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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Aligning Electric Vehicle Customer Charging with Grid Needs

This talk to the National Association of Regulatory Utility Commissioners (NARUC) focuses on "Aligning Electric Vehicle Customer Charging with Grid Needs" to highlight the opportunities associated with managed electric vehicle charging and its value for consumers and the power system. Electric vehicles (EVs) are experiencing a rapid rise in popularity and adoption, and growing EV adoption offers an opportunity to increase electricity demand. With expected widespread EV adoption, supporting charging will require investments in generation, transmission, and distribution systems. Uncoordinated charging of EVs will lead to increased system peak load, possibly exceeding the maximum power that can be supported by distribution systems and generally increasing power system stress. However, vehicles are underutilized assets parked ~96% of the time: managed EV charging can satisfy mobility needs while also supporting the grid. EVs are not a burden for the grid, but a resource: The demand-side flexibility provided by managed EV charging offers significant potential benefits for the grid over multiple timescales and applications., especially for high-renewable systems. Managed charging can support power system planning and operations during normal and extreme conditions, benefitting EV owners and other electricity consumers. Managed charging is shown to consistently provide hundreds of dollars in cost savings per EV each year.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Electric Vehicle Charging for Residential and Commercial Energy Codes: Technical Brief

Numerous studies show that sales of electric vehicles (EVs) have grown consistently over recent years in the U.S. The U.S. Energy Information Administration (EIA) estimated 3 million EVs were on the road in 2022, and the Edison Electric Institute (EEI) forecasts a total of 26.4 million EVs on the road by 2030. Based on this forecast, EEI projects the need for an additional 12.9 million EV charge ports by 2030. If EV charging infrastructure fails to keep pace with sales of EVs it could result in consumers stranded without options to power their vehicles. EVs are capable of providing substantial benefits to the consumers. EVs are less expensive to operate than conventional internal combustion engine vehicles, have lower maintenance costs, and have the convenience of fueling (charging) at home or work. Studies conducted in California show that costs associated with installing EV charging infrastructure can be substantially more expensive for retrofit scenarios compared to new construction, making inclusion of EV infrastructure in new construction codes a cost-effective policy option to increase infrastructure to meet growing demands. PNNL tracks adoption of mandatory EV provisions across the U.S. As of December 20, 2024, 12 states (California, Oregon, Washington, Colorado, New Mexico, Illinois, Maryland, Delaware, New Jersey, Rhode Island, Massachusetts and Vermont) and 53 local governments have added EV provisions to their building codes, local ordinances and zoning requirements. Originally published in 2022, this tech brief has been revised to align with recent model energy code committee discussions and published EV infrastructure code language. This technical brief summarizes market trends, costs and benefits, and provides sample code language for EV charging infrastructure for consideration to be included in model codes, such as the International Energy Conservation Code (IECC) and ANSI/ASHRAE/IES Standard 90.1, as well as directly by states and local governments in their building codes. The technical brief summarizes related efforts undertaken by states and local governments, and builds upon language considered during the 2021 and 2024 IECC development cycles.

2021 IECC↗
DOE OSTI2025

Soft costs and EVSE – Knowledge gaps as a barrier to successful projects

There has been a recent push to increase access to electric vehicle (EV) charging infrastructure. The National Electric Vehicle Infrastructure (NEVI) program, part of the Bipartisan Infrastructure Law (BIL) has made significant funding available for major charging infrastructure projects along state thruways, and many state and local incentives exist for EV owners to install chargers in their homes. However, deployment of these chargers has not kept up with demand, primarily due to issues in project planning, permitting processes, and unforeseen delays. This paper serves as a review of the current understanding of these and other non-hardware costs in EV charging infrastructure projects (collectively known as “soft costs”). We found that soft costs in EV charging infrastructure projects are not well understood. Specifically, there is little agreement on how soft costs should be categorized and tracked, and less agreement still on best practices for controlling these costs and lowering barriers to infrastructure deployment. A broader review of EV charging infrastructure cost analyses shows that these costs can have significant impacts on project outcomes. EV charging infrastructure projects may be able to examine the success of the solar industry in lowering soft costs, and a similar effort may lower project costs significantly. Further work on standardizing and collecting data on EV charging infrastructure costs is required to begin addressing and controlling these costs.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2020

Autonomous Intelligent Charging/Discharging of Electric Vehicles using Distributed Multi-Agent ADMM Framework for Grid Ancillary Services

The increasing popularity of Electric Vehicles (EVs) in the distribution grid along with technological advancement in EV electronics such as vehicle to grid (V2G) technique has enabled them to participate in grid ancillary services. To achieve this, the EVs need to establish a contract with third-party aggregators and connect to a charging unit, either residential or commercial. At any time they are connected, the EVs can decide to take part in the ancillary services program offered to them by the aggregators. If agreed, the aggregators will use the EVs as a power source capable of charging/discharging power according to the input signal, and in return, they will be compensated. This inter-temporal nature of charging/discharging is also transforming the traditional optimal power flow (OPF) problem into a dynamic OPF problem. This chapter aims at developing a multi-layer time-dependent optimization algorithm to utilize EV potential and provide ancillary services while maximizing its utilization function. Specifically, in the upper layer, an autonomous distributed ADMM algorithm is developed to optimize the cost for charging/discharging EVs while using them to regulate the voltage at each bus in the distribution grid. The distributed ADMM algorithm is also expanded to the lower layer where the individual EVs active and reactive power is controlled for voltage regulation while maintaining the desired state of the charge of the vehicle at the end of the charging period. Here, the effectiveness and performance improvement of the proposed multi-layer algorithm is illustrated through analytical analysis and simulation results.

Rahman, Towfiq↗
DOE OSTI2020

Autonomous Intelligent Charging/Discharging of Electric Vehicles using Distributed Multi-Agent ADMM Framework for Grid Ancillary Services

The increasing popularity of Electric Vehicles (EVs) in the distribution grid along with technological advancement in EV electronics such as vehicle to grid (V2G) technique has enabled them to participate in grid ancillary services. To achieve this, the EVs need to establish a contract with third-party aggregators and connect to a charging unit, either residential or commercial. At any time they are connected, the EVs can decide to take part in the ancillary services program offered to them by the aggregators. If agreed, the aggregators will use the EVs as a power source capable of charging/discharging power according to the input signal, and in return, they will be compensated. This inter-temporal nature of charging/discharging is also transforming the traditional optimal power flow (OPF) problem into a dynamic OPF problem. This chapter aims at developing a multi-layer time-dependent optimization algorithm to utilize EV potential and provide ancillary services while maximizing its utilization function. Specifically, in the upper layer, an autonomous distributed ADMM algorithm is developed to optimize the cost for charging/discharging EVs while using them to regulate the voltage at each bus in the distribution grid. The distributed ADMM algorithm is also expanded to the lower layer where the individual EVs active and reactive power is controlled for voltage regulation while maintaining the desired state of the charge of the vehicle at the end of the charging period. The effectiveness and performance improvement of the proposed multi-layer algorithm is illustrated through analytical analysis and simulation results.

Rahman, Towfiq↗
DOE OSTI2021

Sizing Energy Storage System for Energy Arbitrage in Extreme Fast Charging Station

This paper proposes a non-linear programming (NLP) model to optimally size the energy storage system (ESS) and obtain an optimal energy management for energy arbitrage of an extreme fast charging station (XFCS) for electric vehicles (EVs), with minimized total cost of XFCS operation and ESS investment. Different from most reported work on sizing the ESS for EV charging stations, this paper proposes a pragmatic approach to model the ESS life degradation and accurately count the ESS cycles. Moreover, this work incorporates the peak demand charges in the operational cost of the charging station which are often overlooked in the literature. The proposed model is formulated and solved using AIMMS. Finally, a thorough sensitivity analysis is performed to offer insights into how different input parameters impact the ESS sizing and savings from the energy arbitrage perspective.

25 ENERGY STORAGE↗
DOE OSTI2020

Evaluation of smart charging for electric vehicle-to-building integration: A case study

Higher electric vehicle (EV) adoption will stress the importance of demand flexibility to achieve more economic, efficient, and reliable grid operation. Charging technologies will be paramount in shifting temporally to better fit the variable generation of wind and solar. As such, analysis is warranted on the benefits of EV charge scheduling with respect to installation cost, operation cost, difficulty of implementation, and grid flexibility. We tackle this by analyzing the cost savings of implementing an EV charge scheduling infrastructure to reduce demand charges and installation costs. In this paper, we analyze a case study for operation of 16 level 2 chargers and 1 fast charger for two different building types. We then evaluate various test phases for controlling building and charging loads using an adaptive charging network (ACN) algorithm to characterize the ACN’s potential to reduce overall project cost.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Resonant Inelastic X-ray Scattering Calculations of Transition Metal Complexes Within a Simplified Time-Dependent Density Functional Theory Framework

We present a time-dependent density functional theory (TDDFT) approach to compute the light-matter couplings between two different manifolds of excited states relative to a common ground state in the context of 4d transition metal systems. These quantities are the necessary ingredients to solve the Kramers-Heisenberg (KH) equation for resonant inelastic X-ray scattering (RIXS) and several other types of two-photon spectroscopies. The procedure is based on the pseudo-wavefunction approach, where the solutions of a TDDFT calculation can be used to construct excited-state wave- functions, and on the restricted energy window approach, where a manifold of excited states can be rigorously defined based on the energies of the occupied molecular orbitals involved in the excitation process. Thus, the present approach bypasses the need to solve the costly TDDFT quadratic-response equations. We illustrate the applicability of the method to 4d transition metal molecular complexes by calculating the 2p4d RIXS maps of three representative ruthenium complexes and comparing them to experimental results. The method can capture all the experimental features in all three complexes to allow the assignment of the experimental peaks, and relative energies correct to within 0.6 eV at the cost of two independent TDDFT calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Optimal mass evacuation planning for electric vehicles before natural disasters

The electric vehicle (EV) market has significantly expanded because EVs have lower operational costs while leaving less environmental footprint than internal combustion engine vehicles. However, EVs also come with drawbacks, including long charging time and short operational ranges. With these drawbacks and limited charging facilities, efficient long-distance EV evacuation management is challenging and has not been properly addressed. Without an efficient evacuation plan, serious congestion could happen at charging facilities, the evacuation process would be excessively long, and thus human lives may be put at risk. This study is motivated to investigate the optimal mass evacuation planning for EVs considering limited charging facilities. A three-stage method is proposed to efficiently approach this problem. A case study of Florida hurricane evacuation is conducted. Here, the method's effectiveness is verified by comparing it with a benchmark. Management insights and policy indications are drawn through sensitivity analysis of key parameters.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

evmc-supply-curves (Electric Vehicle Managed Charging Supply Curves) [SWR-25-69]

Data and a supporting lightweight Python package that describes possible costs for enabling EV managed charging from 2025 to 2050 for three dispatch mechanisms: Time-of-Use (TOU), Real Time Pricing (RTP), and direct load control (DLC) and four flexibility scenarios (Flat and Low, Mid, and High Flex).

Matsuda-Dunn, Reiko [National Renewable Energy Lab↗
DOE OSTI2022

Can Cobalt Be Eliminated from Lithium-Ion Batteries?

Following the discovery of LiCoO 2 (LCO) as a cathode in the 1980s, layered oxides have enabled lithium-ion batteries (LIBs) to power portable electronic devices that sparked the digital revolution of the 21st century. Since then, LiNi x Mn y Co z O 2 (NMC) and LiNi x Co y Al z O 2 (NCA) have emerged as the leading cathodes for LIBs in electric vehicle (EV) application and have become crucial components in the fight against global warming. However, the surging demand for LIBs has led to an extremely tight supply. The EV sector has already dominated the LIB market, even though EV sales were only 2–3% of total passenger vehicle sales in 2020. EV sales are expected to grow 10-fold by the end of this decade, and close to 90% of the total LIB demand will come from the EV sector. As a result, LIB manufacturers must aggressively ramp up cell production to keep pace with the immense growth of the EV market, a quest that brings scrutiny to the cost and sustainability of current LIB manufacturing practices. Here, cathodes are a critical component that largely determines the energy density and 40–50% of the total cell cost in LIBs. Rigorous consideration of the cathode performance and material cost is crucial in sustaining EV adoption. In this Viewpoint, we discuss why using cobalt in cathodes is unsustainable in the long run and highlight the features of cobalt-free cathodes.

25 ENERGY STORAGE↗
NASA NTRS1982

The flexible magnetic field thruster

The thruster is designed so that ion currents to various internal surfaces can be measured directly; these measurements facilitate calculations of the distribution of ion currents inside the discharge chamber. Experiments are described suggesting that the distribution of ion currents inside the discharge chamber is strongly dependent on the shape and strength of the magnetic field but independent of the discharge current, discharge voltage, and neutral flow rate. Measurements of the energy cost per plasma ion suggest that this cost decreases with increasing magnetic field strength as a consequence of increased anode shielding from the primary electrons. Energy costs per argon plasma ion as low as 50 eV are measured. The energy cost per beam ion is found to be a function of the energy cost per plasma ion, extracted ion fraction, and discharge voltage. Part of the energy cost per beam ion has to do with creating many ions in the plasma and then extracting only a fraction of them into the beam. The balance of the energy goes into accelerating the remaining plasma ions into the walls of the discharge chamber.

Brophy, J. R.↗
DOE OSTI2020

Levelized Cost of Charging Electric Vehicles

This data set includes the levelized cost of charging (LCOC) and lifetime fuel cost savings (LFCS) values as reported in "Levelized Cost of Charging of Electric Vehicles in the United States." Values are reported at the state and national levels for battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). The data set also includes the four annual direct current fast charging (DCFC) station load profiles used to approximate the levelized cost of DCFC charging. Each profile provides 15-min resolved power requirements for one full year. Borlaug, B., Salisbury, S., Gerdes, M., and Muratori, M., Levelized Cost of Charging Electric Vehicles in the United States, Joule (2020), https://doi.org/10.1016/j.joule.2020.05.013.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Analyzing SCM Grid Benefits from Electric Transportation [Slides]

Increasing adoption of EVs and expanding unmanaged charging loads could increase the cost of transportation energy due to increasing load variability and shrinking infrastructure capacity. The actual cost of transportation energy, such as charging an EV, depends on several factors including energy costs, charging infrastructure costs, and applicable grid upgrades. Based on studies from past DOE projects; RECHARGE, DirectXFC, FUSE and 21st Century Truck Partnership (21CTP) the EV-CENTS project will develop a transportation energy cost metric to better quantify these factors and provide a framework for assessing the value potential of new technology solutions, such as smart charge management (SCM), which could reduce these costs for all stakeholders. The initial assessment will focus on the cost of charging, which will vary across vehicle classes such as light-duty vehicles (LDV) or medium and heavy-duty vehicles (MHDV), as well as across different vocations resulting in many different use cases for this metric. Cost of charging results will be developed for each use case in both uncontrolled and controlled scenarios to understand the value potential of different SCM objective functions and their ability to optimize the cost of energy and delay or eliminate the need for electrical upgrades.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Development of a Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) Tool

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles. The main challenge is that light-duty tools do not estimate real-time vehicle mass, a factor that has a disproportionate impact on the energy consumption of large commercial vehicles. The Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) tool advances the state of the art in evaluating electrification potential and infrastructure requirements for fleets of commercial vehicles. In this work, the HEVII tool is demonstrated with non-uniformly sampled telematics data from an existing fleet to assess the suitability for electrification of each individual vehicle, determine optimal locations for charging infrastructure to support a fleet of EVs and analyze associated costs. Payload mass is predicted using sparse ground-truth data for all input drive cycles and an initial data analysis is conducted to assess the characteristics driving behaviors and energy consumption of the fleet using an adaptable vehicle model. Battery size requirements are determined by applying a novel charger placement algorithm to maximize routes that are viable for EVs and balance time delays with infrastructure development costs. This work details and demonstrates the different aspects of the HEVII tool, presenting preliminary results from an example use case.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

From Atoms to Wheels: The Role of Multi-Scale Modeling in the Future of Transportation Electrification

Traditionally, prototype hardware is built for validation testing to ensure battery systems design changes meet vehicle-level requirements, which is expensive both in cost and time. Virtual engineering (VE) of battery systems for electric vehicle (EV) propulsion offers a reduced-cost alternative to the traditional development process and uses multi-scale modeling to virtually probe the impact of design changes in a particular part on the overall performance of the system. This allows for rapid iteration over multiple design spaces, without committing to build hardware. This perspective article discusses current trends in VE for EV applications and proposes improvements to accelerate EV adoption.

Garrick, Taylor R. (ORCID:0000000322518129)↗
DOE OSTI2026

How Improved Forecasting Can Increase the Bulk Power System Value of Price-Responsive Electric Vehicle Managed Charging

Personal light-duty vehicle (LDV) electric vehicle managed charging (EVMC) can reduce power system costs by better aligning electric vehicle (EV) charging with locations and times of low energy cost or infrastructure use. The need to coordinate charging demand across thousands to millions of vehicles while preserving mobility service is a barrier to realizing the value of EVMC. Price-responsive dispatch mechanisms like time-of-use rates (TOU) and hourly real-time prices (RTP) are attractive compared to direct load control (DLC) because they only require one-way communications and local controls. However, increasing participation in price responsive mechanisms can induce costly-to-serve spikes in load and otherwise increase, rather than decrease, production costs. We quantify the ability of improved EVMC forecasting to sustain savings from price responsive mechanisms beyond the limit of 14% of LDVs actively participating observed in previous work. Perfect forecasting of price-responsive EV load makes TOU and RTP value-competitive with a low-error DLC formulation with up to 27% (within-week flexibility) to 45% or more (within-session flexibility) of LDVs participating in EVMC in an envisioned New England power system with 84% clean energy. Additional costs of implementing DLC should be no more than tens of dollars per vehicle-year if DLC is to be value-competitive with accurately forecast price-responsive EVMC for double-digit percentage shares of LDVs participating.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗