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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 181 records · Page 10

Carbon Corrosion in Polymer Electrolyte Fuel Cells: A Complex Interplay between Morphological Changes and Electrochemical Performance

Due to the high gravimetric energy density of hydrogen, the focus of implementation of polymer electrolyte fuel cells (PEFCs) has shifted from light duty passenger vehicles to heavy duty vehicles such as buses, trucks, locomotives and marine vessels. A mechanistic understanding of degradation is therefore necessary to improve durability and efficiency. During start-up and shut-down (SUSD) of PEFC systems, the catalyst (Pt nanoparticles embedded on carbon support) undergoes local potentials ~ 1 - 1.5 V caused by a combination of fuel (H 2 ) starvation, mixed fuel region and cell reversal. This leads to a series of degradation phenomenon including reduction in cathode catalyst layer (cCL) thickness and porosity, loss in electrochemical surface area (ECSA), ionomer degradation and loss in electrical contact, therefore resulting in severe performance loss. The convoluted relationship between these individual degradation mechanisms, their chronology and their effects on electrochemical performance are yet unresolved. Here, the complex interplay between morphological changes due to carbon corrosion and its effects on the electrochemical performance were analyzed using a combination of detailed electrochemical characterization, spectroscopy, and electron microscopy techniques.

25 ENERGY STORAGE↗

Commercial Vehicle Electrification Evaluation Toolkit (CVEET)

The CVEET can identify and characterize charging and battery capacity challenges for class 3-8 commercial vehicle electrification, including battery electric and plug in hybrid vehicles for various trucks, vans and buses, over a wide range of vehicle operation. The tool can help build the understanding of the impact of commercial vehicle electrification on energy consumption, payload penalty, and GHG (i.e., CO2) emissions. The tool employs a quantitative approach to estimate electric vehicle energy consumption, characterize component performance and determine appropriate charging assumptions to conduct a comprehensive priority analysis of MD/HD vehicle electrification using public domain data.

Gao, Zhiming↗

InfraSys (Infrastructure Systems) [SWR-24-42]

This package implements a data store for components and time series in support of Python-based modeling packages. While it is designed to support teams modeling transmission and distribution systems for electrical grids, it can be used by any package that needs to store components (e.g., generators and buses) that have quantities (e.g., power and voltage) which may vary over time. The package was inspired by InfrastructureSystems.jl (https://github.com/NREL-Sienna/InfrastructureSystems.jl).

Thom, Daniel↗

GridOPTICS/GridPACK

GridPACK is a software framework consisting of a set of modules designed to simplify the development of programs that model the power grid and run on parallel, high performance computing platforms. It also contains several fully developed applications, including powerflow, dynamic simulation, state estimation, Kalman filter analysis (dynamic state estimation), contingency analysis and real time path rating. These applications can be used either standalone or as components in more complicated workflows that combine several different types of application together. The framework modules are available as a combination of libraries and software templates and consist of components for setting up and distributing power grid networks, support for modeling the behavior of individual buses and branches in the network, converting the network models to the corresponding algebraic equations, and parallel routines for manipulating and solving large algebraic systems. The framework also contains a module for distributing tasks evenly amongst computing resources, even if individual tasks vary widely in their execution times. Additional modules support input and output, basic statistical analysis of contingency based calculations, distributed data structures, as well as basic profiling and error management.

Palmer, Bruce↗

test-grid-buildouts [SWR-23-112]

Test-grid-buildouts is a set of notebooks for creating buildouts of synthetic transmission grids with various levels of renewable energy. Workflows can be used to augment various test grid with varying levels of wind/solar generation. Aggregated renewable energy time series (actuals and scenarios) at grid buses can then be used for various transmission grid operation problems. Highly customizable workflow consists of parsing of synthetic grid files, setting up SAM & reV simulations, application of exclusion masks, creating of custom renewable generators on the grid, aggregating of power time series, and writing of modified grid description files.

Satkauskas, Ignas↗

Software Control Program For Transportable Microgrid State-of-charge Balancing And Frequency Stability Controls

A deterministic state-of-charge (SOC) balancing approach software control code is introduced as an integral secondary management to primary control layer of an islanded small microgrid or nanogrid system made up of multiple grid-forming inverter/battery/solar combination systems, where each set of batteries with each inverter are on independent DC buses (i.e. non-paralleled on the DC sides). A DERMS-level control approach, algorithm and automation controller program was developed to improve coordination and enable microgrid asset compliance and SOC balancing, enabling provision of a system-level power stability support architecture, load support, and asset scalability. The architecture is configured to treat each unit or micro/nano-grid as a node in a microgrid network, allowing for autonomous DERMS control regarding load and SOC balancing and power stability. As the network grows with the addition of units, greater coordination efforts may be required. The ideal small network microgrid ranges from 2-10 inverter/battery units before additional control parameters must be considered in the existing architecture. The control approach focuses on a deterministic state-of-charge analysis as the primary level control process followed by a secondary control loop using a forced frequency-watt droop strategy to conform off-the-shelf components into behaving under a leader-follower configuration. Adopting this control scheme has been shown to allow for a balanced, unit-coordinated microgrid network, enabling stable power flow. The deterministic state-of-charge approach is introduced as an integral primary control layer of an islanded small network microgrid. A standard strategy for SOC balancing is implementing a battery management system (BMS) to control SOC on the DC side. An alternative approach is to determine how to coordinate sending and receiving power on the AC side with multiple units. The latter approach assesses all the integrated units in the microgrid network. Once the individual units are identified, further system data is required to calculate each unit's total kWh, provided information about its capability to supply or consume kWh and availability. The secondary control layer in the multi-layered small network microgrid methodology uses the primary layer’s decision to initiate frequency setpoint changes, initializing the SOC balancing. The secondary control layer considers numerous system-dependent variables to enable a charging and discharging profile based on adjustable frequency setpoints. The combined architecture will result in stable, coordinated power flow enhancing an AC microgrid's functionalities.

Myers, KurtS [Idaho National Laboratory (INL), Ida↗

An On-Demand Electric Transit Case Study of New Rochelle, New York

Here, this article characterizes the performance and ridership patterns of an on-demand transit (ODT) service utilizing lightweight electric vehicles (EVs) in New Rochelle, New York. Ridership sociodemographics, travel patterns (both temporal and spatiotemporal), and energy use from the service were explored using travel and survey data from September 2019 through December 2023. The ODT service was found to be used more by women (nearly 60%) and younger demographics (>65% under the age of 42), with peak use in the middle of the day and a grocery store as a top origin and destination. The service was utilized primarily for short trips (86% under 2 mi), with approximately one-third of riders using the ODT service to connect to a train or bus. The costs associated with fueling/charging were compared for different types of fleet vehicles, and the small, right-sized EVs were found to have annual charging costs that were roughly half of the refueling costs for conventional hybrid vans, and 24 times lower than a fleet of diesel buses. Evaluating the vehicle fleet and mapping current socio-spatial travel demand can inform system performance, guide service area development, and support future planning such as expansion to nearby communities and transit hubs. The findings in this case study suggest that on-demand electric transit may be a significant and growing space for advancing highly valued public mobility services. Public transport interventions that consider right-sized, electric, on-demand vehicles can help improve mobility access and reduce energy use and refueling costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Decomposable Formulation of Transmission Constraints for Decentralized Power Systems Optimization

One of the most complicating factors in decentralized solution methods for a broad range of power system optimization problems is the modeling of power flow equations. Existing formulations for direct current power flows either have limited scalability or are very dense and unstructured, making them unsuitable for large-scale decentralized studies. Here, in this work, we present a novel sparsified variant of the injection shift factors formulation, which has a decomposable block-diagonal structure and scales well for large systems. We also propose a decentralized solution method, based on the alternating direction multiplier method, that efficiently handles transmission line outages in N-1 security requirements. Benchmarks on multizonal security-constrained unit commitment problems show that the proposed formulation and algorithm can reliably and efficiently solve interconnection-level test systems with up to 6,515 buses with no convergence or numerical issues.

Alternating-method of multipliers (ADMM)↗

Hestia SW-IFL Onroad Fossil Fuel Carbon Dioxide (FFCO2) product: Road segment-level annual FFCO2 emissions across Arizona (2017-2022), version 1.1

The SW-IFL onroad fossil fuel carbon dioxide (FFCO2) emissions data product represents CO2 emissions from the combustion of fossil fuels by motor vehicles (e.g., passenger cars, trucks, buses, motorcycles) traveling on designated roadways. The emissions are represented geographically on each road segment within the state of Arizona spanning the 2017 to 2022 time period. This data product was developed as part of the Southwest Urban Corridor Integrated Field Laboratory (SW-IFL) project, which aims to provide new knowledge and tools that address urban environmental issues by integrating high-resolution observations, modeling, and civic engagement. The emissions data are provided in CSV (input data, ONR_FFCO2_AZ_county.csv) and GeoPackage form (output polyline objects - about 786,000 road segments, XXXX_AZ_v1.1.gpkg) designated by road class (interstates, arterials, collectors, local). The metadata file (Metadata_SW-IFL_Onroad_annualFFCO2_v1.1.docx) provides details about attributes and data formats. The GeoPackage emissions data are provided separately for local roads and nonlocal roads (interstates, arterials, collectors). The method file (Methods_SW-IFL_Onroad_annualFFCO2_v1.1.docx) describes the data processing flow and data sources. Update on 2024-04-17: Updates were made to both the input emission data file (.csv) and output segment-level emission file (.gpkg). There was an update in county-level emission input data (ONR_FFCO2_AZ_county.csv) and the entire road segments were reprocessed to reflect this update.Update on 2024-04-29: Update was made to one output segment-level emission file (Nonlocal_AZ_v1.0.gpkg). There was an error in the AADT values and the data were reprocessed to reflect this update.Update on 2024-10-22: Temporal coverage was extended to include 2022. VMT values were recalculated using new AADT data and the entire road segments were reprocessed to reflect these updates.

54 ENVIRONMENTAL SCIENCES↗

Hydrogen Fuel Cell Vehicles in Tunnels

There are numerous vehicles which utilize alternative fuels, or fuels that differ from typical hydrocarbons such as gasoline and diesel, throughout the world. Alternative vehicles include those running on the combustion of natural gas and propane as well as electrical drive vehicles utilizing batteries or hydrogen as energy storage. Because the number of alternative fuels vehicles is expected to increase significantly, it is important to analyze the hazards and risks involved with these new technologies with respect to the regulations related to specific transport infrastructure, such as bridges and tunnels. This report focuses on hazards presented by hydrogen fuel cell electric vehicles that are different from traditional fuels. There are numerous scientific research and analysis publications on hydrogen hazards in tunnel scenarios; however, compiling the data to make conclusions can be a difficult process for tunnel owners and authorities having jurisdiction over tunnels. This report provides a summary of the available literature characterizing hazards presented by hydrogen fuel cell electric vehicles, including light-duty, medium and heavy-duty, as well as buses. Research characterizing both worst-case and credible scenarios, as well as risk-based analysis, is summarized. Gaps in the research are identified to guide future research efforts to provide a complete analysis of the hazards and recommendations for the safe use of hydrogen fuel cell electric vehicles in tunnels.

33 ADVANCED PROPULSION SYSTEMS↗

SMART Mobility. Advanced Fueling Infrastructure Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019 and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Advanced Fueling Infrastructure Pillar. This Pillar investigated the charging infrastructure needs of electric ride-hailing and car-sharing vehicles, automated shuttle buses, and freight-delivery truck fleets. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant Pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Electric Vehicle Adoption in Illinois

Over 1.3 million plug-in electric vehicles (PEVs) have been sold in the United States since 2010 when the first Chevy Volt and Nissan Leaf vehicles came on the market. Currently, electric vehicles account for about 2% of monthly light-duty vehicle (LDV) sales. More than 40 different electric vehicle models are actively being marketed, and most of these are LDVs. Over the last few years, automakers have brought more models, with varying mileage ranges, to the market, including medium-duty and heavy-duty trucks and buses. Government agencies at the federal, regional, and state levels, as well as utilities across the United States, have provided financial and non-financial incentives to promote electric vehicle adoption and charging infrastructure deployment. Argonne National Laboratory, sponsored by the U.S. Department of Energy, has developed tools and collected data to quantify the energy, economic, and environmental benefits of electric vehicles. This project has three objectives. First, we summarize the policies and other actions of the federal government, various states/regions, and utilities to promote PEV adoption, and evaluate their relative effectiveness based on a review of the literature. Second, we identify possible PEV adoption paths or scenarios that would result in PEVs making up 15% of all on-road vehicles in Illinois, and we quantify the resulting impacts on petroleum consumption and electricity demand. We also summarize the possible reduction in greenhouse gas (GHG) and criteria pollutant emissions due to PEV adoption. The PEV scenarios include private and public adoption of electric cars, light trucks (e.g., sports utility vehicles [SUVs], vans, pickups), medium-duty vehicles (MDVs), and heavy-duty vehicles (HDVs). Third, we estimate hourly charging loads of light-duty PEVs in 2030 associated with these scenarios, incorporating assumptions about vehicle electric range, efficiency, and travel behavior.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The Value of Motorcycle Electrification in Mexico City

Local and city governments worldwide are looking to adopt emerging technologies and mobility approaches, such as electric vehicles, as they pursue their environmental and economic goals. Commercially available electric vehicles include buses, automobiles, motorcycles, and other two-wheelers. Electrifying transportation is critical to achieve urban sustainability, but it also raises challenges for private and public actors. Jurisdictions across countries are seeking to answer big questions, including how the transportation sector can be electrified, what impacts are priorities, and how such impacts can be obtained.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Development of Control System Functional Capabilities within the IES Plug-and-Play Simulation Environment

The concept of an Integrated Energy System (IES) is meant to combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework is used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES. A new RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization (HERON) has been developed for optimizing the production variables of the IES given the demand profile. Currently, only the limits that affect the production variables and their corresponding time rates of change are considered (explicit constraints). However, other variables are additionally subject to constraints, but the associated limits are not accounted for (implicit constraints). In particular, for the power dispatch problem, the optimization algorithm takes into account the limits on the electrical power output and the corresponding hourly power variations but does not consider other constraints on process variables whose response effects the service life of the IES. This report describes a scheme that allows accounting for implicit constraints without increasing the size of the optimization problem. The Reference Governor (RG) algorithm is traditionally used for enforcing state and control constraints by modifying the set-point trajectories supplied to the feedback regulators. In our application, the RG is coupled within an iterative loop with the HERON-power dispatcher to generate optimal trajectories that ensure the operational constraints are met. A data-driven procedure to derive a representation of the dynamics of the controlled system was developed. First, the variables that represented the state of the system are selected (PCA-based approach), and then state-space representation matrices are derived from the collected measurements (DMDc algorithm). A preliminary version of the developed workflow based on Linear Time Invariant matrices was assessed by adopting a two-unit test case. More sophisticated versions of this workflow foreseeing the on-line derivation of system matrices will be deployed in FY 2021. Finally, a “plug-and-play” library of controllers and state observers was developed in Dymola. Some aspects of the current configuration of the IES unit components, e.g., the encapsulation of the control schemes into dedicated blocks, are consistent with the “plug-and-play” philosophy. Other features, e.g., the system buses collecting the input and the output variables, are not. For this reason, once listed and described the limits of the current configuration, necessary modifications to the plant model interface are presented. As a test-case, the interfaces of the SES model in the RAVEN-based HYBRID framework were reworked accordingly, and two different control schemes were applied to the same plant model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Battery Electric Bus Deployment Considerations in Developing Countries

Electrifying the transportation sector holds many promises and there are numerous strategies for doing so, including the deployment of battery electric buses (BEBs). BEBs can improve air quality (particularly in urban areas), may help curb greenhouse gas emissions, and can have positive impacts on public health and the quality of life for city inhabitants. This fact sheet steps through the six essential BEB considerations: 1) implementation planning, 2) technology selection, 3) economic impacts, 4) charging infrastructure, 5) operational, and 6) maintenance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Alternate Fuel Vehicles in Tunnels

Many types of vehicles using fuels that differ from typical hydrocarbons such as gasoline and diesel are in use throughout the world. These include vehicles running on the combustion of natural gas and propane as well as electrical drive vehicles utilizing batteries or hydrogen as energy storage. These alternative fuels pose hazards that are different from traditional fuels and the safety of these vehicles are being questioned in areas such as tunnels and other enclosed spaces. Much scientific research and analysis has been conducted on tunnel and garage hazard scenarios; however, the data and conclusions might not seem to be immediately applicable to highway tunnel owners and authorities having jurisdiction over tunnels. This report provides a comprehensive, concise summary of the literature available characterizing the various hazards presented by all alternative fuel vehicles, including light-duty, medium- and heavy-duty, as well as buses. Research characterizing both worst-case and more plausible scenarios and risk-based analysis is also summarized Gaps in the research are identified in order to guide future research efforts to provide a complete analysis of the hazards and recommendations for the use of alternative fuel vehicles in tunnels.

33 ADVANCED PROPULSION SYSTEMS↗

Robust Distributed State Estimator for Interconnected Transmission and Distribution Networks (Final Report RPPR-1)

This project’s objective is to develop a combined transmission and distribution state estimator which accounts for very large system size and model complexity (by way of distributing the computations) and large number of solar PV units connected to the distribution system on multiple feeders. The project not only provides a robust formulation and solution to this problem but also tests the solution by implementing it on a well-established large utility system. It introduces several improvements with respect to the state of the art in existing state estimation software: (a) The developed state estimator (SE) allows robust and accurate monitoring of bidirectional flows in distribution systems which result due to the distributed energy sources which are not observable and thus not incorporated in generation dispatch; (b) Large utility systems with tens of thousands of transmission buses and hundreds of thousands of distribution nodes are difficult to model as a single integrated system. This shortcoming is addressed by developing a “scalable distributed computational framework” which allows splitting the ultra large system models into several small subsystems and coordinating their solution by a robust and practical state estimation formulation; (c) Measurement errors irrespective of their locations are detected and removed by the developed state estimator. Historically, transmission and distribution systems were analyzed and operated as two independent systems. Given the non-transposed short feeder sections, unevenly loaded phases, strictly radial configuration and unidirectional power flows in the absence of remote generation, distribution system analysis was customized to account for these characteristics. However, some of these assumptions are no longer valid (non-radial configuration, bidirectional power flows) and thus distribution system analysis should be revisited. Furthermore, in the past, the interaction between the transmission and distribution systems was quite passive, where distribution substations were modeled as lumped loads in the transmission system model. With substantial generation injected by renewable generation located in the distribution systems, such modeling will no longer be accurate. The developed state estimator facilitates proper monitoring of the interactions between the transmission and distribution systems and enables smart dispatch of these units which are made observable by the state estimator.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Comprehensive Total Cost of Ownership Quantification for Vehicles with Different Size Classes and Powertrains

In order to accurately compare the costs of two vehicles, the total cost of ownership (TCO) should consist of all costs related to both purchasing and operating the vehicle. This TCO analysis builds on previous work to provide a comprehensive perspective of all relevant vehicle costs of ownership. In this report, we present what we believe to be the most comprehensive explicit financial analysis of the costs that will be incurred by a vehicle owner. This study considers vehicle cost and depreciation, financing, fuel costs, insurance costs, maintenance and repair costs, taxes and fees, and other operational costs to formulate a holistic total cost of ownership and operation of multiple different vehicles. For each of these cost parameters that together constitute a comprehensive TCO, extensive literature review and data analysis were performed to find representative values in order to build a holistic TCO for vehicles of all size classes. The light- and heavy-duty vehicles selected for analysis in this report are representative of those that are on the road today and expected to be available in the future. Important additive analyses in this study include systematic analysis of vehicle depreciation, in-depth examination of insurance premium costs, comprehensive maintenance and repair estimates, analysis of all relevant taxes and fees, and considerations of specific costs applicable to commercial vehicles. We find that cars depreciate faster than light trucks and that older plug-in electric vehicles have a greater depreciation rate than newer electric vehicles. Light-duty vehicle (LDV) insurance costs show comparable costs for different powertrains, and lower costs for larger size classes. Medium- and heavy-duty vehicle (MHDV) insurance costs vary significantly by vocation. Electric and electrified powertrains have lower maintenance and repair costs than internal combustion engine (ICE) powertrains for all vehicle sizes, relative to vehicle price. MHDV maintenance and repair costs depend heavily on vocation and duty cycle. LDV taxes and fees are comparable across powertrain types and size classes, though marginally higher registration fees exist for alternative fuel vehicles. MHDV fees depend on the vocation, weight rating, and state. Many electric tractor trailers would be affected by additional battery weight, reducing the available payload capacity, and this cost can be substantial. Electric vehicle charging for commercial vehicles can be time-consuming; labor rates can cause this cost to dominate TCO. With improved knowledge of each of the cost components, we calculate a lifetime TCO for comparison across vehicles of different types and attributes. For a simulated small sport utility vehicle in 2025, modeled using Autonomie, the hybrid electric vehicle (HEV) has the lowest cost, followed by the conventional ICE vehicle. For MHDV, TCO can be drastically different depending on the vocation. Long-haul vehicles typically have the lowest per-mile costs. Excluding labor costs, the class 4 delivery has a comparable TCO to the day cab. Vocational trucks, refuse trucks, and transit buses have a high per-mile cost of ownership due to maintenance and insurance. For all of these vehicles, the cost of operating the vehicle is heavily weighted by the labor of the driver, followed by the fuel costs. While the HEV begins as the lowest cost powertrain for passenger vehicles, fuel cells are forecast to reach cost parity by 2030 when hydrogen prices reach $\$ 5$/kg while battery electric vehicles (BEV) reach cost parity by 2035 at a battery cost of $\$ 98$ per usable kWh of capacity, with these two technologies being the lowest cost in 2050. For the class 8 day cab tractor, the HEV and ICE vehicle begin as the lowest cost powertrains, and the 250-mile BEV reduces in cost from the most expensive to the least expensive by 2030.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗