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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation

Deep learning architectures have achieved state-of-the-art (SOTA) performance on computer vision tasks such as object detection and image segmentation. This may be attributed to the use of over-parameterized, monolithic deep learning architectures executed on large datasets. Although such large architectures lead to increased accuracy, this is usually accompanied by a larger increase in computation and memory requirements during inference. While this is a non-issue in traditional machine learning (ML) pipelines, the recent confluence of machine learning and fields like the Internet of Things (IoT) has rendered such large architectures infeasible for execution in low-resource settings. For some datasets, large monolithic pipelines may be overkill for simpler inputs. To address this problem, previous efforts have proposed decision cascades where inputs are passed through models of increasing complexity until the desired performance is achieved. However, we argue that cascaded prediction leads to sub-optimal throughput and increased computational cost due to wasteful intermediate computations. To address this, we propose PaSeR (Parsimonious Segmentation with Reinforcement Learning) a non-cascading, cost-aware learning pipeline as an efficient alternative to cascaded decision architectures. Through experimental evaluation on both real-world and standard datasets, we demonstrate that PaSeR achieves better accuracy while minimizing computational cost relative to cascaded models. Further, we introduce a new metric IoU/GigaFlop to evaluate the balance between cost and performance. On the real-world task of battery material phase segmentation, PaSeR yields 179% improvement over SOTA MatPhase model and a 196% improvement over IDK Cascades under the IoU/GigaFlop metric. We also demonstrate PaSeR’s adaptability to complementary models trained on a noisy MNIST dataset, where it outperforms all baselines on IoU/GigaFlop by an average of 44%.

Srikshan, Bharat↗
DOE OSTI2018

Evaluating Accuracy of the Sunnova Pro Platform Shade Measurement

Sunnova's new solar energy design platform, Sunnova Pro, automatically generates a 3D model of a building and surrounding shading objects. The product is designed to automate the process of engineering a system, sizing batteries and preparing sales proposals.

14 SOLAR ENERGY↗
DOE OSTI2012

Comparison of Plug-In Hybrid Electric Vehicle Battery Life Across Geographies and Drive-Cycles

In a laboratory environment, it is cost prohibitive to run automotive battery aging experiments across a wide range of possible ambient environment, drive cycle and charging scenarios. Since worst-case scenarios drive the conservative sizing of electric-drive vehicle batteries, it is useful to understand how and why those scenarios arise and what design or control actions might be taken tomitigate them. In an effort to explore this problem, this paper applies a semi-empirical life model of the graphite/nickel-cobalt-aluminum lithium-ion chemistry to investigate impacts of geographic environments under storage and simplified cycling conditions. The model is then applied to analyze complex cycling conditions, using battery charge/discharge profiles generated from simulations ofPHEV10 and PHEV40 vehicles across 782 single-day driving cycles taken from Texas travel survey data.

25 ENERGY STORAGE↗
DOE OSTI2024

Second harmonic generation for estimating state of charge of lithium-ion batteries

This study applied the nonlinear ultrasonic method, second harmonic generation, to precisely estimate the state of charge (SoC) in lithium-ion batteries. The second harmonic of the longitudinal wave is generated on a pouch cell battery at 5 MHz with a through-transmission setup. The relative nonlinear parameter β' is determined by analyzing the amplitudes at the fundamental and second harmonic frequencies. To enhance the nonlinear parameter's measurement accuracy, multiple excitation amplitudes are employed. Two separate charge/discharge tests (four-cycle and eight-cycle) are conducted on the battery at a rate of C/10. The nonlinear parameter is measured periodically during the charge/discharge process, and temperature compensation is applied to the measurement. The correlation curves between the nonlinear parameter and the actual SoC align well for the four-cycle and eight-cycle tests, and a robust linear relationship is observed for both correlation curves. A linear model and a second-order polynomial model are applied to fit the correlation using all data points from both tests. The two models are employed to validate the SoC prediction on a second battery by using a four-cycle test. Furthermore, the results indicate that both models can predict the SoC with an accuracy of approximately 3%, whereas the polynomial model demonstrates smaller errors in the regions near 0% and 100% SoC. Therefore, the nonlinear parameter β', measured through the second harmonic generation, can effectively predict lithium-ion battery SoC with an accuracy of less than 3%.

25 ENERGY STORAGE↗
NASA NTRS2011

Multi-Mission Power Analysis Tool

Multi-Mission Power Analysis Tool (MMPAT) Version 2 simulates spacecraft power generation, use, and storage in order to support spacecraft design, mission planning, and spacecraft operations. It can simulate all major aspects of a spacecraft power subsystem. It is parametrically driven to reduce or eliminate the need for a programmer. A user-friendly GUI (graphical user interface) makes it easy to use. Multiple deployments allow use on the desktop, in batch mode, or as a callable library. It includes detailed models of solar arrays, radioisotope thermoelectric generators, nickel-hydrogen and lithium-ion batteries, and various load types. There is built-in flexibility through user-designed state models and table-driven parameters.

Broderick, Daniel↗
DOE OSTI2024

Multiscale approaches for optimizing the impact of strain on Na-ion battery cycle life

Abstract The high costs and geopolitical challenges inherent to the lithium-ion (Li-ion) battery supply chain have driven a rising interest in the development of sodium-ion (Na-ion) batteries as a potential alternative. Unfortunately, the larger ionic radius of Na limits the reversibility of cycling because of the extensive atomic rearrangements that accompany Na-ion insertion, which in turn limit diffusion and charging speed, and lead to rapid degradation of the electrodes. The Center for Strain Optimization for Renewable Energy (STORE) was established to address these challenges and develop new electrode materials for Na-ion cells. This article discusses the current state-of-the-art materials used in Na-ion cells and several directions that STORE believes are critical to understand and control the structural and volumetric changes during the reversible (de)insertion of large cations. Graphical abstract Highlights Understanding the fundamental way materials respond to localized strains at the atomic length-scale is a critical first step in the development of highly reversible, long cycle life, Na-ion insertion hosts. This perspective explores a variety of methods that can be employed to mitigate the detrimental effects of large strain. The insights gained from these investigations should help lay the foundation for the creation of more economical and sustainable batteries that could have immediate impact on global energy infrastructure. Discussion Although there is near universal agreement that electrochemical energy storage must be an integral part of a green-energy future, there is less agreement about how to reduce the cost of energy storage. Replacing high-cost lithium-ion cells with lower-cost sodium-ion batteries is one option frequently considered in future energy models, but the details of what can be achieve with optimized sodium cell performance remains unclear. Here we posit that developing methods to mitigating strain on the electrode particle length scale is a key factor for achieving long-cycle-life sodium-ion batteries. Mitigating strain on the atomic scale suppress electrode-level volume change. Allowing for fast cycling in materials without the problems of electrode cracking or delamination. We further posit that understanding volume change in sodium-ion electrodes at a fundamental level will lead to the designing new sodium-ion electrode materials that will allow for efficient, stable, lower-cost energy storage.

Brady, Michael J.↗
DOE OSTI2024

COMPUTER-AIDED LATTICE DESIGN AND ADVANCED MODELING FOR THE DEVELOPMENT OF LIGHTWEIGHT STRUCTURAL MATERIALS

The notion of a “fission battery” conveys a vision focused on realizing very simple “plug-and-play” nuclear systems that can be integrated into a variety of applications requiring affordable, reliable energy in the form of electricity and/or heat and function without operations and maintenance staff. Fission batteries require lightweight structural materials to increase their mobility, and the lightweight materials must demonstrate structural resilience under various conditions. The objective of this work is to develop lattice structured lightweight structural material featuring a good combination of mechanical properties using advanced modeling and simulation together with an advanced additive manufacturing technique such as laser powder bed fusion. The preliminary results show that different lattice structures and types can be successfully meshed using nTopology software, and the lattice structure data can be successfully transformed to Multiphysics Object-Oriented Simulation (MOOSE) Environment input. Finite Element Analysis (FEA) displays that, at macro/engineering scale simulation, the weight saving design has an obvious effect on tensile behavior such as effective elastic modulus and yield stress. The novel approaches of this work are (1) development of lattice structures for improved mechanical properties using advanced simulation and modeling techniques; and (2) model predictions of the mechanical properties (e.g., strength and stress distribution) of macroscopic materials in order to preliminarily select a lattice structure for additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Learning-based Anticipatory Control of Microreactors

Learning-based anticipatory control advances the level of autonomy of microreactor control systems, a key consideration for the unattended operation of fission batteries. In this work, an anticipatory control system is shown to provide accurate load-following for a microreactor by utilizing a model predictive control framework.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Modeling and Compensation Design for a Power Hardware-in-the-Loop Simulation of an AC Distribution System

Power hardware-in-the-loop (PHIL) simulation, where actual hardware under text is coupled with a real-time digital model in closed loop, is a powerful tool for analyzing new methods of control for emerging distributed power systems. However, without careful design and compensation of the interface between the simulated and actual systems, PHIL simulations may exhibit instability and modeling inaccuracies. This paper addresses issues that arise in the PHIL simulation of a hardware battery inverter interfaced with a simulated distribution feeder. Both the stability and accuracy issues are modeled and characterized, and a methodology for design of PHIL interface compensation to ensure stability and accuracy is presented. The stability and accuracy of the resulting compensated PHIL simulation is then shown by experiment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process and down stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Prognostics↗
DOE OSTI2017

Modeling and Compensation Design for a Power Hardware-in-the-Loop Simulation of an AC Distribution System: Preprint

Power hardware-in-the-loop (PHIL) simulation, where actual hardware under text is coupled with a real-time digital model in closed loop, is a powerful tool for analyzing new methods of control for emerging distributed power systems. However, without careful design and compensation of the interface between the simulated and actual systems, PHIL simulations may exhibit instability and modeling inaccuracies. This paper addresses issues that arise in the PHIL simulation of a hardware battery inverter interfaced with a simulated distribution feeder. Both the stability and accuracy issues are modeled and characterized, and a methodology for design of PHIL interface compensation to ensure stability and accuracy is presented. The stability and accuracy of the resulting compensated PHIL simulation is then shown by experiment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

The Roles and Impacts of PV-Battery Hybrids in a Decarbonized U.S. Electricity Supply

In this paper, we explore the potential impacts of growing industry interest in hybrid systems comprising PV and battery technologies on the results and findings of the Solar Futures Study (DOE 2021). We employ similar scenario definitions in the same ReEDS capacity expansion model, but we perform two versions of each scenario: one in which PV and battery technologies must be deployed separately (No Hybrids), and one in which the model has the option of deploying them together as PVB hybrids (With Hybrids). By comparing the No Hybrids and With Hybrids versions of each scenario, we isolate the impacts of hybridization on the outcomes and findings of the Solar Futures Study. We find that PVB hybrid configurations capture a sizable share of PV deployment, and the highest-net-value PVB hybrid configuration depends strongly on policy conditions. A power sector decarbonization policy generally increases the value proposition of a more forward-looking PVB hybrid configuration that involves significant oversizing of the PV arrays, a larger battery (which facilitates greater recovery and utilization of otherwise clipped energy), and a higher capacity factor. The growing deployment of PVB hybrid configurations primarily displaces standalone PV capacity, such that total installed PV capacity is largely unaffected by the availability of PVB hybrid configurations. However, the higher capacity factors associated with PVB hybrid configurations drive a modest (1-2 percentage point) increase in PV's share of U.S. electricity supply in 2050. Finally, introducing the PVB hybrid configurations influences the future role and makeup of battery storage technologies, and it reduces the required transmission expansion, particularly under scenarios that involve a power sector decarbonization policy.

14 SOLAR ENERGY↗
DOE OSTI2022

Deployment of Multifidelity Uncertainty Quantification for Thermal Battery Assessment Part I: Algorithms and Single Cell Results

This report documents the results of an FY22 ASC V&V level 2 milestone demonstrating new algorithms for multifidelity uncertainty quantification. Part I of the report describes the algorithms, studies their performance on a simple model problem, and then deploys the methods to a thermal battery example from the open literature. Part II (restricted distribution) applies the multifidelity UQ methods to specific thermal batteries of interest to the NNSA/ASC program.

25 ENERGY STORAGE↗
DOE OSTI2019

Estimating Cost and Energy Demand in Producing Lithium Hexafluorophosphate for Li-Ion Battery Electrolyte

In this work, the production of Lithium hexafluorophosphate (LiPF 6 ) for Lithium-ion battery application is studied. Spreadsheet-based process models are developed to simulate three different production processes. These process models are then used to estimate and analyze the factors affecting cost of manufacturing, energy demand, and environmental impact due to greenhouse gas (GHG) emissions. Moreover, the results indicate that in a facility with a capacity of making 10,000 metric tons per year of LiPF 6 the cost of production is around $20 per kg of LiPF 6 , energy consumption is around 30 GWh per year, and the emission of greenhouse gases in CO 2- equivalent gases is around 80 metric tons per day. The impact of change in process and economic parameters on the cost of production, energy demand, and emissions is studied. In addition, a few insights on reducing the cost of production are presented. Lastly, the impact of varying LiPF 6 costs on the overall cost of a Li-ion battery ($ kWh -1 ) is presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Analysis of a Li-Ion Nanobattery with Graphite Anode Using Molecular Dynamics Simulations

In this work, molecular dynamics simulations were performed of the initial charging of a Li-ion nanobattery with a graphite anode and lithium hexaflourphosphate (LiPF 6 ) salt dissolved in ethylene carbonate (CO 3 C 2 H 4 ) solvent as the electrolyte solution. The charging was achieved through the application of external electric fields simulating voltage sources. A variety of force fields were combined to simulate the materials of the nanobattery, including the solid electrolyte interphase, metal collectors, and insulator cover. Some of the force field parameters were estimated using ab initio methods and others were taken from the literature. We studied the behavior of Li-ions traveling from cathode to anode through electrolyte solutions of concentrations 1.15 and 3.36 M. Time-dependent variables such as energy, temperature, volume, polarization, and mean square displacement are reported; a few of these variables, as well as others such as current, resistance, current density, conductivity, and resistivity are reported as a function of the external field and charging voltage. A solid electrolyte interphase (SEI) layer was also added to the model to study the mechanism behind the diffusion of the Li-ions through the SEI. As the battery is charged, the depletion of Li atoms in the cathode and their accumulation in the anode follow a linear increase of the polarizability in the solvent, until reaching a saturation point after which the charging of the battery stops, i.e., the energy provided by the external source decays to very low levels. Lastly, the nanobattery model containing the most common materials of a commercial lithium-ion battery is very useful to determine atomistic information that is difficult or too expensive to obtain experimentally; available data shows consistency with our results.

25 ENERGY STORAGE↗
DOE OSTI2017

A Database for Comparative Electrochemical Performance of Commercial 18650-Format Lithium-Ion Cells

Lithium-ion batteries are a central technology to our daily lives with widespread use in mobile devices and electric vehicles. These batteries are also beginning to be widely used in electric grid infrastructure support applications which have stringent safety and reliability requirements. Typically, electrochemical performance data is not available for modelers to validate their simulations, mechanisms, and algorithms for lithium-ion battery performance and lifetime. In this paper, we report on the electrochemical performance of commercial 18650 cells at a variety of temperatures and discharge currents. We found that LiFePO 4 is temperature tolerant for discharge currents at or below 10 A whereas LiCoO 2 , LiNi x Co y Al 1-x-y O 2 , and LiNi 0.80 Mn 0.15 Co 0.05 O 2 exhibited optimal electrochemical performance when the temperature is maintained at 15°C. LiNi x Co y Al 1-x-y O 2 showed signs of lithium plating at lower temperatures, evidenced by irreversible capacity loss and emergence of a high-voltage differential capacity peak. Furthermore, all cells need to be monitored for self-heating, as environment temperature and high discharge currents may elicit an unintended abuse condition. Overall, this study shows that lithium-ion batteries are highly application-specific and electrochemical behavior must be well understood for safe and reliable operation. Additionally, data collected in this study is available for anyone to download for further analysis and model validation.

25 ENERGY STORAGE↗
DOE OSTI2016

A correlation based fault detection method for short circuits in battery packs

Here this paper presents a fault detection method for short circuits based on the correlation coefficient of voltage curves. The proposed method utilizes the direct voltage measurements from the battery cells, and does not require any additional hardware or effort in modeling during fault detection. Moreover, the inherent mathematical properties of the correlation coefficient ensure the robustness of this method as the battery pack ages or is imbalanced in real applications. In order to apply this method online, the recursive moving window correlation coefficient calculation is adopted to maintain the detection sensitivity to faults during operation. An additive square wave is designed to prevent false positive detections when the batteries are at rest. The fault isolation can be achieved by identifying the overlapped cell in the correlation coefficients with fault flags. Simulation and experimental results validated the feasibility and demonstrated the advantages of this method.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗