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Sheppard, Colin

Publications and source records attributed to Sheppard, Colin.

14 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Behavior, Energy, Autonomy, Mobility Modeling Framework (BEAM) v1.0

The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility- maximizing evolutionary algorithm that minimizes each individual’s cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within- day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing. It is designed to integrate with other open source transportation models, such as ActivitySim.

Lazarus, Jessica↗
DOE OSTI2022

Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC)

Review of the FY20 and FY21 progress for the Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC) project. GEMINI-XFC will use first-of-a-kind integrated high-fidelity grid and transport modeling to identify effective pathways for widespread electrification, to design and evaluate integrated vehicle-grid control schemes, and to optimize electric vehicle integration at a full regional scale with individual customer resolution.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
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 OSTI2020

SMART Mobility. Mobility Decision Science 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 Mobility Decision Science Pillar. The Mobility Decision Science Pillar sought to fill gaps in existing knowledge about the human role in the mobility system including travel decision-making and technology adoption in the context of future mobility. The objective was to study how underlying preferences, needs, and contextual factors might constrain or hasten future transportation system scenarios. 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↗
DOE OSTI2020

SMART Mobility. Modeling Workflow Development, Implementation, and Results 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 SMART Mobility Modeling Workflow effort. The SMART Mobility Modeling Workflow was developed to evaluate new transportation technologies such as connectivity, automation, sharing, and electrification through multi-level systems analysis that captures the dynamic interactions between technologies. By integrating multiple models across different levels of fidelity and scale, the Workflow yields insights about the influence of new mobility and vehicle technologies at the system level. For information about the other Pillars, please refer to the relevant pillar’s Capstone Report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

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↗
DOE OSTI2020

Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC)

Increasing vehicle electrification will require extensive use of extreme fast charging (XFC), especially for larger vehicles. Uncoordinated XFC can create grid challenges, particularly at the distribution level. Two strategies can support widespread XFC: gold-plate the grid (i.e., upgrade all systems to enable worst-case, fully coincident loads) or use integrated planning to codesign a smart system based on advanced controls that leverage load flexibility and distributed energy resources. With the right design and control, XFC can simultaneously support both mobility and grid operations. Fully realizing the potential of XFC, however, will require unprecedented coordination among the charging infrastructure, grid, and vehicles. GEMINI-XFC combines high-fidelity grid and transport modeling at an unprecedented level of resolution and codesigns a smart system based on advanced controls that leverage load flexibility and distributed energy resources to optimize the integration of extreme fast charging (XFC) across a full regional scale with individual customer resolution.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

FY2018 Analysis Annual Progress Report

During fiscal year 2018 (FY 2018), the U.S. Department of Energy Vehicle Technologies Office (VTO) funded analysis projects supportive of VTO’s goals to pursue early stage research in vehicle and mobility system technologies to improve energy efficiency, increase energy reliability and security, improve transportation affordability, and promote economic growth. VTO analysis projects result in a foundation of data, analytical models, and applied analyses that provide insights into critical transportation energy problems and assist in research investment prioritization and portfolio planning.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Behavior, Energy, Autonomy, Mobility Modeling Framework

The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility-maximizing evolutionary algorithm that minimizes each individual's cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within-day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing.

Bae, Sangjae↗
DOE OSTI2017

Final Report Feasibility Study for the California Wave Energy Test Center (CalWavesm)

The California Wave Energy Test Center (CalWave) Feasibility Study project was funded over multiple phases by the Department of Energy to perform an interdisciplinary feasibility assessment to analyze the engineering, permitting, and stakeholder requirements to establish an open water, fully energetic, grid connected, wave energy test center off the coast of California for the purposes of advancing U.S. wave energy research, development, and testing capabilities. Work under this grant included wave energy resource characterization, grid impact and interconnection requirements, port infrastructure and maritime industry capability/suitability to accommodate the industry at research, demonstration and commercial scale, and macro and micro siting considerations. CalWave Phase I performed a macro-siting and down-selection process focusing on two potential test sites in California: Humboldt Bay and Vandenberg Air Force Base. This work resulted in the Vandenberg Air Force Base site being chosen as the most favorable site based on a peer reviewed criteria matrix. CalWave Phase II focused on four siting location alternatives along the Vandenberg Air Force Base coastline and culminated with a final siting down-selection. Key outcomes from this work include completion of preliminary engineering and systems integration work, a robust turnkey cost estimate, shoreside and subsea hazards assessment, storm wave analysis, lessons learned reports from several maritime disciplines, test center benchmarking as compared to existing international test sites, analysis of existing applicable environmental literature, the completion of a preliminary regulatory, permitting and licensing roadmap, robust interaction and engagement with state and federal regulatory agency personnel and local stakeholders, and the population of a Draft Federal Energy Regulatory Commission (FERC) Preliminary Application Document (PAD). Analysis of existing offshore oil and gas infrastructure was also performed to assess the potential value and re-use scenarios of offshore platform infrastructure and associated subsea power cables and shoreside substations. The CalWave project team was well balanced and was comprised of experts from industry, academia, state and federal regulatory agencies. The result of the CalWave feasibility study finds that the CalWave Test Center has the potential to provide the most viable path to commercialization for wave energy in the United States.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

All Electric Passenger Vehicle Sales in India by 2030: Value proposition to Electric Utilities, Government, and Vehicle Owners

In India, there is growing interest among policymakers, planners, and regulators for aggressive electrification of passenger vehicles. For example, Piyush Goyal, the Minister of State for India’s Ministry of Coal, Power, New and Renewable Energy, announced an aspirational goal of converting all vehicle sales in India to battery electric vehicles (BEVs) by 2030 (Economic Times, 2016). In 2012, India has already announced the National Mission on Electric Mobility (NMEM) sets a countrywide goal of deploying 6 to 7 million hybrid and electric vehicles (EVs) by 2020 (DHI, 2012). A major policy motivation for transport electrification is to reduce India’s oil import dependency. The objective of this paper is to assess the effect of full electrification of vehicle sales in India by 2030 on the key stakeholders such as BEV owners, electric utilities, and the government. Specifically, we attempt to answer the following questions: (a) How does the total vehicle ownership cost of BEVs compare with the conventional vehicles? (b) What is the additional load due BEV charging? (c) What is the impact on the power sector investments, costs, and utility revenue? (d) How can smart BEV charging help renewable energy grid integration? (e) What is the impact on the crude oil imports? (f) What is the impact on the greenhouse gas (GHG) emissions?

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Modeling plug-in electric vehicle charging demand with BEAM: the framework for behavior energy autonomy mobility

This report summarizes the BEAM modeling framework (Behavior, Energy, Mobility, and Autonomy) and its application to simulating plug-in electric vehicle (PEV) mobility, energy consumption, and spatiotemporal charging demand. BEAM is an agent-based model of PEV mobility and charging behavior designed as an extension to MATSim (the Multi-Agent Transportation Simulation model). We apply BEAM to the San Francisco Bay Area and conduct a preliminary calibration and validation of its prediction of charging load based on observed charging infrastructure utilization for the region in 2016. We then explore the impact of a variety of common modeling assumptions in the literature regarding charging infrastructure availability and driver behavior. We find that accurately reproducing observed charging patterns requires an explicit representation of spatially disaggregated charging infrastructure as well as a more nuanced model of the decision to charge that balances tradeoffs people make with regards to time, cost, convenience, and range anxiety.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Battery Electric Vehicles can reduce greenhouse has emissions and make renewable energy cheaper in India

India's National Mission on Electric Mobility (NMEM) sets a countrywide goal of deploying 6 to 7 million hybrid and electric vehicles (EVs) by 2020. There are widespread concerns, both within and outside the government, that the Indian grid is not equipped to accommodate additional power demand from battery electric vehicles (BEVs). Such concerns are justified on the grounds of India's notorious power sector problems pertaining to grid instability and chronic blackouts. Studies have claimed that deploying BEVs in India will only

33 ADVANCED PROPULSION SYSTEMS↗