Results for “powertrain simulation” — Page 4

NASA NTRS2025

Piloted Evaluation of a Fault Recovery System for an Aircraft with Distributed Electric Propulsion

Electrified aircraft powertrains contain multiple tightly coupled subsystems, making them much more complex than traditional aircraft propulsion systems, both in terms of integration and control. Electrification enables aircraft to have multiple distributed thrust-producing fans that the flight control system can utilize for enhanced maneuverability, further increasing the control complexity. The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a NASA concept aircraft that leverages this technology. SUSAN is a series/parallel partial hybrid electric single-aisle transport aircraft that takes advantage of its electrified powertrain to provide fuel burn and emissions benefits when compared to the state-of-the-art. Achieving these benefits requires an appropriately designed control architecture that coordinates the various powertrain and flight control subsystems. As such, the SUSAN aircraft is designed with a high level of automation, allowing it to properly manage coupled subsystems and react rapidly to failures and anomalies. To do this effectively, algorithms that perform component health management, fault detection, isolation, and accommodation, and continuous optimization, must be developed, tested, validated, and implemented. This paper describes a piloted evaluation of such an algorithm in scenarios with multiple fan failures, performed in a flight simulator, demonstrating failure recovery and continued safe operation up to the limits of the powertrain. These scenarios are subsequently related to certification requirements.

Electrified Aircraft Propulsion↗

NASA NTRSDate not supplied

Piloted Evaluation of a Fault Recovery System for an Aircraft with Distributed Electric Propulsion

Electrified aircraft powertrains contain multiple tightly coupled subsystems, making them much more complex than traditional aircraft propulsion systems, both in terms of integration and control. Electrification enables aircraft to have multiple distributed thrust-producing fans that the flight control system can utilize for enhanced maneuverability, further increasing the control complexity. The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a NASA concept aircraft that leverages this technology. SUSAN is a series/parallel partial hybrid electric single-aisle transport aircraft that takes advantage of its electrified powertrain to provide fuel burn and emissions benefits when compared to the state-of-the-art. Achieving these benefits requires an appropriately designed control architecture that coordinates the various powertrain and flight control subsystems. As such, the SUSAN aircraft is designed with a high level of automation, allowing it to properly manage coupled subsystems and react rapidly to failures and anomalies. To do this effectively, algorithms that perform component health management, fault detection, isolation, and accommodation, and continuous optimization, must be developed, tested, validated, and implemented. This paper describes a piloted evaluation of such an algorithm in scenarios with multiple fan failures, performed in a flight simulator, demonstrating failure recovery and continued safe operation up to the limits of the powertrain. These scenarios are subsequently related to certification requirements.

Electrified Aircraft Propulsion↗

NASA NTRS2023

Harnessing the Digital Transformation for Development of Electrified Aircraft Propulsion Control Systems

Hybrid electric aircraft propulsion is an emerging technology that presents a variety of potential benefits along with technical integration challenges. Developing these new propulsion architectures with their complex control systems, and ultimately proving their benefit, is a multistep process. This process includes concept development and analysis, dynamic simulation, hardware-in-the-loop testing, full-scale testing, and so on. This effort is being revolutionized and indeed enabled by new digital tools that support increasing the technology readiness level throughout the maturation process. As part of this Digital Transformation, NASA has developed a suite of publicly available digital tools that facilitate the path from concept to implementation. This paper describes the NASA-developed tools and puts them in the context of control system development for hybrid electric aircraft propulsion. The three MATLAB®-based software packages are the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS), the Electrical Modeling and Thermal Analysis Toolbox (EMTAT), and the Thermal Systems Analysis Toolbox (TSAT). These tools are interactive, complementary, and compatible with each other. T-MATS is a modular thermodynamic modeling framework designed for creating custom component level models of jet engines. EMTAT is a modeling framework used to simulate a variety of power electronic devices, using both physics-based and power flow calculations. TSAT is a framework for modeling and analysis of dynamic heat transfer. These packages all consist of graphical, drag-and-drop, parameterizable building blocks representing various components of the system to be modeled, e.g., compressors, turbines, motors, energy storage devices, etc. They are designed to enable the user to model and simulate the end-to-end dynamic operation of a hybrid electric gas turbine engine powertrain at the timescale of the turbomachinery, capturing mechanical, electrical, and thermal interactions. This paper demonstrates through multiple examples how these tools have been used successfully in a variety of applications, including several of the early stages of hybrid electric gas turbine engine propulsion system development, from the initial system modeling to real-time interactive pilot-in-the-loop simulation to physical hardware-in-the-loop testing, each step bringing the technology closer to fruition.

hybrid electric propulsion↗

DOE OSTI2022

Real-Sim Interface: Enabling Multi-resolution Simulation and X-in-the-Loop Development for Connected and Automated Vehicles

We report connected and automated vehicles (CAVs) can bring safety, mobility, and energy benefits to transportation systems. Ideally, CAV applications would be fully evaluated and validated prior to real-world implementation. However, many technical challenges in both software and hardware hinder the process. To comprehensively evaluate all aspects of CAV applications, an integrated evaluation environment is needed with various simulation tools from different domains. In the current literature, there lacks a well-developed interface to enable multi-resolution simulation of vehicle, traffic, virtual environment, and hardware-in-the-loop (HIL) simulation. In this work, a modular and flexible interface is developed to enable multi-resolution vehicle and traffic co-simulation for CAV applications. This interface is built upon Oak Ridge National Laboratory’s (ORNL’s) Real-Sim approach, can support various simulation tools and real-time X-in-the-loop (XIL) simulations, and is based on network communication protocols. The network communication delays are analyzed for simultaneous connection of up to 20 simulators. Then, two example applications are studied to demonstrate the potential usage of the Real-Sim interface: (1) a centralized merging scenario where the dynamics of two ego vehicles are emulated with detailed vehicle and powertrain dynamics models and (2) a signal-controller-in-the-loop (SCIL) evaluation where one intersection of the simulated traffic scenario is controlled by a physical signal controller. This Real-Sim interface is a tool that allows researchers to bring real, tangible hardware and software into simulated environments to comprehensively evaluate and support a wide variety of CAV applications.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

A Comprehensive Simulation Study to Evaluate Future Vehicle Energy and Cost Reduction Potential

Under the umbrella of EERE’s Office of Sustainable Transportation, the U.S. Department of Energy’s (DOE) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) seek to develop sustainable, affordable, and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate the potential for vehicle-level fuel savings is critical to understanding DOE’s impact and success in this mission For this study, Argonne National Laboratory (Argonne) simulated technologies funded by VTO and HFTO for light-duty vehicles across the following: Powertrain configurations (conventional, power-split hybrid electric vehicle, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles); Vehicle classes (compact car, mid-size car, small sport utility vehicle [SUV], mid-size SUV, and pickup truck); Fuels (gasoline, diesel, natural gas, hydrogen, and battery electricity). We assessed each technology for five different timeframes: laboratory years 2015 (reference), 2020, 2025, 2030, and 2045. We assumed a delay of 5 years between laboratory year and model year (i.e., the year the technology is introduced into production). Finally, we included uncertainties for both technology performance and cost by considering two cases (note that these cases are not intended as predictions of future performance): Low case , aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on business as usual regulatory and market environments; High case , aligned with aggressive technology advancements based on research and development (R&D) targets developed through support by VTO and HFTO. We estimated the energy and cost impact of different technologies using Autonomie (Argonne undated), a state-of-the-art vehicle system simulation tool developed by Argonne and used to assess the energy consumption, performance, and cost of multiple advanced vehicle technologies. The tool comprises a complete set of vehicle models to assess impacts across a wide range of classes (from light- to heavy-duty), powertrain configurations (from conventional to hybrid electric vehicles [HEVs], fuel cell electric vehicles [FCEVs], plug-in hybrid electric vehicles [PHEVs], and battery electric vehicles [BEVs]), components, and control strategies, including vehicle-level and component-level controls developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies: analyzing various component technologies, sizing powertrain components to meet different vehicle requirements, comparing the benefits of powertrain configurations, optimizing both heuristic and route-based vehicle energy control, and predicting transportation energy use when paired with a traffic modeling tool such as POLARIS. This report documents the assumptions made and the vehicle-level energy consumption benefits and associated technology costs estimated for various types of light-duty vehicles. Details regarding vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.

08 HYDROGEN↗

DOE OSTI2025

AutonomieAI: An efficient and deployable vehicle energy consumption estimation toolkit

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

Autonomie↗

DOE OSTI2021

Comprehensive powertrain modeling for heavy-duty applications: A study of plug-in hybrid electric bus

A comprehensive forward-looking powertrain model with an efficiency-based control strategy was developed to achieve real-time optimization of plug-in hybrid electric buses while considering the real vehicle drivability and the practical operation of all powertrain components under real driving conditions. The control strategy is based on a supervisory control algorithm that alternately employs charge-dominant control and discharge-dominant control to manage multiple powertrain sources, and enable an optimal overall efficiency-based powertrain operation state by maximizing the inherent optimal powertrain efficiency through best all transmission gear choice and the blend of motor and/or engine power. In the model, a component energy efficiency database was developed to rapidly enable an smart and optimal operating state determined from a set of efficiency maps characterizing the component and powertrain control states as a function of the instantaneous vehicle operational condition. The results revealed that the energy savings achieved with the innovative powertrain control improved by 10%–30% compared with the baseline hybrid powertrain control strategy. The benefits of eco-driving with respect to energy consumption were also evaluated using the powertrain model. The powertrain control model was implemented into a real hybrid bus. Measured energy savings with the optimized control strategy were similar to the simulated results.

33 ADVANCED PROPULSION SYSTEMS↗

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↗

DOE OSTI2024

Investigation of powertrain system decarbonization using electrically assisted turbocharging and hybridization in off-road vehicles

In recent years, the causes, effects, and existential threat of a global anthropogenic climate shift have drawn significant attention and stimulated mitigation efforts from all genres of scientific, political, and industrial bodies. The greenhouse effect and the detrimental environmental impact of excess greenhouse gas (GHG) emissions, like carbon dioxide, is well studied and targeted as the primary culprit for aversive action. However, some fields exist where reducing GHG emissions is met by considerable challenges. One such field is the production and operations of off-road vehicles. Applications of these vehicles are highly diverse and are often characterized by rugged, especially transient, and power intensive duty cycles that make one-fits-all vehicle configuration solutions impractical. This research surveys existing literature to identify the modern technologies packages and challenges that face development and configuration of powertrain systems which currently navigate the regulated emissions and unique duty cycle requirements of this market space. A novel powertrain concept is proposed and evaluated with respect to the pinnacle objectives of load performance improvement, criteria pollutant reduction, and reducing the life cycle GHG intensity of its operation. A sure-fire path to vehicular GHG reduction is through improving the fuel efficiency of internal combustion engines (ICEs), an entrenched component of the off-road vehicle sector. Unfortunately, this is more easily said than done. An approach that has proven successful in this endeavor is engine downsizing and turbocharging, where a larger engine is replaced with a smaller one with added air system boosting via turbocharger to reduce frictional and pumping losses while also enabling access to additional fuel energy. However, practical realization of these potential benefits is often impeded by the transient response capability of the smaller engine across the operating space. For this reason, downsized ICE powertrains have turned to electrified forced induction systems (EFISs) for a decoupling of exhaust energy and engine speed from boost capability. Platformed on 48V hybrid technology, these systems introduce the need for more sophisticated controls around the engine gas-exchange process for the management of boost performance and exhaust gas emissions. On this account, simulation studies utilizing a GT-POWER model of a turbocharged 4.5 L engine outfit with an EFIS using an electrically driven compressor (eBooster®) are conducted to provide insight into the performance of this air handling architecture on an off-road engine. The results show that improvement in transient torque response time in sync with reduction in engine-out soot and NOx emissions are possible with an engine recalibration that leverages the transient air-fuel ratio authority of the eBooster®. Benefits are further demonstrated when duty cycle simulations of the 48V mild-hybrid engine concept are exercised, showing an acute decrease in cumulative fuel usage and soot production. Powertrain hybridization is another technological pathway achieving pronounced GHG reduction successes in modern on-road vehicle applications through integration of Li-ion battery technology. In the off-road vehicle segment, a review of available literature concludes that hybridized architectures are present but generally lack the depth of technologies that have both high specific energy and power capabilities, and thus are limited in their inclusion of Li-ion batteries for ICE assistance and enhanced energy storage capability. Therefore, building on the mild-hybrid engine results, the downsized and eBoosted engine concept was integrated into a larger high-voltage battery-hybrid series-electric powertrain system. Hybrid powertrain parameter sensitivity studies were carried out in a numerical charge-sustaining framework, providing novel insights into power flows between the battery and the engine and how their respective capabilities and operation contribute to GHG and criteria pollutant emissions of diverse duty cycles. Application of supervisory power management introduced robustness into the power sourcing and battery SOC control process and showed that optimum specification of battery properties can yield synergies between GHG emissions and battery energy capacity. Furthermore, examination of recent literature on Li-ion batteries has shown that pack manufacturing is a highly energy intensive process, thereby producing considerable quantities of GHGs that scale with energy storage capacity. Also scaling with a battery’s energy storage capacity is its investment cost. In consideration of these factors, an inclusive technoeconomic and GHG life cycle analysis is conducted. This analysis systematically compares the carbon footprint and total cost of ownership associated with the proposed hybrid powertrain concept to reference and alternative powertrain configurations, facilitating a thorough evaluation of the decarbonization effectiveness and economic viability.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Pathways to a Sustainable Aviation Ecosystem: Aviation Energy Research and Operation Simulator (AEROSim)

The National Renewable Energy Laboratory (NREL) has developed highly accessible tools for ground vehicle modeling, making it easy to quickly compare powertrains and estimate the impact of technology improvements on fuel/energy consumption, performance, cost, and battery life. With AEROSim, NREL advanced aircraft, enabling the same level of ease and versatility in comparing aircraft designs across a range of scenarios and conditions.

advanced air mobility↗

NASA NTRSDate not supplied

Control Concept of Operations and Flight Deck Design for the SUbsonic Single Aft eNgine (SUSAN) Electrofan

Explore the source record for details and available documents.

Electrified powertrain modeling↗

DOE OSTI2022

Connected and automated vehicles, driving systems, and control logic for info-rich eco-autonomous driving

A method for controlling automated driving operations of a vehicle includes determining vehicle origin and destination data, and generating a graphical representation of a road network with multiple candidate routes between the vehicle's origin and destination. Road-level data, including speed, turn angle, and/or gradient data, is received for each candidate route, and respective total energy uses are estimated for the vehicle to traverse across the candidate routes. Multiple candidate driving strategies, each having respective speed and acceleration profiles, are determined for the candidate route with the lowest estimated total energy use. An optimal candidate driving strategy is selected through a cost evaluation of the associated speed and acceleration profiles and forward movement simulations of the vehicle over a prediction horizon. Command signals are transmitted to the vehicle's steering and/or powertrain systems to execute control operations based on the optimal driving strategy and the candidate route with the lowest energy use.

Zhao, Junfeng↗

DOE OSTI2022

Development and Demonstration of a Fuel-Efficient, Class 8 Tractor & Trailer Engine System (SuperTruck II)

Navistar presents the SuperTruck II (ST II) Final Report to the Unites States Department of Energy (US DOE), which covers the five Budget Periods (BPs) from 10-1-2016 through 6-30-2022. For ST II, Navistar built on the achievements of the SuperTruck I (ST I) Program as a catalyst to continue critical research, design and development, testing, and operations to reach the ambitious goals of the ST II project. This approach allowed Navistar to continue contributing to the essential needs of our nation for safe, efficient, and cost-effective delivery of goods and services, as we reduced negative environmental effects and improved operational productivity. This document contains information specified in DOE F 4600.2, Final Scientific/Technical Report DOE F 241.3, B. SCIENTIFIC/TECHNICAL REPORTS, explaining how we met and exceeded program requirements. Throughout this Final Report, Navistar extracted information from documents prepared during the project that represent our management, design and development, building, and testing efforts to meet and exceed SuperTruck II project goals. Navistar followed Plan requirements to achieve / exceed Project Objectives: a) >100% improvement in vehicle freight efficiency (FE) (on ton-MPG basis) relative to 2009 baseline with stretch goal of 140% improvement [actual: 170%); b) >55% engine brake thermal efficiency (BTE) demonstrated in operational engine at a 65-mph cruise point on a dynamometer – ≥31% increase from 2009 baseline [actual: 55.20% of combined BTE) ; and c) development and implementation of commercially cost effective technologies (in terms of a simple payback). Technology selection / development path focused on developing technologies applicable for production within 3-year approach, while ensuring technology readiness and cost of ownership for end users. The Program was organized into five budget periods: Requirements / Technology Assessment and Initial Hardware Testing; Technology Development and Concept Readiness Demonstration; Technology Finalization and Validation Tractor / Trailer Fabrication, Integration and Commissioning Demonstration; and Fuel Economy (FE) and Brake Thermal Efficiency (BTE) and Program Completion. Leadership was provided by DOE, with tasks performed by laboratories (Argonne National Laboratory, Lawrence Livermore National Laboratory); partners at Bosch, TPI, Dana, and J.B. Hunt; , and support from University of Michigan and Clemson University. Navistar lead this team with Principal Investigator / Contracting Officer; Project Manager (PM); Vehicle, Engine, and Aftertreatment Engineers; Finance Manager, Technical Program Leads, and Legal/IP; and other key personnel. Work also included personnel in risk management; funding / budget / finance. Work involved analysis, development, testing, and down selection of individual/system engine, aftertreatment, and vehicle technologies, with integration of selected technologies into a prototype vehicle for demonstration of fuel-efficiency gain. Work also included component/integrated system level development of truck and trailer aerodynamics, base engine efficiency, advanced aftertreatment, combustion efficiency, waste heat recovery, hybrid powertrain, reduced rolling resistance, weight reduction, idle reduction, and driver feedback. As ST II progressed, Navistar performed computer-based modeling / simulations of technologies focused on the primary operational areas: Engine, Aftertreatment, and Vehicle. During the ST II Program, the COVID Virus outbreak unexpectedly challenged by the effects of, which affected staffing, scheduling, design, supplies, availability of materials, production procedures, and testing. The DOE responded by extending the program by three quarters to ensure that project tasks were completed for this vital project. Focus continued on analyzing, developing, testing, and down selecting individual-/system-level engine and vehicle technologies for integration of the final selected technologies into a prototype vehicle that would demonstrate fuel-efficiency gains made possible through these technologies. This included component/integrated system-level development of truck and trailer aerodynamics, base engine efficiency, advanced aftertreatment, combustion efficiency, waste heat recovery, solar power, distributed and intelligent vehicle power, hybrid powertrain, reduced rolling resistance, weight reduction, idle reduction, and driver feedback. Throughout the program, function, reliability, and performance at all levels were ensured through testing. Proof of this approach was demonstrated in multiple, on-road demonstrations: Scenario A (Flatland) Fuel Economy, Scenario B (Hilly) Fuel Economy, and City Cycle Tests. Other benefits derived from ST II included new/improved products, publications, patents, and next-step capabilities related to electric/hydrogen vehicles and autonomous driving.

Zukouski, Russ↗

DOE OSTI2026

Scenario-based analysis of electric vehicle adoption in the United States: Technology, infrastructure, and electricity pricing

This work investigates the impact of battery technology advancement, charging infrastructure development, and time-of-use (TOU) electricity pricing on vehicle adoption by 6 powertrain types in the United States through 2050. Using the Market Acceptance of Advanced Automotive Technologies (MA3T) model, we simulate 15 scenarios, examining individual cost factors and their combinations. We assess outcomes through market share, consumer surplus, and energy consumption. Results show that battery cost reductions are the strongest driver of EV adoption, increasing 2050 battery electric vehicle (BEV) share by 27 percentage points over baseline, raising annual consumer surplus by $511 per household, and reducing cumulative energy consumption by 16,610 trillion Btu. These gains are two to five times larger than those from other individual factors. Reducing home charging installation costs produces moderate impact, while TOU pricing alone yields only small gains, raising 2050 BEV market share by 1–2 percentage points. However, when cost factor improvements are combined, their effects are amplified beyond simple additivity. Pairing modest battery cost reductions with charging installation cost reductions and TOU pricing results in the largest 2050 BEV sales combined impact. The analysis demonstrates that moderate progress targeting multiple cost barriers may be more impactful than focusing on any single barrier.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Refining Control, Charging, and Battery Chemistry for CO 2 e Savings in Heavy-Duty Off-Road Plug-In Series Hybrid

With current and future regulations continuing to drive reductions in carbon dioxide equivalent (CO 2 e) emissions in the on-road industry, the off-road industry is also likely to be regulated for fuel and CO 2 e savings. This work focuses on converting a heavy-duty off-road material handler from a conventional diesel powertrain to a plug-in series hybrid, achieving a 49% fuel reduction and 29% CO 2 e reduction via simulation. Control strategies were refined for energy savings, including a regenerative braking strategy to increase regenerative braking and a load-following hydraulic strategy to decrease electrical energy consumption. The load-following hydraulic control shuts off the hydraulic electric machine when it is not needed—an approach not previously seen in a load-sensing, pressure-compensated system. Furthermore, these strategies achieved a 24.1% fuel savings, resulting in total savings of 61% in fuel and 41% in CO 2 e in the plug-in series compared to the conventional machine. Beyond control strategies, this study evaluated battery chemistry and charging strategy refinements for total cost of ownership (TCO) and lifetime CO 2 e. LFP batteries emerged as the most cost-effective and least emitting due to their longer lifespan, which reduced replacement frequency. Charging comparisons showed that Level 2 charging (L2C) typically resulted in lower TCO but higher lifetime CO 2 e than DC fast charging (DCFC). DCFC costs were heavily influenced by local demand charges, and DCFC emissions were heavily influenced by local grid emissions.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

Optimal Control Allocation for Distributed Electric Propulsion in A Series/Parallel Partial Hybrid Powertrain

The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a NASA concept transport aircraft representative of technology anticipated for a 2040 entry-into-service date. The powertrain consists of a single thrust-producing geared turbofan engine with generators driving a series/parallel partial hybrid power/propulsion system. The architecture includes 16 underwing contrarotating fans, eight on each side. The distributed fans can be used by the flight control system to augment or replace the rudder function. This paper sets up the optimal control problem of setpoint determination for individual wingfans in the distributed propulsion system, accounting for electrical string efficiencies, saturations, and failures. The solution minimizes power consumption while maintaining thrust and torque on the airframe for maneuvering. Additionally, thrust that would have been lost due to temporary fan speed or power saturation is optimally redistributed to maintain overall desired thrust and torque on the aircraft. A simulation of a coordinated turn utilizing the distributed electric propulsion for yaw rate control in a multiple wingfan failure scenario demonstrates the robustness of the powertrain design to failures and helps define its limitations.

Distributed Electric Propulsion↗

NASA NTRSDate not supplied

Optimal Control Allocation for Distributed Electric Propulsion in A Series/Parallel Partial Hybrid Powertrain

The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a NASA concept transport aircraft representative of technology anticipated for a 2040 entry-into-service date. The powertrain consists of a single thrust-producing geared turbofan engine with generators driving a series/parallel partial hybrid power/propulsion system. The architecture includes 16 underwing contrarotating fans, eight on each side. The distributed fans can be used by the flight control system to augment or replace the rudder function. This paper sets up the optimal control problem of setpoint determination for individual wingfans in the distributed propulsion system, accounting for electrical string efficiencies, saturations, and failures. The solution minimizes power consumption while maintaining thrust and torque on the airframe for maneuvering. Additionally, thrust that would have been lost due to temporary fan speed or power saturation is optimally redistributed to maintain overall desired thrust and torque on the aircraft. A simulation of a coordinated turn utilizing the distributed electric propulsion for yaw rate control in a multiple wingfan failure scenario demonstrates the robustness of the powertrain design to failures and helps define its limitations.

Distributed Electric Propulsion↗