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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Driving an Industry: Medium and Heavy Duty Fuel Cell Electric Truck Component Sizing

Medium and heavy duty (MD and HD respectively) vehicles are responsible for 26 percent of the total U.S. transportation petroleum consumption [1]. Hydrogen fuel cells have demonstrated value as part of a portfolio of strategies for reducing petroleum use and emissions from MD and HD vehicles. [2] [3], but their performance and range capabilities, and associated component sizing remain less clear when compared to other powertrains. This paper examines the suitability of converting a representative sample of MD and HD diesel trucks into Fuel Cell Electric Trucks (FCETs), while ensuring the same truck performance, in terms of range, payload, acceleration, speed, gradeability and fuel economy.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2017

Planetary Load Sharing in Three-Point- Mounted Wind Turbine Gearboxes: Preprint

Wind turbine gearboxes do not achieve their expected design life. The cost of gearbox replacements and rebuilds and the downtime associated with these failures increase the cost of wind energy. In 2007, the U.S. Department of Energy established the National Renewable Energy Laboratory (NREL) Gearbox Reliability Collaborative (GRC). Its goals are to understand the root causes of premature gearbox failures and improve their reliability. To date, the GRC has focused on a 750-kW drivetrain with a three-stage, three-point-mounted gearbox. A nonproprietary version of the gearbox containing CRBs with C3 clearances in the planetary stage was customized. Two of these gearboxes, GB1 and GB2, were manufactured and then tested in the National Wind Technology Center's 2.5-MW dynamometer and in the field. Major GRC findings include the detrimental effect of rotor moments on planetary load sharing and predicted fatigue, and the risk of bearing sliding in low-torque conditions for three-point configuration drivetrains. Based on the knowledge gained from testing and analysis of the original design, the GRC gearbox was redesigned to improve its load-sharing characteristics and predicted fatigue. This new gearbox is named GB3. As shown in Figure 1, its key improvement is the incorporation of preloaded TRBs that support the planet carrier and planets. Roller loads can be optimized and bearing life maximized with a small preload [4]. These preloaded bearings, along with interference-fitted planet pins, improve alignments and load-sharing characteristics. A semi-integrated planet bearing design also increases capacity and eliminates outer race fretting. Romax Technology, with Powertrain Engineers and the Timken Company (Timken), completed the redesign. Timken manufactured and instrumented the planet gears and bearings. Brad Foote Gearing manufactured the other gearing and assembled the gearbox.

17 WIND ENERGY↗
DOE OSTI2016

Implications of Successes and Failures of BEV-Focused Incentive Support for PEVs in the U.S., Canada and Europe

An international comparative analysis of causes of variation of PEV sales rates per capita in selected U.S. states, Canadian provinces, and European Nations is conducted. 2014-15 light-duty PEV registrations/sales by make and model are examined, drawing heavily on 2014 data for aggregate comparisons. States, provinces, and nations with PEV success, but with widely varying PEV purchase incentives and charging infrastructure installations are examined. The paper focuses particularly presence or absence of long daily distance charging options for PEVs. Four questions are addressed. (1) European evidence is that PHEV powertrains are a very marketable option for large family vehicles. For small BEVs BMW i3 sales patterns indicate that range extension beyond 120 km but less than 240 km via gasoline significantly increases market share. BEV inter-city functionality provided by aggressive installation of DC fast charging was critical to overall PEV success in Norway. Norway, like Northern Europe and Canada has a utility network that is winter peaking, which allows provision of peak summer BEV long-distance charging needs without difficulty. This is not the case for the U.S., which is summer peaking. The reviewed states, provinces, and nations vary considerably in seasonal peak temperature extremes. These climate differences have a significant bearing on the local viability of PHEVs vs. BEVs. The long distance DC fast charging infrastructure investments needed to support BEV market success are not as large when PHEVs are preferred by consumers.

Battery electric vehicle↗
DOE OSTI2017

Personal Fuel Economy in Conventional and Hybrid Electric Vehicles: Integrating Driving Cycle Predictions with Power Distribution Optimization

Improving fuel efficiency and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel efficiency in laboratory controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This study uses standard driving cycles as a base for comparison, while aiming to develop fuel efficiency estimates based on personalized driving cycles that can inform consumers’ vehicle purchase and use decisions. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: aggressive, normal, or calm. Then the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate personal fuel efficiency for conventional vehicles and HEVs. To optimize personal fuel efficiency estimates, the Equivalent Consumption Minimization Strategy (ECMS) is applied. Depending on the driving style (aggressive, normal, or calm) and the driving scenario (city or highway driving), conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel efficiency was 13% lower in calm city driving, but almost 34% higher for aggressive highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on application of case-based reasoning and used to tune the power distribution in HEVs, their fuel consumption can improve by up to 12% in city driving. Implications and limitations of the findings are discussed in the paper.

Liu, Jun↗
DOE OSTI2016

Personalized Vehicle Energy Efficiency & Range Predictor/MyGreenCar

MyGreenCar provides users with the ability to predict the range capabilities, fuel economy, and operating costs for any vehicle for their individual driving patterns. Users launce the MyGreeCar mobile app on their smartphones to collect their driving patterns over any duration (e.g. serval days, weeks, months, etc) using a phones's locational capabilities. Using vehicle powertrain models for any user-specified vehicle type, MyGreenCar, calculates the component-level energy and power interactions for the chosen vehicle to predict several important quantities, including: 1. For Evs: Alleviating range anxiety 2. Comparing fuel economy, operating costs, and payback time across models and types.

SAXENA, SAMVEG↗
DOE OSTI2017

Navigation API Route Fuel Saving Opportunity Assessment on Large-Scale Real-World Travel Data for Conventional Vehicles and Hybrid Electric Vehicles: Preprint

The green routing strategy instructing a vehicle to select a fuel-efficient route benefits the current transportation system with fuel-saving opportunities. This paper introduces a navigation API route fuel-saving evaluation framework for estimating fuel advantages of alternative API routes based on large-scale, real-world travel data for conventional vehicles (CVs) and hybrid electric vehicles (HEVs). The navigation APIs, such Google Directions API, integrate traffic conditions and provide feasible alternative routes for origin-destination pairs. This paper develops two link-based fuel-consumption models stratified by link-level speed, road grade, and functional class (local/non-local), one for CVs and the other for HEVs. The link-based fuel-consumption models are built by assigning travel from a large number of GPS driving traces to the links in TomTom MultiNet as the underlying road network layer and road grade data from a U.S. Geological Survey elevation data set. Fuel consumption on a link is calculated by the proposed fuel consumption model. This paper envisions two kinds of applications: 1) identifying alternate routes that save fuel, and 2) quantifying the potential fuel savings for large amounts of travel. An experiment based on a large-scale California Household Travel Survey GPS trajectory data set is conducted. The fuel consumption and savings of CVs and HEVs are investigated. At the same time, the trade-off between fuel saving and time saving for choosing different routes is also examined for both powertrains.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

NREL/Industry Range-Extended Electric Vehicle for Package Delivery

Range-extended electric vehicle (EV) technology can be a viable option for reducing fuel consumption from medium-duty (MD) and heavy-duty (HD) engines by approximately 50 percent or more. Such engines have wide variations in use and duty cycles, however, and identifying the vocations/duty cycles most suitable for range-extended applications is vital for maximizing the potential benefits. This presentation provides information about NREL's research on range-extended EV technologies, with a focus on NREL's real-world data collection and analysis approach to identifying the vocations/duty cycles best suited for range-extender applications and to help guide related powertrain optimization and design requirements. The presentation also details NREL's drive cycle development process as it pertains to package delivery applications.

25 ENERGY STORAGE↗
DOE OSTI2013

Vehicle-to-grid Simulator

Vehicle-to-grid Simulation Platform (V2G-Sim) -- A recent activity at LBNL has begun to create a simulation platform to generate temporarily--and spatially--resolved predictions of grid impacts and opportunities from increased plug-in vehicle (PEV) deployment. Currently in the prototype stage, the V2G-Sim models for: a) driver behavior, b) automated generation of trip specific drive cycles (velocity-time profiles), c) vehicle powertrain models of energy usage during a trip, d) vehicle charging, and e) vehicle response to managed charging algorithms.

Saxena, Samveg↗
DOE OSTI2018

National-Level Energy Impacts of Cooperative Adaptive Cruise Control (CACC) Systems

This poster highlights a study of the potential national-level energy impact of connected and automated vehicles and technologies (e.g., cooperative adaptive cruise control (CACC), eco-signals, automated mobility districts, etc.) and how varying levels of CACC adoption affect on-road fuel economy for different vehicle powertrains as well as potential changes in vehicle-miles-traveled distributions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Energy-Efficient Cruise Control Using Optimal Control for a Hybrid Electric Vehicle

Increasing connectivity in passenger vehicles provides for a large amount of look-ahead information about driving conditions. An intelligent control algorithm is presented that takes advantage of this information to obtain the operation strategy for the powertrain of a parallel hybrid electric vehicle, in an uncongested highway cruising situation. In order to guarantee sufficient computational efficiency to meet future online requirements, the algorithm is based on Pontryagin’s Minimum Principle. The whole driving/operation strategy is composed of a series of solutions to the optimal control sub-problem for each separate route segment. The control sequence computed offline is then evaluated in Autonomie. The simulation result shows 6% fuel savings compared to a baseline rule-based controller with no speed optimization.

Shen, Daliang↗
DOE OSTI2014

Trip Prediction Using GIS for Vehicle Energy Efficiency

In this paper, we present a novel trip prediction process whose main applications include vehicle energy efficiency optimization. The process is composed of multiple parts. First, a Markov chain model is built from quality-checked, real-world vehicle speed data. Secondly, a geographical information system (GIS), ADAS-RP, is used to define a real-world itinerary on a map-based interface. Finally, an algorithm runs the Markov chains under constraints from the GIS. As a result, any number of second-by-second stochastic speed profiles can be generated for a given itinerary. These speed profiles can then be inputs to vehicle powertrain models such as Autonomie, which can be used to predict energy consumption and other operational parameters. An example of the application of speed prediction is also described, involving route-based energy management for plug-in hybrid electric vehicles, in which the knowledge of future speed profiles can be used to find the optimal energy split.

energy efficiency↗
DOE OSTI2019

Impact of CAV Technologies on Energy Consumption of Advanced Electrified Vehicles

Transportation system simulation is a widely accepted approach to evaluate the impact of transport policy deployment. In developing a transportation system deployment model, the energy impact of the model is extremely valuable for sustainability and validation. It is expected that different penetration levels of Connected-Automated Vehicles (CAVs) will impact the travel behavior due to changes in potential factors such as congestion, miles traveled, etc. Along with such impact analyses, it is also important to further quantify the regional energy impact of CAV deployment under different factors of interest. The objective of this paper is to study the different penetration levels of CAVs deployment in the City of Chicago and how it impacts the energy consumption of electrified vehicles in the future. The paper will further provide a statistical analysis of the results to evaluate the impact of the different penetration levels on the different electrified powertrains used in the study.

Islam, Ehsan Sabri↗
DOE OSTI2018

Reliability Indexing of Intra-Facility Dispatch Decisions

The Powertrain Reliability Index (PRI) quantifies the trade-offs made in system reliability for efficiency, water use, and environmental purposes for relevant stakeholders. Using historical data from five North American hydroelectric facilities on a shared river system, this metric characterizes the annual probabilistic reliability of intra-facility dispatch decisions. The metric characterizes the operational risk of actual dispatch based on the lowest risk-exposed dispatch and the highest risk-exposed dispatch. By normalizing facility reliability by the number of components described within the facility, constraining operation by the historic dispatch total of unit starts and generating hours, and bounding by required grid flexibility, each facility’s dispatch can be scored for reliability.

Signore, Stephen R.↗
DOE OSTI2019

Development of a Real-Time Mobility Control and Visualization System with Predictive Vehicle Speed Control for Connected and Automated Vehicles (CAVs)

The Oak Ridge National Laboratory is developing a cloud-based platform for the deployment and evaluation of mobility- and efficiency-oriented connected and automated vehicles (CAVs) applications that will include fundamental data management, communications and visualization elements. This system will serve as a data portal, providing indirect communications that are relevant to efficiency applications, and can be used for both real world CAVs deployments and virtual systems that allow evaluation of new CAVs applications and algorithms. Predictive vehicle speed control algorithms have been developed and will be integrated with the real-time queryable transportation database and geospatial platform to achieve optimized energy efficiency and improved traffic flow in off-freeway driving. This paper describes the technology and summarizes evaluations being conducted for a corridor in Chattanooga, Tennessee, where the technology is expected to be initially deployed. To evaluate the fuel savings resulting from the vehicle speed control and the RTMCS data framework, vehicle powertrain simulations will be completed using results from a calibrated microscopic traffic model.

Laclair, Tim↗
DOE OSTI2018

Co-Optimization of Fuels & Engines: FY17 Year in Review

Researchers from national laboratories and universities across the United States are putting their combined scientific might behind the Co-Optima initiative to bring better fuels and better vehicles to market sooner. Our team members are exploring synergies across a range of fuel, engine, and powertrain options, with a focus on opportunities to increase fuel diversification and provide greater flexibility for refiners and fuel producers. We have adopted a composition-agnostic approach to identify new blendstocks that can be combined with conventional petroleum-based fuels to deliver significantly improved performance in advanced internal combustion engines. In fiscal year 2017 (FY17), our research expanded to encompass solutions for the entire on-road fleet, from light-duty passenger cars to heavy-duty freight trucks. This report spotlights some of FY17's most significant Co-Optima accomplishments.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Exhaust gas heat recovery system

A powertrain includes an engine, an exhaust system including an exhaust passageway in fluid communication with the engine, an exhaust gas heat exchanger, a transmission having a transmission cooling system, a transmission heat exchanger, a heater core, a pump, and an engine coolant circuit. The engine coolant circuit provides fluid communication between the engine, the exhaust gas heat exchanger, the transmission heat exchanger, the heater core, and the pump. The exhaust gas heat exchanger is operatively connected to the exhaust system and configured to transfer heat between the engine coolant circuit and the exhaust system. The transmission heat exchanger is operatively connected to the transmission cooling system and configured to transfer heat between the engine coolant circuit and the transmission cooling system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

A Comprehensive Approach to Measure the Efficiency of Freight Transport: Freight Mobility Energy Productivity (F-MEP) Metric

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement is critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregated in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance of freight systems. Addressing this research need, this paper presents the 'Freight Mobility Energy Productivity' metric to quantify freight performance of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight performance. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight efficiency as a result of vehicle powertrain technology improvement. Results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the efficiency of existing as well as future freight trends and technologies.

25 ENERGY STORAGE↗
DOE OSTI2020

Deep Learning-Based Queue-Aware Eco-Approach and Departure System for Plug-In Hybrid Electric Buses at Signalized Intersections: A Simulation Study

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency. Based on optimal solutions obtained from the graph-based trajectory planning algorithm (GTPA), a deep neural network (DNN) is developed to learn the optimal vehicle speed for the next time step given its current state. It is demonstrated that the trained DNN can provide energy-efficient trajectories with high computational efficiency and high flexibility adopting to dynamic changes in the surrounding environment. To address the impact of downstream traffic, a queue prediction model is further developed using data from radars and connected vehicles (CVs), as well as signal timing data from SPaT messages. A comprehensive simulation study in the microscopic traffic modeling software PTV VISSIM shows that the proposed DLQ-EAD can achieve 18.7%-24.0% energy efficiency improvements for a single PHEB on various traffic congestion levels. The proposed queue prediction model can be of practical significance even at low penetration rates of CVs. Specifically, additional energy savings of 2.0%-8.2% can be further achieved with 20% vehicles in the network.

Ye, Fei↗