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DOE OSTI2020

Energy Consumption and Cost Reduction of Future Light-Duty Vehicles through Advanced Vehicle Technologies: A Modeling Simulation Study Through 2050

The U.S. Department of Energy’s (DOE’s) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) aim to develop sustainable, affordable and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate vehicle-level fuel savings potential is critical for understanding DOE’s impact. In this work, we simulated technologies funded by VTO and HFTO for light duty vehicles. The simulations were performed across: Multiple powertrain configurations (i.e., conventional, power-split, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles), Vehicle classes (i.e., compact car, midsize car, small sport utility vehicle [SUV], midsize SUV, and pickup trucks); and Fuels (i.e., gasoline, diesel, hydrogen, and battery electricity). These various technologies are assessed for six different timeframes: laboratory years 2015, 2020, 2025, 2030, and 2045. A delay of 5 years is assumed between laboratory year and model year (year technology is introduced into production). Finally, uncertainties are included for both technology performance and cost aspects by considering two cases: Low case, aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on regulations, business as usual; and High case, aligned with aggressive technology advancements based on R&D targets developed through support by VTO & HFTO. These scenarios are not intended as predictions of future performances. The energy and cost impact of different technologies were estimated using Autonomie (www.autonomie.net), Argonne vehicle system simulation tool. Autonomie is a state-of-the-art vehicle system simulation tool used to assess the energy consumption, performance and cost of multiple advanced vehicle technologies across classes (from light to heavy duty), powertrains (from conventional to HEVs, FCEVs, PHEVs and BEVs), components and control strategies. Autonomie is packaged with a complete set of vehicle models for a wide range of vehicle classes, powertrain configurations and component technologies, including vehicle level and component level controls. These controls were developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies including analyzing various component technologies, sizing powertrains components for 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 and estimates the vehicle-level energy consumption benefits and associated technology costs for the various types of light duty vehicles. All details of vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1980

Reduced order adaptive controller studies

The use of a reduced-order adaptive controller, can arise from a desire to reduce control complexity with a commensurate reduction in controller sensitivity or from necessity in an attempt to use a finite dimensional controller on an infinite dimensional system. An interest in developing adaptive controllers for flexible structures by application of existing lumped-parameter system (LPS) adaptive controller strategies to truncated expansion descriptions of the distributed parameter system (DPS) behavior of flexible structures has led to two qualitative descriptions of the misbehavior of reduced-order adaptive controllers. A summary is provided of these interpretations of the additional difficulties facing reduced-order adaptive controllers, which are bypassed by exact-order adaptive controllers. A test problem, which initializes attempts to quantify the qualitative insights, is also formulated.

Johnson, C. R., Jr.↗

NASA NTRS1989

Precision Segmented Reflector figure control system architecture

A control system architecture for an actively controlled segmented reflector is described along with a design realization for achieving precision alignment of reflector panels. Performance requirements are derived in part from the Large Deployable Reflector, which is a representative mission, and error allocations are made which consider mirror panel surface errors, position measurement and figure estimation, and position control of both quasi-static and dynamic disturbances. The design uses multiple wavelength interferometric edge sensors and voice coil actuators in conjunction with a hybrid control strategy to correct panel position errors. A unit cell shown to be central to the concept is analyzed. The cell integrates the sensing, actuation, and mechanical functions of a control module together with a reflector panel to form a unitized assembly.

Mettler, E.↗

DOE OSTI2026

Demand response event simulator and risk-aware bidding tool for industrial customers

Incentive Based Demand Response (IBDR) program participation delivers financial benefits to the consumers and resiliency benefits to the electricity grid. Effectively participating in these programs as an industrial consumer requires bidding strategies that balance financial risk with operational constraints. Existing bidding tools tend not to fully incorporate stochastic IBDR event modeling, program specific baseline and payment/penalty calculations, or demand reduction process control schemes that account for the cascading impacts of shutdown in complex facilities. Here, this work presents an IBDR event simulator and risk-aware bidding framework tool integrating three key components: a flexible, parameterized demand response event generator that rigorously accounts for program structures and stochasticity, a demand response operational simulation model that generates explicit control strategies for load reduction, and a Monte Carlo simulator to evaluate financial risk for varied capacity bids. A case study at a wastewater treatment plant participating in PG&E's Capacity Bidding Program demonstrates the framework's utility. In the peak capacity price month of August, optimal bidding by the wastewater treatment plant nets a mean IBDR benefit of $101,000 (67% of the August electricity bill) with 0.4% probability of a financial loss. This framework enables industrial operators to make informed bidding decisions, negotiate better program terms with demand response load aggregators, and analyze energy flexibility investments at their facilities. Ultimately, this work reduces participation barriers in IBDR programs and supports the broader goal of enhancing grid reliability and renewable energy integration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRSDate not supplied

Real-time Hardware-in-the-Loop Evaluation of a Partially Turboelectric Propulsion Control Design

In support of aviation fuel burn and emission reduction goals, NASA is pursuing high-payoff research investments that promise to transform aviation. This includes investments in Electrified Aircraft Propulsion (EAP). Multiple technology challenges must be addressed to unlock the full potential of EAP. This includes addressing challenges related to propulsion controls, which will be vital for ensuring efficient coordinated operation of EAP subsystems. This paper presents results from real-time hardware-in-the-loop testing of a control design for a single aisle partially-turboelectric aircraft propulsion concept conducted at the NASA Electric Aircraft Testbed (NEAT) facility. The control system under test is designed for a propulsion concept consisting of two wing-mounted turbofan engines that produce thrust and generate electrical power to drive a boundary layer ingesting tailfan propulsor via an electrical motor. An integrated control strategy is applied to ensure coordinated operation of the turbofan and tailfan subsystems during steady-state and transient operation throughout the flight envelope. The NEAT test of this integrated control design consists of a partially hardware-in-the-loop, partially simulated configuration. A subscale representation of the electrical system design is implemented in hardware and mechanically coupled to electric machines that emulate turbomachinery and propulsor shaft dynamics. The hardware configuration is then operated under the control of a real-time computer application that runs a simulation of the propulsion system and the developed control logic. The NEAT facility test campaign includes a series of experiments that subject the control design to throttle transients conducted throughout the flight envelope and full-flight mission profiles. Testing under simulated performance degradation is also conducted to evaluate control design robustness. This includes constant and abrupt changes in degradation levels. Results from the hardware-in-the-loop test are presented and shown to be in good agreement with pre-test simulation predictions demonstrating the efficacy of the integrated control design approach.

Electrified Aircraft Propulsion↗

NASA NTRSDate not supplied

Real-time Hardware-in-the-Loop Evaluation of a Partially Turboelectric Propulsion Control Design

Electrified Aircraft Propulsion↗

NASA NTRS2023

Real-Time Hardware-in-the-Loop Evaluation of A Partially Turboelectric Propulsion Control Design

In support of aviation fuel burn and emission reduction goals, NASA is pursuing high-payoff research investments that promise to transform aviation. This includes investments in Electrified Aircraft Propulsion (EAP). Multiple technology challenges must be addressed to unlock the full potential of EAP. This includes addressing challenges related to propulsion controls, which will be vital for ensuring efficient coordinated operation of EAP subsystems. This paper presents results from real-time hardware-in-theloop (HIL) testing of a control design for a single aisle partially-turboelectric aircraft propulsion concept conducted at the NASA Electric Aircraft Testbed (NEAT) facility. The control system under test is designed for a propulsion concept consisting of two wing-mounted turbofan engines that produce thrust and generate electrical power to drive a boundary layer ingesting tailfan propulsor via an electrical motor. An integrated control strategy is applied to ensure coordinated operation of the turbofan and tailfan subsystems during steady-state and transient operation throughout the flight envelope. The NEAT test of this integrated control design consists of a partially HIL, partially simulated configuration. A subscale representation of the electrical system design is implemented in hardware and mechanically coupled to electric machines that emulate turbomachinery and propulsor shaft dynamics. The hardware configuration is then operated under the control of a real-time computer application that runs a simulation of the propulsion system and the developed control logic. The NEAT facility test campaign includes a series of experiments that subject the control design to throttle transients conducted throughout the flight envelope and full-flight mission profiles. Testing under simulated performance degradation is also conducted to evaluate control design robustness. This includes constant and abrupt changes in degradation levels. Results from the HIL test are presented and shown to be in good agreement with pretest simulation predictions demonstrating the efficacy of the integrated control design approach.

Electrified Aircraft Propulsion↗

DOE OSTI2021

Learning-based CO 2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation

There have been increasing concerns over the air quality inside buildings as high levels of bio-effluents can cause nausea, dizziness, headaches, and fatigue to the people working in those spaces. First published in 2004 as Standard 62.1, ASHRAE Standard 62.2-2019 requires highly occupied spaces to implement heating, ventilation, and air conditioning (HVAC) that can dilute contaminants produced by occupants. In this regard, occupant-centric ventilation control has been regarded as an effective practice to maintain a satisfactory indoor air quality (IAQ) when dealing with highly variable occupancy environments. However, few established models in current literature and practice consider dynamic occupancy behavior and adaptive IAQ control. To address this gap, a dynamic indoor CO2 model is constructed using machine learning algorithms to forecast CO2concentrations across a range of forecasting horizons. Herein, we tuned and compared six state-of-the-algorithms—including Support Vector Machine, Ada Boost, Random Forest, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. The algorithms’ performances are validated using CO 2 and historical meteorological data collected from a campus classroom with a variable occupancy rate. Simulation results showed that Multilayer Perceptron can strongly predict the volatile CO 2 behavior and also outperforms other algorithms in terms of accuracy. Furthermore, a control strategy capable of modeling and detecting dynamic patterns of CO 2 level is utilized to modulate the ventilation rate in real-time and also reduce the energy consumption. The proposed controller reduced the HVAC fan’s energy consumption by 51.4% and provide ventilation as needed per the ASHRAE standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Impedance Emulation Control of Wave Energy Converters

Modeling and control of wave energy conversion (WEC) systems for maximum power extraction is challenging due to complex multiphysics that include fluids, mechanics, and machine drives. To uncover an intuitive model that clearly depicts WEC system operation, we utilize a force-current equivalent circuit framework which then enables us to design an impedance emulation control strategy. To provide context for our framework, we focus on a standard reference model-3 (RM3) point absorber device coupled to a permanent magnet synchronous generator. After the proposed extremum seeking controller is computed, we experimentally validate its performance on a platform that consists of two back-to-back connected inverters that emulate the WEC system.

wave energy conversion, impedance emulation, machi↗

DOE OSTI2022

Autonomous Closed-Loop Experimental Characterization and Dynamic Model Validation of a Scaled Underwater Kite

This paper presents the closed-loop experimental framework and dynamic model validation for a 1/12-scale underwater kite design. The pool-based tow testing framework described herein, which involves a fully actuated, closed-loop controlled kite and flexible tether, significantly expands upon the capabilities of any previously developed open-source framework for experimental underwater kite characterization. Specifically, the framework has allowed for the validation of three closed-loop flight control strategies, along with a critical comparison between dynamic model predictions and experimental results. In this paper, we provide a detailed presentation of the experimental tow system and kite setup, describe the control algorithms implemented and tested, and quantify the level of agreement between our multi-degree-of-freedom kite dynamic model and experimental data. Finally, we also present a sensitivity analysis that helps to identify the most influential parameters to kite performance and further explain the remaining mismatches between the model and data.

42 ENGINEERING↗

NASA NTRS2002

Vibration Reduction of Helicopter Blade Using Variable Dampers: A Feasibility Study

In the report, the investigation of controlling helicopter-blade lead-lag vibration is described. Current practice of adding passive damping may be improved to handle large dynamic range of the blade with several peaks of vibration resonance. To minimize extra-large damping forces that may damage the control system of blade, passive dampers should have relatively small damping coefficients, which in turn limit the effectiveness. By providing variable damping, a much larger damping coefficient to suppress the vibration can be realized. If the damping force reaches the maximum allowed threshold, the damper will be automatically switched into the mode with smaller damping coefficient to maintain near-constant damping force. Furthermore, the proposed control system will also have a fail-safe feature to guarantee the basic performation of a typical passive damper. The proposed control strategy to avoid resonant regions in the frequency domain is to generate variable damping force in combination with the supporting stiffness to manipulate the restoring force and conservative energy of the controlled blade system. Two control algorithms are developed and verified by a prototype variable damper, a digital controller and corresponding algorithms. Primary experiments show good potentials for the proposed variable damper: about 66% and 82% reductions in displacement at 1/3 length and the root of the blade respectively.

Lee, George C.↗

DOE OSTI2020

Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models

One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.

machine learning↗

NASA NTRSDate not supplied

Enhancement of an Electrified Tilt-Wing Propulsion System using Turbine Electrified Energy Management

Hybrid gas-electric aircraft propulsion architectures provide flexibility in the way that power and energy is managed when compared to their traditional pure-gas counterparts. In this paper, investigations are conducted for the impact this added flexibility has on the operability of turbomachinery. Specifically, the Turbine Electrified Energy Management (TEEM) concept is applied. It takes a controls approach to improving operability of the turbomachinery by utilizing electric hardware. In this paper, TEEM is applied to a propulsion system for a 15 passenger vertical lift concept vehicle. This is the first application of TEEM to a turbine engine that generates power. The study establishes TEEM as being applicable to this smaller thrust/power class of air transportation vehicle and explores how power can be otherwise managed in the propulsion system to benefit the aircraft. The simulation study demonstrates significant improvements in transient operability that expands the engine design space to enable a more efficient and lighter weight engine design. Simulation results also demonstrate tighter regulation of the power turbine and rotor speeds, a slight decrease in bulk fuel burn, and an increase in the maximum thrust of ~7%. This is achieved through the power management control strategy and modestly sized electric machines with re usable energy storage.

Turbine Electrified Energy Management↗

DOE OSTI2020

Motion Control of Hydraulic Actuators In the Presence of Discrete Pressure Rail Switching

Conventional off-highway mobile machines are hydraulically actuated and have poor energy efficiencies. The goal to increase system efficiency and to reap the benefits of electrification have led to the creation of a novel Hybrid Hydraulic-Electric Architecture (HHEA) which could significantly increase efficiency, decrease the sizes and cost of electrical components, and maintain control performance. The key concept is to utilize a set of common pressure rails to provide the majority of power via the power dense hydraulics and to modulate that power by small electric components, which are less power dense, for precise control. The paper presents the motion control strategy for this architecture. The architecture presents a distinct challenge in that as the energy minimization algorithm selects different pressure rails, the system could experience large discrete jumps in pressures that impact control performance, especially when the electric components are torque limited. To meet this challenge, a passivity based back-stepping controller is used as the nominal controller between pressure rail switching events and a separate transition controller is used to deal with the pressure rail switching events. Two transition controllers are proposed, one uses the electric motor torque as the input, and the other also uses valve timing as a second input. For both transition controllers, a least norm control approach is used to steer the system states to the exact desired states at the end of the short transition period (~ 40 ms). The transition controllers are able to reduce the tracking error and the required electric motor torque, and hence achieve better control performance even with smaller electric components.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1989

Robust adaptive regulation without persistent excitation

A globally convergent adaptive regulator for minimum- or nonminimum-phase systems subject to bounded disturbances and unmodeled dynamics is presented. The control strategy is designed for a particular input-output representation obtained from the state space representation of the system. The leading coefficient of the representation is the product of the observability and controllability matrices of the system. The controller scheme uses a Least-Squares identification algorithm a with dead zone. The dead zone is chosen to obtain convergence properties on the estimates and on the covariance matrix as well. This allows the definition of modified estimates which secure well-conditioned matrices in the adaptive control law. Explicit bounds on the plant output are given.

Lozano-Leal, Rogelio↗

NASA NTRS1991

Implementation of a three degree of freedom, motor/brake hybrid force output device for virtual environment control tasks

The advent of high resolution, physical model based computer graphics has left a gap in the design of input/output technology appropriate for interacting with such complex virtual world models. Since virtual worlds consist of physical models, it is appropriate to output the inherent force information necessary for the simulation to the user. The detailed design, construction, and control of a three degree freedom force output joystick will be presented. A novel kinematic design allows all three axes to be uncoupled, so that the system inertia matrix is diagonal. The two planar axes are actuated through an offset gimbal, and the third through a sleeved cable. To compensate for friction and inertia effects, this transmission is controlled by a force feedforward and a closed force feedback proportional loop. Workspace volume is a cone of 512 cubic inches, and the device bandwidth is maximized at 60 Hz for the two planar and 30 Hz for the third axis. Each axis is controlled by a motor/proportional magnetic particle brake combination fixed to the base. The innovative use of motors and brakes allows objects with high resistive torque requirements to be simulated without the stability and related safety issues involved with high torque, energy storing motors alone. Position, velocity, and applied endpoint force are sensed directly. Different control strategies are discussed and implemented, with an emphasis on how virtual environment force information, generated by the MIT Media Lab Computer Graphics and Animation Group BOLIO system, is transmitted to the device controller. The design constraints for a kinesthetic force feedback device can be summarized as: How can the symbiosis between the sense of presence in the virtual environment be maximized without compromising the interaction task under the constraints of the mechanical device limitations? Research in this field will yield insights to the optimal human sensory feedback mix for a wide spectrum of control and interaction problems. A flexible research tool that is designed as an easily reproducible product prototype has been constructed to explore the variety of possible force interaction.

Russo, Massimo↗