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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Electrochemical Performance of Nanosized Disordered LiVOPO 4

ε-LiVOPO4 is a promising multielectron cathode material for Li-ion batteries that can accommodate two electrons per vanadium, leading to higher energy densities. However, poor electronic conductivity and low lithium ion diffusivity currently result in low rate capability and poor cycle life. To enhance the electrochemical performance of ε-LiVOPO4, in this work, we optimized its solid-state synthesis route using in situ synchrotron X-ray diffraction and applied a combination of high-energy ball-milling with electronically and ionically conductive coatings aiming to improve bulk and surface Li diffusion. We show that high-energy ball-milling, while reducing the particle size also introduces structural disorder, as evidenced by 7Li and 31P NMR and X-ray absorption spectroscopy. We also show that a combination of electronically and ionically conductive coatings helps to utilize close to theoretical capacity for ε-LiVOPO4 at C/50 (1 C = 153 mA h g–1) and to enhance rate performance and capacity retention. The optimized ε-LiVOPO4/Li3VO4/acetylene black composite yields the high cycling capacity of 250 mA h g–1 at C/5 for over 70 cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)

This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Optimization of plasma amplifiers

Here, plasma amplifiers offer a route to side-step limitations on chirped pulse amplification and generate laser pulses at the power frontier. They compress long pulses by transferring energy to a shorter pulse via the Raman or Brillouin instabilities. We present an extensive kinetic numerical study of the three-dimensional parameter space for the Raman case. Further particle-in-cell simulations find the optimal seed pulse parameters for experimentally relevant constraints. The high-efficiency self-similar behavior is observed only for seeds shorter than the linear Raman growth time. A test case similar to an upcoming experiment at the Laboratory for Laser Energetics is found to maintain good transverse coherence and high-energy efficiency. Effective compression of a 10kJ, nanosecond-long driver pulse is also demonstrated in a 15-cm-long amplifier.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

A Minimum Principle-Based Algorithm for Energy-Efficient Eco-Driving of Electric Vehicles in Various Traffic and Road Conditions

Our report presents an optimization algorithm for energy-efficient driving of electric vehicles. The algorithm is based on Pontryagin’s Minimum Principle by considering the driving mission as an optimal control problem. On a planned route, diverse application cases may occur due to various physical traits such as powertrain (e.g., switching between motor and generator operation), environmental variations (e.g., terrain grade change), traffic laws (e.g., speed limits), and safety concerns (e.g., headway to the leading vehicle). The proposed algorithm handles these real-world challenges by considering the perturbation to the system dynamics and the constraints to the control and state variables.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

An OpenStreetMaps based tool to study the energy demand and emissions impact of electrification of medium and heavy-duty freight trucks

In this paper, we present the mathematical formulation of an OpenStreetMaps (OSM) based tool that compares the costs and emissions of long-haul medium and heavy-duty (M&HD) electric and diesel freight trucks, and determines the spatial distribution of added energy demand due to M&HD EVs. The optimization utilizes a combination of information on routes from OSM, utility rate design data across the United States, and freight volume data, to determine these values. In order to deal with the computational complexity of this problem, we formulate the problem as a convex optimization problem that is scalable to a large geographic area. In our analysis, we further evaluate various scenarios of utility rate design (energy charges) and EV penetration rate across different geographic regions and their impact on the operating cost and emissions of the freight trucks. Our approach determines the net emissions reduction benefits of freight electrification by considering the primary energy source in different regions. Such analysis will provide insights to policy makers in designing utility rates for electric vehicle supply equipment (EVSE) operators depending upon the specific geographic region and to electric utilities in deciding infrastructure upgrades based on the spatial distribution of the added energy demand of M&HD EVs. To showcase the results, a case study for the U.S. state of Texas is conducted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Li-ion battery design through microstructural optimization using generative AI

Lithium-ion batteries are used across various applications, necessitating tailored cell designs to enhance performance. Optimizing electrode manufacturing parameters is a key route to achieving this, as these parameters directly influence the microstructure and performance of the cells. However, linking process parameters to performance is complex, and experimental or modeling campaigns are often slow and expensive. This study introduces a fast computational optimization framework for electrode manufacturing parameters. A generative model, trained on a small dataset of microstructural images associated with different manufacturing parameters, efficiently generates representative microstructures for new parameters. This model is integrated into a Bayesian optimization loop that includes microstructure generation, characterization, and simulation, aiming to find optimal manufacturing parameters for a particular application. Significant improvement in the energy density of a 4680 cell is achieved through bespoke cell design, highlighting the importance of cell-scale normalization. The framework’s modularity allows its application to various advanced materials manufacturing scenarios.

batteries↗
DOE OSTI2019

Influence of scan pattern and geometry on the microstructure and soft-magnetic performance of additively manufactured Fe-Si

The influence of geometry and scan pattern on the microstructure evolution and magnetic performance of additively manufactured Fe-3Si components was investigated. To reduce eddy current losses, novel geometries were designed and built and the microstructure and properties of these samples were characterized. The laser scan pattern was shown to strongly influence both the as-built grain structure and strength of the crystallographic texture, resulting in measurable changes in the as-built magnetic performance. In thin wall samples, heat treatment resulted in an increase in the maximum relative magnetic permeability and decrease in power losses in most samples, consistent with grain growth. However, decreases in the spacing between thin walls to increase the stacking factor of the cross-section was shown to result in unwanted electrical shorting between walls and an increase in eddy current losses. Compared to simple parallel plate construction and a mesh structure, a novel cross-section design based on the Hilbert space filling curve was found to produce the lowest power losses. The mechanisms behind these results were explored using a combination of heat conduction and electromagnetic simulations, providing a route for future component and process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2015

A new n-type half-Heusler thermoelectric material NbCoSb

Highlights: • Half-Heusler alloy NbCoSb with 19 valence electron count was studied as TE material. • It is surprising that NbCoSb is n-type. • A maximum ZT of ∼0.4 is achieved at 700 °C without optimization. • It opens up a new route to develop new half-Heusler thermoelectric materials. • It is very interesting that a traditionally thought of VEC of 18 is not required. - Abstract: We surprisingly made a new n-type thermoelectric compound NbCoSb with half-Heusler (HH) structure having valence electron count of 19, different from the traditional 18, which opens up a new route to develop new half-Heusler thermoelectric materials not following the traditional valence electron count of 18. The samples are made by arc melting followed by ball milling and hot pressing. The effect of hot pressing temperature on the thermoelectric properties of NbCoSb samples has been studied. A maximum thermoelectric figure-of-merit (ZT) of ∼0.4 is achieved at 700 °C in NbCoSb sample that is hot pressed at 1000 °C. This work add a new member to HH compounds for thermoelectric applications, although the peak ZT of ∼0.4 is still lower than that of the traditional HHs. Moreover, it is very interesting to see that a traditionally thought of valence electron counts of 18 is not required.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Ab initio reaction pathways for photodissociation and isomerization of nitromethane on four singlet potential energy surfaces with three roaming paths

Photodissociation pathways of nitromethane following π → π{sup *} electronic excitation are reported. The potential energy surfaces for four lowest singlet states are explored, and structures of many intermediates, dissociation limits, transition states, and minimum energy conical intersections were determined using the automated searching algorism called the global reaction route mapping strategy. Geometries are finally optimized at CASSCF(14e,11o) level and energies are computed at CAS(14o,11e)PT2 level. The calculated preferable pathways and important products qualitatively explain experimental observations. The major photodissociation product CH{sub 3} and NO{sub 2} ({sup 2}B{sub 2}) is formed by direct dissociation from the S{sub 1} state. Important pathways involving S{sub 1} and S{sub 0} states for production of various dissociation products CH{sub 3}NO + O ({sup 1}D), CH{sub 3}O(X{sup 2}E) + NO (X{sup 2}Π), CH{sub 2}NO + OH, and CH{sub 2}O + HNO, as well as various isomerization pathways have been identified. Three roaming processes also have been identified: the O atom roaming in O dissociation from CH{sub 3}NO{sub 2}, the OH radical roaming in OH dissociation from CH{sub 2}N(O)(OH), and the NO roaming in NO dissociation from CH{sub 3}ONO.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Quantum annealing correction with minor embedding

Quantum annealing provides a promising route for the development of quantum optimization devices, but the usefulness of such devices will be limited in part by the range of implementable problems as dictated by hardware constraints. To overcome constraints imposed by restricted connectivity between qubits, a larger set of interactions can be approximated using minor embedding techniques whereby several physical qubits are used to represent a single logical qubit. However, minor embedding introduces new types of errors due to its approximate nature. Here we introduce and study quantum annealing correction schemes designed to improve the performance of quantum annealers in conjunction with minor embedding, thus leading to a hybrid scheme defined over an encoded graph. We argue that this scheme can be efficiently decoded using an energy minimization technique provided the density of errors does not exceed the per-site percolation threshold of the encoded graph. We test the hybrid scheme using a D-Wave Two processor on problems for which the encoded graph is a two-level grid and the Ising model is known to be NP-hard. The problems we consider are frustrated Ising model problem instances with “planted” (a priori known) solutions. Applied in conjunction with optimized energy penalties and decoding techniques, we find that this approach enables the quantum annealer to solve minor embedded instances with significantly higher success probability than it would without error correction. Our work demonstrates that quantum annealing correction can and should be used to improve the robustness of quantum annealing not only for natively embeddable problems but also when minor embedding is used to extend the connectivity of physical devices.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Controlling phonon lifetimes via sublattice disordering in AgBiSe 2

Understanding and controlling microscopic heat transfer mechanisms in solids is critical to material design in numerous technological applications. Yet, the current understanding of thermal transport in semiconductors and insulators is limited by the difficulty in directly measuring individual phonon lifetimes and mean free paths, and studying their dependence on the microscopic state of the material. Here in this paper, we report our measurements of microscopic phonon scattering rates in AgBiSe 2 , which exhibits a controllable, reversible change directly linked to microstructure evolution near a reversible structural phase transition, that directly impacts the thermal conductivity. We demonstrate a steplike doubling of phonon scattering rates resultant from the cation disordering at the structural transition. To rationalize the neutron scattering data, we leverage a stepwise approach to account for alterations to the thermal conductivity that are imparted by distinct scattering mechanisms. These results highlight the potential of tunable microstructures housed in a stable crystal matrix to provide a practical route to tailor phonon scattering to optimize thermal transport properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment

This paper provides a comparative assessment of three economic optimal control strategies, aimed at minimizing the fuel consumption of heavy-duty trucks in a highway environment, under a representative lead vehicle model informed by traffic data. These strategies fuse a global, offline dynamic programming (DP) optimization with online model predictive control (MPC). We then show how two of the three strategies can be adapted to accommodate the presence of traffic and optimally navigate signalized intersections using infrastructure-to-vehicular (I2V) communication. The MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. The three candidate economic MPC formulations that are evaluated include: a nonlinear time-based formulation that directly penalizes predicted fuel consumption, a nonlinear time-based formulation that penalizes braking effort as a surrogate for fuel consumption, and a linear distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity profile obtained from DP. Using a medium-fidelity Simulink model, based on a Volvo truck's longitudinal and engine dynamics, we analyze the optimization's performance on four highway routes under various traffic scenarios. Results demonstrate 3.7-8.3% fuel economy improvement on highway routes without traffic and 6.5-10% on the same routes with traffic included. Furthermore, we present a detailed analysis of energy usage by "type" (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control strategy.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Neural Architecture and Feature Search for Predicting the Ridership of Public Transportation Routes

Accurately predicting the ridership of public-transit routes provides substantial benefits to both transit agencies, who can dispatch additional vehicles proactively before the vehicles that serve a route become crowded, and to passengers, who can avoid crowded vehicles based on publicly available predictions. The spread of the coronavirus disease has further elevated the importance of ridership prediction as crowded vehicles now present not only an inconvenience but also a public-health risk. At the same time, accurately predicting ridership has become more challenging due to evolving ridership patterns, which may make all data except for the most recent records stale. One promising approach for improving prediction accuracy is to fine-tune the hyper-parameters of machine-learning models for each transit route based on the characteristics of the particular route, such as the number of records. However, manually designing a machine-learning model for each route is a labor-intensive process, which may require experts to spend a significant amount of their valuable time. To help experts with designing machine-learning models, we propose a neural-architecture and feature search approach, which optimizes the architecture and features of a deep neural network for predicting the ridership of a public-transit route. Our approach is based on a randomized local hyper-parameter search, which minimizes both prediction error as well as the complexity of the model. We evaluate our approach on real-world ridership data provided by the public transit agency of Chattanooga, TN, and we demonstrate that training neural networks whose architectures and features are optimized for each route provides significantly better performance than training neural networks whose architectures and features are generic.

Ayman, Afiya↗
DOE OSTI2025

AutonomieAI: An efficient and deployable vehicle energy consumption estimation toolkit

Here, this paper presents AutonomieAI, a novel toolkit designed for efficient energy estimation of vehicles across diverse trip scenarios, routes, and drive cycles, applicable to a broad range of vehicle powertrain technologies. It leverages state-of-the-art Machine Learning techniques to deliver real-time energy prediction of vehicles, enabling co-simulation with transportation level system tools and opening doors for large-scale optimization at city, network or national level. Benchmark results show that AutonomieAI achieves high accuracy, with an average percentage error below 2% for most powertrain types, and computational efficiency capable of processing over 10,000 trips per second. Applications of AutonomieAI have potential to offer the flexibility to assist in solving eco-routing problems, optimize for vehicle and powertrain selection, study charging decision behavior, and optimize for charging station placement. AutonomieAI is the result of large neural network based model architectures, trained on very large and unique high fidelity vehicle simulation data. It is lightweight, deployable, efficient and has accuracy comparable to specialized and complex physics based simulation softwares.

Autonomie↗
DOE OSTI2017

Maximization of Platoon Formation Through Centralized Routing and Departure Time Coordination

Platooning allows vehicles to travel with a small intervehicle distance in a coordinated fashion because of vehicle-to-vehicle connectivity. When applied at a larger scale, platooning creates significant opportunities for energy savings because of reduced aerodynamic drag, as well as increased road capacity and a reduction in congestion resulting from shorter vehicle headways. These potential savings are maximized, however, if platooning-capable vehicles spend most of their travel time within platoons. Ad hoc platoon formation may not ensure a high rate of platoon driving. This paper considers here the problem of central coordination of platooning-capable vehicles. Coordination of their routes and departure times can maximize the fuel savings afforded by platooning vehicles. The resulting problem is a combinatorial optimization problem that considers the platoon coordination and vehicle routing problems simultaneously. The methodology is demonstrated through evaluation of the benefits of a coordinated solution and comparison with the uncoordinated case when platoons form only in an ad hoc manner. The coordinated and uncoordinated scenarios are compared on a grid network with various assumptions about demand and the time vehicles are willing to wait.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

A Facile Method to Fine‐Tune Polymer Aggregation Properties and Blend Morphology of Polymer Solar Cells Using Donor Polymers with Randomly Distributed Alkyl Chains

Abstract The device performance of polymer solar cells (PSCs) is strongly dependent on the blend morphology. One of the strategies for improving PSC performance is side‐chain engineering, which plays an important role in controlling the aggregation properties of the polymers and thus the domain crystallinity/purity of the donor–acceptor blends. In particular, for a family of high‐performance donor polymers with strong temperature‐dependent aggregation properties, the device performances are very sensitive to the size of alkyl chains, and the best device performance can only be achieved with an optimized odd‐numbered alkyl chain. However, the synthetic route of odd‐numbered alkyl chains is costly and complicated, which makes it difficult for large‐scale synthesis. Here, this study presents a facile method to optimize the aggregation properties and blend morphology by employing donor polymers with a mixture of two even‐numbered, randomly distributed alkyl chains. In a model polymer system, this study suggests that the structural and electronic properties of the random polymers comprising a mixture of 2‐octyldodecyl and 2‐decyltetradecyl alkyl chains can be systematically tuned by varying the mixing ratio, and a high power conversion efficiency (11.1%) can be achieved. This approach promotes the scalability of donor polymers and thus facilitates the commercialization of PSCs.

Yao, Huatong↗
DOE OSTI2024

Deploying Mobility-On-Demand for All by Optimizing Paratransit Services

While on-demand ride-sharing services have become popular in recent years, traditional on-demand transit services cannot be used by everyone, e.g., people who use wheelchairs. Paratransit services, operated by public transit agencies, are a critical infrastructure that offers door-to-door transportation assistance for individuals who face challenges in using standard transit routes. However, with declining ridership and mounting financial pressure, public transit agencies in the USA struggle to operate existing services. We collaborate with a public transit agency from the southern USA, highlight the specific nuances of paratransit optimization, and present a vehicle routing problem formulation for optimizing paratransit. We validate our approach using real-world data from the transit agency, present results from an actual pilot deployment of the proposed approach in the city, and show how the proposed approach comprehensively outperforms existing approaches used by the transit agency. To the best of our knowledge, this work presents one of the first examples of using open-source algorithmic approaches for paratransit optimization.

Pavia, Sophie↗
DOE OSTI2021

Robust vehicle routing under uncertainty via branch-price-and-cut

Here, this paper contemplates how branch-price-and-cut solvers can be employed along with the robust optimization paradigm to address parametric uncertainty in the context of vehicle routing problems. In this setting, given postulated uncertainty sets for customer demands and vehicle travel times, one aims to identify a set of cost-effective routes for vehicles to traverse, such that the vehicle capacities and customer time window constraints are respected under any anticipated demand and travel time realization, respectively. To tackle such problems, we propose a novel approach that combines cutting-plane techniques with an advanced branch-price-and-cut algorithm. Specifically, we use deterministic pricing procedures to generate "partially robust" vehicle routes and then utilize robust versions of rounded capacity inequalities and infeasible path elimination constraints to guarantee complete robust feasibility of routing designs against demand and travel time uncertainty. In contrast to recent approaches that modify the pricing algorithm, our approach is both modular and versatile. It permits the use of advanced branch-price-and-cut technologies without significant modification, while it can admit a variety of uncertainty sets that are commonly used in robust optimization but could not be previously employed in a branch-price-and-cut setting.

42 ENGINEERING↗