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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Economic NMPC for a Reversible Solid Oxide Cell

Reversible solid oxide fuel cells (rSOCs) offer the flexibility to operate in tandem with the electric grid by switching between fuel cell and electrolysis modes based on real-time electricity prices. However, their complex, tightly coupled dynamic behavior poses significant challenges in determining optimal operating strategies. In this work, we present an economic nonlinear model predictive control (E-NMPC) framework to optimize the operation of rSOCs. The proposed E-NMPC is applied to a detailed rSOC flowsheet model that includes a utility scale rSOC module as well as balance-of-plant equipment necessary for thermal management. Our results demonstrate that in fuel cell mode, the E-NMPC strategy reduces hydrogen consumption compared to conventional set-point tracking NMPC, while maintaining the same level of electricity output. Also, in electrolysis mode, the E-NMPC yields a marginal improvement in hydrogen production. In addition, we explore the integration of a battery with the rSOC system to enhance flexibility in meeting electricity production and consumption targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2019

Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment

This paper provides a comparative assessment of three economic optimal control strategies, aimed at minimizing the fuel consumption of heavy-duty trucks in a highway environment, under a representative lead vehicle model informed by traffic data. These strategies fuse a global, offline dynamic programming (DP) optimization with online model predictive control (MPC). We then show how two of the three strategies can be adapted to accommodate the presence of traffic and optimally navigate signalized intersections using infrastructure-to-vehicular (I2V) communication. The MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. The three candidate economic MPC formulations that are evaluated include: a nonlinear time-based formulation that directly penalizes predicted fuel consumption, a nonlinear time-based formulation that penalizes braking effort as a surrogate for fuel consumption, and a linear distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity profile obtained from DP. Using a medium-fidelity Simulink model, based on a Volvo truck's longitudinal and engine dynamics, we analyze the optimization's performance on four highway routes under various traffic scenarios. Results demonstrate 3.7-8.3% fuel economy improvement on highway routes without traffic and 6.5-10% on the same routes with traffic included. Furthermore, we present a detailed analysis of energy usage by "type" (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control strategy.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Hierarchical Control of Megawatt-Scale Charging Stations for Electric Trucks with Distributed Energy Resources

Electrifying medium- and heavy-duty trucks is critical to decarbonizing the transportation sector. Energy needs of electric trucks will likely require megawatt-scale charging stations, which could significantly stress the electric distribution grid. Distributed energy resources (DER) can alleviate this stress and reduce charging costs with proper management. To that end, this work develops a hierarchical predictive control algorithm for future multi-port megawatt-scale charging stations that can provide real-time energy management for stations, decide charging rates, dispatch energy storage system (ESS), and provide grid voltage support. We integrate three algorithmic components: (i) an energy management optimization (EMO) that provides supervisory control to DER assets and charging loads at minute scale, (ii) a real-time energy management system (RT-EMS) that heuristically compensates for fast disturbances at sub-second scale, and (iii) a model predictive control (MPC)-based battery management system (BMS) that communicates future charging demands to the EMO, to manage the overall megawatt-scale site. Additionally, validation in a controller hardware-in-the-loop (CHIL) environment shows that the hierarchical controller can reduce the total energy consumption from the grid by approximately 28% compared to an uncontrolled case for the station configuration in this paper, without impacting charging time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Field testing and validation of a low-cost MPC for demand flexibility for grid-interactive K-12 schools

K-12 school buildings account for the highest energy consumption within the public sector. Implementing advanced HVAC controls in grid-interactive K-12 schools could bring substantial economic advantages and grid flexibility. Our previous study demonstrated that a low-cost model predictive control (MPC) solution, which coordinates multiple packaged units, can enable demand flexibility without major hardware upgrades. However, a significant gap remains between academic pilots and market-ready scalable solutions. This paper extends the previous single-site pilot to a multi-site demonstration involving three school campuses (95 total units) through a commercial technology transfer process. Addressing the challenge of verifying performance with sparse field data, we present a new statistical approach using Bayesian methods to estimate the MPC’s effect on peak demand. Unlike traditional methods, this approach robustly quantifies uncertainty in non-normal, limited datasets. The results confirm the solution’s replicability, achieving a 21.6–38.9% reduction in HVAC peak demand (10.8–22.1% at the site-level) with > 98% probability across diverse locations. Finally, we document critical barriers to scaling software-as-a-service (SaaS) solutions–such as API instability and diverse legacy systems–and offer practical strategies to accelerate the commercial adoption of grid-interactive efficient buildings.

Ham, Sang Woo↗
NASA NTRS2007

Pseudo-Waypoint Guidance for Proximity Spacecraft Maneuvers

A paper describes algorithms for guidance and control (G&C) of a spacecraft maneuvering near a planet, moon, asteroid, comet, or other small astronomical body. The algorithms were developed following a model-predictive-control approach along with a convexification of the governing dynamical equations, control constraints, and trajectory and state constraints.

Acikmese, Ahmet↗
DOE OSTI2023

ActiveBAS: A Low-cost, Scalable Control Solution for Grid-Interactive Small and Medium Sized Commercial Buildings

This project aims to develop and enhance a low-cost, highly scalable control solution for Small and Medium-Sized Commercial Buildings (SMCB), assess the business potential at multiple sites, and perform commercialization efforts. The technology can be applied to any buildings served by multiple units, with the benefits being greatest for open-spaced buildings, such as banks, retail stores, restaurants, and factories. This project aims to develop an affordable control solution for: 1) SMCB grid responsiveness, 2) reduction of GHG by changing unit operations, 3) greater reduction in utility costs, and 4) rapid adoption in the marketplace. The proposed technology will be built on a previously developed and demonstrated MPC solution. The minimal sensor requirement and less need of control expertise are the unique feature of the algorithm that leads to low capital and maintenance costs, and short installation and implementation time. These attributes contribute to low capital and maintenance costs, as well as a short installation and implementation time. However, these advantages come with a trade-off: increased difficulties and unreliability when applying traditional modeling and MPC control approaches due to limited information. This final report describes the modeling approaches developed and tested to overcome these challenges. It begins by outlining the modeling challenge posed by minimal sensor requirements, then delves into the proposed modeling approaches, which primarily involve system identification. Finally, preliminary test results for a simulation case study are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Energy-Efficient Driving in Connected Corridors via Minimum Principle Control: Vehicle-in-the-Loop Experimental Verification in Mixed Fleets

Connected and automated vehicles (CAVs) can plan and actuate control that explicitly considers performance, system safety, and actuation constraints in a manner more efficient than their human-driven counterparts. In particular, eco-driving is enabled through connected exchange of information from signalized corridors that share their upcoming signal phase and timing (SPaT). This is accomplished in the proposed control approach, which follows first principles to plan a free-flow acceleration-optimal trajectory through green traffic light intervals by Pontryagin's Minimum Principle in a feedback manner. Urban conditions are then imposed from exogeneous traffic comprised of a mixture of human-driven vehicles (HVs) - as well as other CAVs. As such, safe disturbance compensation is achieved by implementing a model predictive controller (MPC) to anticipate and avoid collisions by issuing braking commands as necessary. The control strategy is experimentally vetted through vehicle-in-the-loop (VIL) of a prototype CAV that is embedded into a virtual traffic corridor realized through microsimulation. Up to 36% fuel savings are measured with the proposed control approach over a human-modelled driver, and it was found connectivity in the automation approach improved fuel economy by up to 26% over automation without. Additionally, the passive energy benefits realizable for human drivers when driving behind downstream CAVs are measured, showing up to 22% fuel savings in a HV when driving behind a small penetration of connectivity-enabled automated vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

HEMS: Home Energy Management System [SWR-15-12] - Stochastic Optimal Scheduling of Residential Appliances with Renewable Energy Sources

The HEMS algorithm schedules residential appliance operation to minimize the weighted sum of occupant discomfort, total energy cost, peak electricity consumption and carbon footprint, weighted according to occupant preferences. A centralized mixed integer linear programming (MILP) based optimization approach is taken, with the HEMS managing all appliances directly, following a Model Predictive Control (MPC) methodology which determines the optimal schedule over the user-specified scheduling horizon and repeats the optimization at configurable time intervals. The HEMS is developed on the General Algebraic Modeling System (GAMS) using the MILP solver. This system is able to effectively deal with the stochasticity in the distributed system by utilizing Monte Carlo simulation where uncertainties in future electricity price, outdoor temperature, water usage, solar and wind generation can be accounted for. The HEMS controls the operation of the heating, ventilation and air conditioning (HVAC) system, water heater, refrigerator, residential batteries, electric vehicles, standalone micro combined heat and power (CHP) generator, dishwasher, washer/dryer, pool pumps, and lighting.

Pratt, Annabelle↗
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 OSTI2020

High precision control and deep learning-based corn stand counting algorithms for agricultural robot

This paper presents high precision control and deep learning-based corn stand counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor, and stand counting, are measured manually. This is highly labor-intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm designed to enable an ultra-compact ground robot to count corn stands by driving through the fields autonomously. The algorithm leverages a deep network to detect corn plants in images, and a visual tracking model to re-identify detected objects at different time steps. We collected data from 53 corn plots in various fields for corn plants around 14 days after emergence (stage V3 - V4). The robot predictions have agreed well with the ground truth with C robot =1.02×C human -0.86 and a correlation coefficient R=0.96. The mean relative error given by the algorithm is -3.78%, and the standard deviation is 6.76%. These results indicate a first and significant step towards autonomous robot-based real-time phenotyping using low-cost, ultra-compact ground robots for corn and potentially other crops.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Seamless Transition Between Dual Operating Modes of A Single-Phase Quasi-Z-Source Inverter

A model predictive control (MPC) based seamless transition between standalone and grid-connected operations is proposed for the active power filter (APF) integrated single-phase quasi-Z-source inverter (qZSI) power system. The predictive models of AC output, qZS network, and APF branch are established. The qZSI operating modes are analyzed to form corresponding cost functions. With the predictions, references, and switching states, the minimum cost function is determined to find the optimal switching state for qZSI power devices. The APF branch compensates completely the dc-side double-line-frequency (2ω) ripple under MPC, so low qZS inductance and capacitance are achieved without loss of performance. Simulation results demonstrate the proposed solution to have an unseamed transition.

Li, Xiao↗
DOE OSTI2022

A Tri-Port Current-Source Soft-Switching Medium-Voltage String Inverter for Large-Scale Solar-Plus-Storage Farms

This article presents a tri-port current-source soft-switching medium-voltage string inverter (TMVSI) to reduce the Levelized cost of energy (LCOE) of large-scale solar-plus-storage (SPS) farms. Throughout this work, the topology, operating principle, circuit simulation, control, and hardware design of the TMVSI are introduced. In addition, three key challenges compromising the performance of the TMVSI are addressed in this article. First, a new feed-forward compensation for model predictive control of the TMVSI is proposed to compensate for sampling and computational delay and high dc-link ripple with low computational cost and high scalability. Second, the use of laminated permanent magnets is proposed to reduce the losses of the MV medium-frequency transformer (MFT) in the TMVSI while increasing its saturation current. Finally, an improved U-shape winding pattern is proposed to decrease the leakage inductance of the MV MFT and reduce the voltage stress across semiconductor switches without adding cost or complexity. Here, the effectiveness of the TMVSI is validated by experiments at up to 20kW/500V in different test cases with custom-built prototypes. The contributions of this work make the TMVSI a viable solution to SPS farms and unleash its potential for LCOE reduction.

14 SOLAR ENERGY↗
DOE OSTI2018

An Ensemble Optimization Framework for Coupled Design of Hydropower Contracts and Real-Time Reservoir Operating Rules

Often, revenues from hydropower generation depend on the operator's ability to provide firm power in the presence of uncertain inflows. The primary options available for optimizing revenue are negotiation of a firm power contract before operations begin and adjustment of the reservoir release during operations. Contract and release strategy optimization are closely coupled and most appropriately analyzed with stochastic real-time control methods. Here we use an ensemble-based approach to stochastic optimization that provides a convenient way to construct nonparametric revenue probability distributions to explore the implications of uncertainty. The firm power contract is a simplified bilateral fixed price agreement that partially insulates operator and buyer from price fluctuations. The release control laws and firm energy target are jointly optimized to maximize the operator's expected revenue. Revenue probability distributions and related spill performance statistics indicate that predictive operating strategies such as stochastic dynamic programming and model predictive control can give significantly better performance than standard deterministic operating rules. The performance obtained from batch optimization with perfect inflow information establishes a convenient upper bound on potential revenue and provides a baseline for assessing the significance of differences between real-time operating strategies. Sensitivity analysis indicates that the benefits of predictive operational strategies are greatest for reservoirs with medium nondimensional residence times and less important for reservoirs with large residence times. Overall, probabilistic analysis of the coupled hydropower contract-operations problem provides a realistic way to assess revenue and risk for reservoirs that must provide firm power when inflows are uncertain.

13 HYDRO ENERGY↗
DOE OSTI2019

Automated Vehicles in Hazardous Merging Traffic: A Chance-Constrained Approach

To realize the energy efficiency and productivity benefits of automated driving, control algorithms must function safely among conventional vehicles. Prototype tests on public roads have revealed a trend of human-driven vehicles rear-ending automated ones. A popular belief holds that unusually conservative control algorithms play a role in such collisions. In August 2018, an automated vehicle was rear-ended while waiting to merge. Inspired by that incident, this paper examines a resemblant scenario in simulation using model predictive control for the automated vehicle. Constraint setup alternatives to avoid collisions with inattentive following vehicles are proposed and assessed in this simulation environment. In one variant, imminent rear-end collisions are detected and constraints are modified to promote more aggressive merging during such an event. Results show that higher-performing chance constraint designs can reduce collision probability, but may have other adverse effects depending on the particular algorithm.

Dollar, R. Austin↗