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Results for “Route optimization”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mechanism of shape symmetry breaking in surfactant-mediated crystal growth

We present a dynamical model of crystal growth, in which it is possible to reliably achieve asymmetric products, beginning from symmetric initial conditions and growing within an isotropic environment. The asymmetric growth is the result of a positive feedback mechanism that amplifies the effect of thermal fluctuations in the coverage of surfactants on the growing crystalline facets. Within our simple model, we are able to understand the kinetic and thermodynamic factors involved in both the onset of symmetry breaking and the persistence of anisotropic growth. We demonstrate that the mechanism is general by studying models with increasing complexity. We argue that this mechanism of symmetry breaking underpins observations of colloidal, seed-mediated syntheses of single crystalline metal nanorods capped with strongly interacting surfactants. The parameters within our model are related to experimental observables such as the concentration, hydrophobicity, and binding strength of the surfactants, which suggests a potential route to optimize the yield of asymmetric products in colloidal nanoparticle syntheses.

Oaks-Leaf, Sam↗
DOE OSTI2019

Joint Fleet Sizing and Charging System Planning for Autonomous Electric Vehicles

This paper studies the joint fleet sizing and charging system planning problem for a company operating a fleet of autonomous electric vehicles (AEVs) for passenger and goods transportation. Most of the relevant published papers focus on intracity scenarios and adopt heuristic approaches, e.g., agent based simulation, which do not guarantee optimality. In contrast, we propose a mixed integer linear programming model for intercity scenarios. This model incorporates comprehensive considerations of 1) limited AEV driving range; 2) optimal AEV routing and relocating operations; 3) time-varying origin-destination transport demands; and 4) differentiated operation cost structure of passenger and goods transportation. The proposed model can be computational expensive when the scale of the transportation network is large. We then exploit the structure of this program to expedite its solution. Numerical experiments are conducted to validate the proposed method. Our experimental results show that AEVs in passenger and goods transportation have remarkable planning and operation differences. We also demonstrate that intelligent routing and relocating operations, charging system and vehicle parameters, e.g., charging power, battery capacity, driving speed etc., can significantly affect the economic efficiency and the planning results of an AEV fleet.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Machine-learning-aided cognitive reconfiguration for flexible-bandwidth HPC and data center networks [Invited]

This paper proposes a machine-learning (ML)-aided cognitive approach for effective bandwidth reconfiguration in optically interconnected datacenter/high-performance computing (HPC) systems. The proposed approach relies on a Hyper-X-like architecture augmented with flexible-bandwidth photonic interconnections at large scales using a hierarchical intra/inter-POD photonic switching layout. We first formulate the problem of the connectivity graph and routing scheme optimization as a mixed-integer linear programming model. A two-phase heuristic algorithm and a joint optimization approach are devised to solve the problem with low time complexity. Then, we propose an ML-based end-to-end performance estimator design to assist the network control plane with intelligent decision making for bandwidth reconfiguration. Numerical simulations using traffic distribution profiles extracted from HPC applications traces as well as random traffic matrices verify the accuracy performance of the ML design estimator ( < <#comment/> 9 % <#comment/> error) and demonstrate up to 5 × <#comment/> throughput gain from the proposed approach compared with the baseline Hyper-X network using fixed all-to-all intra/inter-portable data center interconnects.

Chen, Xiaoliang (ORCID:0000000278056237)↗
DOE OSTI2026

Formation of Non-Doped Cubic Lithium Lanthanum Zirconium Oxide Nanofibers: Insights from In Situ Synchrotron X-Ray Scattering

This study investigates the formation mechanism of non-doped cubic lithium lanthanum zirconium oxide (c-LLZO) nanofibers using in situ synchrotron X-ray scattering techniques. Electrospun polymer precursor nanofibers were annealed at temperatures up to 800 °C, enabling real-time tracking of phase transitions via simultaneous small-angle X-ray scattering (SAXS), wide-angle X-ray scattering (WAXS), and evolved CO 2 gas analysis. The results reveal a three-step transformation pathway: polymer decomposition, formation of La 2 Zr 2 O 7 (LZO), and direct conversion of LZO to c-LLZO without intermediate tetragonal phases detected within the sensitivity of our in situ WAXS measurement. Cryo-electron energy loss spectroscopy (EELS) further elucidates the role of lithium diffusion, showing Li enrichment at fiber surfaces and Li deficiency in the interior, which stabilizes the cubic phase. This Li segregation effect in nanostructured LLZO materials extends beyond the previously reported size effect. This work advances the understanding of c-LLZO formation mechanisms and provides practical insights for optimizing synthesis routes to achieve phase-pure c-LLZO for solid-state battery applications.

LLZO phase stability↗
DOE OSTI2020

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Transport spatial model for the definition of green routes for city logistics centers

This paper presents a transport spatial decision support model (TSDSM) for carrying out the optimization of green routes for city logistics centers. The TSDSM model is based on the integration of the multi-criteria method of Weighted Linear Combination (WLC) and the modified Dijkstra algorithm within a geographic information system (GIS). The GIS is used for processing spatial data. The proposed model makes it possible to plan routes for green vehicles and maximize the positive effects on the environment, which can be seen in the reduction of harmful gas emissions and an increase in the air quality in highly populated areas. The scheduling of delivery vehicles is given as a problem of optimization in terms of the parameters of: the environment, health, use of space and logistics operating costs. Each of these input parameters was thoroughly examined and broken down in the GIS into criteria which further describe them. The model presented here takes into account the fact that logistics operators have a limited number of environmentally friendly (green) vehicles available. The TSDSM was tested on a network of roads with 127 links for the delivery of goods from the city logistics center to the user. The model supports any number of available environmentally friendly or environmentally unfriendly vehicles consistent with the size of the network and the transportation requirements. - Highlights: • Model for routing light delivery vehicles in urban areas. • Optimization of green routes for city logistics centers. • The proposed model maximizes the positive effects on the environment. • The model was tested on a real network.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Isovalent Alloying and Heterovalent Substititution as Routes to Accelerate the Development and Optimization of Super-Efficient Halide Perovskite PV Solar Cells

Halide perovskites represent one of the fastest growing photovoltaic (PV) device technologies in the last decade, with research efficiencies among the highest confirmed. However, the research needed to advance tandem perovskite solar cells requires a specialized understanding base, but there is no such base for this young group of materials with the same depth as in semiconductor alloy research. Whereas IV-IV, II-V and II-VI semiconductors have a sophisticated science foundation refined over decades due of their use in electronics, lasers, LEDs and night vision applications, this is not yet the case for halide perovskites. At the same time, these materials behave rather differently than conventional semiconductors. Within the rapidly unfolding field of PV, the emerging halide-perovskite alloy research has largely been based on empiricism. Clearly, a concerted and comprehensive application to halide perovskite alloys of the modern quantum theory of solids brought us over the years a broad understanding of the critical factors enabling conventional inorganic PV could leapfrog the Edisonian, trial-and-error search for solutions to critical problems that are poorly understood. These theory approaches are required to accelerate the progress toward commercially ready hybrid-perovskites by providing a theory-guided experiment component to the EERE investments in this high-priority R&D. During this project we have studied three different rungs on the ladder of knowledge about hybrid perovskites. This kind of single halide perovskites represent the first level of development (rung 1) of solar cells, and includes materials such as APbI 3 , ASnI 3 , APbBr 3 , ASnBr 3 with different A cations, such as methylammonium (MA) with formamidinium (FM). Rung 2 is obtained by mixing B cations (Sn and Pb), and X anions (Cl and Br or I) producing a disordered alloy (A, A’)(M,M’)(X,X’) 3 -space. The most advanced rung corresponds to heterovalent-substitution of halide perovskites, creating an ordered double-perovskite compound. Although the focus of the project was on Rungs 2 and 3, we have made important discoveries regarding the nature of pure perovskites. We’ve observed that cubic perovskites should be modeled by a polymorphous network, instead of the basic single unit cell approach. This leads to the conclusion that perovskites have a fluid-like nature. An article regarding this work has been submitted to Nature Materials. Using the approach described above, we have also simulated alloys of halide perovskites, representing rung 2. We have focused on mixtures of [Cs,FA][Sn,Pb]I 3 with different concentrations, being able to disentangle the structural and electronic factors that determines the variation (bowing) of properties such as band gaps, mixing enthalpies and decomposition energies. For rung 3 compounds, we’ve closely interacted with experimental groups clarifying the chemical routes to obtain a certain compound, and what should be the most stable structure for them. As the final product, and consequent dissemination of the research, we’ve submitted two papers for publication, that are currently under evaluation. The interaction with experimental groups was also an important focus. We have participated in several local, national and international meetings, and reached out more closely to groups from CU Boulder and Arkansas.

14 SOLAR ENERGY↗
DOE OSTI2018

Optimal Operation of a Plug-In Hybrid Vehicle

In this work, we present a convex optimization control method that has been shown in simulations to increase the fuel efficiency of a plug-in hybrid electric vehicle by over 10%. Using information on energy demand and energy use profiles, the problem is defined to preferentially use battery resources sourced from the grid over petroleum resources. We pose the general nonlinear optimal resource management problem over a predetermined route as a convex optimization problem using a reduced model of the vehicle. This problem is computationally efficient enough to be optimized “on the fly” on the on-board vehicle computer and is thus able to adapt to changing vehicle conditions in real time. Lastly, using this reduced model to generate control inputs for the detailed vehicle simulator autonomie, we record efficiency gains of over 10% as compared to the industry standard charge depleting charge sustaining controller over synthetic mixed urban-suburban routes.

02 PETROLEUM↗
DOE OSTI2025

Mapping Spiking Neural Networks to Heterogeneous Crossbar Architectures using Integer Linear Programming

Advances in novel hardware devices and architectures allow Spiking Neural Network (SNN) evaluation using ultra-low power, mixed-signal, memristor crossbar arrays. As individual network sizes quickly scale beyond the dimensional capabilities of single crossbars, networks must be mapped onto multiple crossbars. Crossbar sizes within modern Memristor Crossbar Architectures (MCAs) are determined predominately not by device technology but by network topology; more, smaller crossbars consume less area thanks to the high structural sparsity found in larger, brain-inspired SNNs. Motivated by continuing increases in SNN sparsity due to improvements in training methods, we propose utilizing heterogeneous crossbar sizes to further reduce area consumption. This approach was previously unachievable as prior compiler studies only explored solutions targeting homogeneous MCAs. Our work improves on the state-of-the-art by providing Integer Linear Programming (ILP) formulations supporting arbitrarily heterogeneous architectures. By modeling axonal interactions between neurons, our methods produce better mappings while removing inhibitive a priori knowledge requirements. We first show a 16.7-27.6% reduction in area consumption for square-crossbar homogeneous architectures. Then, we demonstrate 66.9-72.7% further reduction when using a reasonable configuration of heterogeneous crossbar dimensions. Next, we present a new optimization formulation capable of minimizing the number of inter-crossbar routes. When applied to solutions already near-optimal in area, an 11.9-26.4% routing reduction is observed without impacting area consumption. Finally, we present a profile-guided optimization capable of minimizing the number of runtime spikes between crossbars. Compared to the best-area-then-route optimized solutions, we observe a further 0.5-14.8% inter-crossbar spike reduction while requiring 1–3 orders of magnitude less solver time.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗
DOE OSTI2018

Energy impact evaluation for eco-routing and charging of autonomous electric vehicle fleet: Ambient temperature consideration

This paper studies the heterogeneous energy cost and charging demand impact of autonomous electric vehicle(EV) fleet under different ambient temperature. A data-driven method is introduced to formulate a two-dimensional grid stochastic energy consumption model for electric vehicles. This model aids in analyzing EV energy cost and describing uncertainties under variable traffic and ambient temperature conditions. According to this energy consumption model, an optimal eco-routing and charging decision making framework is designed for autonomous EV fleet. This optimization framework can improve the capability of autonomous EV's energy management and help to find their minimum energy cost routes with consideration of charging demand and travel time requirements. Based on these promising models and technologies, comprehensive case studies are performed on a data-driven simulated transportation network in New York City. Detailed results show us the heterogeneous energy impact and charging demand under different ambient temperature. By giving the same travel demand and charging station information, there may be more than 20% difference of overall energy cost and 60% difference of charging demand in a specific charging station between low and high ambient temperature of a month. All these studies will help to construct sustainable infrastructure for autonomous electric vehicle fleet management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

RouteE API

This is the API endpoint for RouteE energy prediction, which can be used to get both single vehicle link or route energy estimates and transportation network-wide energy consumption estimates for a variety of vehicles. This enables external researchers and transportation engineers to access and utilize NREL's growing library of pre-trained vehicle models for prediction of transportation energy consumption. This API provides three endpoints: • /route: Energy estimation of a vehicle over a planning link or sequence of links (route). • /network: Network-wide estimation of energy consumption for all vehicle traffic in the desired area. • /compass: Energy-optimal “eco-routing” between input origin and destination coordinates (Currently in beta for Denver metro area only).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

RouteE API

This is the API endpoint for RouteE energy prediction, which can be used to get both single vehicle link or route energy estimates and transportation network-wide energy consumption estimates for a variety of vehicles. This enables external researchers and transportation engineers to access and utilize NREL's growing library of pre-trained vehicle models for prediction of transportation energy consumption. This API provides three endpoints: - /route: Energy estimation of a vehicle over a planning link or sequence of links (route). - /network: Network-wide estimation of energy consumption for all vehicle traffic in the desired area. - /compass: Energy-optimal “eco-routing” between input origin and destination coordinates (Currently in beta for Denver metro area only).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

RouteE API

This is the API endpoint for RouteE energy prediction, which can be used to get both single vehicle link or route energy estimates and transportation network-wide energy consumption estimates for a variety of vehicles. This enables external researchers and transportation engineers to access and utilize NLR's growing library of pre-trained vehicle models for prediction of transportation energy consumption. This API provides three endpoints: - /route: Energy estimation of a vehicle over a planning link or sequence of links (route). - /network: Network-wide estimation of energy consumption for all vehicle traffic in the desired area. - /compass: Energy-optimal “eco-routing” between input origin and destination coordinates (Currently in beta for Denver metro area only).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Here, results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

Drone energy consumption↗