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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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DOE OSTI2020

Eco Look-Ahead Control of Battery Electric Vehicles and Roadway Grade Effects

Roadway grade is one of the major variables that affects a vehicle’s energy consumption. This study demonstrates the potential benefits of an eco look-ahead control for battery electric vehicles (BEVs). The proposed eco look-ahead controller was developed for the lead vehicle of a platoon of BEVs. The eco look-ahead control predicts the optimum speed and acceleration levels within a preset speed window to minimize energy consumption considering the instantaneous energy used and the regenerated energy. The developed BEV eco look-ahead control system integrates a BEV energy consumption model and a powertrain model to save fuel while maintaining vehicle speed within a user-specified window. In addition, the study demonstrates the effects of roadway grade on BEV energy consumption. The results show that the energy consumption of BEVs are significantly reduced on downhill roads compared with internal combustion engine vehicles owing to regenerative braking. Specifically, testing showed that BEVs could even produce extra energy on downhill roads. The study also tested the eco look-ahead control on an 18-km section of I-81 and found that the system produced savings of 8.51% and 26.88% in the uphill and downhill directions, respectively. This study demonstrated that regenerative energy in BEVs is a critical factor in energy efficiency and the developed eco look-ahead control significantly improved the energy efficiency for BEVs.

Engineering↗

NASA NTRS2002

Modeling and control for closed environment plant production systems

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.

NASA Discipline Life Support Systems↗

NASA NTRS2006

Real-Time Feedback Control of Flow-Induced Cavity Tones: Fixed-Gain Control - Part 1

A generalized predictive control (GPC) algorithm was formulated and applied to the cavity flow-tone problem. The control algorithm demonstrated multiple Rossiter-mode suppression at fixed Mach numbers ranging from 0.275 to 0.38. Controller performance was evaluated with a measure of output disturbance rejection and an input sensitivity transfer function. The results suggest that disturbances entering the cavity flow are collocated with the control input at the cavity leading edge. In that case, only tonal components of the cavity wall-pressure fluctuations can be suppressed and arbitrary broadband pressure reduction is not possible with the present sensor/actuator arrangement. In the control-algorithm development, the cavity dynamics were treated as linear and time invariant (LTI) for a fixed Mach number. The experimental results lend support to that treatment.

Kegerise, M. A.↗

DOE OSTI2013

SICS: A Sensor-Based In-Line Control System for the Surfaces of Continuously Cast Slabs

The goal of this project was to develop and demonstrate a SICS test unit that meet the need of the steel industry for enhancing the continuous casting process for improved surface quality for the targeted benefits and that is composed of an In-line Imaging Module and a Hybrid Predictive Control Module. The In-line Imaging Module provides the real-time or near real-time Product Information to the Hybrid Predictive Control Module. The latter is able to fuse the Product Information with the Process Information. In order to support the goal, this project had the technical objectives for indexes such as inspection accuracy and in-line service life, based on the inspection coverage of the entire cast slab. The deployment of the SICS systems has the potential to provide benefits in reduction of energy waste, CO 2 emission, waste water amount, toxic waste, and so forth. It will help to uphold the competitiveness of the industry that employs approximately 150,000 people nationwide and consumes about 3% of the energy in the country. The Phase II project has been carried out in accordance to the proposed tasks and budget, based on the original and extended schedule. The R&D team designed and implemented the test unit for the full width coverage, installed the unit in a caster. The development work further included enhanced image processing, in-depth defect study and process control models. The function, operation, and maintenance of the SICS was thoroughly studied during the Phase II research. The experience indicates additional hardware and procedures are required to make the SICS a commercially ready product in operation and maintenance aspect. Such developments have been finished and the team is contacting potential customers for the first commercial installation of SICS. Additionally, OGT is exploring the possibility to team up with a US company that specializes in surface cleaning for slabs/blooms/billets such that the in-line surface inspection can be integrated with in-line surface clean up for the maximum benefit to the steel industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

Advanced Models and Controls for Prediction and Extension of Battery Lifetime (Presentation)

Predictive models of capacity and power fade must consider a multiplicity of degradation modes experienced by Li-ion batteries in the automotive environment. Lacking accurate models and tests, lifetime uncertainty must presently be absorbed by overdesign and excess warranty costs. To reduce these costs and extend life, degradation models are under development that predict lifetime more accurately and with less test data. The lifetime models provide engineering feedback for cell, pack and system designs and are being incorporated into real-time control strategies.

25 ENERGY STORAGE↗

DOE OSTI2021

Human-automated vehicle interactions

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗

DOE OSTI2021

HUMAN-AUTOMATED VEHICLE INTERACTIONS

Guo, Longxiang↗

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 OSTI2020

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying H ∞ design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1993

Prediction and control of vortex-dominated and vortex-wake flows

This progress report documents the accomplishments achieved in the period from December 1, 1992 until November 30, 1993. These accomplishments include publications, national and international presentations, NASA presentations, and the research group supported under this grant. Topics covered by documents incorporated into this progress report include: active control of asymmetric conical flow using spinning and rotary oscillation; supersonic vortex breakdown over a delta wing in transonic flow; shock-vortex interaction over a 65-degree delta wing in transonic flow; three dimensional supersonic vortex breakdown; numerical simulation and physical aspects of supersonic vortex breakdown; and prediction of asymmetric vortical flows around slender bodies using Navier-Stokes equations.

Kandil, Osama↗

DOE OSTI2022

Predicting and Controlling Corrosion (Abbreviated Final Report)

Corrosion accumulates significant replacement costs in the transportation, utility, manufacturing, and infrastructure sectors. It also underpins several of LLNL’s core missions in stockpile stewardship, defense, and energy security. Whereas most corrosion and aging models are empirically parameterized to describe well-defined conditions late in the reaction, the factors that determine the early stages of corrosion—during which mitigation could be most impactful— are poorly understood. This project addressed the critical need for new approaches to predict the kinetics of corrosion initiation based on firm physical and chemical understanding. The activities encompassed degradation of relevant metals in both hydrogen-rich and environmental corrosion scenarios. The team integrated state-of-the-art multiscale simulation, in situ characterization, and data science within three technical thrusts: hydriding of Ti alloys; aqueous corrosion of Al and Ni-Cr alloys; and degradation of additively manufactured 316L stainless steel. In each case, novel capabilities were developed to identify and track the impacts of key atomistic, compositional, and microstructural features on the metal systems. For hydriding, protocols were developed to tightly integrate multiscale models, advanced multimodal characterization, and machine learning to determine how hydrogen interacts with native passivating surface oxides and nucleates new undesired phases, shedding new light on the critical role of grain boundaries, interfaces, and atomically disordered regions. For aqueous corrosion, the project demonstrated methods to predict dissolution rates of metal surfaces in corrosive solutions, to measure and understand microstructural and grain orientation effects on corrosion susceptibility, and to investigate competing growth and dissolution kinetics of surface oxides. For additively manufactured metals, analysis using state-of-the-art microscopy techniques revealed the role of specific heterogeneities invoked during laser processing, including cellular structure, dislocations, and precipitates, on corrosion susceptibility. In addition to new capabilities and understanding, the project provided an avenue for workforce development, as well as key partnerships with stakeholders in corrosion science.

08 HYDROGEN↗

DOE OSTI2024

Autonomous Anomaly Detection for MPC Forecasts of HVAC Systems in Residential Communities

The use of residential heating, ventilation, and air conditioning (HVAC) to shift peak demand or provide ancillary services is a potential solution in the presence of older grids and distributed renewables. However, to ensure the efficient use of devices, utilities need to accurately forecast the load and adopt error correction schemes when necessary. While significant theoretical research exists in the area of predictive control of HVAC, little experimental evidence exists. The lack of experimental data in turn causes researchers to be unprepared for unsystematic errors which emerge due to the higher complexity of the data generating process. This study offers an anomaly detection methodology that uses unsupervised machine learning algorithms to detect and isolate these errors with different forecast error ranges. The results of anomaly detection procedure can then be used for error correction and would eventually help develop better predictive controllers. The methodology is tested using real world data from a smart neighborhood that currently operates in Atlanta. GA.

Lebakula, Viswadeep↗

DOE OSTI2025

Hybrid Energy Management with Real-Time Control of a High-Power EV Charging Site

Decarbonization of transportation systems is driving higher capacity energy storage and faster charging power requirements in electric vehicles (EVs). Given the potential advantages - such as increased efficiency, reduced inverter capacity, and less total cable mass - there is a demand in the industry for more DC distribution for high-power charging (HPC) hubs. However, the cost-effective, adaptive, and robust operation of the DC-coupled HPC hub necessitates a robust site energy management system (SEMS). Validating SEMS operation using a digital twin of an HPC hub in a real-time simulator (RTS) platform is crucial before field deployment. In this study, we propose a hybrid energy management site controller designed to achieve high-level, long-term operational objectives while managing low-level power sharing control between hub assets. We develop a centralized model predictive controller (MPC) to optimize hub operating points and use these points to update the droop parameters of the site energy storage system (ESS). This approach ensures the hub follows an optimal operating point while maintaining the flexibility to respond to load surges. We tested and verified our proposed approach both offline and on a Controller Hardware-in-the-loop (C-HIL) simulation platform integrated with a SEMS framework, demonstrating real-time site operation and validating a cost-effective and robust site controller.

ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1993

Transition prediction and control in subsonic flow over a hump

The influence of a surface roughness element in the form of a two-dimensional hump on the transition location in a two-dimensional subsonic flow with a free-stream Mach number up to 0.8 is evaluated. Linear stability theory, coupled with the N-factor transition criterion, is used in the evaluation. The mean flow over the hump is calculated by solving the interacting boundary-layer equations; the viscous-inviscid coupling is taken into consideration, and the flow is solved within the separation bubble. The effects of hump height, length, location, and shape; unit Reynolds number; free-stream Mach number, continuous suction level; location of a suction strip; continuous cooling level; and location of a heating strip on the transition location are evaluated. The N-factor criterion predictions agree well with the experimental correlation of Fage; in addition, the N-factor criterion is more general and powerful than experimental correlations. The theoretically predicted effects of the hump's parameters and flow conditions on transition location are consistent and in agreement with both wind-tunnel and flight observations.

Masad, Jamal A.↗

DOE OSTI2020

Toward the Prediction and Control of Glass Transition Temperature for Donor–Acceptor Polymers

Semiconducting donor–acceptor (D–A) polymers have attracted considerable attention toward the application of organic electronic and optoelectronic devices. However, a rational design rule for making semiconducting polymers with desired thermal and mechanical properties is currently lacking, which greatly limits the development of new polymers for advanced applications. Here, polydiketopyrrolopyrrole (PDPP)-based D–A polymers with varied alkyl side-chain lengths and backbone moieties are systematically designed, followed by investigating their thermal and thin film mechanical responses. The experimental results show a reduction in both elastic modulus and glass transition temperature (Tg) with increasing side-chain length, which is further verified through coarse-grained molecular dynamics simulations. Informed from experimental results, a mass-per-flexible bond model is developed to capture such observation through a linear correlation between Tg and polymer chain flexibility. Using this model, a wide range of backbone Tg over 80 °C and elastic modulus over 400 MPa can be predicted for PDPP-based polymers. This study highlights the important role of side-chain structure in influencing the thermomechanical performance of conjugated polymers, and provides an effective strategy to design and predict Tg and elastic modulus of future new D–A polymers.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Centipod WEC, Advanced Controls, MPC Controller MATLAB Code

This archive contains the MATLAB code for the model predictive control (MPC) controller developed in this project. The archive containing the WaveDyn models used for analysis of the Centipod with the MPC controller is linked in this submission.

16 TIDAL AND WAVE POWER↗

DOE OSTI2016

Optimal Power Flow for Distribution Systems under Uncertain Forecasts

The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2012

Positron Emission Tomography (PET) Evaluation After Initial Chemotherapy and Radiation Therapy Predicts Local Control in Rhabdomyosarcoma

Purpose: 18-fluorodeoxyglucose positron emission tomography (PET) is already an integral part of staging in rhabdomyosarcoma. We investigated whether primary-site treatment response characterized by serial PET imaging at specific time points can be correlated with local control. Patients and Methods: We retrospectively examined 94 patients with rhabdomyosarcoma who received initial chemotherapy 15 weeks (median) before radiotherapy and underwent baseline, preradiation, and postradiation PET. Baseline PET standardized uptake values (SUVmax) and the presence or absence of abnormal uptake (termed PET-positive or PET-negative) both before and after radiation were examined for the primary site. Local relapse-free survival (LRFS) was calculated according to baseline SUVmax, PET-positive status, and PET-negative status by the Kaplan-Meier method, and comparisons were tested with the log-rank test. Results: The median patient age was 11 years. With 3-year median follow-up, LRFS was improved among postradiation PET-negative vs PET-positive patients: 94% vs 75%, P=.02. By contrast, on baseline PET, LRFS was not significantly different for primary-site SUVmax {<=}7 vs >7 (median), although the findings suggested a trend toward improved LRFS: 96% for SUVmax {<=}7 vs 79% for SUVmax >7, P=.08. Preradiation PET also suggested a statistically insignificant trend toward improved LRFS for PET-negative (97%) vs PET-positive (81%) patients (P=.06). Conclusion: Negative postradiation PET predicted improved LRFS. Notably, 77% of patients with persistent postradiation uptake did not experience local failure, suggesting that these patients could be closely followed up rather than immediately referred for intervention. Negative baseline and preradiation PET findings suggested statistically insignificant trends toward improved LRFS. Additional study may further understanding of relationships between PET findings at these time points and outcome in rhabdomyosarcoma.

62 RADIOLOGY AND NUCLEAR MEDICINE↗