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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Harnessing the Digital Transformation for Development of Hybrid Electric 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 that goes from concept, to analysis, to dynamic simulation, to hardware in the loop testing, to full scale testing and beyond. This effort is being revolutionized and indeed enabled by new digital tools that support the increasing technology readiness level throughout the maturation process. NASA has developed and made available a suite of digital tools that ease the path from concept to implementation. The three 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 powertrain at the timescale of the turbomachinery, capturing mechanical, electrical, and thermal interactions. This paper will describe through multiple examples how these tools have been used successfully in several of the early stages of hybrid electric 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.

Electrified Propulsion↗
NASA NTRSDate not supplied

Control and Scaling Approach for the Emulation of Dynamic Subscale Torque Loads

Research and development of electrified aircraft propulsion powertrains is moving toward the use electro-mechanical systems to emulate the loads a system imparts on another. Replacing a prime mover with a model driving an electro-mechanical system capable of emulating loads that regulate the system to produce a desired response is a lower risk, lower cost alternative to using the traditional prime mover for control system verification. This paper outlines a control and scaling approach for emulating scaled dynamic torque loads using electric machine (EM) hardware for electrified aircraft propulsion research and development purposes. The approach, known as the Sliding Mode Impedance Controller with Scaling (SMICS), drives a mechanically coupled two-EM system that provides a scaled hardware representation of an electrified turbomachinery shaft. One EM reflects inertial dynamics and load of the shaft under steady-state and transient operation while the second EM represents a motor/generator connected to the shaft. This closed loop system applies impedance and sliding mode control schemes to match desired dynamics in real-time along with parameter scaling to effectively scale full scale torque inputs, a full-scale desired inertia, and sub-scale speed feedback. The result is a sub-scale hardware implementation of a coupled EM system that is command-able by a model and control system designed for a full-scale electrified aircraft propulsion powertrain. The paper elaborates on the need for closed loop control and scaling as well as impedance and sliding mode control theory, shows a derivation of the controller and scaling, its implementation, and presents a comparison of theoretical and actual simulation results acquired during hardware-in-the-loop testing of a partial turboelectric propulsion concept at the NASA Electric Aircraft Testbed (NEAT).

Santino J Bianco↗
DOE OSTI2025

OR-AGENT framework – Architecting electrified heavy-duty drayage applications

The widespread adoption of zero-emission vehicles in heavy-duty (HD) commercial freight transportation faces considerable technoeconomic challenges. For heavy-duty trucks, ensuring high uptime, cost parity with diesel, and safety standards is especially critical as these vehicles operate over long distances with heavy loads, where any downtime or off-nominal behaviors significantly impacts logistics, productivity, and the total cost of ownership. Unlike traditional diesel refueling, BEV charging infrastructure must be co-optimized with vehicle deployment, operational demands, and grid capacity to ensure cost-effective and reliable freight operations. However, the lack of a standardized ownership and service model has led to a fragmented approach—where commercial vehicle operators may invest in, own, and maintain both vehicle/batteries and charging/energy infrastructure. This disconnect may exclude energy service providers from the equation, forcing fleet operators to explore ‘behind-the-fence’ energy solutions that increase capital investment, operational downtime, overhead costs, and, in some cases, net carbon emissions. To address these issues, this study introduces OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport), a comprehensive modeling framework that integrates powertrain architectures, charging infrastructures, and energy backbone systems into a cohesive strategy. In this paper, OR-AGENT is applied to develop an interconnected systems architecture for energy efficiency and resiliency enhancement of heavy-duty drayage vehicles at the Port of Savannah, GA. This framework showcases an interconnected systems approach to electrifying heavy-duty drayage vehicles at the Port of Savannah, GA. The study assessed BEVs with 400–1200 kWh battery capacities, accounting for seasonal variations in weather and freight routing. A diverse charging mix (150 kW–1250 kW) was evaluated alongside grid capacity constraints, cost, and carbon intensity analysis, leading to the development of a strategic microgrid/Distributed Energy Resources (DER) deployment architecture to ensure a reliable and sustainable transition. However, the findings also highlight the need for alternative zero-emission solutions for remaining trips, such as larger batteries, electrified roadways, hydrogen powertrains, or net-zero emission fuels. In conclusion, the findings are incorporated into a Total Cost of Ownership (TCO) model to identify optimal architectures for an interconnected electrified ecosystem.

Commercial vehicles↗
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 NTRSDate not supplied

SUSAN Power/Propulsion System Emulation Test Predictions

The development of all-electric and hybrid-electric propulsion systems for transport aircraft presents an opportunity for new designs that can reduce fuel consumption and emissions from commercial aviation while enabling safer and more reliable aircraft. The SUbsonic Single Aft eNgine (SUSAN) is a conceptual design for a single-aisle transport aircraft with a series/parallel partial-hybrid propulsion system that is being developed by NASA as a reference design for single-aisle transport with a high degree of electrification. The SUSAN aircraft incorporates multiple tightly coupled power, propulsion, and flight control systems. This coupling leads to challenges in the aircraft control design process, requiring a hierarchical and more coordinated control architecture. This presentation will summarize the plans for a Hardware-in-the-Loop (HIL) test performed in the Hybrid Propulsion Emulation Rig (HyPER) facility at NASA Glenn Research Center (GRC). During this test, a real-time reference model of the full-scale SUSAN powertrain and controllers will be run alongside a sub-scale electro-mechanical system representing a portion of the electrified components in the SUSAN hybrid powertrain. This test will allow the performance of the SUSAN controllers to be evaluated using real powertrain components and allows side-by-side comparison between the real and modeled subsystems. This presentation will summarize the full-scale system model, steps towards integration of representative hardware for future control testing, and predicted results.

Jonah J. Sachs-Wetstone↗
NASA NTRSDate not supplied

Development of a Digital Twin for Electrified Aircraft Powertrain Health Management

The augmentation of aircraft powertrains with electrical power systems is a promising path to reducing aircraft fuel consumption, emissions, and noise. Like conventional propulsion systems, electrified aircraft propulsion (EAP) systems will be subject to wear and tear throughout their lifecycles. System health management for EAP will enable efficient flight and maintenance scheduling, realizing economic, safety, and reliability benefits. A digital twin is, broadly, a dynamically updated virtual representation of an individual physical asset. This paper presents a Kalman filter-based approach for the development of a digital twin for an electrified powertrain and applies the approach to an EAP controls testbed. Measurements from nominal testbed operations are used to update a nonlinear model of the testbed. A Kalman filter is then created and used to identify and isolate anomalous testbed behavior based on measurements from off- nominal operations. Results show that the Kalman filter-based digital twin can monitor individual powertrain components’ health for degradation or other changes in performance. The applicability of the presented digital twin approach to any hybrid- or fully-electrified powertrain is emphasized.

Electrified Aircraft Propulsion↗
NASA NTRS2023

Control and Scaling Approach for the Emulation of Dynamic Subscale Torque Loads

Research and development of electrified aircraft propulsion powertrains are relying on the use of electromechanical systems to emulate turbomachinery/rotor loads. Replacing a physical turbomachinery/rotor with a model driving a subscale electromechanical system capable of emulating subscale torque loads and responses is a lower risk, lower cost alternative to using the full-scale turbomachinery/rotor for initial control system verification. This paper outlines a novel control and scaling approach for emulating dynamic subscale torque loads using electric machine (EM) hardware for electrified aircraft propulsion research and development purposes. The approach, known as the Sliding Mode Impedance Controller with Scaling (SMICS), drives a mechanically coupled, two-EM system to behave like a subscale hardware representation of a hybrid-electric turbomachinery shaft. One EM reflects the inertial dynamics and torque load of the subscale turbomachinery under steady-state and transient operation while the second EM represents a motor/generator connected to the shaft, which is intended to hybridize the turbomachinery. This closed loop control and scaling algorithm applies impedance and sliding mode control schemes, along with parameter scaling, to match subscale, desired dynamics in real-time and to allow this system to be driven by a full-scale hybrid-electric turbomachinery model and control. The paper elaborates on the concept of the closed loop control and scaling approach and explains the significance of using impedance and sliding mode control. It shows a derivation of the closed loop control and scaling algorithm, its implementation, and presents a comparison of theoretical and actual simulation results acquired during hardware-in-the-loop testing of a partial turboelectric propulsion concept aircraft at the NASA Electric Aircraft Testbed (NEAT).The results show that the intended dynamic responses of the hardware and the aircraft model are achieved in both the time and frequency domain. Full scale propulsion control systems can be tested using this hardware and software approach.

Emulation↗
NASA NTRSDate not supplied

Advancement of the General Aviation Synthesis Program Using Python to Enable Optimization-Based Hybrid-Propulsion Aircraft Design

In support of the Electrified Powertrain Flight Demonstrator and Advanced Air Transport Technologies projects at NASA, a new tool has been developed at NASA’s Ames and Glenn Research Centers to enable coupled engine and airframe optimization and analysis. The new tool combines the engineering-level analysis methods and empirical models of the FORTRAN General Aviation Synthesis Program (GASP) with the Python-based OpenMDAO framework to provide a modular framework for efficient gradient-based optimization with the aim of incorporating new subsystem models for unconventional configurations. The tool has been verified against GASP analyses of several aircraft models and mission formulations. Preliminary efforts have been made to integrate pyCycle, a thermodynamic cycle analysis tool, to enable simultaneous optimization of hybrid propulsion system and vehicle parameters while taking full mission performance and constraints into account. This will improve current capabilities to assess impacts of electrified powertrain technologies on future aircraft designs.

Kenneth R. Lyons↗
NASA NTRSDate not supplied

Advancement of the General Aviation Synthesis Program Using Python to Enable Optimization-Based Hybrid-Propulsion Aircraft Design

In support of the Electrified Powertrain Flight Demonstrator and Advanced Air Transport Technologies projects at NASA, a new tool has been developed at NASA's Ames and Glenn Research Centers to enable coupled engine and airframe optimization and analysis. The new tool combines the engineering-level analysis methods and empirical models of the FORTRAN General Aviation Synthesis Program (GASP) with the Python-based OpenMDAO framework to provide a modular framework for efficient gradient-based optimization with the aim of incorporating new subsystem models for unconventional configurations. The tool has been verified against GASP analyses of several aircraft models and mission formulations. Preliminary efforts have been made to integrate pyCycle, a thermodynamic cycle analysis tool, to enable simultaneous optimization of hybrid propulsion system and vehicle parameters while taking full mission performance and constraints into account. This will improve current capabilities to assess impacts of electrified powertrain technologies on future aircraft designs.

Kenneth R Lyons↗
NASA NTRSDate not supplied

Parametric Modeling and Mission Performance Analysis of a True Parallel Hybrid Turboprop Aircraft for Freighter Operations

Hybrid-electric propulsion systems for short-haul, cargo carrying aircraft have emerged as promising solutions for an environmentally sustainable future for commercial aviation. The novel propulsion architecture presents significant complexity and requires the development of new methodologies to account for the unique coupling and integration between critical design variables. This paper presents a comprehensive study on the modeling, performance assessment, and design space exploration of a C-130H freighter aircraft retrofitted with a parallel hybrid electric powertrain. Details on the development of parametric models for both the baseline and true parallel hybrid (TPH) aircraft and the integrated electrified aircraft propulsion (EAP) system sizing approach are presented along with detailed performance analyses of payload/range capabilities for short-haul cargo missions. For 2030, 2040, and 2050 EAP technology levels, the TPH C-130H configuration with a ~2.12 MW class EAP system has a range capability of 485-1,028 nautical miles and block fuel savings of 27-44%.

efficiency↗
NASA NTRS2017

Propulsion Powertrain Real-Time Simulation Using Hardware-in-the-Loop (HIL) for Aircraft Electric Propulsion System

It is essential to design a propulsion powertrain real-time simulator using the hardware-in-the-loop (HIL) system that emulates an electrified aircraft propulsion (EAP) systems power grid. This simulator would enable us to facilitate in-depth understanding of the system principles, to validate system model analysis and performance prediction, and to demonstrate the proof-of-concept of the EAP electrical system. This paper describes how subscale electrical machines with their controllers can mimic the power components in an EAP powertrain. In particular, three powertrain emulations are presented to mimic 1) a gas turbo-=shaft engine driving a generator, consisting of two permanent magnet (PM) motors with brushless motor drives, coupled by a shaft, 2) a motor driving a propulsive fan, and 3) a turbo-shaft engine driven fan (turbofan engine) operation. As a first step towards the demonstration, experimental dynamic characterization of the two motor drive systems, coupled by a mechanical shaft, were performed. The previously developed analytical motor models1 were then replaced with the experimental motor models to perform the real-time demonstration in the predefined flight path profiles. This technique can convert the plain motor system into a unique EAP power grid emulator that enables rapid analysis and real-time simulation performance using hardware-in-the-loop (HIL).

turbo-electric propulsion↗
DOE OSTI2020

Development and Demonstration of a Class 6 Range-Extended Electric Vehicle for Commercial Pickup and Delivery Operation: Preprint

Range-extended hybrids are an attractive option for medium- and heavy-duty commercial vehicle fleets because they offer the efficiency of an electrified powertrain with the driving range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy (DOE)-funded project for Medium- and Heavy-Duty Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric Class 6 truck with a range-extending engine designed for pickup and delivery application. The National Renewable Energy Laboratory (NREL) assisted by developing representative workday drive cycles for Class 6 operation and an adapted cycle to enable vehicle track testing. A novel, automated driving system was utilized by Southwest Research Institute (SwRI) to improve the repeatability of the track testing conducted to quantify vehicle energy consumption. Cummins used a drivetrain model to design the hybrid control system for increased fuel savings. The control system functionality and fuel savings objective of 50% or more were confirmed by track testing described in this paper.

ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

Analysis and Optimization of Baseline Single Aisle Aircraft for Future Electrified Powertrain Flight Demonstrator Comparisons

The purpose of this study is to provide baseline single-aisle vehicles for future comparisons with NASA’s Electrified Powertrain Flight Demonstration (EPFD) turbofan-powered Vision Systems. State-of-the-art single-aisle transports with varying design capacities of 100 to 150 passengers are modeled using NASA Ames Research Center’s General Aviation Synthesis Program (GASP) as well as GASPy. GASPy is a modernized Python-based version of GASP built on the OpenMDAO framework to allow for future, efficient gradient-based optimization and coupled airframe-propulsion design. In order to meet projected NASA Aeronautics goals for 2035, advanced aircraft technologies must be incorporated into these vehicle systems. Methodology to parametrically infuse baseline aircraft models with advanced technologies simulating improvements in aerodynamics, structures, and propulsion systems is detailed, along with the results of technology sensitivity studies. Comparison of the baseline and advanced configurations will allow for future analysis of the benefits of future hybrid and fully electric aircraft concepts in the EPFD project, where fuel consumption and emissions will be modeled and assessed. This study has been conducted under the EPFD project to establish benchmark turbofan models and demonstrate System Analysis capabilities in multi-disciplinary aircraft design, analysis, and optimization for advanced turbofan concepts.

Carl J. Recine↗
DOE OSTI2020

RouteE: A Vehicle Energy Consumption Prediction Engine

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

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

An MBSE Framework to Identify Regulatory Gaps for Electrified Transport Aircraft

A typical aircraft certification process consists of obtaining a type, production, airworthiness, and continued airworthiness certificates. During this process, a type certification plan is created that includes the intended regulatory operating environment, the proposed certification basis, means of compliance, and a list of documentation to show compliance. Earlier work by the authors demonstrated a model-based framework for the management of these certification artifacts for normal category airplanes. Presently, it is expanded and adapted to consider certification for transport category airplanes regulated under 14 CFR Part 25, providing clear transparency and traceability between the text of the regulations and imposed requirements, contextual information, and specified test activities. In particular, a capability to identify potential gaps in the applicability of regulations for novel architectures such as electrified aircraft is proposed. This capability, based on mismatches between the functional intent and the corresponding prescribed physical implementation, is developed. A sample implementation of the proposed capability is presented for a notional electrified powertrain aircraft architecture.

electrified transport aircraft↗
DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

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

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Optimizing Long Term Hydrogen Fueling Infrastructure Plans on Freight Corridors for Heavy Duty Fuel Cell Electric Vehicles

The development of a future hydrogen energy economy will require the development of several hydrogen market and industry segments including a hydrogen based commercial freight transportation ecosystem. For a sustainable freight transportation ecosystem, the supporting fueling infrastructure and the associated vehicle powertrains making use of hydrogen fuel will need to be co-established. This paper develops a long-term plan for refueling infrastructure deployment using the OR-AGENT (Optimal Regional Architecture Generation for Electrified National Transportation) tool developed at the Oak Ridge National Laboratory, which has been used to optimize the hydrogen refueling infrastructure requirements on the I-75 corridor for heavy duty (HD) fuel cell electric commercial vehicles (FCEV). This constraint-based optimization model considers existing fueling locations, regional specific vehicle fuel economy and weight, vehicle origin and destination (OD), vehicle volume by class and infrastructure costs to characterize in-mission refueling requirements for a given freight corridor. The authors applied this framework to determine the ideal long term public access locations for hydrogen refueling (constrained by existing fueling stations and dispensing technology), the minimal viable cost to deploy sufficient hydrogen fuel dispensers, and associated equipment, to accommodate a growing population of hydrogen fuel cell trucks. So the framework discussed in this paper can be expanded and applied to additional electrified powertrains as well as a larger interstate system, expanded regional corridor, or other transportation networks.

08 HYDROGEN↗
NASA NTRSDate not supplied

Adaptive Control and Scaling Approach for the Emulation of Dynamic Sub-scale Torque Loads

Previous research by the authors proposed a control and scaling approach for emulating dynamic sub-scale torque loads. This approach produced an emulation controller that successfully regulated the dynamic behavior of a sub-scale electro-mechanical system intended to be a dynamical representation of the sub-scale turboelectric powertrain of a single-aisle commercial aircraft. The sub-scale system provides an environment without turbomachinery or rotors for the initial testing of electrified aircraft propulsion (EAP) control algorithms as they would be applied to a full-scale EAP system. The sub-scale turbomachinery/rotor torque loads were produced by electric machines (EMs) driven by the control and scaling approach, a full-scale turboelectric powertrain model, and an advanced EAP control algorithm. Although successfully tested, this approach produces an emulation controller that does not guarantee asymptotic stability. Modifying the original control law and integrating adaptive control techniques into the emulation controller allows the designer to guarantee asymptotic stability. This paper introduces the idea behind the emulation controller modifications, derives the controller, proves asymptotic stability, describes the implementation of the controller on a sub-scale electro-mechanical system intended to represent a parallel hybrid-electric turbofan engine, and describes the testing of a full-scale advanced EAP control algorithm. The turbofan engine and emulation controller performance are compared to results obtained using the previous, non-adaptive control and scaling approach.

adaptive↗