Results for “hybrid powertrain” — Page 8

NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗

NASA NTRS2024

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗

NASA NTRSDate not supplied

A Call To Action To Engage The Community To Meet The Challenges That Must Be Tackled To Make Electrified Aircraft Propulsion Real

Technology risk reduction is essential, as it is necessary to demonstrate the potential of Electrified Aircraft Propulsion (EAP). However, more is needed for implementation. The industry is leading EAP by developing a diverse community of novel vehicles from short-haul, small, urban-focused electric vertical takeoff and landing (eVTOL) to regional air mobility (RAM) and hybrid-electric, single-aisle transport category airplanes. There are a variety of novel EAP technologies for each of these novel vehicles. And the industry is not only looking at novel technology to advance the state of the art. Instead, the industry is looking to certify these novel aircraft through their regulatory authorities, such as the US Federal Aviation Administration (FAA), the European Union Aviation Safety Authority (EASA), Transport Canada Civil Aviation (TCCA), and Brazil’s Agência Nacional de Aviação Civil (National Civil Aviation Agency, ANAC), as well as other regulatory authorities. The NASA Electrified Powertrain Flight Demonstration (EPFD) project has partnered with two industry partners to advance integrated MW-class powertrain system technology demonstration that includes an assessment of their regulatory and standards gaps in their technology. The EPFD has conducted a generic regulatory gap analysis of hybrid electric engines that aligns with the industry partners’ efforts. The EPFD regulations and standards team is integrated into the industry standards community. The international industry standards community is wrestling with critical key challenges to certification. While some certification elements are proprietary, several technology elements cut across company propriety in aircraft engines (US 14 CFR Part 33 and EASA CS-E, regulations that only reflect reciprocating and turbine engines). The approach that several of these regulatory authorities have taken is to collaborate to address their challenges. The Certification Management Team (CMT) consists of the EASA, FAA, TCCA, and ANAC, and they have begun to address common questions, such as the Loss of Power Control (LOPC) for electric engines. They have reached out to the standards community to seek answers. The industry standards development organizations (SDO) have also looked ahead to address current regulations and standards gaps. The ASTM has built key committees in its ASTM F44 General Aviation Committee and F39 Aircraft Systems Committee. The SAE has established the E-40 Electric Propulsion and AE-10 High Voltage committees.

Standards↗

NASA NTRSDate not supplied

A Call To Action To Engage The Community To Meet The Challenges That Must Be Tackled To Make Electrified Aircraft Propulsion Real

Technology risk reduction is essential, as it is necessary to demonstrate the potential of Electrified Aircraft Propulsion (EAP). However, more is needed for implementation. The industry is leading EAP by developing a diverse community of novel vehicles from short-haul, small, urban-focused electric vertical takeoff and landing (eVTOL) to regional air mobility (RAM) and hybrid-electric, single-aisle transport category airplanes. There are a variety of novel EAP technologies for each of these novel vehicles. And the industry is not only looking at novel technology to advance the state of the art. Instead, the industry is looking to certify these novel aircraft through their regulatory authorities, such as the US Federal Aviation Administration (FAA), the European Union Aviation Safety Authority (EASA), Transport Canada Civil Aviation (TCCA), and Brazil’s Agência Nacional de Aviação Civil (National Civil Aviation Agency, ANAC), as well as other regulatory authorities. The NASA Electrified Powertrain Flight Demonstration (EPFD) project has partnered with two industry partners to advance integrated MW-class powertrain system technology demonstration that includes an assessment of their regulatory and standards gaps in their technology. The EPFD has conducted a generic regulatory gap analysis of hybrid electric engines that aligns with the industry partners’ efforts. The EPFD regulations and standards team is integrated into the industry standards community. The international industry standards community is wrestling with critical key challenges to certification. While some certification elements are proprietary, several technology elements cut across company propriety in aircraft engines (US 14 CFR Part 33 and EASA CS-E, regulations that only reflect reciprocating and turbine engines). The approach that several of these regulatory authorities have taken is to collaborate to address their challenges. The Certification Management Team (CMT) consists of the EASA, FAA, TCCA, and ANAC, and they have begun to address common questions, such as the Loss of Power Control (LOPC) for electric engines. They have reached out to the standards community to seek answers. The industry standards development organizations (SDO) have also looked ahead to address current regulations and standards gaps. The ASTM has built key committees in its ASTM F44 General Aviation Committee and F39 Aircraft Systems Committee. The SAE has established the E-40 Electric Propulsion and AE-10 High Voltage committees.

Standards↗

NASA NTRS2024

Sizing and Performance Analysis of a MW-Class Electrified Aircraft Propulsion (EAP) System for a Parallel Hybrid Turboprop Concept

Aircraft electrification has emerged as a pivotal research and development focus in the 21st century, gaining international momentum as a strategic approach to curtail fuel consumption and emissions in the civil aviation sector. Through the Electrified Powertrain Flight Demonstration (EPFD) project, NASA is collaborating with industry partners to develop and demonstrate Megawatt (MW) class Electrified Aircraft Propulsion (EAP) systems using regional turboprops, as well as single-aisle commercial transports. With the rapid emergence of novel EAP concepts, current efforts are focused on developing the capabilities to evaluate the impacts of electrification on vehicle-level performance. This requires parametric modeling at the conceptual design stage, using uncertainty propagation techniques to account for the high variability in EAP system architecture. This paper details an integrated approach to parametric sizing of multi-MW EAP systems in a large turboprop freighter concept with a parallel hybrid configuration. The primary goal is to assess vehicle-level performance sensitivities, such as range and fuel burn reduction, to variations in electrical system component-level performance metrics, including specific power, efficiency, and energy density, based on near-term EAP technology levels.

electrified aircraft propulsion↗

DOE OSTI2023

Energy-Efficient Maneuvering of Connected and Automated Vehicles (CAVs) with Situational Awareness at Intersections (Final Progress Report)

The increased development of Connected and Automated Vehicle (CAV) systems, currently used for safety and driver convenience, presents new opportunities to improve the energy efficiency of vehicles. Southwest Research Institute (SwRI) achieved a 20% energy consumption reduction in a 2017 Toyota Prius Prime plug-in hybrid by using connectivity (V2V, V2I, V2X) as part of the Next Generation Energy Technologies for Connected and Automated on-Road Vehicles (NEXTCAR) program. The energy consumption gains were achieved by a combination of vehicle dynamics and powertrain control algorithms with a focus on SAE L1 and L2 automated vehicles where a human is still responsible for safe operation. SwRI is now involved in NEXTCAR-II, focusing on energy-efficient control tech for SAE Level 4/5 automated vehicles, aiming for a 30% energy reduction compared to stock hybrids. The rise of Mobility as a Service (MaaS) is driving investments in L4 and L5 automated vehicles. A study by the University of Michigan shows these vehicles might increase energy use and emissions by 3-20%. Technology similar to NEXTCAR can enhance energy efficiency in highly automated vehicles, leveraging improved sensing and actuation capabilities. While the NEXTCAR programs targeted energy efficiency improvements for a single vehicle, this program adopts a more expansive approach. It places its focus on understanding and testing the cumulative effects within a region or corridor, aiming to assess how a subset of vehicles equipped with NEXTCAR-style technologies influence the overall energy consumption of all vehicles traveling within that area. Additionally, the program explores infrastructure-based mobility solutions to optimize efficiency, and seeks to understand and quantify public perception and likelihood of technology adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

ELECTRIFICATION OF A HEAVY-DUTY OFF-ROAD MATERIAL HANDLER: ENERGY SAVINGS AND EMISSION REDUCTIONS

Federal regulations are driving the adoption of electrification technologies to reduce carbon dioxide equivalent (CO2e) emissions, a metric that quantifies the global warming potential of various greenhouse gases in terms of carbon dioxide (CO2). Although no specific CO2 regulations exist for heavy-duty off-road machines, future reductions are likely, given stricter emissions standards for on-road vehicles. The heavy-duty off-road sector offers significant fuel-saving potential, as its focus has traditionally been on reliability and performance rather than fuel efficiency. This dissertation examines fuel and CO2e savings opportunities on a heavy-duty off-road material handler, the Pettibone Cary-Lift 204i, from stock configuration to simple modifications to a complete teardown and reconfiguration of the machine with a plug-in series hybrid architecture using electrified hydraulics. The study begins by modeling the baseline machine’s fuel and energy consumption, calibrating with experimental data from custom operating cycles. An energy analysis identifies key areas for fuel savings. Two simple powertrain modifications result in a combined 16.2% fuel savings. Next, a Pugh-style analysis narrows a list of electrified architectures, leading to high-fidelity models that evaluate total lifetime CO2e and costs. Higher electrification levels reduce CO2e emissions but increase costs, and electricity grid emissions significantly impact CO2e for plug-in architectures. A plug-in series hybrid is chosen for the project. In its base control form, 49% fuel and 29% CO2e savings are expected from the plug-in series hybrid compared to the baseline machine. Further savings are pursued through regenerative braking (6.3%) and load-following hydraulic control (17.8%), totaling 24.1% fuel savings, and leading to a total of 61% fuel and 41% CO2e savings compared to the baseline. Battery chemistries and charging strategies are also analyzed for cost and CO2e impacts, finding LFP batteries as superior due to longevity, and overnight level 2 charging usually at a lower cost but resulting in higher emissions than opportunity DC fast-charging (DCFC). DCFC emissions are highly dependent on grid emissions, and DCFC cost is highly dependent on grid demand charges. Finally, artificial intelligence is applied to operating cycle recognition. Neural network accuracy ranges from 81% to 99%, with applications to worksite efficiency and safety improvements.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2023

Advanced 2030 Turboprop Aircraft Modeling for the Electrified Powertrain Flight Demonstration Program

Electrified aircraft propulsion concepts are rapidly emerging due to their huge potential in fuel saving and mitigating negative environmental impact. In order to perform a linear technology progression and fairly assess the impacts of powertrain electrification, it is important to first establish parametric state-of-the-art baseline vehicle models with advanced technologies matured by 2030. For a regional turboprop (50-passenger) size class and a thin haul (19-passenger) turboprop size class, a current state-of-the-art technology reference aircraft (TRA) is identified and modeled using a multi-disciplinary analysis and optimization environment. Viable technologies for airframe and conventional propulsion system are then identified which are expected to be available by 2030. These technologies are parametrically infused in the TRA models to create advanced technology aircraft models, which will serve as the baseline models for future studies of powertrain electrification.

epfd↗

NASA NTRSDate not supplied

Sizing and Performance Analysis of a MegaWatt-Class Electrified Aircraft Propulsion (EAP) System for a Parallel Hybrid Turboprop Concept

Aircraft electrification has emerged as a pivotal research and development focus in the 21st century, gaining international momentum as a strategic approach to curtail fuel consumption and emissions in the civil aviation sector. Under the Electrified Powertrain Flight Demonstration (EPFD) project, NASA and industry partners are raising the technical readiness, addressing integration challenges and gathering data for future standards and regulations for demonstrate Megawatt (MW) class Electrified Aircraft Propulsion (EAP) systems which can be utilized on regional turboprop or single aisle transports. With the rapid emergence of novel EAP concepts, risk reduction efforts are focused on developing the capabilities to evaluate the impacts of electrification at the conceptual design level where high variability in the EAP system architecture must be accounted for. This paper details an integrated, approach to parametric sizing of multi-MW EAP systems with the primary goal of assessing vehicle-level performance sensitivities to variations in electrical system component-level performance metrics. The focus of this work is to investigate the degree to which EAP component parameters such as specific power, efficiency, and energy density impact range and overall fuel savings.

EAP↗

NASA NTRSDate not supplied

Sizing and Performance Analysis of a MW-Class Electrified Aircraft Propulsion (Eap) System for A Parallel Hybrid Turboprop Concept

Aircraft electrification has emerged as a pivotal research and development focus in the 21st century, gaining international momentum as a strategic approach to curtail fuel consumption and emissions in the civil aviation sector. Under the Electrified Powertrain Flight Demonstration (EPFD) project, NASA and industry partners are raising the technical readiness, addressing integration challenges and gathering data for future standards and regulations for demonstrate Megawatt (MW) class Electrified Aircraft Propulsion (EAP) systems which can be utilized on regional turboprop or single aisle transports. With the rapid emergence of novel EAP concepts, risk reduction efforts are focused on developing the capabilities to evaluate the impacts of electrification at the conceptual design level where high variability in the EAP system architecture must be accounted for. This paper details an integrated, approach to parametric sizing of multi-MW EAP systems with the primary goal of assessing vehicle-level performance sensitivities to variations in electrical system component-level performance metrics. The focus of this work is to investigate the degree to which EAP component parameters such as specific power, efficiency, and energy density impact range and overall fuel savings.

EAP↗

DOE OSTI2026

Autonomie Simulation Datasets in Support of U.S. DOT-NHTSA Advanced Vehicle Technology Research

Understanding how new vehicle technologies affect fuel economy and energy use is critical to the regulatory work performed by the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA), which sets Corporate Average Fuel Economy (CAFE) standards under the Energy Policy and Conservation Act of 1975. In order to support this work, Argonne National Laboratory uses Autonomie, a full-vehicle simulation tool, to evaluate advanced powertrain architectures and their effects on vehicle energy consumption and performance. A wide range of vehicle classes has been assessed (i.e., internal combustion engine vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, battery-electric vehicles, and fuel cell electric vehicles), as well as the effects of various technology improvements such as lightweighting, aerodynamic refinements, and low-rolling-resistance tires. Simulations have been run across multiple drive cycles to capture fuel and electricity use under realistic operating conditions. The resulting datasets include detailed vehicle-level results, model assumptions, and validation reports, all of which have been made publicly available through NHTSA in support of the 2023 notice of proposed rulemaking covering light-duty vehicles for model years 2027 to 2035. These data are critical to stakeholders working in fuel economy regulation, vehicle technology assessment, and energy policy analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2017

Dynamic Modeling, Controls, and Testing for Electrified Aircraft

Electrified aircraft have the potential to provide significant benefits for efficiency and emissions reductions. To assess these potential benefits, modeling tools are needed to provide rapid evaluation of diverse concepts and to ensure safe operability and peak performance over the mission. The modeling challenge for these vehicles is the ability to show significant benefits over the current highly refined aircraft systems. The STARC-ABL (single-aisle turbo-electric aircraft with an aft boundary layer propulsor) is a new test proposal that builds upon previous N3-X team hybrid designs. This presentation describes the STARC-ABL concept, the NASA Electric Aircraft Testbed (NEAT) which will allow testing of the STARC-ABL powertrain, and the related modeling and simulation efforts to date. Modeling and simulation includes a turbofan simulation, Numeric Propulsion System Simulation (NPSS), which has been integrated with NEAT; and a power systems and control model for predicting testbed performance and evaluating control schemes. Model predictions provide good comparisons with testbed data for an NPSS-integrated test of the single-string configuration of NEAT.

Connolly, Joseph↗

DOE OSTI2023

Intelligent driving passive pedal control

A method for assignment of vehicle control includes receiving route data indicating a route between a starting location of a vehicle and a destination location, and determining an optimal vehicle configuration for the route based on a target vehicle speed and a hybrid torque split. The method further includes receiving a driver requested torque value and determining a passive pedal torque value based on the route data and vehicle powertrain data. The method further includes selectively assigning control of the vehicle to a vehicle system or to a driver of the vehicle based on the driver requested torque value and the passive pedal torque value.

Aggoune, Karim↗

DOE OSTI2023

RouteE-Powertrain [SWR-19-19]

RouteE-Powertrain is a tool for predicting energy usage over a set of road links. RouteE-Powertrain is a Python package that allows users to work with a set of pre-trained mesoscopic vehicle energy prediction models for a varity of vehicle types. Additionally, users can train their own models if "ground truth" energy consumption and driving data are available. RouteE-Powertrain models predict vehicle energy consumption over links in a road network, so the features considered for prediction often include traffic speeds, road grade, turns, etc. The typical user will utilize RouteE's catalog of pre-trained models. Currently, the catalog consists of light-duty vehicle models, including conventional gasoline, diesel, hybrid electric (HEV), and battery electric (BEV). These models can be applied to link-level driving data (in the form of pandas dataframes) to output energy consumption predictions. Users that wish to train new RouteE models can do so. The model training function of RouteE enables users to use their own drive-cycle data, powertrain modeling system, and road network data to train custom models. https://pypi.org/project/nrel.routee.powertrain/ pip install nrel.routee.powertrain

Holden, Jacob↗

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↗

DOE OSTI2021

Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035

From daily commutes to cross-country road trips, millions of light-duty vehicles are on the road every day. The transportation sector is one of the United States’ largest sources of greenhouse gas emissions, and fuel is an important cost for drivers. The period from 2025-2035 could bring the most fundamental transformation in the 100-plus year history of the automobile. Battery electric vehicle costs are likely to fall and reach parity with internal combustion engine vehicles. New generations of fuel cell vehicles will be produced. Connected and automated vehicle technologies will become more common, including likely deployment of some fully automated vehicles. These new categories of vehicles will for the first time assume a major portion of new vehicle sales, while internal combustion engine vehicles with improved powertrain, design, and aerodynamics will continue to be an important part of new vehicle sales and fuel economy improvement.This study is a technical evaluation of the potential for internal combustion engine, hybrid, battery electric, fuel cell, nonpowertrain, and connected and automated vehicle technologies to contribute to efficiency in 2025-2035. In addition to making findings and recommendations related to technology cost and capabilities, Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy - 2025-2035 considers the impacts of changes in consumer behavior and regulatory regimes.

MARCHANT, GARY↗

NASA NTRS2020

Multidisciplinary Systems Analysis of a Six Passenger Quadrotor Urban Air Mobility Vehicle Powertrain

There is a high degree of research interest in the design space for electric vertical takeoff and landing (eVTOL) vehicles, because these vehicles are seen as key enablers for urban air mobility (UAM). This work further explores the eVTOL design space, by presenting analysis of a six-passenger eVTOL quadrotor powertrain, with integrated power, propulsion, and thermal management systems modeled using the Numerical Propulsion System Simulation (NPSS) and the NPSS Power System Library. Four architectures are modeled at the same design point and compared over a design mission. Results from an architecture comparison study show that a hybrid architecture performs best in terms of cruise range, however, the hybrid needs batteries with high specific energy and specific power in order to obtain a benefit vs turboelectric architectures. Sensitivity studies are conducted to show correlations between component parameters and system metrics. This data provides useful indicators for further technological improvement. Lastly, an initial TMS model is presented, and sensitivity studies on TMS design parameters are presented as well, to show high level TMS design trends.

electrified propulsion↗

NASA NTRSDate not supplied

Multidisciplinary Systems Analysis of a Six Passenger Quadrotor Urban Air Mobility Vehicle Powertrain

There is a high degree of research interest in the design space for electric vertical takeoff and landing (eVTOL) vehicles, because these vehicles are seen as key enablers for urban air mobility (UAM). This work further explores the eVTOL design space, by presenting analysis of a six-passenger eVTOL quadrotor powertrain, with integrated power, propulsion, and thermal management systems modeled using the Numerical Propulsion System Simulation (NPSS) and the NPSS Power System Library. Four architectures are modeled at the same design point and compared over a design mission. Results from an architecture comparison study show that a hybrid architecture performs best in terms of cruise range, however, the hybrid needs batteries with high specific energy and specific power in order to obtain a benefit vs turboelectric architectures. Sensitivity studies are conducted to show correlations between component parameters and system metrics. This data provides useful indicators for further technological improvement. Lastly, an initial TMS model is presented, and sensitivity studies on TMS design parameters are presented as well, to show high level TMS design trends.

evtol↗