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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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DOE OSTI2023

A Review on Simulation Platforms for Agent-Based Modeling in Electrified Transportation

As the use of combustion engine vehicles plays a deciding role in global warming, we can observe a trend to replace them with electric vehicles (EV) driven by new environmentally conscious policies and increasing technological capabilities. With improvements in driving range and reduction in prices come new challenges that may hamper the progress towards complete battery driven transportation. A major challenge for the increasing EV adoption is the planning of extensions to existing infrastructure or the inclusion of new infrastructure components in the planning process. This demands increasingly complex planning tools that can simulate the interplay between different stakeholders in modern transportation scenarios such as EVs, charging stations, energy providers, and general transportation participants. Simulation platforms for agent-based modeling in transportation have been developed as effective interactive tools that allow planners to explore different trade-offs across different scenarios with the ability to simulate the impact of policy or infrastructure decisions on the different stakeholders in the simulation. This article surveys several of the major simulation platforms that include modern EV-based forms of transportation and allow the simulation of relevant infrastructure components alongside the well established transportation simulations. These tools allow researchers to analyze expected traffic flow, identify possible charging station locations based on area demand, predict electrical grid demand, and more. Here this survey intends to make it easier for researchers to identify and apply a simulation platform in the context of supporting the increasing electrification of the transportation sector, enabling more efficient simulation and planning capabilities in this domain.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life

Agent-based models (ABMs) in transportation modeling simulate activity and travel decisions at the disaggregate level of households and individuals. To do this, ABMs require detailed and realistic information on agents’ socioeconomic and demographic characteristics. Various synthetic population generators have been proposed to address this need. However, most of those currently in practice are cross-sectional in nature and do not account for the dynamics within households and individuals as they progress through life events over time. This is a major shortcoming, as literature has shown that transportation decisions are affected by the transition between and co-occurrence of life cycle events. While some demographic evolution simulators have been proposed to address this issue, they are developed using cross-sectional data and capture only a small set of life cycle events and their interdependence. Addressing these drawbacks, we propose a demographic microsimulator (DEMOS) that captures the “continuum of life” by considering a range of household- and individual-level life cycle events. DEMOS is developed using the Panel Survey of Income Dynamics, one of the world’s longest-running longitudinal surveys. The DEMOS submodels consider key life cycle events that are influenced by agents’ demographic variables. DEMOS is applied to evolve the population of the San Francisco Bay Area over a 9-year horizon. Results demonstrate how DEMOS generates life trajectories and how DEMOS outputs match the observed demographic trends. DEMOS is expected to enable longitudinal analysis in the context of ABMs and expand ABMs analyses relating to dynamic processes such as household-level vehicle transactions.

Demographic evolution↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

An integrated transportation-power system model for a decarbonizing world

Rising demand for electricity from electric vehicles (EVs) will require new paradigms to guarantee reliable and low-cost electricity. This study couples an agent-based travel demand simulator and an electricity grid model to assess the economic costs of supplying power to meet EVs' added demand across the Chicago region. Results suggest that shifting from personal EVs to a fleet of shared, fully-automated all-electric vehicles (SAEVs) could lower per-mile emissions, congestion, and embodied vehicle and charging infrastructure emissions. Further, the results should compel policymakers to shift the cost of providing power onto commercial customers, like electric ride-hail fleets, through price-indexed electricity prices, which can shift charging to off-peak periods or away from resource-scarce hours.

Integrated modeling↗
DOE OSTI2023

Approaches for Synthesis and Deployment of Controller Models on Automated Vehicles for Car-following in Mixed Autonomy

This paper describes the software design patterns and vehicle interfaces that were employed to transition vehicle controllers from simulation environments to open-road field experiments. The approach relies on a life cycle that utilizes model-based design and code generation, along with agile software development, and both software and hardware-in-the-loop testing, with additional safety margins. Autonomous designs should consider the dynamics of mixed autonomy in traffic to safely operate among humans. The software that provides a vehicle’s behavior intelligence is often developed through simulation, which may have a mismatch between dynamics, or as a result of a reinforcement learning workflow, which may be a black box with challenges to analyze. In each of these cases, it is important to have research interfaces that provide strongly typed data streams accessible to researchers who are not software experts while continuing to satisfy safety and liveness constraints. This paper describes how we design the hardware platform interfaces and software design process for a mixed autonomy traffic experiment with a leader-follower scenario. Controller synthesis for these vehicles requires clearly articulated vehicle interfaces and software design patterns for successful onboard deployment. Testing strategies for such controllers are also described before algorithms are transitioned to full-scale field experiments with safety operators for the vehicles. Testing strategies include software-in-the-loop simulation testing, hardware-in-the-loop simulation, ghost-car testing, and read-only testing in live traffic. With our approach, we were not only able to validate our controller synthesized in scripts and simulation, but also able to scale deployment to multiple vehicles.

Bhadani, Rahul↗
DOE OSTI2026

Mauka Energy FEVER Tool Dataset

Mauka Energy’s dataset, developed under the Forestry Electric Vehicle Energy Routing (FEVER) project and funded by the U.S. Department of Energy’s Small Business Innovation Research program, is a high-resolution geospatial resource designed to support energy modeling for electric log trucks in complex forestry environments. The dataset integrates detailed spatial and road network data to enable accurate simulation of vehicle performance across varied terrain. At its core, the dataset incorporates lidar-derived elevation models, road alignments, and surface classifications from Oregon State University’s McDonald-Dunn Research Forest. These data capture fine-scale variations in slope, curvature, and surface conditions across forest road systems, allowing for vehicle-level analysis of energy consumption and recovery. The dataset also includes data collected on the surrounding public and private road networks in Benton County, Oregon, used in real-world haul routes. These connecting segments provide critical context for modeling transitions between forest operations and regional transportation infrastructure, incorporating attributes such as grade profiles, elevation change, and speed constraints. This combined dataset underpins the development of Mauka Energy’s rolldown tool, which quantifies energy use and regenerative braking potential on downhill and variable-grade segments. By leveraging high-resolution terrain and road data, the FEVER project enables more accurate assessment of electric vehicle feasibility and performance in forestry applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Pedestrian-Involved Traffic Signal Optimization Using Decentralized Graph-based Multi-Agent Reinforcement Learning

Incorporating pedestrian movements in traffic signal timing optimization is essential for ensuring smooth and safe urban transportation systems. To develop an effective signal timing plan, it is crucial to comprehend how various pedestrian accommodation strategies impact both vehicle and pedestrian flow. This study explores the application of the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) method for signal timing optimization. It considers both pedestrian and vehicle traffic state and assesses the effects of fixed and adaptive pedestrian request response on traffic and signal timing. The evaluation of DGMARL encompasses vehicles Eco_PI which is fuel consumption impact related to vehicle stops and stop delay, and pedestrian waiting time to ensure overall system efficiency. The proposed approach was evaluated using a Digital Twin microscopic traffic simulation model of MLK Smart Corridor in Chattanooga, Tennessee. The evaluation outcomes of vehicles Eco_PI, pedestrian waiting time and serving time are compared with the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall to determine the most suitable approaches for various traffic conditions. The results indicated that, on average, the strategy of Signal timing optimization with adaptive pedestrian request response improved the Eco_PI by 32.43%, and Signal timing optimization with both automated pedestrian traffic demand and adaptive pedestrian request response improved the Eco_PI by 31.62% compared to the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall.

K Kumarasamy, Vijayalakshmi↗
DOE OSTI2023

Vehicle Powertrain Simulation Accuracy for Various Drive Cycle Frequencies and Upsampling Techniques

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory's Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation. A large data set of 1 Hz trajectories is used to quantify the accuracy loss when simulating energy consumption for conventional, hybrid, and battery electric powertrains using less than 1 Hz data. Techniques to upsample lower frequency drive cycle data in order to increase accuracy are also explored. Median energy consumption errors when simulating energy consumption for a 1/10 Hz trajectory are found to be 3-6% when compared to 1 Hz trajectories. Applying upsampling and interpolation techniques are shown to reduce the simulation errors by roughly 50%. The findings in this work can guide connected vehicle data collection specifications and processing techniques applied when using collected data for powertrain simulation.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

FY2020 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2020 focused on analytical research and large-scale modeling and simulation to understand the impacts that new mobility technologies and services will have at the vehicle-, traveler-, and overall transportation system-level. This research included the development of a multi-fidelity, end-to-end transportation system models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results from projects within each of the EEMS activity areas. The Computational Modeling and Simulation key activity area summarizes work within the sub-areas of (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) Artificial Intelligence, High-Performance Computing, and Data Analytics, and (3) Core Simulation and Evaluation Tools. Additionally, the program’s advanced R&D projects are summarized within (4) the Connectivity and Automation Technology key activity area. Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Holistic Fuel Cell Electric Vehicle/Hydrogen Station Optimization Model (CRADA CRD-18-00745 Final Report)

The National Renewable Energy Lab (NREL), Argonne National Lab (ANL), Sandia National Lab (SNL), and Frontier Energy, Inc comprise a team that will perform a review of currently available models that can simulate both a hydrogen station and a fuel cell electric vehicle (FCEV), and whose owners are willing to make them available freely. We will then down select to a single model or model pair, secure agreements for their free use, and validate the resulting model or coupled models. For example, we may evaluate an existing 1-D hydrogen fueling model developed by Kyushu University for the Japanese New Energy and Industrial Technology Development Organization (NEDO). The validation will take place using data from various sources, potentially including the European Joint Research Center (JRC) HyTransfer project, SAE testing performed by Powertech to validate SAE J2601, and whole-station validation using the NREL’s Hydrogen Infrastructure Testing and Research Facility (HITRF). The team will make the model and all the validation data open to the public at the end of the project.

1-D hydrogen fueling model↗
DOE OSTI2025

Enhancing Grid Resilience with HIVE: Decentralized V2G Coordination for Black Starts

This paper proposes the HIVE (Harmonized Integration of Vehicle Energy for Grid Support) model, a novel game-theoretic framework for decentralized coordination of electrified vehicles to enable black start and load restoration during grid outages. In the absence of a central controller, HIVE employs a cooperative game to model vehicle interactions, allowing autonomous decision-making while admitting to a Nash equilibrium for grid restoration. The framework addresses the heterogeneity of vehicles and their operational constraints, selecting a lead vehicle for grid-forming and coordinating grid-following vehicles to support prioritized loads. Applied to a hospital blackout scenario, HIVE demonstrates robust performance in forming an islanded microgrid and sustaining critical loads under varying vehicle availability, state of charge, and power constraints. Simulation results highlight the model’s effectiveness in ensuring decentralized coordination of energy allocation and prioritizing loads, offering a scalable solution for resilient grid operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2025

HD ADOPT: Heavy-Duty Vehicle Choice Model Documentation

HD ADOPT is a logit consumer vehicle choice and stock model that analyzes the Class 8 tractor market. The model projects future technology shares, fuel consumption, and greenhouse gas (GHG) emissions under input assumptions of technology progress, energy prices, and policies. ADOPT is distinguished from other vehicle choice models through inclusion of non-linear and heterogenous consumer preferences and characterization of the full range of market options rather than use of composite vehicles. In addition, ADOPT has integrated vehicle simulation capabilities that enable performance assessment and optimization of endogenous technology evolution. Optionally, the model is able to adjust this evolution to enforce compliance with fuel economy and GHG emissions regulations. Primary results include projection of technology shares and future in-use fleet energy demand, petroleum consumption, and GHG emissions. This enables analysis and comparison of future scenarios of technology improvements, economic conditions, and national policies. Recent new features for the HD modeling also enable examination of different on-board hydrogen fuel storage technologies from the lens of consumer preferences for vehicle cost and range. This report documents current ADOPT capabilities and methodologies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Energy consumption and charging load profiles from long-haul truck electrification in the United States

Abstract The urgent need to decarbonize the transportation sector combined with falling battery prices has spurred industry and policy interest in long-haul truck electrification. The charging behavior and resulting loads from electrified long-haul freight trucks are crucial for the smooth operation of the electric grid and have far-reaching environmental impacts (e.g., greenhouse gas and other air pollutant emissions). However, the aggregate energy impact of a fleetwide shift to electrified long-haul freight trucking has not been explored. This study combines electric truck design scenarios, bottom-up truck weight modeling, vehicle energy modeling, large-scale truck traffic data, and simulation of likely operation and charging behaviors to estimate end-use energy consumption and location-specific hourly charging loads for a national fleet of long-haul electric trucks. Relative to a fleet of future diesel trucks, electrification would reduce direct end-use energy consumption by 0.9 × 10 18 J (0.9 quadrillion BTU), but electrification might increase life cycle energy consumption depending on the electricity source. The electricity required to charge long-haul electric trucks is equivalent to five percent of annual electricity consumption in the United States (US). The simulated truck charging loads peak during the day across the US grid regions, but the charging peaks’ exact timing is sensitive to when trucks are dispatched for operation. The load shapes suggest that electric trucks’ charging loads can coincide with peaks in solar power generation, and planning could enable on- or off-site integration between truck charging stations and renewable electricity generation.

Tong, Fan (ORCID:0000000346613956)↗
DOE OSTI2024

A Large-Scale Analysis to Optimize the Control and V2V Communication Protocols for CDA Agreement-Seeking Cooperation

Cooperative driving automation (CDA) Class C, agreement-seeking cooperation, is an innovative and practical solution that can promote cooperation among general passenger vehicles on the road. However, more comprehensive studies are needed before establishing the standard protocols of agreement-seeking cooperation, such as communication frequency and the duration of cooperation. Here, this article presents an initiative study on the impacts of communication capabilities on agreement-seeking cooperation. Through a large-scale analysis by regulating vehicle-to-vehicle (V2V) communication metrics, this work suggests desirable system parameters that can maximize the benefits of cooperation and ensure reliable operability while avoiding exhaustive communication loads. As the first step, an example agreement-seeking cooperation system is created for a car-following scenario, including decision-making and control algorithms for autonomous vehicles. Then, software-in-the-loop tests explore the performance of the developed system as it encounters various communication risks, such as latency and message packet drops. The system performance metrics are evaluated from various angles, including the time consumed for the agreement-seeking process, cooperation ratio, and the ratio of faulty cooperation. Energy saving from the cooperation is assessed by using simulation software that can run multiple high-fidelity vehicle models simultaneously. Based on the analyses, this article suggests the V2V communication requirements for the reliable operation of CDA agreement-seeking, which can be referred to when developing the standard protocols of agreement-seeking cooperation.

42 ENGINEERING↗
DOE OSTI2021

I.6.C Battery Safety Testing

Abuse tests are designed to determine the safe operating limits of HEV\PHEV energy storage devices. Testing is intended to achieve certain worst-case scenarios to yield quantitative data on cell\module\pack response, allowing for failure mode determination and guiding developers toward improved materials and designs. Standard abuse tests with defined start and end conditions are performed on all devices to provide comparison between technologies. New tests and protocols are developed and evaluated to more closely simulate real world failure conditions. While robust mechanical models for vehicles and vehicle components exist, there is a gap for mechanical modeling of EV batteries. The challenge with developing a mechanical model for a battery is the heterogeneous nature of the materials and components (polymers, metals, metal oxides, liquids).

25 ENERGY STORAGE↗
DOE OSTI2024

Integrating Human Factors in Dynamic Rideshare Assignment: Willingness-To-Pay for Delay

Dynamic ride-sharing (DRS) has been projected to be a key solution to lowering system-wide congestion. Despite recent developmental progress, demand studies for DRS suggest low levels of willingness for travelers to use such services. The disconnect between DRS system designs and user preferences limits the application impacts of DRS in the real world. Therefore, this paper aims to design a new DRS trip/vehicle assignment strategy by considering the user preferences of choices under different levels of service. In this study, an agent-based simulation approach is used to model a fleet of shared vehicles that allows DRS. An optimization model is developed to match riders to vehicles while accounting for traveler delay and delay acceptance. Travelers are also dynamically issued predictive discounts, catered to their expected willingness to pay, to incentivize them to accept longer trip delays. Results show that the proposed approach can improve system efficiency by increasing average vehicle occupancy by up to 1.0 persons/trip and DRS acceptance by up to 38.9% depending on fleet size. Additionally, congestion is eased through the decrease of empty vehicle miles traveled by up to 7.1%.

Paul, Joseph↗
DOE OSTI2021

OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies

Electrification and the growth of distributed energy resources (DERs), including flexible loads, are changing the energy landscape of electric distribution systems and creating new challenges and opportunities for electric utilities. Changes in demand profiles require improvements in distribution system load models, which have not historically accounted for device controllability or impacts on customer comfort. Although building modeling research has focused on these features, there is a need to incorporate them into distribution load models that include DERs and can be used to study grid-interactive buildings. In this paper, we present the Object-oriented, Controllable, High-resolution Residential Energy (OCHRE) model. OCHRE is a controllable thermal-electric residential energy model that captures building thermal dynamics, integrates grid-dependent electrical behavior, contains models for common DERs and end-use loads, and simulates at a time resolution down to 1 minute. It includes models for space heaters, air conditioners, water heaters, electric vehicles, photovoltaics, and batteries that are externally controllable and integrated in a co-simulation framework. Using a proposed zero energy ready community in Colorado, we co-simulate a distribution grid and 498 all-electric homes with a diverse set of efficiency levels and equipment properties. We show that controllable devices can reduce peak demand within a neighborhood by up to 73% during a critical peak period without sacrificing occupant comfort. We also demonstrate the importance of modeling load diversity at a high time resolution when quantifying power and voltage fluctuations across a distribution system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Next Generation Multi-Use Heavy-Duty High-Flow Direct-Fill Hydrogen Refueling Station with Standardized Design (CRADA Final Report)

The Contractor will build a liquid hydrogen (LH2) fueling station model that is capable of simulating the amount of hydrogen transferred from the liquid storage tank to the vehicle, depending on the station’s design and control scheme. This modeling work will allow the Parties to seek the design and control scheme of an LH2 station that can fill a targeted number of vehicles while meeting a time-average flow rate of 10 kg/min for each fill. While evaluating a high dispensing performance station design and control scheme, the Contractor will analyze the design and control scheme are economically viable.

08 HYDROGEN↗