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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Comparison of Optimal Energy Management Strategies Using Dynamic Programming, Model Predictive Control, and Constant Velocity Prediction

Due to the recent advancements in autonomous vehicle technology, future vehicle velocity predictions are becoming more robust which allows fuel economy (FE) improvements in hybrid electric vehicles through optimal energy management strategies (EMS). A real- world highway drive cycle (DC) and a controls-oriented 2017 Toyota Prius Prime model are used to study potential FE improvements. We proposed three important metrics for comparison: (1) perfect full drive cycle prediction using dynamic programming, (2) 10-second prediction horizon model predictive control (MPC), and (3) 10-second constant velocity prediction. These different velocity predictions are put into an optimal EMS derivation algorithm to derive optimal engine torque and engine speed. The results show that the constant velocity prediction algorithm outperformed the baseline control strategy but underperformed the MPC strategy with an average 1.58% and 2.45% of FE improvement with highway and city-highway DC. Also, using a 10-second prediction window MPC strategy provided FE improvement results close to the full drive cycle prediction case. MPC has the potential to achieve 60%-65% and 70% - 80% of global FE improvement over highway and city-highway DC respectively.

Patel, Amol Arvind↗
DOE OSTI2016

Predictive Control over Charge Density in the Two-Dimensional Electron Gas at the Polar-Nonpolar NdTiO 3 / SrTiO 3 Interface

Through systematic control of the Nd concentration, we show that the carrier density of the twodimensional electron gas (2DEG) in SrTiO3=NdTiO3=SrTiO3 ð001Þ can be modulated over a wide range. We also demonstrate that the NdTiO3 in heterojunctions without a SrTiO3 cap is degraded by oxygen absorption from air, resulting in the immobilization of donor electrons that could otherwise contribute to the 2DEG. This system is, thus, an ideal model to understand and control the insulator-to-metal transition in a 2DEG based on both environmental conditions and film-growth processing parameters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Nowcasting, predictive control, and feedback control for temperature regulation in a novel hybrid solar-electric reactor for continuous solar-thermal chemical processing

A novel 15 kW reactor for the hybridization of concentrated solar and conventional electric heat was fabricated for renewable and continuous chemical processing at temperatures up to 1700 °C. Solar-electric controllers based on feedback or predictive linear models were used to regulate the device at 925 °C for the production of syngas via the gasification of carbon. The system was challenged with cloud transients programmed on a 45 kW high-flux solar simulator that approximated weather observed in the San Luis Valley (Colorado, USA). In experiment it was found that model predictive control with a 1 min ahead nowcast of incipient clouding best regulated solar-electric reactor temperatures, potentially averting thermal fatigue. Upon clouding, model predictive control with a nowcast yielded temperature disturbances of ±10 °C, whereas feedback control alone featured ±25 °C excursions. Overall, the performance of model predictive control with a nowcast was 75% better than feedback as indicated by the integral squared error of temperature excursion residuals. The importance of nowcast accuracy was explored, and although the model-based controller was robust to forecast amplitude inaccuracy, temporal forecast inaccuracy wholly negated the benefits of predictive control.

14 SOLAR ENERGY↗
DOE OSTI2022

Data-Enabled Predictive Control for Building HVAC Systems

Model predictive control is widely used as a control technology for the computation of optimal control inputs of building heating, ventilating, and air conditioning (HVAC) systems. However, both the benefits and widespread adoption of model predictive control (MPC) are hindered by the effort of model creation, calibration, and accuracy of the predictions. In this paper, we apply the data-enabled predictive control (DeePC) algorithm for designing controls for building HVAC systems. The algorithm solely depends on input/output data from the system to predict future state trajectories without the need for system identification. The algorithm relies on the idea that a vector space of all input–output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given the input signal is persistently exciting. Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated building modeled in EnergyPlus is a modified commercial large office prototype building served by an air handling unit-variable air volume HVAC system. Temperature setpoints of zones are used as control variables to minimize the HVAC energy cost of the building considering a time-of-use electricity rate structure. Furthermore, sensitivity analysis is conducted to gain insights into the effect of parameter tuning on DeePC performance. Simulation results are used to illustrate the performance of the algorithm and compare the algorithm with model-based MPC and occupancy-based setpoint controller. Overall, DeePC achieves similar performance compared to MPC for lower engineering effort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Hardware-in-the-Loop Simulation of a Distribution System with Air Conditioners under Model Predictive Control: Preprint

Many have proposed that responsive load provided by distributed energy resources (DERs) and demand response (DR) are an option to provide flexibility to the grid and especially to distribution feeders. However, because responsive load involves a complex interplay between tariffs and DER and DR technologies, it is challenging to test and evaluate options without negatively impacting customers. This paper describes a hardware-in-the-loop (HIL) simulation system that has been developed to reduce the cost of evaluating the impact of advanced controllers (e.g., model predictive controllers) and technologies (e.g., responsive appliances). The HIL simulation system combines large-scale software simulation with a small set of representative building equipment hardware. It is used to perform HIL simulation of a distribution feeder and the loads on it under various tariff structures. In the reported HIL simulation, loads include many simulated air conditioners and one physical air conditioner. Independent model predictive controllers manage operations of all air conditioners under a time-of-use tariff. Results from this HIL simulation and a discussion of future development work of the system are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Grey-box and ANN-based building models for multistep-ahead prediction of indoor temperature to implement model predictive control

Model-based predictive control (MPC) strategies for heating, ventilation, and air-conditioning (HVAC) systems present an opportunity to lower building energy consumption and operational costs. Such approaches rely on the development of a model to precisely forecast building thermal dynamics, such as room air temperature or heating/cooling rate, and make control-related decisions. The control-oriented modeling of building energy systems should be accurate in predicting indoor conditions and present low computational complexity. These features are the key challenge of implementing advanced control methods such as MPC. Extant studies on building modeling for MPC have focused on step-ahead forecasting techniques to forecast building thermal dynamics, while multistep-ahead forecasting is essential. Moreover, machine learning model suitable in case of the domain-based engineering expertise are also not available. To this aim, we perform a comparative analysis of the grey-box model based on a resistance-capacitance (RC) thermal network and a machine learning model composed of an artificial neural network (ANN) for multistep-ahead prediction of building thermal dynamics using current and historical data. Actual experimental data obtained from the Flexible Research Platform (FRP) in Oak Ridge National Laboratory (US) are used for estimation and validation purposes. The average root mean squared error (RMSE) of the grey-box and ANN models are 0.89 °C and 1.02°C, respectively. Finally, the results indicate that the grey-box model outperforms the ANN model in the considered validation periods in terms of accuracy and prediction stability.

42 ENGINEERING↗
DOE OSTI2022

Neural Lyapunov Differentiable Predictive Control

We present a learning-based predictive control methodology using the differentiable programming framework with sufficient Lyapunov-based guarantees. The neural Lyapunov differentiable predictive control (NLDPC) learns the policy by constructing a computational graph encompassing the system dynamics, state, and input constraints, and the necessary Lyapunov certification constraints, and thereafter using the automatic differentiation to update the neural policy parameters. In conjunction, our approach jointly learns a Lyapunov function that certifies the regions of state-space with stable dynamics. We also provide an initial condition sampling-based statistical guarantee for the training of NLDPC. Our offline training approach provides a computationally efficient and scalable alternative to classical model predictive control solutions, which now directly comes with Lyapunov based guarantees as shown in this paper. We substantiate the advantages of the proposed approach with simulations to stabilize the double integrator model and on an example of controlling an aircraft model.

Mukherjee, Sayak↗
DOE OSTI2025

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads. This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities. One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies. In this work, we propose an advanced control method, called adaptive neural parameter-varying model predictive control (ANPV-MPC), to control the temperature and energy consumption of a building via its Heating, Ventilation, and Air Conditioning system. ANPV-MPC combines key ideas in parameter-varying control, adaptive control, and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control. The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model. The Bayesian neural network additionally provides uncertainty estimates, triggering online training to capture evolving building system conditions. We show that ANPV-MPC can approximate the building system dynamics with a 28.39% higher accuracy than traditional linear model predictive control, resulting in 36.23% better control performance without increasing complexity of the optimal control problem. ANPV-MPC also adapts in real time to previously unseen conditions using online learning, further improving its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Optimal Control of Distributed Energy Resources using Model Predictive Control

In an isolated power system (rural microgrid), Distributed Energy Resources (DERs) such as renewable energy resources (wind, solar), energy storage and demand response can be used to complement fossil fueled generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation. The problem is formulated as a multi-objective optimization problem with the goals of minimizing fuel costs and changes in power output of diesel generators, minimizing costs associated with low battery life of energy storage and maintaining system frequency at the nominal operating value. Two control modes are considered for controlling the energy storage to compensate either net load variability or wind variability. Model predictive control (MPC) is used to solve the aforementioned problem and the performance is compared to an open-loop look-ahead dispatch problem. Simulation studies using high and low wind profiles, as well as, different MPC prediction horizons demonstrate the efficacy of the closed-loop MPC in compensating for uncertainties in wind and demand.

Mayhorn, Ebony T.↗
DOE OSTI2022

Fast and fault-tolerant model predictive control of MMCs under selective harmonic elimination

Selective harmonic elimination (SHE) is a suitable pulse-width modulation (PWM) tech-nique for some applications of modular multilevel converters (MMC) with lower numberof semiconductor submodules (SM). However, SHE can make it difficult to control circu-lating currents in the MMC. Moreover, the MMC is vulnerable to SM switch failures thatreduce its output power magnitude and quality. This paper proposes a fault-tolerant modelpredictive control strategy for MMCs operating with SHE (FT SHE-MPC). The effects ofswitch failure on phase voltage waveforms are studied first. Then, a method is proposedto design the FT SHE waveform to preserve its harmonic performance. Next, the pro-posed FT SHE-MPC is designed to increase the capacitor voltages during an SM failure tomaintain the fundamental component of the faulty phase voltage and control the circulat-ing currents, maintaining balance among line voltages and the output power of the MMC.Additionally, the proposed method offers as much as 60% faster FT transition time and asmuch as 36% lower capacitor voltage ripple. The advantages of the proposed method areverified through offline and real-time simulation studies on a three-phase nine-level MMCin PSCAD/EMTDC software and RTDS.

42 ENGINEERING↗
DOE OSTI2022

Learning Stochastic Parametric Differentiable Predictive Control Policies

We present a scalable unsupervised learning-based method for obtaining explicit control policies for model predictive control problems for stochastic linear systems with additive uncertainties subject to nonlinear chance constraints. We call the proposed method stochastic parametric differentiable predictive control (SP-DPC), which extends the recently proposed deterministic DPC policy optimization algorithm. We formulate the SP-DPC as a deterministic approximation to the stochastic parametric constrained optimal control problem via independent sampling of the problem's parameters and uncertainties. This formulation allows us to directly compute the policy gradients via automatic differentiation of the problem's value function, evaluated over sampled parameters and uncertainties. In particular, the computed expectation of the problem's value function is backpropagated through the finite-time closed-loop system rollouts parametrized by a known nominal system dynamics model and neural control policy. We also provide theoretical probabilistic guarantees on closed-loop stability and chance constraints satisfaction for systems controlled by learned neural policies. We demonstrate the computational efficiency and scalability of the proposed policy optimization algorithm in three numerical examples, including systems with a large number of states or subject to nonlinear constraints.

Drgona, Jan↗
DOE OSTI2018

SEMI-ACTIVE CONTROL FOR TWO-BODY OCEAN WAVE ENERGY CONVERTER BY USING HYBRID MODEL PREDICTIVE CONTROL

Model predictive control (MPC) has been considered as one important feed-forward optimal control strategy for ocean wave energy converter (WEC) targeted on power maximization. The capability of MPC to handle system constraints (ex. stroke, velocity, actuator limitations), and the availability to provide optimal solution for linear system provide potential for the implementation of such algorithm in the WEC control. However, currently, only active MPC control has been introduced for single and two-body WECs. Such control strategy may introduce negative power during the optimization process, since the power take-off (PTO) damping has no constraint. In this paper, we proposed a hybrid MPC strategy in limiting both the PTO damping force and PTO damping to avoid negative power generation during cost function minimization (negative power minimization) for the two-body WEC. The problem is formulated into a quadratic programming (QP) problem targeted at power maximization. However, the standard QP problem formulation cannot be directly applied to the semi-active control problem due to the PTO damping constraints. Therefore, the problem is reformulated as a Mixed-integer Quadratic Programming (MIQP) problem, which contains logical switch to select constraint matrices based on the sign of the relative velocity between the buoy and submerged body. The optimal solution is compared with those of the active MPC control strategy and the passive model with the same irregular wave input.

16 TIDAL AND WAVE POWER↗
DOE OSTI2018

Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication: Preprint

Wind plant control strategies, including axial induction and wake steering control, aim to improve the performance of wind farms, including increasing energy production and decreasing turbine loads. This paper presents a linear model of wake characteristics for use with a distributed model predictive control method for the purpose of optimizing axial induction and yaw misalignment setpoints. In particular, we use an iterative, distributed control method with nearest neighbor communication to coordinate turbine control actions that account for wake interactions between turbines. Simulations of the model and controller are performed on a 2x3 array of turbines using a modified version of the FLOw Redirection and Induction in Steady-state (FLORIS) model to dynamically track the relevant wake parameters. Preliminary results show the controller's ability to follow an arbitrary wind farm power reference signal for the purpose of providing active power control (APC) ancillary services for power grid stability. This efficient distributed control strategy can enable real-time wind farm optimization and control, even for very large scale farms.

17 WIND ENERGY↗
DOE OSTI2024

Ontologies at Work: Analyzing Information Requirements for Model Predictive Control in Buildings

Model Predictive Control (MPC) has shown significant potential for improving energy efficiency, indoor air quality and occupant comfort of buildings. MPC-based control algorithms have also shown the ability to shift loads and optimize for multiple objectives, including but not limited to reducing the green-house gas emissions, energy costs and peak demand. However, one of the main implementation challenges of these control algorithms is the integration and configuration effort needed to deploy a supervisory MPC controller in a building. By assigning standardized references to information sources and control points in buildings, existing studies have shown that semantic ontologies and corresponding queries have the potential to ease the deployment of such controllers. Yet, the use of semantic information to ease the deployment processes of MPC controllers is still limited. In this paper, we review three MPC experiments and synthesize the information requirements of these optimization problems. We then turn to existing and upcoming semantic ontologies such as Brick, SAREF and ASHRAE Standard 223 to represent these requirements, evaluating their potential to support the implementation of an MPC controller. This investigation concludes with a discussion of existing opportunities and open questions that the community should explore to support more streamlined MPC implementations.

Prakash, Anand Krishnan↗
DOE OSTI2021

Robust Predictive Control for Modular Solid-State Transformer With Reduced DC Link and Parameter Mismatch

This paper presents the analysis and implementation of a predictive control method for dc-link regulation and voltage balance in a cascaded modular reduced dc-link solid-state transformer (SST). Passive components like bulky dc links limit the power density of power converters, especially medium-voltage (MV) SST. Reduced dc-link or low-inertia converters can dramatically reduce the size, cost, and weight by tolerating larger dc-link ripples and improve the reliability with electrolytic capacitor-less dc link. However, a small dc link leads to tight coupling between the input and the output stages, which is a challenge for control design. In stacked low-inertia converters (SLIC), the low-inertia converter modules are stacked for MV applications, resulting in coupling between the modules and making the control more challenging. A new model predictive control method which can achieve deadbeat regulation on the dc link without weighting factors has been proposed to address this novel problem. This paper focuses on analyzing the condition of the low-inertia dc link up to 80% ripple, the robustness of the control under parameter mismatches, high-order terms, and important implementation issues such as model-based sampling and computation delay compensation. Significantly, the high-order terms are introduced because of the large dc-link ripple. These high-order terms are unique to the SLIC and negligible in conventional high-inertia converters. A discrete-time large-signal model is built to capture the dc-link’s nonlinear dynamics, and the eigenvalues of a small-signal Jacobian matrix are analyzed with Floquet theory to evaluate stability, using the modular soft-switching solid-state transformer (M-S4T) as an example of the SLIC. Simulation and experimental results of an MVDC M-S4T verify the analysis and the predictive control method. Finally, the general application of the predictive control to low-inertia converters is compared against a conventional PI controller using a reduced dc-link active-front-end (AFE) rectifier as an example.

14 SOLAR ENERGY↗
DOE OSTI2022

Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller

Some novel applications require the tracking/rejection of biased sinusoidal reference/distur-bances. According to the internal model principle (IMP), a controller must embed the model of a biased sinusoidal signal to track references and also reject perturbations modeled through the aforementioned signal. However, the design of that kind of controller is not straightforward, especially when they are implemented in digital processors. This paper presents a controller, based on generalized predictive control (GPC), designed for tracking/rejection of biased sinusoidal signals. In general, GPC is based on the prediction of the plant responses through an augmented prediction model. The proposed approach develops an augmented model that predicts the future errors. The prediction model and the control law used in the proposed approach embed the discrete-time model of a biased sinusoidal signal. Thus, the proposed controller can track/reject biased sinusoidal references/disturbances. The predicted errors and the future inputs of the proposed augmented model are used to define the cost function that measures the control performance. An optimization technique was applied to obtain the solution of the cost function, which is the optimal sequence of future model inputs that allows defining the control law. Experimental tests prove that the proposed controller can asymptotically track and reject biased sinusoidal signals.

42 ENGINEERING↗