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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Learning electric vehicle driver range anxiety with an initial state of charge-oriented gradient boosting approach

This manuscript focuses on the modeling of electric vehicle (EV) driver’s range anxiety, a fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery pack, to reach its destination and would strand its occupants. Despite numerous research studies on the modeling of charging behaviors, modeling efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To this end, an ensemble learning model based on gradient boosting is developed. The model sequentially fits new predictors to new residuals of the previous prediction and, then, minimizes the loss when adding the latest prediction. A total of 18 features are defined and extracted from the multisource data, which cover information on driver, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5-year’s charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. Here, the result shows the proposed model achieved a satisfactory result with a R square value of 0.54 and root mean square error of 0.14, both better than multiple linear regression model and random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users or that are further from residential areas.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Mobility Data and Models Informing Smart Cities

This presentation explores the interactions of Emerging Technology, Changing Urban Environments, and Urban Travel Behavior on Mobility and Energy Impacts. Key findings include quantifying impacts of emerging mobility, access and infrastructure associated with new mobility choices and services in urban areas. The results demonstrate new data collection methods and capabilities to understand and identify key levers to improve energy productivity of future mobility systems and of quickly evolving MaaS and COVID-19 contexts to monitor rapidly shifting urban mobility patterns. Under development are mode choice models for new modes versus traditional modes. Initial estimates are developed on the potential of workforce mobility strategies in shaping future energy-efficient mobility system impacts. For mode replacement and workforce mobility, this presentation offers observability into the changes in number of transactions per mode, after ride-hailing/TNC/MaaS introduction and in origins of workforce to enable user need-driven reimagining of efficient mobility systems: Seattle (SEA-TAC) airport: for every 100 new TNC transactions for ground transportation, ~27% replaced transit, 35% replaced parking, 17% replaced car rentals, and 21% replaced taxis. Similarly, at Denver International (DEN), ride-hailing transactions replaced transit, parking, car rental and taxis at a rate of 34.7%, 39.0%, 16.6%, and 9.7%, respectively. A workforce mobility tool, analysis/visualization techniques, and capabilities to understand and identify key job commute needs, new choices in access to job opportunities, and affordability aspects as levers to improve energy productivity of future integrated urban mobility systems.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2021

Human Mobility to Parks Under the COVID-19 Pandemic and Wildfire Seasons in the Western and Central United States

In 2020, people's health suffered a great crisis under the dual effects of the COVID-19 pandemic and the extensive, severe wildfires in the western and central United States. Parks, including city, national, and cultural parks, offer a unique opportunity for people to maintain their recreation behaviors following the social distancing protocols during the pandemic. However, massive forest wildfires in western and central US, producing harmful toxic gases and smoke, pose significant threats to human health and affect their recreation behaviors and mobility to parks. In this study, we employed the geographically and temporally weighted regression (GTWR) Models to investigate how COVID-19 and wildfires jointly shaped human mobility to parks, regarding the number of visits per capita, dwell time, and travel distance to parks, during June - September 2020. We detected strong correlations between visitations and COVID-19 incidence in southern Montana, western Wyoming, Colorado, and Utah before August. However, the pattern was weakened over time, indicating the decreasing trend of the degree of concern regarding the pandemic. Moreover, more park visits and lower dwell time were found in parks further away from wildfires and less air pollution in Washington, Oregon, California, Colorado, and New Mexico, during the wildfire season, suggesting the potential avoidance of wildfires when visiting parks. This study provides important insights on people's responses in recreation and social behaviors when facing multiple severe crises that impact their health and wellbeing, which could support the preparation and mitigation of the health impacts from future pandemics and natural hazards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Measurement of electronic structure and surface reconstruction in the superionic Cu 2-x Te

Recently, layered copper chalcogenides Cu 2 X family (X=S, Se, Te) has attracted tremendous research interests due to their high thermoelectric performance, which is partly due to the superionic behavior of mobile Cu ions, making these compounds "phonon liquids."Here, we systematically investigate the electronic structure and its temperature evolution of the less studied single crystal Cu 2-x Te by the combination of angle resolved photoemission spectroscopy (ARPES) and scanning tunneling microscope/spectroscopy (STM/STS) experiments. While the band structure of the Cu 2-x Te shows agreement with the calculations, we clearly observe a 2×2 surface reconstruction from both our low temperature ARPES and STM/STS experiments which survives up to room temperature. Interestingly, our low temperature STM experiments further reveal multiple types of reconstruction patterns, which suggests the origin of the surface reconstruction being the distributed deficiency of liquidlike Cu ions. Our findings reveal the electronic structure and impurity level of Cu 2 Te, which provides knowledge about its thermoelectric properties from the electronic degree of freedom.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Inaugural Technologist in Communities Scanning Tour: Automated and Electrified On-Demand Mobility (Follow-On Summary Report: April 24-25, 2023) [Slides]

In 2023, the Technologist in Communities (TIC) program hosted its inaugural scanning tour across the American Southwest, with a goal of investigating the region's leading public mobility systems and their integration with on-demand public mobility services. The scanning tour concept was inspired by and named after a U.S. Department of Transportation Federal Highway Administration practice of hosting "scanning tours," both domestically and abroad, to explore innovative transportation practices throughout the 1990s and early 2000s. Guided by the idea that "seeing is believing," scanning tours created opportunities for government and industry representatives to collectively experience new technologies and best practices firsthand. In 2023, the National Renewable Energy Laboratory (NREL) followed suit by leading the inaugural TIC scanning tour. TIC, part of the Technology Integration program within the U.S. Department of Energy's (DOE's) Vehicle Technologies Office, supports positive energy and mobility outcomes in communities across rural to urban contexts. TIC enables technical experts to work directly with leaders and stakeholders in communities and assist them in implementing promising new technologies. This first TIC scanning tour invited NREL, DOE, and industry experts to survey innovative public mobility practices and their integration into public transit systems across the Southwest. The tour's goals included examining the state of innovation in public mobility in cities, communities, and airports; facilitating collaboration between entities; and sparking potential new partnerships. The following report summarizes key findings from the scanning tour.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

EV Load Forecasting Guide: A Report by the Energy Systems Integration Group’s EV Load Forecasting Task Force

Forecasting electricity usage is a foundational planning activity for utilities, underpinning billions of dollars in grid investments that ensure system reliability. Historically, forecasting relied on trends in economic and population growth; however, transportation electrification presents a new and complex planning challenge. Unlike conventional loads, electric vehicle (EV) charging has relatively limited usage history. In addition, charging is driven by complex human behaviors, is mobile, and at the same time can concentrate geographically in ways that, without proper planning, can quickly overwhelm local distribution systems.

Giraldez, Julieta [Electric Power Engineers, Austi↗
DOE OSTI2022

Use of a 360-Degree Underwater Camera to Characterize Artificial Reef and Fish Aggregating Effects around Marine Energy Devices

Marine energy devices must be attached to the seafloor by their foundations, pilings, or anchors, and will have other parts in the water column like the devices themselves, mooring lines, and power export cables running along the seafloor. The installation and presence of these artificial structures will create physical changes that can disrupt or create new habitats, and potentially alter the behavior of mobile organisms such as fish around a device by attracting them to these new artificial reefs and fish aggregating devices. In this study, we tested a new approach for monitoring fish activity around a marine energy device anchor: a 360-degree underwater camera to keep the target (a wave energy converter’s anchor) in the field of view of the camera. The camera was deployed in three configurations (hand-held, tripod, video lander) at sites with different hydrodynamics and underwater visibilities. The video lander was the best configuration: very stable, versatile, and easy to handle. The 360-degree field of view enabled observing and counting fishes, which were more abundant at dusk than dawn or noon, around the anchor. Despite remaining challenges, 360-degree cameras are useful tools for monitoring animal interactions with marine energy devices.

360-degree camera↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

CORSAIR: A Framework for Human Mobility Prediction through Visit Characterization and Spatial Behavior Modeling

The rapid advancement of location acquisition technologies has led to the daily collection of vast amounts of mobile trajectory data, facilitating in-depth research on human mobility and enabling more accurate mobility prediction models. However, existing methodologies often fall short in capturing the intricate dynamics of human navigation and spatial behavior. This paper addresses this gap by exploring the multifaceted relationships between individuals and their environments, considering the diverse influences of personal preferences and experiences. Some places hold sentimental value and are visited frequently, while others serve as transient points of passage. To model these differences, we introduce CORSAIR, a novel visit characterization framework that leverages visitation patterns and dwell times to delineate an individual’s relationship with specific places. CORSAIR classifies visits into seven distinct types: casual, occasional, routine, special, anchor, important, and resettling. Also, we show that explicitly recognizing these distinct visit types and incorporating nuanced visit intents into mobility prediction models leads to a substantial improvement in prediction accuracy. This distinction allows for more precise modeling of the individual’s transitions, enhancing the personalization and relevance of location-based services. Our findings suggest that a deeper understanding of the complexities of individual-environment interactions is crucial for developing effective predictive tools in mobility research.

Amichi, Licia [ORNL] (ORCID:0000000177631394)↗
DOE OSTI2021

Non-local corrections to the typical medium theory of Anderson localization

We use the recently developed finite cluster typical medium approach to study the Anderson localization transition in three dimensions. Applying our method to the box and binary alloy disorder distributions, we find a fast convergence with the cluster size. We demonstrate the importance of the typical medium environment and the non-local spatial correlations for the proper characterization of the localization transition. As the cluster size increases, our typical medium cluster method recovers the correct critical disorder strength for the transition. Our findings highlight the importance of the non-local cluster corrections for capturing the localization behavior of the mobility edge trajectories. Our results demonstrate that the typical medium cluster approach developed here provides a consistent and systematic description of the Anderson localization transition in the framework of the effective medium embedding schemes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Evaluate the System-Level Impact of Connected and Automated Vehicles Coupled with Shared Mobility: An Agent-based Simulation Approach

With the rapid growth of information and communication technologies, Connected and Automated Vehicles (CAVs) are deemed to be disruptive with the potential to significantly improve overall transportation system efficiency, however, may bring Vehicle Miles Traveled (VMT) increase or other issues. Further, shared mobility systems are another disruptive force that is reshaping our travel patterns. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance and energy efficiency, we develop a mesoscopic simulation-based framework for mobility and energy efficiency evaluation considering the disruptive transportation technologies. Under this framework, we develop novel models for energy intensity and modal activity, and evaluated a variety of energy scenarios for different combinations of CAV applications, various levels of automation, roadway characteristics, and traffic conditions, while also varying different vehicle types and fuel/powertrain technologies. Based applying this modeling suite to a calibrated BEAM simulation network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes.

42 ENGINEERING↗
DOE OSTI2026

Climatic imprint on interfacially-controlled platinum-palladium resources

Abstract Iron oxide-rich laterites, soils, and regolith formed from the weathering of ultramafic rocks represent untapped unconventional resources for the critical minerals platinum and palladium, but the fundamental surficial geochemistry of these elements remains poorly understood. Depletion of Pd relative to Pt occurs in some weathering zones in semi-arid climates. The accepted model attributes this platinum-palladium chemical fractionation to preferential complexation of Pd by dissolved chloride. However, similar fractionation is not observed in laterites of humid equatorial regions despite substantial wet deposition of chloride. The established mechanistic model for Pt and Pd behavior during weathering thus inaccurately predicts the distribution of these critical minerals in many settings, hindering global resource assessment. We show through mineral-fluid partitioning experiments coupled to element-specific spectroscopy that this canonical explanation for platinum-palladium fractionation is invalid: chloride complexation does not differentially mobilize Pd versus Pt. Instead, mineral-specific interfacial reactions control Pd and Pt accumulation. Modeling of platinum-palladium fractionation in representative weathering zone profiles demonstrates sub-equal retention in goethite-rich settings and Pd depletion in hematite-rich zones, accurately predicting trends observed in soils and laterites. Iron oxide mineralogy, reflecting modern and past regional climate conditions, is likely the primary determinant of Pt and Pd endowment in weathering zone resources. This new model for Pt and Pd mobilization and accumulation behavior provides a mechanistic foundation for exploration and recovery of platinum group elements from novel ultramafic regolith deposits.

58 GEOSCIENCES↗
DOE OSTI2020

Understanding Superionic Conductivity in Lithium and Sodium Salts of Weakly Coordinating Closo -Hexahalocarbaborate Anions

Solid-state ion conductors based on closo-polyborate anions combine high ionic conductivity with a rich array of tunable properties. Cation mobility in these systems is intimately related to the strength of the interaction with the neighboring anionic network and the energy for reorganizing the coordination polyhedra. In this paper, we explore such factors in solid electrolytes with two anions of the weakest coordinating ability, [HCB 11 H 5 Cl 6 ] – and [HCB 11 H 5 Br 6 ] – , and a total of 11 polymorphs are identified for their lithium and sodium salts. Our approach combines ab initio molecular dynamics, synchrotron X-ray powder diffraction, differential scanning calorimetry, and AC impedance measurements to investigate their structures, phase-transition behavior, anion orientational mobilities, and ionic conductivities. We find that M(HCB 11 H 5 X 6 ) (M = Li, Na, X = Cl, Br) compounds exhibit order–disorder polymorphic transitions between 203 and 305 °C and display Li and Na superionic conductivity in the disordered state. Through detailed analysis, we illustrate how cation disordering in these compounds originates from a competitive interplay among the lattice symmetry, the anion reorientational mobility, the geometric and electronic asymmetry of the anion, and the polarizability of the halogen atoms. These factors are compared to other closo-polyborate-based ion conductors to suggest guidelines for optimizing the cation–anion interaction for fast ion mobility. This study expands the known solid-state poly(carba)borate-based materials capable of liquid-like ionic conductivities, unravels the mechanisms responsible for fast ion transport, and provides insights into the development of practical superionic solid electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem

The widespread adoption of electric vehicles (EVs) in recent years has necessitated the development of effective charging infrastructures. However, charging infrastructure expansion is a multifaceted problem that requires careful consideration of the existing infrastructure, spatiotemporal distribution of charging demands, power-grid capacity, and budget constraints. Here, to approach this complex problem, we present CHARGE-MAP, a data-driven simulation-optimization framework, focused on ensuring meaningful charging experience for individual EV owners. CHARGE-MAP integrates three modules: an agent-based simulation module that estimates spatiotemporal distribution of charging demands by modeling EV adopter mobility and charging behavior; an optimization module that determines optimal new charging station/charger locations and capacities, while minimizing expected detour distances and wait-times with a limited number of new stations; and a power module that determines how to connect the stations to the power grid while maintaining its stability. Using the state of Virginia (consisting of 95 counties and 38 independent cities) as a case study, our results show that CHARGE-MAP can meet the demand of ~198,600 predicted EVs with 1,305 new public charging stations and 2,164 new chargers. It reduces average detour distances for charging by 66% and wait-times at stations by 72% compared to the existing infrastructure. Furthermore, transformer capacity requirement analysis reveals that only 1.8% of residential transformers require upgrades, while over 80% of commercial charging locations can be supported with modest transformer infrastructure (25 to 50 kVA). This indicates that targeted investments can facilitate cost-effective EV integration. Consequently, CHARGE-MAP provides policymakers and urban planners with crucial data-driven insights for effective EV charging infrastructure expansion. Sign up for PNAS alerts.

charging infrastructure↗
DOE OSTI2020

High‐Performance Zinc Tin Oxide TFTs with Active Layers Deposited by Atomic Layer Deposition

Abstract New deposition techniques for amorphous oxide semiconductors compatible with silicon back end of line manufacturing are needed for 3D monolithic integration of thin‐film electronics. Here, three atomic layer deposition (ALD) processes are compared for the fabrication of amorphous zinc tin oxide (ZTO) channels in bottom‐gate, top‐contact n‐channel transistors. As‐deposited ZTO films, made by ALD at 150–200 °C, exhibit semiconducting, enhancement‐mode behavior with electron mobility as high as 13 cm 2 V −1 s −1 , due to a low density of oxygen‐related defects. ZTO deposited at 200 °C using a hybrid thermal‐plasma ALD process with an optimal tin composition of 21%, post‐annealed at 400 °C, shows excellent performance with a record high mobility of 22.1 cm 2 V –1 s –1 and a subthreshold slope of 0.29 V dec –1 . Increasing the deposition temperature and performing post‐deposition anneals at 300–500 °C lead to an increased density of the X‐ray amorphous ZTO film, improving its electrical properties. By optimizing the ZTO active layer thickness and using a high‐ k gate insulator (ALD Al 2 O 3 ), the transistor switching voltage is lowered, enabling electrical compatibility with silicon integrated circuits. This work opens the possibility of monolithic integration of ALD ZTO‐based thin‐film electronics with silicon integrated circuits or onto large‐area flexible substrates.

Allemang, Christopher R.↗
DOE OSTI2026

Elucidating the role of surface species in CO oxidation catalyzed by boron nitride nanotube supported transition metal oxides

Boron nitride nanotube (BNNT) is considered a highly promising catalyst support due to its outstanding thermal stability and chemical inertness. These characteristics make BNNT an attractive alternative for high-temperature applications. However, most studies to date have focused on incorporating platinum group metals (PGMs) to achieve high activity. Although BNNT-supported PGM catalysts are highly effective, their scarcity and high cost hinder widespread use in industrial processes. In this study, BNNT-supported transition metal oxides (TMO x /BNNT; TM = Fe, Co, Ni, and Cu) catalysts were investigated, and CO oxidation was applied as a model reaction to evaluate their catalytic performance. Several characterization techniques, including SEM-EDX, TEM, SXRD, H 2 -TPR, and XPS, were employed to examine their physicochemical properties. Notably, the particle size of the metal oxides differed significantly depending on the metal type. This variation is primarily attributed to the inherent metal–support interactions and the thermodynamic stability of each oxide during synthesis. These properties also affected catalytic activity, and various parameters, such as oxygen mobility and redox behavior, played important roles in determining performance. Finally, in situ DRIFTS, CO-TPSR, reaction-order analysis, and 18 O 2 isotope-labeling experiment were used to investigate the reaction mechanism. In conclusion, the findings provide insights into the design of cost-effective BNNT-supported catalysts and highlight their potential applicability in oxidation reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.

33 ADVANCED PROPULSION SYSTEMS↗