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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Identifying Electric Vehicles to Best Serve University Fleet Needs and Support Sustainability Goals

University fleets represent an enticing opportunity to explore the near-term feasibility of achieving net-zero-carbon emissions in transportation. In many instances, universities operate much like a small, self-contained ecosystem with all the same transportation needs as a larger municipality, but with a smaller geographic footprint. Their fleets often include a wide variety of vehicle types serving the campus, including low-speed vehicles (e.g., golf carts), light-duty sedans, SUVs, and pickups, as well as medium-duty trucks and delivery vehicles. The mix of vehicle and operational needs combined with broader activities related to net-zero campuses makes universities and colleges unique microcosms to determine the feasibility of and path to achieving net-zero fleets. As the availability of electric drivetrains expands beyond light-duty sedans, fleets need to understand when it will be operationally and financially appropriate to start adding electric drivetrains to their fleets. To better understand these opportunities, NREL contracted Sawatch Labs to analyze the role electric vehicles (EVs) can have in helping universities meet net-zero emissions and fleet sustainability goals they have instituted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Enabling Extreme Fast Charging through Control of Li Deposition Overpotential on Graphite Electrodes

Currently produced electric vehicles (EVs) rely on the use of lithium ion battery technology due to its high energy and power density. However, a major barrier facing the adoption of electric vehicles is that currently utilized Li-ion batteries take significantly longer to recharge (~ 30 minutes) compared to the time necessary to refuel vehicles powered by internal combustion engines (< 10 minutes). Thus, the need to develop Li-ion batteries which can be charged in approximately 10 minutes (6 C rate) without sacrificing range, cost, or cycle life is critical for the widespread implementation of EVs.

25 ENERGY STORAGE↗
DOE OSTI2023

Silicon Carbide Inverter for Off-Road Heavy-Duty Applications: The Importance of Thermal and Thermomechanical Design in Power Electronics Packaging

Electrification of drivetrain systems is now seen as a major opportunity by the transportation industry across the globe to reduce the greenhouse gas emissions and revolutionize the travel patterns of millions of people. The cumulative number of plug-in hybrid and battery electric vehicles (EVs) sold in the United States has now surpassed 2 million in 2021, according to the International Energy Agency. The introduction of EVs in different classes of passenger vehicles and the continued drop in prices spurred by government incentives have attracted the attention of consumers despite certain barriers, such as higher initial cost and range anxiety. In the United States, the EV market share is now growing at an exponential pace, with the domestic automakers allocating a lion's share of new and future car sales to EVs. Additionally, different market studies project decreasing cost and rising sales of medium- and heavy-duty electric trucks. Although electrification initiatives are strongly pursued in the on-road passenger vehicle market, off-highway sectors, such as construction, mining, and agriculture, are also gradually implementing electric drivetrain technologies in their machineries. As EVs grow in popularity on a global scale, innovative drivetrain technologies must meet the increasing energy demand by significantly increasing system efficiency.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Grid-Integrated Electric Mobility Model (GEM) v1.0

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. The convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, but requires careful integration with rapidly evolving electricity systems. We have developed the GEM Model (Grid-Integrated Electric Mobility) to examine these interactions with a U.S.-wide simulation framework encompassing private electric vehicles (EVs); shared automated EVs (SAEVs); charging infrastructure; controlled EV charging; and a grid economic dispatch model to simulate mobility futures exclusively using EVs. We find that an SAEV fleet 9% the size of today's active vehicles can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Controlled EV charging can also reduce electricity demand variability, significantly reducing GHG emissions and decreasing solar curtailment by about one-third. While private EVs with uncontrolled charging would reduce GHG emissions by 53% compared to gasoline vehicles, SAEVs could achieve a 70% reduction.

Sheppard, Colin↗
DOE OSTI2025

Improving Resiliency in Planning MW-Scale Medium and Heavy Duty EV Charging Stations Considering TSCOTS Optimization

Electrification of heavy-duty (HD) vehicles marks an important milestone and technical challenge in the electric vehicle (EV) industry and the public grid. However, implementing EV charging at this scale will necessitate that traditional truck stops be updated with EV charging infrastructure that could represent 10's of MW in electricity consumption. Furthermore, as the transportation sector is represented as critical infrastructure, supporting resiliency considerations in EV charging infrastructure will be critical. This paper proposes an optimization-based approach for optimally designing a MW-scale microgrid charging network. This approach transforms conventional designed truck stops into a reliable HDEV charging stations capable of overnight slow charging and 30-minute to 1 hour fast charging. Using a mixed-integer linear program formulation blending capacity planning and reliability constraints, an optimal network configuration can be solved for a proposed EV charging station that includes photovoltaic and battery energy storage capabilities.

Ponce, Moises [University of Tennessee, Knoxville ↗
DOE OSTI2022

Extremum-Seeking-Based Ultra-local Model Predictive Control and Its Application to Electric Motor Speed Regulation

Electric vehicle (EV) market is rapidly expanding. As a critical component of EV, an electric motor needs to accurately follow a reference speed signal while respecting the electrical current constraint for safety. Those requirements are usually formulated as a model predictive control (MPC) problem. However, the performance of traditional model-based MPC depends on the accuracy of the system model, which may not always be guaranteed in reality. Therefore, we utilize a data-driven, model-free predictive control strategy, called ultra-local MPC (ULMPC), to control the speed of an electric motor. To further enhance the control performance of ULMPC, we employ the extremum-seeking control (ESC) to tune the control gain of the ULMPC online. Simulation and hardware experiments demonstrate the enhancement of the extremum-seeking-based ULMPC over a constant-gain ULMPC.

Zhou, Yujing↗
DOE OSTI2023

Coordinating the electric vehicle transition and electricity grid decarbonization in the U.S. is not essential to achieving substantial long-term carbon dioxide emissions reductions

Abstract How quickly the US can decarbonize light-duty vehicle (LDV) transportation depends on the rates of change of electric vehicle (EV) sales, stock turnover, and grid decarbonization. We build a stock turnover model to assess how sensitive achieving 2050 LDV decarbonization targets is to these rates. We estimate carbon dioxide (CO 2 ) reductions of 70%–85% by 2050, including emissions from vehicles and upstream electricity generation, provided that new vehicle sales transition to 100% EVs and substantial grid decarbonization are accomplished by 2050. This result is robust to continuation of long-term trends of increasing vehicle longevity, and to whether the timing of EV sales growth and grid decarbonization are coordinated. If the two key goals are met, the annual contribution of EV electricity use to CO 2 emissions will be small over the entire period.

Leard, Benjamin↗
DOE OSTI2021

Tri-Level Scheduling Model Considering Residential Demand Flexibility of Aggregated HVACs and EVs Under Distribution LMP

Residential loads, especially heating, ventilation and air conditioners (HVACs) and electric vehicles (EVs), have great potentials to provide demand flexibility which is an attribute of grid-interactive efficient buildings (GEB). Under this new paradigm, first, EV and HVAC aggregator models are developed in this paper to represent the fleet of GEBs, in which the aggregated parameters are obtained based on a new approach of data generation and least squares parameter estimation (DG-LSPE), which can deal with heterogeneous HVACs. Then, a tri-level bidding and dispatching framework is established based on competitive distribution operation with distribution locational marginal price (DLMP). Furthermore, the first two levels form a bilevel model to optimize the aggregators’ payment and to represent the interdependency between load aggregators and the distribution system operator (DSO) using DLMP, and the third level is to dispatch the optimal load aggregation to all residents by the proposed priority list-based demand dispatching algorithm. Finally, case studies on a modified IEEE 33-Bus system illustrate three main technical reasons of payment reduction due to demand flexibility: load shift, DLMP step changes, and power losses. They can be used as general guidelines for better decision-making for future planning and operation of demand response programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Control and Real-Time Simulation of Microgrids with EV Fast-Charging and Grid-Forming Resources

Fast charging stations (FCSs) for electric vehicles (EVs) can behave as constant power loads, which is problematic for the operation of grid-forming (GFM) inverter-based resources (IBRs). To tackle this problem, this paper proposes a control suite that makes FCSs responsive to ac voltage and frequency disturbances. To test the performance of the FCS controls, the paper also sets forth theory to compensate for time-delays appearing in the real-time simulation of microgrid models divided into several central processing units (CPUs). Furthermore, these contributions are showcased via multi-CPU real-time simulations of a faulted microgrid having EV FCSs and GFM IBRs powered by photovoltaic solar arrays as well as power hardware-in-the-loop experiments. These contributions are significant to address NERC recommendations and IEEE standards.

14 SOLAR ENERGY↗
DOE OSTI2026

Reverse Logistics Ev Battery Recycling Agent Base Model

This represents the initial regional tier of the electric vehicle (EV) battery recycling agent base model. Through this model, we can ascertain the number of EV purchases at both the state and regional levels. We employ census data to develop a diverse household profile to inform decisions regarding the acquisition of new or used EVs. The number of EV purchases at the state level will affect the future demand for recycling, reuse, and repurposing of end-of-life EV batteries. Additionally, tax credits, EV rebate programs, and the financial capacity of households will influence the number of EV purchases, thereby further impacting the demand for EV battery recycling.

Alam, Lamia [Idaho National Laboratory (INL), Idah↗
DOE OSTI2025

A Dynamic Pricing Method to Manage the Impact of EV Charging on the Grid Using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

EV charging, dynamic pricing, grid-informed chargi↗
DOE OSTI2025

A dynamic pricing method to manage the impact of EV charging on the grid using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Optimal Electric Vehicle Charging and Discharging Strategies Under DER Compensation Programs: Preprint

The adoption of electric vehicles (EVs) is becoming increasingly popular because of environmental concerns, the greater availability of models, and increased cost-competitiveness with gas vehicles. Because EVs have both charging and discharging capabilities, they provide great potential to help electric utilities with grid operation. When the grid demand is high, EVs can discharge to the grid to reduce the peak load, and vice versa; therefore, electric utilities have designed different policies to encourage EV charging station operators to charge or discharge at certain time periods. The New York State Public Service Commission established the Value of Distributed Energy Resources (VDER), or the Value Stack, to compensate for energy created by distributed energy resources, including EVs. This paper presents an optimization-based approach to identify the "golden hours" and "golden spots," i.e., the effective time periods and geographic locations for EV charging station operators to charge or discharge under the VDER program that can provide them the highest benefit. The proposed methodology can be applied to other compensation mechanisms and distribution systems as well. By working with industry partner NineDot Energy, realistic charging station information is used in this study, and the proposed approach is tested on a distribution feeder. The results from this study can help electric utilities and EV charging station operators determine the ideal charging/discharging time and the ideal locations for the charging station(s) in their distribution systems to achieve maximized benefit.

electric vehicle↗
DOE OSTI2026

Bounding the costs of electric vehicle managed charging—supply curves for scenarios from 2025 to 2050

As electric vehicle (EV) adoption increases, the resulting EV battery charging will increase demand on the electric power grid. Through EV managed charging (EVMC) programs, charging can be shifted in time to support electric grid reliability and reduce electricity costs. EVMC can offer an alternative to additional supply-side generation, but the costs of EVMC implementation must be understood to evaluate the cost-benefits of EVMC. This paper presents bottom-up, forward-looking (from 2025 through 2050) estimates of the incremental costs associated with different EVMC dispatch mechanisms available to electric utilities. The costs of enabling EVMC for a range of customer participation levels are presented in the form of supply curves, which provide per-EV costs for a targeted level of participation. The largest drivers of cost variation are assumptions about future charging flexibility paradigms described in four scenarios. These supply curves can be used to quantify the expected costs of EVMC programs and enable comparison with supply-side or other demand flexibility alternatives.

25 ENERGY STORAGE↗
DOE OSTI2026

Analysis and Overview of Hybrid Wired and Wireless Bi-Directional EV Charger Systems

Here, this paper analyzes and overviews hybrid wired and wireless bi-directional Electric Vehicle (EV) charging systems with a primary focus on resonant compensation methods that enable a unified power conversion architecture. Four compensation configurations based on series–series and LCC–LCC resonant networks are systematically evaluated for both wired transformer-based and wireless coupler-based operation. The analysis examines how coupling conditions, resonant component selection, and auxiliary compensation tuning influence voltage gain characteristics, resonant tank current magnitude and phase, and operating frequency requirements. Normalized frequency-domain results are presented to directly compare reactive current behavior and voltage regulation capability under wired and wireless operating conditions. A 60 kW bi-directional charger case study is used to demonstrate the feasibility of retaining a common hardware platform while accommodating distinct coupling scenarios through compensation tuning rather than structural modification. The presented results provide design-oriented insights into resonant network selection and compensation strategies for scalable and flexible hybrid EV charging systems.

Hybrid↗
DOE OSTI2022

Sustainable Electric Vehicle Batteries for a Sustainable World: Perspectives on Battery Cathodes, Environment, Supply Chain, Manufacturing, Life Cycle, and Policy

Abstract Li‐ion batteries (LIBs) can reduce carbon emissions by powering electric vehicles (EVs) and promoting renewable energy development with grid‐scale energy storage. However, LIB production and electricity generation still heavily rely on fossil fuels at present, resulting in major environmental concerns. Are LIBs as environmentally friendly and sustainable as expected at the current stage? In the past 5 years, a skyrocketing growth of the EV market has been witnessed. LIBs have garnered huge attention from academia, industry, government, non‐governmental organizations, investors, and the general public. Tremendous volumes of LIBs are already implemented in EVs today, with a continuing, exponential growth expected for the years to come. When LIBs reach their end‐of‐life in the next decades, what technologies can be in place to enable second‐life or recycling of batteries? Herein, life cycle assessment studies are examined to evaluate the environmental impact of LIBs, and EVs are compared with internal combustion engine vehicles regarding environmental sustainability. To provide a holistic view of the LIB development, this Perspective provides insights into materials development, manufacturing, recycling, legislation and policy, and beyond. Last but not least, the future development of LIBs and charging infrastructures in light of emerging technologies are envisioned.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Open Circuit Switch Fault Management Method of a Multi-Phase Synchronous Buck Converter for EV Charging Application

Multi-phase synchronous buck converter (MSBC) is becoming popular for the electric vehicle (EV) charging application because of its higher efficiency, smaller passive component sizes, bidirectional power flow, and output current ripple reduction through interleaving. This converter also provides higher reliability than the traditional single phase converter since its modular structure enables fault tolerant operation. Fault management in the converter requires the detection of switch fault, shut down of the faulty phase, and reconfiguration of the PWM signals for the healthy phases to avoid circulating current and high output current ripple. In addition, it is desirable that the fault management method can be implemented using the micro-controller unit (MCU) or digital signal processor (DSP), which are widely used to control power converters. This paper presents a fault management method for detecting switch faults and reconfiguring healthy phases using a DSP. The method is validated through simulations and experiments using a commercial off-the-shelf power stack.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A Hybrid Fuel Cell and Battery Storage Power Management for Grid-Interactive EV Charging Station

With the increasing adoption of renewable energy sources in grid-interactive Electric Vehicle (EV) charging stations, the role of energy storage systems has become critical. While large energy storage systems have mitigated the intermittency of renewable energy, integrating multi-source energy management with prioritized charging can further enhance the reliability of charging stations (CS). This paper presents a decentralized energy management (DEM) approach combining battery energy storage (BES) and fuel cell (FC) systems using a rule-based line resistance correction droop (LRCD) control technique. The proposed droop control dynamically adjusts the gain to balance the state-of-charge (SoC) of the BES, enhancing power support longevity and improving battery life under varying capacity conditions by reducing current stress. Additionally, the paper addresses the challenges of using fuel cells in linear regions to optimize efficiency and manage various charging scenarios. The CS integrates unity power factor grid interaction, and power support for auxiliary loads, maintaining harmonic distortion within 5% during grid islanding. The approach evaluates DC bus voltage regulation under various scenarios of PV array power fluctuations and dynamic load variations, in both grid-connected and standalone operations. In conclusion, the proposed control strategy is validated on a laboratory prototype through various dynamic load variation and grid islanding scenarios.

Khalid, Mohd [Oak Ridge National Laboratory (ORNL)↗