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Results for “Hybrid Electric Vehicles”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Online Identification of Power Required for Self-Sustainability of the Battery in Hybrid Electric Vehicles

Hybrid electric vehicles have shown great potential for enhancing fuel economy and reducing emissions. Deriving a power management control policy to distribute the power demanded by the driver optimally to the available subsystems (e.g., the internal combustion engine, motor, generator, and battery) has been a challenging control problem. One of the main aspects of the power management control algorithms is concerned with the self-sustainability of the electrical path, which must be guaranteed for the entire driving cycle. This paper considers the problem of identifying online the power required by the battery to maintain the state of charge within a range of the target value. An algorithm is presented that realizes how much power the engine needs to provide to the battery so that self-sustainability of the electrical path is maintained.

fuel economy↗
DOE OSTI2014

A Consumer-Oriented Control Framework for Performance Analysis in Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) have attracted considerable attention due to their potential to reduce fuel consumption and emissions. Our objective with this paper is to enhance our understanding of the associated tradeoffs among the HEV subsystems, e.g., the engine, the motor, and the battery, and investigate the related implications for fuel consumption and battery capacity and lifetime. Addressing this problem can provide insights on how to prioritize these objectives based on consumers needs and preferences. The results of the proposed optimization approach can also be used to investigate the implications for HEV costs related to ownership and warranty.

25 ENERGY STORAGE↗
DOE OSTI2023

Integrated Optimization of Powertrain Energy Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Hybrid Electric Vehicles (HEVs) and autonomous vehicles have been widely studied recently for on-road transportation. In the study of autonomous HEVs, the control of the vehicle's external dynamics and powertrain dynamics are often treated separately. Optimizing these two problems together can significantly improve fuel economy. In this paper, an autonomous HEV following a leader is considered. First, the augmented model to integrate the abovementioned dynamics is presented. Second, the optimization problem is defined to find the optimum fuel consumption of the follower in pursuit of a leader in a drive cycle. A customized control strategy based on Approximate Dynamic Programming (ADP) is then explored in which the optimal cost-to-go at each time step is approximated using neural networks. Also, the accuracy of the optimization solution is enhanced by applying the concept of the reachable sets. At last, three case studies show that the examined integrated control strategy outperforms the one with the separated optimization method by an additional 7.4%, 4.6%, and 11.8% improvement in fuel consumption, respectively.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1997

Hybrid Turbine Electric Vehicle

Hybrid electric power trains may revolutionize today's ground passenger vehicles by significantly improving fuel economy and decreasing emissions. The NASA Lewis Research Center is working with industry, universities, and Government to develop and demonstrate a hybrid electric vehicle. Our partners include Bowling Green State University, the Cleveland Regional Transit Authority, Lincoln Electric Motor Division, the State of Ohio's Department of Development, and Teledyne Ryan Aeronautical. The vehicle will be a heavy class urban transit bus offering double the fuel economy of today's buses and emissions that are reduced to 1/10th of the Environmental Protection Agency's standards. At the heart of the vehicle's drive train is a natural-gas-fueled engine. Initially, a small automotive engine will be tested as a baseline. This will be followed by the introduction of an advanced gas turbine developed from an aircraft jet engine. The engine turns a high-speed generator, producing electricity. Power from both the generator and an onboard energy storage system is then provided to a variable-speed electric motor attached to the rear drive axle. An intelligent power-control system determines the most efficient operation of the engine and energy storage system.

Viterna, Larry A.↗
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

Navigation Application Programming Interface Route Fuel Saving Opportunity Assessment on Large-Scale Real-World Travel Data for Conventional Vehicles and Hybrid Electric Vehicles

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 application programming interface (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). Navigation APIs, such as 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 many global positioning system driving traces to the links in TomTom MultiNet and road grade data from the U.S. Geological Survey elevation data set. Fuel consumption on a link is computed by the proposed 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 global positioning system 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 travel time due to choosing different routes is also examined for both powertrains.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

Emissions from Medium-Duty Conventional and Diesel-Electric Hybrid Vehicles; NREL (National Renewable Energy Laboratory)

This presentation discusses the results of emissions testing for medium-duty conventional and diesel-electric hybrid vehicles. Testing was based on a field evaluation approach that utilized the Fleet DNA drive cycle database and NREL’s Renewable Fuels and Lubricants (ReFUEL) Laboratory chassis dynamometer. Vehicles tested included parcel delivery (Class 6 step vans), beverage delivery (Class 8 tractors), and parcel delivery (Class 7 box trucks) vehicles, all with intended service class medium/heavy heavy-duty diesel (MHDD). Results for fuel economy and tailpipe NOx emissions included: diesel hybrid electric vehicles showed an average fuel economy advantage on identified test cycles: Class 6 Step Vans: 26%; Class 7 Box Trucks: 24.7%; Class 8 Tractors: 17.3%. Vehicle miles traveled is an important factor in determining total petroleum and CO2 displacement. Higher NOx emissions were observed over some test cycles: highly drive cycle dependent; engine-out differences may result from different engine operating point; and selective catalyst reduction temperature may play a role, but does not explain the whole story.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2012

Within-Day Recharge of Plug-In Hybrid Electric Vehicles: Energy Impact of Public Charging Infrastructure

This paper examines the role of public charging infrastructure in increasing the share of driving on electricity that plug-in hybrid electric vehicles might exhibit, thus reducing their gasoline consumption. Vehicle activity data obtained from a global positioning system tracked household travel survey in Austin, Texas, is used to estimate gasoline and electricity consumptions of plug-in hybrid electric vehicles. Drivers within-day recharging behavior, constrained by travel activities and public charger availability, is modeled. It is found that public charging offers greater fuel savings for hybrid electric vehicles s equipped with smaller batteries, by encouraging within-day recharge, and providing an extensive public charging service is expected to reduce plug-in hybrid electric vehicles gasoline consumption by more than 30% and energy cost by 10%, compared to the scenario of home charging only.

Dong, Jing↗
NASA NTRS1992

Electric and hybrid electric vehicle study utilizing a time-stepping simulation

The applicability of NASA's advanced power technologies to electric and hybrid vehicles was assessed using a time-stepping computer simulation to model electric and hybrid vehicles operating over the Federal Urban Driving Schedule (FUDS). Both the energy and power demands of the FUDS were taken into account and vehicle economy, range, and performance were addressed simultaneously. Results indicate that a hybrid electric vehicle (HEV) configured with a flywheel buffer energy storage device and a free-piston Stirling convertor fulfills the emissions, fuel economy, range, and performance requirements that would make it acceptable to the consumer. It is noted that an assessment to determine which of the candidate technologies are suited for the HEV application has yet to be made. A proper assessment should take into account the fuel economy and range, along with the driveability and total emissions produced.

Schreiber, Jeffrey G.↗
DOE OSTI2016

Data-driven reinforcement learning–based real-time energy management system for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off between real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. Here, a case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Vehicle Platooning: An Energy Consumption Perspective

Urban traffic congestion is a chronic problem faced by many cities in the US and worldwide. It results in inefficient infrastructure use as well as increased vehicle fuel consumption and emission levels. Excessive fuel consumptions add extra costs to commuters as well as transportation businesses. Consuming less fuel and thus reducing costs by a single percentage digit can have a significant impact on the balance sheet as well as the protection of the environment. Researchers have developed, and continue to develop, tools and systems to optimize the operations of fleets as well as engines in order to burn less fuel and therefore generate less CO2 emissions. Platooning is one such tool that attempts to maintain relatively small distances (i.e. pre-determined time gap) between consecutive vehicles. It has the potential to increase the capacity of the road as well as reduce the consumed fuel. In this paper, we use a fuel consumption model for internal combustion light-duty vehicles, electric vehicles, hybrid electric vehicles, buses and trucks in order to determine and quantify the effects of platooning on a fleet fuel consumption. The results suggest that a reduction of up to 3%, 3.5%, 4.5 %, 10%, and 15% in fuel consumption can be achieved for internal combustion engine vehicles, hybrid electric vehicles, electric vehicles, buses and trucks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Downloadable Dynamometer Database (D3): Public Test Data on Advanced-Technology Vehicles

Access to high-quality, independent vehicle test data is critical to advancing energy-efficient transportation research. The Downloadable Dynamometer Database (D3) is a public repository of dynamometer test data on advanced-technology vehicles, generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory and hosted by the Transportation and Power Systems Division. The database has been made available to support researchers, students, and professionals engaged in energy-efficient vehicle research, development, and education. A wide range of vehicle categories has been tested (i.e., alternative fuel vehicles, conventional gasoline and diesel vehicles, all-electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles), as well as various drive cycles and test conditions documented in the accompanying D3 user presentation. Stakeholders can select a vehicle type, identify a vehicle of interest, and download the associated test data for use in their own analyses. Data downloaded from D3 must be accompanied by the required attribution: "This data is from the Downloadable Dynamometer Database and was generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory." These data are critical to vehicle modeling, validation, technology assessment, and educational use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network based Equivalent Model

The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.

Parameter optimization, V2X, Powertrain modeling↗
NASA NTRS1985

Power Train for Hybrid Electric Vehicle

Power train of hybrid electric vehicle enables internal-combustion engine and electric motor to operate near same constant speed except when off or idling.

Vivian, H. C.↗
DOE OSTI2012

A Fully Directional Universal Power Electronic Interface for EV, HEV, and PHEV Applications

This study focuses on a universal power electronic interface that can be utilized in any type of the electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles (PHEVs). Basically, the proposed converter interfaces the energy storage device of the vehicle with the motor drive and the external charger, in case of PHEVs. The proposed converter is capable of operating in all directions in buck or boost modes with a noninverted output voltage (positive output voltage with respect to the input) and bidirectional power flow.

Bidirectional dc/dc converters↗
DOE OSTI2014

2011 Hyundai Sonata 3539 - Hybrid Electric Vehicle Battery Test Results

The U.S. Department of Energy’s Advanced Vehicle Testing Activity Program consists of vehicle, battery, and infrastructure testing on advanced technology related to transportation. The activity includes tests on hybrid electric vehicles, including testing hybrid electric vehicle batteries when both the vehicles and batteries are new and at the conclusion of 160,000 miles of on-road fleet testing. This report documents battery testing performed for the 2011 Hyundai Sonata Hybrid (VIN KMHEC4A47BA003539). Battery testing was performed by Intertek Testing Services NA. The Idaho National Laboratory and Intertek collaborate on the Advanced Vehicle Testing Activity for the Vehicle Technologies Program of the U.S. Department of Energy.

33 ADVANCED PROPULSION SYSTEMS↗