CO2 emissions associated with electric vehicle charging: The impact of electricity generation mix, charging infrastructure availability and vehicle type
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McLaren, Joyce (ORCID:0000000309600283)↗
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Results for “CHARGING VEHICLES”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Improving Resiliency for Electric Vehicle Charging
Electric vehicles are seeing growing adoption. However, challenges with range anxiety persists. While charging infrastructure is anticipated to expand, EV charger systems have not been as robust to challenges. This paper discusses potential outage conditions associated with EV charging and presents new technology in development to improve EV charging resilience.
Electric vehicle charging, electric vehicle chargi↗
Battery charging control methods, electrical vehicle charging methods, battery charging control apparatus, and electrical vehicles
Battery charging control methods, electrical vehicle charging methods, battery charging control apparatus, and electrical vehicles are described. In one arrangement, battery charging control methods include accessing price information for electrical energy supplied by an electrical power distribution system and controlling an adjustment of an amount of the electrical energy from the electrical power distribution system used to charge a rechargeable battery at different moments in time using the price information. Other arrangements are described.
25 ENERGY STORAGE↗
Impact of public electric vehicle charging infrastructure
Our work uses market analysis and simulation to explore the potential of public charging infrastructure to spur US battery electric vehicle (BEV) sales, increase national electrified mileage, and lower greenhouse gas (GHG) emissions. By employing both scenario and parametric analysis for policy driven injection of public charging stations we find the following: (1) For large deployments of public chargers, DC fast chargers are more effective than level 2 chargers at increasing BEV sales, increasing electrified mileage, and lowering GHG emissions, even if only one DC fast charging station can be built for every ten level 2 charging stations. (2) A national initiative to build DC fast charging infrastructure will see diminishing returns on investment at approximately 30,000 stations. (3) Some infrastructure deployment costs can be defrayed by passing them back to electric vehicle consumers, but once those costs to the consumer reach the equivalent of approximately 12¢/kWh for all miles driven, almost all gains to BEV sales and GHG emissions reductions from infrastructure construction are lost.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Grid Impact Analysis of Heavy-Duty Electric Vehicle Charging Stations
This paper presents a grid impact analysis of heavy-duty electric vehicle (EV) charging stations. In this work, heavy-duty EVs have battery capacities high enough to provide a range of 250-500 miles on a single charge, such as long-haul trucks. Heavy-duty EVs will require extremely fast charging rates to reduce charging time and will induce very high charging loads (at the multiple-megawatt scale) if several vehicles charge at the same time. Therefore, analysis is needed to understand the impact of charging station loads on the electric power grid and set the baseline for developing mitigation plans and necessary system upgrades. We develop a systematic procedure to analyze the potential impact of the placement of charging stations on the grid. Charging load is modeled using a DC fast-charging station model. A voltage load sensitivity matrix approach is leveraged to investigate the challenges of placing charging stations on the feeder. Given the charging load profiles and suggested charging station locations, time-series simulations are performed on various connection points on the feeder to understand the impact. The analysis is performed on both the IEEE 34-bus system and a realistic feeder from California. Initial mitigation solutions are developed based on insights from this analysis.
47 OTHER INSTRUMENTATION↗
Incorporating Residential Smart Electric Vehicle Charging in Home Energy Management Systems
Electric vehicles (EVs) are expected to drastically increase residential electricity consumption and could provide a significant source of flexible demand. Aggregating smart EV charge controllers with other smart home devices through a home energy management system can lead to more optimal outcomes that benefit homeowners, utilities, and grid operators. Control strategies should consider occupant convenience by accounting for the need for fully charged EVs near the EV departure time. In this paper, we develop an EV charging framework that accounts for occupant convenience using OCHRE, a residential energy model, and foresee, a home energy management system. We simulate a community with high EV penetration and show that integrated, smart EV charging reduces peak demand and smooths night-time energy consumption. Simulation results show that the proposed control strategy nearly eliminates peak period EV charging and reduces the daily peak demand from EVs by 23%.
ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach
This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.
Engineering↗
Severe Temperature Impact Study on Ferrite Electromagnetic Shielding for Wireless Electric Vehicle Charging
Wireless charging of an electric vehicle (EV) is an emerging technology that enables noncontact charging, which has advantages in terms of convenience and flexibility. However, the embedded charging pads under and on the ground expose the charging coils, ferrite shielding, and related components to a potentially severe external environment. To study the electromagnetic shielding performance under such extreme temperature conditions, ferrite shielding material's permeability characteristics are tested under temperatures as low as -50°C to as high as 240°C. Referring to the official recorded lowest and highest temperature of the United States, in this paper, it is assumed that the operation temperature ranges from -50°C to 57°C as the coldest winter and hottest summer environment for WPT application. Electromagnetic performance, shielding, and associated stray magnetic emissions are simulated by using three-dimensional (3D) transient simulations to study the impact of low and high temperature on electromagnetic shielding performance. Based on ferrite testing data, from -50°C low temperature to 57°C high temperature, the ferrite's relative permeability ranges from 2550 to 3727. The simulation study indicates that there won't be significant impact on the EM shielding performance for WPT operated under those severe environment temperature conditions.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Understanding the linkage between electric vehicle charging network coverage and charging opportunity using GPS travel data
Using GPS travel survey data from three metropolitan areas, this paper estimates the relationship between plug-in electric vehicle (PEV) public charging infrastructure coverage and charging opportunity. The former is defined as the percentage of public areas with charging stations installed and the latter represents the drivers probability of accessing a public charger at their trip stops (stop-based) or during at least one stop of their travel day (trip chain-based). Understanding this relationship is important to PEV manufacturers and electric vehicle supply equipment (EVSE) providers, as well as government agencies, for evaluating different levels of EVSE deployment and estimating the impact of its availability on PEV adoption. Heuristic procedures are developed to estimate stop- and trip chain-based charging opportunities. Charging opportunity reflects the visibility of EVSE as the incremental security that charging stations enable, while evaluating the potential of a driver to access EVSE at travel stops or during the day, for different levels of EVSE deployment on the transportation network. The metrics proposed do not assess the probability of completing trips with electric vehicles of a given range. The analysis results indicate that drivers’ trip destinations concentrate in a few popular places and EVSE deployment in these areas could achieve relatively high levels of charging opportunity. This finding is also robust to regional variations.
33 ADVANCED PROPULSION SYSTEMS↗
Inventory of Public Key Cryptography in US Electric Vehicle Charging
Electric vehicles (EVs) and charging infrastructure are networked systems, which employ high-level communications in support of charging and grid service decisions. Public key cryptography (PKC) underlies much of the security and privacy protections of the information exchange. We are entering a new epoch where quantum computing threats must be seriously considered. A sufficiently large quantum computer, so named Cryptographically Relevant Quantum Computer (QRQC), will be able to perform the mathematical operations to efficiently attack the underpinnings of traditional PKC, thus jeopardizing the digital foundations for trust, communications security, and data security. Estimates suggest a QRQC can break public key encryption and digital signatures in the manner of tens to hundreds of hours, compared to traditional computing that would demand more than 10 18 years in a brute force-style attack. A consensus belief of quantum theorists, quantum experimenters, and cryptographers suggest that the quantum threat will be likely realized in the next twenty years. To address the threat, post-quantum cryptography, which is cryptosystems that are designed to be secure against both traditional and quantum computing threats, must be adopted. Migration from traditional PKC to quantum-resilient cryptography is a global undertaking and likely represents the largest transition in computing history. The nascent state of EV public key infrastructure, combined with limited adoption of the vehicle secure charging features, presents an opportunity to establish a preference for quantum-resistant cryptography as a step on the migration path. Delays will stunt the efforts as rapidly accelerating EVs sales and huge infrastructure investments will create large growing bases of long-lived vehicles and infrastructure. Migration preparations can commence while NIST continues the process to standardize post-quantum cryptography (PQC), which are quantum-resilient algorithms designed to be secure against traditional and quantum computing threats. The first step in preparing EV charging is to identify the presence of traditional public key cryptography algorithms and applications. With this objective in mind, this report is intended to advise the vehicle manufacturers, charging station manufacturers, charging station operators, charge network providers and other EV charging stakeholders with information on traditional PKC application and the potential risks when PKC becomes insecure. This report, the first in a series of reports discussing the topics existing at the confluence of post-quantum cryptography adoption and EV charging, identifies traditional public key applications employed and identifies potential consequences of leaving EV charging infrastructure vulnerable to quantum computing. The focus remains squarely on the of EV charging and infrastructure with respect to PKC and is believed by the authors to complement the NIST SP 1800-38 Migration to Post-Quantum Cryptography. While the report is centered on infrastructure, there are implications to vehicles.
33 ADVANCED PROPULSION SYSTEMS↗
Emissions Associated with Electric Vehicle Charging: Impact of Electricity Generation Mix, Charging Infrastructure Availability, and Vehicle Type
With the aim of reducing greenhouse gas emissions associated with the transportation sector, policy-makers are supporting a multitude of measures to increase electric vehicle adoption. The actual level of emission reduction associated with the electrification of the transport sector is dependent on the contexts that determine when and where drivers charge electric vehicles. This analysis contributes to our understanding of the degree to which a particular electricity grid profile, vehicle type, and charging patterns impact CO2 emissions from light-duty, plug-in electric vehicles. We present an analysis of emissions resulting from both battery electric and plug-in hybrid electric vehicles for four charging scenarios and five electricity grid profiles. A scenario that allows drivers to charge electric vehicles at the workplace yields the lowest level of emissions for the majority of electricity grid profiles. However, vehicle emissions are shown to be highly dependent on the percentage of fossil fuels in the grid mix, with different vehicle types and charging scenarios resulting in fewer emissions when the carbon intensity of the grid is above a defined level. Restricting charging to off-peak hours results in higher total emissions for all vehicle types, as compared to other charging scenarios.
33 ADVANCED PROPULSION SYSTEMS↗
Charging Infrastructure Technologies: Smart Electric Vehicle Charging for a Reliable and Resilient Grid (RECHARGE)
Annual Merit review of the Smart Electric Vehicle Charging for a Reliable and Resilient Grid (RECHARGE) project. This project will demonstrate the value of smart charge management to reduce the impact of Electric Vehicles at Scale. Assess management of Plug-in Electric Vehicle (PEV) charging at scale to avoid negative grid impacts, identify critical strategies and technologies, and enhance value for PEV / EVSE / grid stakeholders.
47 OTHER INSTRUMENTATION↗
Highly Resolved Projections of Passenger Electric Vehicle Charging Loads for the Contiguous United States: Results From and Methods Behind Bottom-Up Simulations of County-Specific Household Electric Vehicle Charging Load (Hourly 8760) Profiles Projected Through 2050 for Differentiated Household and Vehicle Types
This report documents enhancements made to the TEMPO (Transportation Energy & Mobility Pathway Options TM ) model to project spatially, demographically, and temporally resolved national-scale EV charging load profiles and describes three scenarios and corresponding datasets created for the NREL demand-side grid (dsgrid) project in support of bulk power systems modeling. In brief, TEMPO was enhanced to disaggregate national and annual energy demand projections into household and county-level projections of passenger electric vehicle (EV) hourly charging load profiles (8760 profiles), accounting for consumer, travel, and temperature variations that impact EV energy demand. In alignment with NREL's forward-looking grid modeling, three scenarios for EV adoption covering 2020-2050 were created-- Annual Energy Outlook (AEO) Reference Case, Electrification Futures Study (EFS) High Electrification, and All EV Sales by 2035 --and associated datasets have been included in the dsgrid platform for public use.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Results from the vehicle charging and potential experiment on STS-3
The Vehicle Charging and Potential Experiment formed part of the scientific experiments comprising the NASA Office of Space Sciences-1 pallet flown at an altitude of 250 km and an orbital inclination of 40 deg on the third flight of the Space Shuttle Orbiter in March 1982. Details of the objectives, the instrumentation of the experiment, and some preliminary results from the individual instruments are presented. The experiment studied passive vehicle charging using thermal plasma probes, and it is concluded that apart from moderate v x B (L)-induced electrical potential resulting from the large size and restricted plasma contact area, the Space Shuttle Orbiter behaved similarly to smaller spacecraft orbiting in the same ionospheric region. However, an unexpected passive orbital result was the detection of enhanced plasma density in the vicinity of the Orbiter under restricted orbital conditions, apparently composed of contaminant molecular species and with an electron component having a temperature considerably higher than the ambient ionosphere electrons. The active vehicle charging experiments were performed by using a 100 mA/1 keV electron beam emitted out of the payload bay. Charging measurements made during short electron beam pulse emissions provide an upper limit of about 1 mF for the Orbiter capacitance. Steady-state charging measurements showed that for the vehicle attitudes covered by the mission, the induced electrical potential was normally only a few volts, except during some nighttime conditions when it rose above 40 V.
Banks, P. M.↗
Data Files for “The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure"
This data set includes modeling results from “The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure” including region-specific [i.e., national, state, and core-based statistical area (CBSA)—cities/towns] electric vehicle supply equipment (EVSE) port count requirements in 2025 and 2030 for multiple scenarios described in the study. Please cite as: Wood, E., B. Borlaug, M. Moniot, D.-Y. Lee, Y. Ge, F. Yang, and Z. Liu. 2023. The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5400-85654.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Learning Assisted Demand Charge Mitigation for Workplace Electric Vehicle Charging
Not Available
33 ADVANCED PROPULSION SYSTEMS↗
Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022
Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format. Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035. The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip. These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.
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