Exploring the future energy-mobility nexus: The transportation energy & mobility pathway options (TEMPO) model
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Muratori, Matteo↗
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The Transportation Energy and Mobility Pathway Options (TEMPO) Model: Overview and Validation of V1.0 [Slides]
This presentation documents the approaches and methods used in the Transportation Energy & Mobility Pathway Options™ (TEMPO) model to evaluate passenger and freight demand for transportation and mobility services, project vehicle ownership and technology adoption decisions, and determine transport mode choices to derive scenarios of future energy use and emissions. TEMPO is an all-inclusive transportation demand model that covers the entire United States, with an implicit spatial resolution and an hourly temporal resolution that allows for generating time-resolved energy use profiles to assess multisectoral integration aspects. Key features of the TEMPO model include the ability to perform endogenous out-of-sample forecasting to extrapolate recent emerging trends and analyze impacts of disruptive technological breakthroughs and behavioral changes. TEMPO employs an innovative representation of passenger mobility demand stemming from household-level decisions that determine vehicle adoption, ownership, and use based on sociodemographics (e.g., income, household composition), technology attributes (e.g., travel cost, time), geography (e.g., urban, suburban, rural) and population-specific multiday mobility and travel requirements. This representation enables a more forward-looking perspective on the use of new mobility options and the adoption of alternative fuel vehicles, as well as a more accurate representation of their energy usage profiles than previous modeling approaches. A comparison with the U.S. Energy Information Administration’s Annual Energy Outlook showcases the ability of TEMPO to accurately replicate widely accepted projections by representing the key elements of the entire transportation sector at the appropriate level of resolution. TEMPO is intended to generate future scenarios of technology adoption, energy use, and emissions in the transportation sector to compare alternatives, inform decision makers, and assess integration with energy infrastructure and supply systems at an appropriate spatiotemporal resolution.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Energy Systems Decarbonization: Perspectives on Electric Vehicles and Grid Integration
The imperative for strong and rapid emissions reductions to mitigate global warming and enhance air quality necessitates a transition to net-zero emissions. This shift requires significant changes throughout the entire energy supply-demand ecosystems, tailored to various sectors. Transportation stands as the least-diversified energy sector and the largest source of U.S. GHG emissions. As the primary catalyst for vehicle decarbonization when paired with clean electricity, electric vehicles (EVs) will play a pivotal role in the future. EVs are poised to drive substantial growth in electricity demand and presents a unique opportunity to provide demand-side flexibility that is crucial for future renewable-dominated electricity systems. Smart integration of EVs can strengthen the grid, reducing costs and enhancing resilience. At NREL, laying the scientific groundwork to actualize this vision: our research is finding ways for EVs to support grid planning and operations across several timescales and to fully exploit the synergies between EVs and renewables.
ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
Electric Vehicles: A Sustainable Solution for Transportation Systems
The imperative for strong and rapid emissions reductions to mitigate global warming and enhance air quality necessitates a transition to net-zero emissions. This shift requires significant changes throughout the entire energy supply-demand ecosystems, tailored to various sectors. Transportation stands as the least-diversified energy sector and the largest source of U.S. GHG emissions. As the primary enabler for vehicle decarbonization when paired with clean electricity, electric vehicles (EVs) will play a pivotal role in the future. This talk summarizes current status of EV technologies and markets, future projections, and opportunities associated with a conversion to EV for all on-road vehicles. Moreover, EVs are poised to drive substantial growth in electricity demand and presents a unique opportunity to provide demand-side flexibility that is crucial for future renewable-dominated electricity systems. Smart integration of EVs can strengthen the grid, reducing costs and enhancing resilience.
ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
Toward Human-Centric Transportation and Energy Metrics: Influence of Mode, Vehicle Occupancy, Trip Distance, and Fuel Economy
Traditional metrics measuring transportation and energy outcomes can be augmented to better represent impacts on people's lives and systems-level performance. In this context, this study introduces two novel metrics: road capacity (as number of people traveling and accessing services) and energy intensity (as energy use for people traveling and accessing services). Current national-level distributions of available data in the United States for factors contributing to the two new integrated metrics are used as context to evaluate potential outcomes. These factors include vehicle occupancy, mode share, fuel economy, and trip distance. Variations in input values provide insights on how these factors shape efficiencies in road capacity and energy intensity. Parametric sensitivity analysis indicates that the impact of each input depends upon the metric being evaluated. For the human-centered road capacity mobility metric, increasing vehicle occupancy has the largest effect – twice that of increasing mode share for bike, walk, and transit. For the energy intensity mobility metric, the effect of improving fuel economy is the largest. However, when the focus is on accessibility (instead of mobility), for both metrics the effect of lowering average trip distance is the largest. Additionally, a novel interactive tool to visualize the results for various parameter combinations makes the metrics practitioner ready. The findings suggest that the diffusion of new human-centric metrics that benchmark outcomes associated with road capacity and energy may be significant in motivating new sustainable transportation investments and efficient utilization of infrastructure, mobility assets, and services.
ADVANCED PROPULSION SYSTEMS↗
Decision Support Tool for Planning Neighborhood-Scale Deployment of Low-Speed Shared Automated Shuttles
Increasing interest and investment in connected, automated, and electric vehicles as well as mobility-as-a-service (MaaS) concepts are paving the way for the next major transformation in transportation through automated and shared mobility. The initial excitement toward rapid deployment and adoption of automated vehicles (AVs) has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geofenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities in relation to benefits or disadvantages of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This paper presents a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as the Automated Mobility District modeling and simulation toolkit, the proposed decision support tool intends to help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles (SAVs) in geofenced regions. This paper describes the toolkit, as well as a sample scenario analysis for the deployment of low-speed automated shuttles in Greenville, South Carolina, U.S. Results from the scenario study demonstrate the effectiveness of the proposed simulation toolkit in planning for advanced mobility systems.
47 OTHER INSTRUMENTATION↗
Unmanned Aerial Vehicle Synthetic Aperture RADAR for Surface Change Monitoring
Space-based and airplane-based synthetic aperture RADAR (SAR) can monitor ground height using interferometric SAR (InSAR) collections. However, fielding the airplane-based SAR is expensive and coordinating the frequency and timing of ground experiments with space-based SAR is challenging. This research explored the possibility of using a small, mobile unmanned aerial vehicle- base (UAV) SAR to see if it could provide a quick and inexpensive InSAR option for the Source Physics Experiment (SPE) Phase III project. Firstly, a local feasibility collection using a UAV-based SAR showed that InSAR products and height measurements were possible, but that in-scene fiducials were needed to assist in digital elevation model (DEM) construction. Secondly, an InSAR collection was planned and executed over the SPE Phase III site using the same platform configuration. We found that the image formation by the SAR manufacturer creates discontinuities, and that noise impacted the generation and accuracy of height maps. These processing artifacts need to be overcome to generate an accurate height map.
47 OTHER INSTRUMENTATION↗
Community Public Mobility Using On-Demand, Low-Speed Electric Vehicles: A Case Study in Downtown St. Louis, Missouri
Legacy fixed route transit systems designed to serve commuters struggle to provide efficient and effective service for short neighborhood trips and for population groups unable to access and egress transit stops using active modes (e.g., elderly, disabled). Neighborhood on-demand transit (ODT) services using low-speed electric vehicles (LSEV) are an innovative technological solution that can help fill this gap in service (e.g., short, high-frequency trips) for diverse populations and trip types. This study evaluated user characteristics and travel behavior for a neighborhood ODT service (using LSEVs) in downtown St. Louis, Missouri using responses from a community survey (n=244), ridership data, and vehicle trajectory information. A comparative analysis between neighborhood ODT, fixed route transit, and transportation network companies (TNC) was also conducted from the perspectives of total travel time, cost, and greenhouse gas emissions. Ultimately, the goal of the analysis was to motivate and inform holistic public mobility systems where different services are optimized to meet specific community needs. Findings indicate that the neighborhood ODT was effective at reaching diverse populations (elderly (20%), lower income (27%), and households with limited access to private vehicles (34%)). ODT reduced total travel time by 32% compared to fixed route transit, produced 2.4 - 4.3 times less greenhouse gas emissions per passenger mile (compared to transit and TNCs), and was more affordable (free to users) than alternative options ($1 for transit, $10-12 for TNCs). Overall satisfaction rates were high, with 80% of respondents rating the service a 4 or 5 out of 5.
ADVANCED PROPULSION SYSTEMS↗
Mobility Energy Productivity Evaluation of Prediction-Based Vehicle Powertrain Control Combined with Optimal Traffic Management
Transportation vehicle and network system efficiency can be defined in two ways: 1) reduction of travel times across all the vehicles in the system, and 2) reduction in total energy consumed by all the vehicles in the system. The mechanisms to realize these efficiencies are treated as independent (i.e., vehicle and network domains) and, when combined, they have not been adequately studied to date. This research aims to integrate previously developed and published research on Predictive Optimal Energy Management Strategies (POEMS) and Intelligent Traffic Systems (ITS), to address the need for quantifying improvement in system efficiency resulting from simultaneous vehicle and network optimization. POEMS and ITS are partially independent methods which do not require each other to function but whose individual effectiveness may be affected by the presence of the other. In order to evaluate the system level efficiency improvements, the Mobility Energy Productivity (MEP) metric is used. MEP specifically measures the connectedness of a system while accounting for time and energy externalities of modes that provide mobility in a given location. A SUMO model is developed to reflect real traffic patterns in Fort Collins, Colorado and data is collected by a probe SUMO vehicle which is validated against data collected on a real vehicle driving the same routes through the city. Individual vehicle and system level efficiencies are calculated using SUMO outputs for scenarios which integrate POEMS and ITS independently as well as jointly. Results from application of POEMS and ITS show improvement in energy consumption and travel times respectively when compared to the respective baseline scenarios. Our conclusion is that there are promising synergistic benefits to travel time and energy efficiency when POEMS and ITS are combined.
ADVANCED PROPULSION SYSTEMS↗
The ABCs of On-Demand Transit (ODT)
On-demand mobility - also referred to as on-demand transit (ODT) - is a form of public mobility that is flexible with respect to where and when service is provided, and ODT deployments have increased significantly in recent years. Transit agencies are becoming increasingly interested in ODT, and due to differing definitions and various service design and business model options, it can be difficult to learn about the emerging industry. This work provides an overview of the definitions of ODT, recent trends internationally and in the U.S., ODT's benefits and challenges (particularly compared to fixed-route transit), three primary service design options, system costs and funding considerations, and a metrics framework for evaluating ODT systems to ensure continued successful performance. Identified benefits include increased service areas, short ride and wait times, increased user flexibility, potential to reduce energy consumption and emissions through shared trips and smaller, right-sized vehicles, increased safety and comfort through door-to-door service, and rich data streams including granular spatio-temporal data that can be analyzed to continuously improve the service. Challenges include scaling ODT service up as small increases in ridership require additional supply to keep service quality high, serving peak times including keeping low wait times, the lack of fixed schedule being challenging for commuters, integrating ODT services with nearby transit systems, and equity for riders without smartphones who cannot track the vehicle in a mobile app. Finally, an overview of seven ODT case studies (in Texas, Missouri, New York, and Ontario, Canada) performed by NREL and related analysis of travel time, energy and emissions, costs, and equity are presented. Initial key findings include: ODT can be cost- and energy-effective compared to fixed-route transit, ODT serves more people than other transit options, and ODT system deployments can be followed by rapid growth.
24 POWER TRANSMISSION AND DISTRIBUTION↗
How Improved Forecasting Can Increase the Bulk Power System Value of Price-Responsive Electric Vehicle Managed Charging
Personal light-duty vehicle (LDV) electric vehicle managed charging (EVMC) can reduce power system costs by better aligning electric vehicle (EV) charging with locations and times of low energy cost or infrastructure use. The need to coordinate charging demand across thousands to millions of vehicles while preserving mobility service is a barrier to realizing the value of EVMC. Price-responsive dispatch mechanisms like time-of-use rates (TOU) and hourly real-time prices (RTP) are attractive compared to direct load control (DLC) because they only require one-way communications and local controls. However, increasing participation in price responsive mechanisms can induce costly-to-serve spikes in load and otherwise increase, rather than decrease, production costs. We quantify the ability of improved EVMC forecasting to sustain savings from price responsive mechanisms beyond the limit of 14% of LDVs actively participating observed in previous work. Perfect forecasting of price-responsive EV load makes TOU and RTP value-competitive with a low-error DLC formulation with up to 27% (within-week flexibility) to 45% or more (within-session flexibility) of LDVs participating in EVMC in an envisioned New England power system with 84% clean energy. Additional costs of implementing DLC should be no more than tens of dollars per vehicle-year if DLC is to be value-competitive with accurately forecast price-responsive EVMC for double-digit percentage shares of LDVs participating.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Evaluating Direct and Indirect Influence on EV Charging Stations Across the US
The adoption of new technology for electric vehicles (EV) and mobility applications can bring underappreciated vulnerabilities to the power grid. One area of potential fraud and adversarial influence is through the business ecosystem of startups that own and deploy EV technology. Yet, there are no models or analyses that map the network of organizations and people that have direct and indirect influence over technologies currently deployed in the grid. To fill this gap, we develop a multilayer network model to measure direct and indirect influence on EV charging stations. First, we create and adversarial socio-technical network (ASTN) model via a data fusion pipeline for different US regions of interest (ROI). Then, we develop an integrated ASTN for Chicago, Los Angeles, New York, and Philadelphia. We rank EV charging companies direct influence within each geographic region as well as indirect influence via social network analysis. While some companies have strong direct and indirect influence (i.e., ChargePoint) others show a mismatch between their influence over charging stations and their position within the social network. For example, Tesla has strong direct influence on stations and weak indirect influence over competitors. In contrast, 7Charge has weak direct influence over stations, but strong indirect influence over competitors.
29 - ENERGY PLANNING, POLICY AND ECONOMY↗
Carbon decorated Li 3 V 2 (PO 4 ) 3 for high-rate lithium-ion batteries: Electrochemical performance and charge compensation mechanism
Fast charging and high-power delivering batteries are highly demanded in mobile electronics, electric vehicles and grid energy storage, but there are full of challenges. The star-material Li 3 V 2 (PO 4 ) 3 is demonstrated as a promising high-rate cathode material meeting the above requirements. Herein, we report the carbon decorated Li 3 V 2 (PO 4 ) 3 (LVP/C) cathode prepared via a facile method, which displays a remarkable high-rate capability and long-term cycling performance. Briefly, the prepared LVP/C delivers a high discharge capacity of 122 mAh g -1 (~93% of the theoretical capacity) at a high rate up to 20 C and a superior capacity retention of 87.1% after 1000 cycles. Importantly, by applying a combination of X-ray absorption spectroscopy and full-range mapping of resonant inelastic X-ray scattering, we clearly elucidate the structural and chemical evolutions of LVP upon various potentials and cycle numbers. Furthermore, we show unambiguous spectroscopic evidences that the evolution of the hybridization strength between V and O in LVP/C as a consequence of lithiation/delithiation is highly reversible both in the bulk and on the surface during the discharge-charge processes even over extended cycles, which should be responsible for the remarkable electrochemical performance of LVP/C. Our present study provides not only an effective synthesis strategy but also deeper insights into the surface and bulk electrochemical reaction mechanism of LVP, which should be beneficial for the further design of high-performance LVP electrode materials.
25 ENERGY STORAGE↗
Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models
The environmental impacts of global warming driven by methane (CH 4 ) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH 4 . Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH 4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH 4 as a classification problem and (ii) predict the intensity of CH 4 as a regression problem. The classification model performance for CH 4 detection was evaluated using accuracy, F1 score, Matthew’s Correlation Coefficient (MCC), and the area under the receiver operating characteristic curve (AUC ROC), with the top-performing model being 97.2%, 0.972, 0.945 and 0.995, respectively. The R 2 score was used to evaluate the regression model performance for CH 4 intensity prediction, with the R 2 score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH 4 detection and intensity prediction can be used with fixed environmental sensors deployed on the ground or with sensors mounted on unmanned aerial vehicles (UAVs) for mobile detection.
Majumder, Reek↗
Rapid Laser Reactive Sintering of Garnet Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 and Li 6.1 La 3 Zr 2 Al 0.3 O 12 and Solid-State Electrolytes
All solid-state lithium-ion batteries (ASSLIBs) have gained significant interest in recent years due to their wide range of applications, including mobile devices, electric vehicles, and grid storage. Garnet-type Li 7 La 3 Zr 2 O 12 (LLZO) solid-state electrolyte (SSE) continues to be a significant player in the fabrication of ASSLIBs with excellent ionic conductivity on the order of 10 -3 S•cm -1 with the integration of dopants and sintering aids to assist conductivity and structural evolution during the sintering procedure. Traditional sintering techniques, such as solid-state sintering (SSS), have been proven to yield desirable crystal structures. However, the low ionic conductivity results from inferior microstructure, and lithium loss is a significant challenge. Recently, a relatively novel sintering process called rapid laser sintering (RLS) has shown great potential for achieving fully dense solid oxide electrolytes with less lithium loss, resulting in high ionic conductivity [1] [2] [3] [4]. Here, we applied a modified RLS method using precursor powders instead of pre-synthesized LLZO powders to fabricate Al-doped LLZO (Li 6.1 La 3 Zr 2 Al 0.3 O 12 + 13 wt% Li 2 CO 3 ) and Ta-doped LLZO (Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 + 5 wt% LiOH•H 2 O) dense electrolytes. This rapid laser reactive sintering (RLRS) could potentially reduce the number of processing steps and therefore lower the manufacturing cost. We hope the rapid high-temperature sintering can densify the electrolyte and avoid the lithium loss for achieving high lithium-ion conductivity. In this work, we optimized a combination of laser parameters (speed and power), initial precursor composition, and controlled atmospheres to discover the optimal conditions for reaching the efficacious crystal structure, microstructure, and ionic conductivity.
25 ENERGY STORAGE↗
Mechanical, Electrochemical & Thermal Models - Training (CRADA CRD-19-00798 Final Report)
Under the proposed effort in partnership with Hyundai Motor Company (HMC), the National Renewable Energy Laboratory (NLR) will host Dr. Jaeyoung Lim from HMC for a period of one year to jointly develop mathematical models for battery cells and modules subject to mechanical crush. NLR will assist with the development of mathematical models that Dr. Lim will incorporate into his research effort on new concepts of mobility with electric vehicles.
33 ADVANCED PROPULSION SYSTEMS↗
Rapid Laser Reactive Sintering of Garnet Li 6.1 La 3 Zr 2 Al 0.3 O 12 Solid-State Electrolyte
All solid-state lithium-ion batteries (ASSLIBs) have gained significant interest in recent years due to their wide range of applications, including mobile devices, electric vehicles, and grid storage. Garnet-type Li 7 La 3 Zr 2 O 12 (LLZO) solid-state electrolyte (SSE) continues to be a significant player in the fabrication of ASSLIBs with excellent ionic conductivity on the order of 10 -3 S•cm -1 with the integration of dopants and sintering aids to assist conductivity and structural evolution during the sintering procedure. Traditional sintering techniques, such as solid-state sintering (SSS), have been proven to yield desirable crystal structures. However, the low ionic conductivity results from inferior microstructure, and lithium loss is a significant challenge. Recently, a relatively novel sintering process called rapid laser reactive sintering (RLRS) has shown great potential for achieving fully dense solid oxide electrolytes with less surface element loss, resulted in high ionic conductivity. Here, we applied the same RLRS method to fabricate LLZO dense electrolyte with different amounts of aluminum dopants. We hope the rapid high-temperature sintering can densify the electrolyte and avoid the lithium loss for achieving high lithium-ion conductivity. In this work, we optimized multiple combinations of laser parameters (speed, power, and defocus distance), initial precursor composition, and controlled atmospheres to discover the optimal conditions for reaching the efficacious crystal structure, microstructure, and ionic conductivity.
25 ENERGY STORAGE↗
Architecture for Web-Based Visualization of Large-Scale Energy Domains: Preprint
With the growing penetration of inverter-based distributed energy resources and increased loads through electrification, power systems analyses are becoming more important and more complex. Moreover, these analyses increasingly involve the combination of interconnected energy domains with data that are spatially and temporally increasing in scale by orders of magnitude, surpassing the capabilities of many existing analysis and decision-support systems. We present the architectural design, development, and application of a high-resolution web-based visualization environment capable of cross-domain analysis of tens of millions of energy assets, focusing on scalability and performance. Our system supports the exploration, navigation, and analysis of large data from diverse domains such as electrical transmission and distribution systems, mobility and electric vehicle charging networks, communications networks, cyber assets, and other supporting infrastructure. We evaluate this system across multiple use cases, describing the capabilities and limitations of a web-based approach for high-resolution energy system visualizations.
grid modernization↗