Results for “public charging” — Page 6

DOE OSTI2024

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Fourth Quarter 2023

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's (DOE's) Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the fourth calendar quarter of 2023 (Q4 2023) by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared to the infrastructure requirement scenario outlined in the National Renewable Energy Laboratory (NREL) report, The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the sixteenth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and NREL publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: First Quarter 2024

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's (DOE's) Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the first calendar quarter of 2024 (Q1 2024) by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared to the infrastructure requirement scenario outlined in the National Renewable Energy Laboratory (NREL) report, "The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure" (https://www.nrel.gov/docs/fy23osti/85654.pdf). This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the 17th report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and NREL publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Second Quarter 2024

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's (DOE's) Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the second calendar quarter of 2024 (Q2 2024) by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared to the infrastructure requirement scenario outlined in the National Renewable Energy Laboratory (NREL) report, "The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure" (https://www.nrel.gov/docs/fy23osti/85654.pdf). This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the 18th report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and NREL publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Charging Hub Scenario Sheet Screenshot

The Excel-based CHECT tool estimates the LCOC ($/kWh) by charger type (Level 1, Level 2, and DC fast charging) for a given charging hub scenario. CHECT requires users to input certain charging hub scenario parameters, including the number of chargers, daily utilization, and charger replacement frequency by charger type. Additional inputs such as the charging schedule, local utility rates, capital/operational costs, and financial inputs can be customized by the user, or the tool can generate results using appropriate default values from literature for the charging hub scenario and service location(s). Using the above inputs, CHECT performs a robust techno-economic analysis to generate the LCOC by charger type, broken down by cost category (e.g., capital, operational, utility, taxes) for various combinations of charging hub types (multiunit dwelling or public) and ownership models (residential, utility, or private company). It also outputs the annual discounted cash flows and determines the most sensitive input variables. In addition, the tool allows users to easily compare the LCOC across various ownership models or across different states.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator (First Quarter 2023)

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the first calendar quarter of 2023 by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with a 2030 infrastructure requirement scenario. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the thirteenth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Second Quarter 2023

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the second calendar quarter of 2023 by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with a 2030 infrastructure requirement scenario. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the fourteenth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Third Quarter 2022

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's (DOE) Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the third calendar quarter of 2022 (Q3 2022) by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with two different 2030 infrastructure requirement scenarios. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the eleventh report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator (Fourth Quarter 2022)

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's (DOE) Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the fourth calendar quarter of 2022 (Q4 2022) by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with two different 2030 infrastructure requirement scenarios. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the twelfth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Revenue-Maximizing Shared Parking and Electric Vehicle Charging Management in Multi-Unit Dwellings

In urban areas, searching for parking and electric vehicle (EV) charging can result in cruising, congestion, and environmental externalities. Recognizing the business opportunity of offering private parking and charging infrastructure access within multi-unit dwellings (MUDs) during daytime, we model a shared parking and EV charging management system. We maximize the revenue of MUD charging hubs in mixed land use, catering to public demand. Our approach accounts for the objectives of the two stakeholders involved: a demand model is fitted on the choices of EV charging users, and the supply model optimizes the allocation of parking and charging requests in an MUD parking lot. A binary integer linear programming model for the allocation of parking and charging spaces with a rolling horizon is integrated with matching rules that handle both parking and charging requests. In our numerical experiments in a neighborhood of Chicago, Illinois, we estimate the performance of the MUD parking and charging system with metrics that include revenue, number of matchings, and utilization rates. At any given time, MUDs with lower prices attract more charging requests, particularly those of longer duration, resulting in higher revenue and greater charging utilization. Dynamic pricing facilitates a more equitable distribution of requests; as MUD parking lots reach capacity and their fees increase, other MUDs become more competitive, attracting additional requests. Comparing our method against first-come-first-served and optimal-solution benchmarks, we demonstrate our model’s effectiveness in dynamically managing mixed parking and charging demand in MUD charging hubs.

electric vehicle, multi-unit dwelling, charging in↗

DOE OSTI2024

Real-Time Anomaly Detection for Charge-Based Triggering in LArTPCs

Modern particle detectors, including liquid argon time projection chambers (LArTPCs), collect a vast amount of data, making it impractical to save everything for offline analysis. As a result, these experiments need to employ different down-selection techniques during data acquisition, referred to as triggering. In this talk, I will present a framework that would enable real-time, data-driven triggering for LArTPCs, using anomaly detection algorithms implemented on Field-Programmable Gate Arrays (FPGAs). Drawing on a study that makes use of collected charge data from the MicroBooNE LArTPC Public Dataset, I will discuss the overall performance of such algorithms and potential applications for future neutrino experiments.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Overview of the Electrification of Transportation in Hawaii

This document is a summary of electric vehicle (EV) experiences in Hawaii. It is meant to be informative but does not present any new technical analysis except for the development of key lessons learned that could be applied in similar contexts. The electrification of transportation is essential for Hawaii's energy goal. An electrification of transportation strategy complements other energy policy goals, increases clean energy impacts, and provides customer value. By the end of 2020, there were over 12,000 EVs registered in Hawaii (about 1 percent of all cars). That number is expected to grow, based on the results from recent surveys and studies in Hawaii. Surveys pointed out the need for more charging stations, especially in places where people do business or park for long periods of the day. Participation in controlled charging programs should have attractive incentives since a majority of EV owners would not be willing to interrupt their EV charging for demand response. Various studies have confirmed the EV potential in Hawaii. For example, the JUMPSmart Maui demonstration project, a public-private partnership with Japan, helped to establish the EV charging station infrastructure in Maui and provided important information about charging behaviors. A critical backbone study commissioned by the utility recommended that 3,600 public chargers be installed by 2030 on the five islands, which confirms the need for infrastructure improvements expressed in earlier surveys. The process that emerged in Hawaii can be an example to other locations, which could heed the lessons from Hawaii's EV experiences: The importance of an overarching energy goal/objective based on a shared vision; planning and pilot projects; a strategic plan (roadmap) leveraging on initial experiences; evaluation of the effectiveness/success of actions; fine-tuning as needed; close regulatory oversight and stakeholder participation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Consequence-Driven Cybersecurity for High-Power Electric Vehicle Charging Infrastructure

Cybersecurity of high-power charging infrastructure for electric vehicles (EVs) is critical to the safety, reliability, and consumer confidence in this publicly accessible technology. Cybersecurity vulnerabilities in high-power EV charging infrastructure may also present risks to broader transportation and energy-infrastructure systems. Here, this paper details a methodology used to analyze and prioritize high-consequence events that could result from cybersecurity sabotage to high-power charging infrastructure. The highest prioritized events are evaluated under laboratory conditions for the severity of impact and the complexity of cybersecurity manipulation. Mitigation solutions and strategies are presented to secure the vulnerabilities that potentially lead to high-consequence events. These mitigations can be immediately implemented by industry or executed during the design stage.

25 ENERGY STORAGE↗

DOE OSTI2025

EVs@Scale High Power Charging Pillar

Slide deck assesses a portfolio of EVs, EVSEs, and Fleets that are expected to utilize High Power Charging (>200kW) to understand charging rates, grid impacts, and asset utilization. Provide DOE, project partners, stakeholders, and the public with analysis on the capability of HPC performance of today's charging infrastructure.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

EV Watts Public Database

With the rapid increase in vehicle electrification, there is a need for up-to-date, publicly available national data to understand end user charging and driving patterns, as well as vehicle and infrastructure performance, to inform research planning. Energetics worked with various partners to collect and analyze plug-in electric vehicle (PEV) and electric vehicle supply equipment (EVSE) data from 2019 to 2022. All sensitive attributes have been removed from this publicly available dataset. Researchers from one of the partner national labs under non-disclosure agreement (NDA) can request access to additional attributes by reaching out to evwattsdata@energetics.com.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Chlorophylls as primary electron acceptors in reaction centers

Photosynthetic Reaction Centers (RCs) can be considered blueprints for highly efficient energy transfer. Embedded with an array of cofactors, including (bacterio)chlorophyll ((B)Chl) and (B)pheophytin ((B)Pheo) molecules, RCs function with a high quantum yield that spans a wide spectral range. Understanding the principles that underlie their function can influence the design of the next generation of artificial photosynthetic devices. We are particularly interested in the factors that influence the early stages of light-driven charge separation in RCs. With the recent publication of several highly anticipated RC structures and advanced computational methods available, it is possible to probe both the geometric and electronic structures of an array of RCs. In this chapter, we review the electronic and geometric structures of the (B)Chl and (B)Pheo primary electron acceptors from fiveRCs, comprising both Type I and Type II RCs and representing both heterodimeric and homodimeric systems. We showcase the dimeric A0•- state of Type I RCs, whereby the unpaired electron is delocalized, to various extents, over two (B)Chl molecules (B)Chl2 and (B)Chl3. This delocalization is controlled by several factors, including the structure of the (B)Chls, interactions with the surrounding protein matrix, and the orientation and distances of the cofactors themselves. In contrast, the primary acceptors of Type II RCs are entirely monomeric, with electron density residing solely on the (B)Pheo. We compare the natural design of the primary acceptors of the Type I and Type II RCs from both an evolutionary and application based perspective.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2020 Excel)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET is developed as a multidimensional spreadsheet model in Microsoft Excel. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. It includes two sub-models named Fuel-Cycle Model (GREET 1, contains data on fuel cycles and vehicle operations) and Vehicle-Cycle Model (GREET 2, evaluates the energy and emission effects associated with vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗

DOE OSTI2021

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2021 Excel)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET is developed as a multidimensional spreadsheet model in Microsoft Excel. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. It includes two sub-models named Fuel-Cycle Model (GREET 1, contains data on fuel cycles and vehicle operations) and Vehicle-Cycle Model (GREET 2, evaluates the energy and emission effects associated with vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗

DOE OSTI2022

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2022 Excel)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET is developed as a multidimensional spreadsheet model in Microsoft Excel. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. It includes two sub-models named Fuel-Cycle Model (GREET 1, contains data on fuel cycles and vehicle operations) and Vehicle-Cycle Model (GREET 2, evaluates the energy and emission effects associated with vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗