installing EV charging stations.
The town of Stratford plans to develop and own a 6 kilowatt (kW) solar array on the roof of a municipal-owned public pavilion on the town green. A SolarEdge level 2 EV charging station will be installed at the pavilion to be used by the public. The charging station will have one charging port. The town will install 3 air source heat pumps in the library located adjacent to the pavilion to reduce the use of propane to heat the library. The town will install 4 air source heat pumps in the Town Hall to reduce the use of #2 heating oil to heat the facility. The 6 kW solar array will provide electricity to the library to offset electricity usage, and when an EV plugs into the charging station the solar will provide electricity to the charging station. There is no ground disturbance or ground trenching associated with this project. The 6 kW solar array on the roof of the pavilion will include 20 (390) watt all black panels. A SolarEdge level 2 EV charger will be installed at the pavilion with the 240 V inverter. The solar array will provide electricity to the library and when a vehicle plugs into the EV charger the solar array will switch to power the charger. Any excess electricity that is generated by the array and not used by the library or the EV charger will be fed onto the grid and the town will receive a net metering credit applied towards future consumption. There is no ground disturbance associated with the EV charger or solar array, and the town will install signage to direct the public to the charger. The library will install 2 (18,000) BTU ductless air source heat pumps and 1 (24,000) BTU unit. This will significantly reduce the amount of propane required to heat the library. The Town Hall will install 2 (36,000) BTU ductless air source heat pumps and 2 (9,000) BTU units to reduce the amount of #2 heating oil required to heat the building.
EV charging station site design and cost estimation tool, developed for users to input information on where they would like to install EV charging stations in relation to existing electrical and surface infrastructure. The tool calculates electrical upgrade requirements, estimates costs, and provides a project design.
This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.
This presentation is an overview of the VTO Annual Merit Review for EVSE soft costs project.
Once a site location is selected for the installation of an electric vehicle (EV) charging station, the next step is to plan and design the specific station. Local and state governments can require and/ or incentivize accessible and safe design elements, so the station is constructed in an equitable manner.
Public streetlight charging, whether on streets in central business districts or residential areas, provides easy charging access for apartment residents and homeowners alike. While most electric vehicle (EV) drivers charge at home, they do so in garages or on driveways they own. For renters and residents of multifamily housing (MFH), however, this may not be an option. EVs have a lower cost of ownership compared to conventional vehicles, and a used EV may be an affordable option for a lower-income household. But without easy access to charging, even a low-cost used EV may not be an option for a prospective buyer. An affordable curbside charging network has the potential to expand EV adoption into neighborhoods that have to date seen minimal interest and uptake of the technology and associated charging infrastructure. Streetlight charging networks can provide an economical, scalable, and effective approach to providing equitable and convenient charging. Metropolitan Energy Center (MEC) is dedicated to the mission of creating resource efficiency, environmental health, and economic vitality in the Kansas City region and beyond. Since 1983, MEC has provided resources, outreach, and training to make alternative fuels and energy efficiency commonplace. MEC led a streetlight charging pilot project that installed limited EV charging infrastructure on the streetlight system in Kansas City, Missouri, to demonstrate and test the benefits of curbside charging for EVs at existing on-street parking locations. The project aimed to cost-effectively expand the charging network in Kansas City to support residential charging and provide infrastructure in one or more charging deserts throughout the city. This pilot evaluates the impact and overall success of streetlight charging based on community feedback, utilization of charging infrastructure, technical feasibility, and cost. The project has pursued a data- and community-driven site selection process designed to identify sites with high demand and high opportunity for EV charging. This project was funded by the U.S. Department of Energy (DOE) and awarded to MEC through a competitive proposal process. The novelty and complexity of this project required an organization that could facilitate collaboration across levels of government, community members, and industry partners. For the past 25 years, through Kansas City Regional Clean Cities, MEC has worked with numerous public and private fleets on a variety of projects to improve the environmental performance and efficiency of the regional vehicle fleet. To advance affordable, efficient, and clean transportation efforts, DOE Clean Cities and Communities coalitions create local networks of public and private sector stakeholders and engage communities. Rooted within their local communities, the coalitions serve as experts and ambassadors, bringing to bear the collective knowledge, experience, and practical know-how of the entire network from within DOE, its national laboratories, and diverse stakeholders in the field. MEC and its project partners made in-kind contributions to leverage federal dollars for the benefit of the Kansas City community. Findings from this project will help determine the best applications for streetlight charging technologies to maximize funding impact and serve community needs. The team evaluated locations based on expected charging demand, technical feasibility, safety considerations, and enhanced charging network siting needs. Throughout the project, the team gathered feedback and evaluated ways to make public charging for EVs available to all community members. The insights will help Kansas City and other communities streamline future efforts to support EV drivers through public charging in the city right-of-way. Furthermore, this project will inform citywide guidance for future installations. MEC is committed to a transparent and publicly accessible approach that encourages the collaborative evaluation of streetlight charging. The project has engaged the community to proactively identify and evaluate the benefits and impacts of streetlight charging. It was a priority for the project to ensure the benefits of this pilot are distributed equitably to all members of the Kansas City community and that new charging opportunities and associated resources are available in diverse neighborhoods across the city. The charging infrastructure supports an affordable curbside charging network that will enable more drivers to choose EVs and provide easy charging access for all community members interested in driving an EV. The community feedback received through this project informed future resources and opportunities to make EVs more accessible to all members of the Kansas City community. MEC worked with several community partners on this project, including Missouri University of Science and Technology (MST), Pennsylvania State University (Penn State), the National Renewable Energy Laboratory (NREL); the city of Kansas City, Missouri; Evergy; Black and McDonald (B&M); LilyPad EV; EVNoire; and Westside Housing Organization (WHO). Project partners contributed to the cost match required for DOE grants through capital expenditures, personnel, and other in-kind contributions. Detailed descriptions of project team organizations can be found in Appendix A. Project Partners. Analysts at NREL and MST/PennState developed site maps based on demand and equity considerations. MEC conducted outreach to community members to garner input on project design and site selection, and received approval from the Missouri Public Service Commission (PSC) for Evergy’s EV charging station ownership. MEC worked with all partners to gather additional siting criteria and developed a site selection evaluation checklist, and partners conducted site visits to proposed installation sites. Next, B&M, Evergy, and the city executed all site agreements, conducted site-specific engineering design, acquired associated permits, and issued notices to proceed site by site or in small batches. Finally, from January to April 2023, the project team installed 23 EV charging stations built on Kansas City’s streetlight system in six council districts. Evergy will own, operate, and monitor the stations for 10 years, sharing charging data with MEC for at least 1 year.
This document serves as a comprehensive resource for Federal agencies in developing their own program resources that promote the efficient and effective use of electric vehicles (EVs) for home-to-work travel while ensuring compliance with Federal regulations and sustainability objectives. One mission of the U.S. Department of Energy's Federal Energy Management Program (FEMP) Fleet program is to help federal fleet managers meet or exceed statutory requirements related to energy and environmental performance while improving overall fleet efficiency, reducing costs, and meeting mission requirements. To further this mission, FEMP provides resources to support Federal agencies with increasing alternative fuel vehicle (AFV) acquisitions and reducing petroleum use. EVs are AFVs and help agencies meet federal fleet requirements. Federal fleets include government-owned EVs used for home-to-work travel. The purpose of this document is to serve as a guide for Federal agencies in developing their own internal program documents to manage government-owned EVs used for home-to-work travel. Federal agencies should consult their counsel and consider their own policies and authorities in the implementation of any policies or best practices regarding government-owned EVs used for home-to-work travel. The guide provides key considerations for agencies, including launching a pilot program to fine-tune best practices, conducting a cost-benefit analysis to compare home versus public charging, and exploring cost-effective solutions, such as installing standard outlets instead of dedicated charging stations. The guide underscores the importance of legal and financial considerations, such as verifying agency authority to install home charging infrastructure at an employee's home, ensuring the availability and appropriateness of using agency funds for home charging infrastructure, and understanding the tax implications of reimbursements.
Teams of automated battery-powered electric vehicles have the potential to execute complex mission tasks in off-road environments for agriculture, military, and other applications. Limited onboard energy reserves hinder their adoption in large-scale resource-constrained environments, where recharging is a necessity. It may be infeasible to install a network of static charging stations in off-road environments. For this reason, dedicated mobile host vehicles with charging capabilities are proposed as a means to increase range and capabilities of the multivehicle team. Here, in this study, we consider an ad hoc planning framework, where results from a high-confidence trajectory planner are leveraged to plan charging rendezvous between a host and other worker vehicles in a receding horizon fashion to provide high confidence that energy reserves will not be prematurely exhausted. The core problem is posed so as to minimize the impact of recharging on the mission in terms of task delays, overall energy utilization, and costs of fast charging. Through extensive Monte Carlo simulations of an off-road mission, we show a decrease in task delays without substantial increases in energy needs by updating the charging rendezvous plan during the mission. However, if updates are made too often, model mismatch may cause unnecessary cycling and mission failure.
To achieve a more affordable and reliable transportation system, we need to smartly upgrade our power systems and install a large number of charging stations, but conventional planning methods are not up to the task. By applying advanced simulation and optimization tools, we can design a smarter, more cost-effective electric transportation network. The initial focus was on public transit systems, demonstrating how this approach can deliver broader economic, reliability, and air quality benefits nationwide.
On this eighth day of the STS-88 mission, the flight crew, Commander Robert D. Cabana, Pilot Frederick W. Sturckow, and Mission Specialists Nancy J. Currie, James H. Newman, Jerry L. Ross, and Sergei Krikalev enter the International Space Station where Krikalev and Currie install a new battery charging unit. Sturckow and Currie remove launch restraint bolts from some of the panels inside Zarya. Cabana, Ross, and Newman check the communications system's videoconferencing capability. Then Ross, Newman, and Krikalev transfer equipment and supplies from Endeavour for future inhabitants of the Space Station. The crew then participates in an interview with KNX Radio in Los Angeles and KARE-TV in Minneapolis, Minnesota.
In order to facilitate the use of electric vehicles at NASA Langley Research Center (LaRC), charging stations should be made available to LaRC employees. The implementation of charging stations would decrease the need for gasoline thus decreasing CO2 emissions improving local air quality and providing a cost savings for LaRC employees. A charging station pilot program is described that would install stations as the need increased and also presents a business model that pays for the electricity used and installation at no cost to the government.
Here, this paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.