Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “voltage prediction”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images

Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). Overall, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R 2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R 2 of up to 0.73.

14 SOLAR ENERGY↗
DOE OSTI2020

Radio frequency and DC high voltage breakdown of high pressure helium, argon, and xenon

Motivated by the possibility of guiding daughter ions from double beta decay events to single-ion sensors for barium tagging, the NEXT collaboration is developing a program of R&D to test radio frequency (RF) carpets for ion transport in high pressure xenon gas. This would require carpet functionality in regimes at higher pressures than have been previously reported, implying correspondingly larger electrode voltages than in existing systems. This mode of operation appears plausible for contemporary RF-carpet geometries due to the higher predicted breakdown strength of high pressure xenon relative to low pressure helium, the working medium in most existing RF carpet devices. In this paper we present the first measurements of the high voltage dielectric strength of xenon gas at high pressure and at the relevant RF frequencies for ion transport (in the 10 MHz range), as well as new DC and RF measurements of the dielectric strengths of high pressure argon and helium gases at small gap sizes. We find breakdown voltages that are compatible with stable RF carpet operation given the gas, pressure, voltage, materials and geometry of interest.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A “Master Curve” Describing Reaction Inhomogeneity and Plating Onset during Fast-Charging of Graphite Electrodes

Lithium plating in porous graphite electrodes is a major limitation for fast charging. Theoretical evidence suggests plating during fast charging is largely due to inhomogeneous intercalation through the electrode thickness (caused by ionic/mass transfer limitations and inherent thermodynamic properties of the graphite material). Numerical and analytical predictions of plating onset during fast charging have been proposed but not compared directly with experiments. This work validates these model predictions against plating onset measurements via the “dOCV” method in graphite half-cells, for various electrode thicknesses and C-rates. Remarkably, it is shown that experimental and theoretical trends in plating onset (vs C-rate and electrode thickness) collapse to a single “master curve” using a nondimensional “reaction inhomogeneity” parameter. This observation supports the hypothesized reaction inhomogeneity mechanism for accelerating plating onset and provides practical guidance for electrode design. Furthermore, this work develops theory to quantify reaction inhomogeneity in situ directly from voltage V vs capacity Q data, using dQ / dV analysis. Here again, experiments and numerical predictions show good agreement, where peaks that correspond to LiC 6 correlate with plating onset. This work provides experimental validation of theoretical tools that can predict plating onset, aid electrode design, and give insight for plating onset mechanisms during fast charging.

25 ENERGY STORAGE↗
DOE OSTI2023

Integration of Nuclear Material Accounting Data and Process Monitoring Data for Improvement on Detection Probability in Safeguarding Electrochemical Processing Facilities (Final Technical Report)

The KAERI advanced spent fuel conditioning process (ACP) process is a critical component of the US- South Korean nuclear cooperation and the following “123 Agreement.” Its development has received considerable attention in both countries. The ACP is an electrochemical processing (pyroprocessing) that recycles over 96% of the used nuclear fuel (UNF). It is also intrinsically proliferation-resistant in theory. In normal operation, the U/TRU product is very hot radiologically. In addition, the Cm provides a high level of spontaneous neutrons, making the product unsuitable for weapon use. However, as pointed in some study, “the need for safeguards to protect against the diversion and misuse of separated plutonium applies essentially equally to all grades of plutonium.” As pointed by many studies, the well-established traditional Nuclear Material Accounting (NMA) approach cannot be directly applied to electrochemical processing because of the lack of an input accountability tank, the non-continuous material flow, and the unsatisfactory level of confidence in sampling methods. Therefore, nuclear safeguards remain a grand challenge in the developing of commercial electrochemical separations facilities, especially around the heart of such facilities, the electrorefiner (ER) systems. In contrast to NMA data, process monitoring (PM) data is normally an indirect measurement of the SNM and is acquired much more frequently. In a broad sense, PM includes monitoring by various types of equipment, e.g. radiation detectors, cameras, voltage, current sensors. Because it is already being collected by the operator, the additional cost to safeguards is low. It has long been believed that PM data can supplement NMA data and help improve safeguards, although the benefits are hard to quantify. The U.S. DOE’s Material Protection, Accounting, and Control Technology (MPACT) campaign has made substantial investments into innovative PM sensor technology and predictive model development for real- or near real-time measurement and prediction of molten salt density and level, salt composition and actinide concentration especially Pu, the cell voltage, and the cell current to supplement traditional NMA. For aqueous-based reprocessing facilities, it is reported that PM, integrated with traditional NMA, have a high detection probability for specific diversions. For electrochemical reprocessing, preliminary studies have shown that PM data can support traditional NMA in various ways by providing a basis to estimate some of the in-processing nuclear material inventories. Despite early success, further studies on fusion of PM data and NMA data are still needed, which is the goal of this proposed work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Realizing continuous cation order-to-disorder tuning in a class of high-energy spinel-type Li-ion cathodes

Conventional Li-ion cathode materials are dominated by well-ordered structures, in which Li and transition metals occupy distinct crystallographic sites. Here, we show in this paper that profoundly new degrees of freedom for the optimization of electrochemical properties may be accessed if controllable cation disorder is introduced. In a class of high-capacity spinel-type cathode materials, we identify cation to anion ratio in synthesis as a key parameter for tuning the structure continuously from a well-ordered spinel, through a partially ordered spinel, to rocksalt. We find that the varying degree of cation disorder modifies the voltage profile, rate capability, and charge-compensation mechanism in a rational and predictable way. Our results indicate that spinel-type order is most beneficial for achieving high-rate performance as long as the cooperative 8a to 16c phase transition is suppressed, while more rocksalt-like disorder facilitates O redox, which can increase capacity. Our findings reveal an important tuning handle for achieving high energy and power in the vast space of partially ordered cathode materials.

25 ENERGY STORAGE↗
DOE OSTI2021

Design, modeling and experimental validation of a micro cantilever beam with an electro-controllable twisting ability

The structure design of micro cantilever beams (MCBs), which has a significant effect on MCBs' controllable actuation range, is usually restricted by the establishment of the inner electric circuit. This paper proposes the design of a micro electro-controllable twisting cantilever beam (METCB). The architecture and the fabrication diagram of the proposed METCB are presented before its features are theoretically and experimentally validated. In detail, the analytical validation includes a sequence of two models: electro-thermal and thermo-mechanical. Through these two models, the thermal distribution along the METCB and the deformation brought by the thermal distribution are investigated, respectively. On the basis of the design and equations, the METCB samples have been manufactured using a planar deposition technique with an e-beam evaporator. Two in-site experiments are then conducted to sequentially figure out the upper voltage limit of the inner electric circuit and the maximum temperature limit of the METCB. Finally, another experiment is carried out to explore the relation between the applied voltage and the relative twisting angle of the METCB sample. Here, the theoretical prediction and the observed experiment result both prove the feasibility of the proposed METCB and its electro-controllable twisting ability.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Empirical Comparison of Machine Learning Approaches for Black-Box Modeling of Power Conversion System Dynamics

Inverter-based resources are key components in modern power systems, but accurately modeling their complex behavior can be challenging. Standard, generic converter models often oversimplify inverter dynamics, leading to significant errors in predicting performance. In this work, we compare several data-driven machine learning (ML) approaches for inverter modeling, performing experiments on power conversion systems, systematically varying input conditions, and recording the resulting voltages and currents. The ML models were then trained on this measured data to capture the inverter's dynamic response and to predict the inverter's output current. A performance comparison between the four ML models under study is conducted, laying the foundation for future work on hardware implementation for real-time inference.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

A Convolutional Neural Network Model for Battery Capacity Fade Curve Prediction using Early Life Data

Herein, early prediction of battery performance degradation trends can facilitate research of new materials and cell designs, rapid deployment of batteries in real-world applications, timely replacement of batteries in critical applications, and even the secondary use market. In this study, we design a convolutional neural network model to predict the entire battery capacity fade curve - a critical indicator of battery performance degradation - using first 100 cycles of data (~ three weeks of testing). We use the discharge voltage-capacity curves as input to the model and automate the feature extraction process through the convolutional layers of the network. Our approach can predict the per cycle capacity fade rate and rollover cycle (knee point) in the capacity fade curve, which indicate the onset of rapid capacity decay. On the publicly available graphite/LiFePO 4 battery dataset, optimized networks predict the capacity fade curves, rollover cycle, and end of life with 3.7% (worst-case), 19%, and 17% mean absolute percentage errors, respectively.

25 ENERGY STORAGE↗
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 OSTI2021

Lithium-ion battery physics and statistics-based state of health model

A pseudo-2d model using COMSOL Multiphysics® software is developed to simulate performance and performance degradation of Li-ion batteries consisting of layered and olivine cathodes with graphite anode when subjected to peak shaving grid service. Multiple degradation pathways are considered, including solid electrolyte interphase (SEI) formation and breakdown at the anode, cathode dissolution and its synergistic effect on SEI formation at the anode. The model is validated by simulating commercial cylindrical cell performance. A global model is developed to simulate performance across all chemistries, along with individual chemistry models using global model parameters as initial values. There is good agreement between these models for various optimization parameters such as SEI equilibrium potential, cathode dissolution exchange current density, solvent diffusivity in the SEI and SEI ionic conductivity. To circumvent time constraints related to the COMSOL model, a 0d global model is developed which fits data well and provides more clarity on differences in cathode dissolution exchange current density. Again, good agreement for various optimization parameters is obtained among the COMSOL global & individual chemistry models and the 0-d model. The lessons learned from the physics-based model is used to develop a top down statistics-based model using current, voltage and anode volumetric change per mole lithium intercalated, along with their interactions as degradation predictors. This model predicts out of sample degradation for multiple grid services and electric vehicle drive cycle with high accuracy and provides the pathway to develop an efficient battery management system combining machine learning and findings from physics-based computationally intensive algorithms.

Crawford, Aladsair J.↗
DOE OSTI2022

Performance Evaluation of an Advanced Distributed Energy Resource Management Algorithm

This paper presents performance evaluation of a new distributed energy resource management system (DERMS) algorithm via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of sub-transmission and distribution networks, the DERMS software controller, and 84 power hardware solar photovoltaic (PV) inverters, standard communication protocols, and a capacitor bank controller. The DERMS algorithm is also called, Grid-Optimization of Solar (GO-Solar) platform which includes predictive state estimation (PSE) and online multiple objective optimization (OMOO) to dispatch the legacy devices and distributed energy resources (e.g., PV). The voltage regulation performance is evaluated under three scenarios, volt-var smart inverter (baseline), and DERMS control for 100% and 30% of PV. The results show that controlling 30% of PV systems with the GO-Solar platform may provide the best balance of control performance and implementation cost.

distributed energy resource management system (DER↗
DOE OSTI2020

Auto-BLAST (AutoBLAST) [SWR-20-93]

Battery life modeling often involves a lot of manual parameter fitting and is not easy for users to adopt the model and use it. To reduce the difficulties for users to adopt battery lifetime models, an automatic battery lifetime modeling analysis and simulation tool suite, Auto-BLAST, has been developed. Auto-BLAST includes a lithium-loss-base life model, a battery electric model, and an algorithm which automatically fits all key parameters in the electric and life models using user provided data. The models and algorithm are packaged into two user-friendly GUIs, Auto-LifeMod, for easy battery life prognostic model fitting and Auto-LifeSim, for easy battery lifetime simulation. The lithium-loss-based life model adopts a similar model framework that models degradations using aging rate models, using battery cycling data to predict battery degradation and expected lifetime. The electric model is an equivalent circuit model which simulates battery voltage responses based on current/power demand profiles. The auto-fitting algorithm uses the user-input data to generate custom battery life model(s). Two GUIs, wrapping around the life model and the auto-fitting algorithm, provide friendly interfaces for users to generate a life model and use it for case study. One GUI requires summary data from accelerated battery life degradation tests as an input and produces battery life models predicting (a) capacity degradation, and (b) resistance growth of the battery. The GUI displays the electrical model response and the input experimental data against the life model predictions with fitted model parameters and fitting error. The GUI also generates an output file to save the fitted model parameters. The second GUI uses the life model from the first GUI and a user-defined battery cycling profile to predict battery lifetime degradation and expected lifetime. Predicted capacity and resistance vs. time are plotted in the GUI and saved to output file. There is a user guide for both GUIs

Mishra, Partha↗
DOE OSTI2026

Theory of the thermionic current beyond the traditional space charge limit enabled by trapped ions in the virtual cathode

We show that trapped ions in virtual cathode potential wells can raise the transmitted current of emitted electrons into a plasma much closer to the full emission than is predicted by cathode sheath theories without trapped ions. The transmitted current is controlled by the well barrier voltage, which must adjust to balance the creation of low-energy ions within the well, and their loss. Our model considers the case of a plasma-facing cathode where trapped ions are created passively via charge-exchange collisions and lost passively via thermal leakage over the well. We quantify these rates and estimate the current in terms of system parameters for thermionic emission into a plasma with several cathode geometries. A general prediction is that the current as a function of emitted flux does not saturate at the traditional space charge limit (the onset of a well) but can reach far higher values until the trapped ion balance breaks down, causing instability. The maximum stable current depends on parameters but in principle can be arbitrarily high if active techniques are used to manipulate the trapped ion balance. We conclude that major improvements in plasma technologies with hot cathodes might be achieved by optimizing the current enhancement enabled by trapped ions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Hygrothermal aging effects on polyimide and acrylate-based adhesive materials in high-performance flat-flex cable assemblies

Flat-flex assemblies are widely used across a diverse range of technologies, including consumer electronics, automotive systems, aerospace and defense applications, and medical devices, due to their compact form factor and flexibility. However, environmental stressors like temperature and humidity are known to degrade performance over time, though the main mechanisms for degradation remain unknown. Here, in this work, we examined the aging behavior of copper/polyimide/adhesive laminate systems under varying temperature and humidity conditions, focusing on material degradation and its effects on mechanical and dielectric properties. Results show that peel strength and breakdown voltage decrease with exposure time, temperature, and humidity; spectroscopic characterization revealed that the adhesive, and not the polyimide, is the weak link in the material stack-up. The adhesive, identified as a butyl acrylate-acrylonitrile (BA-AN) copolymer, exhibited age-related spectral changes that correlated with exposure severity and duration. Hydrolysis at BA ester and AN nitrile groups was identified as the primary degradation mechanism, producing amides, acids, and alcohols. We developed a mechanistic model that connects kinetic parameters for BA-AN spectral band decay to peel strength and breakdown voltage, and implicates hydrolysis at the AN moiety, not BA, in performance decrements. Modeling predictions at ambient conditions indicate faster decline in peel strength than dielectric strength at early times, followed by plateauing of both properties at longer times. These findings provide critical insights into laminate aging mechanisms and highlight the importance of addressing BA-AN degradation to improve the long-term reliability of these systems in high-humidity environments.

organic↗
DOE OSTI2023

First-Principles Examination of Multiple Criteria of Organic Solvent Oxidative Stability in Batteries

Oxidative instability of the liquid electrolyte at or near battery cathode oxide surfaces has significant detrimental effects on batteries. Organic solvent molecules are often the fuel and precursors of such degradation processes, releasing electrons and protons that react with cathode oxides and electrolyte anions. These reactions contribute to cathode–electrolyte interphase (CEI) film formation, transition-metal ion dissolution, and phase transformation of the surface regions of the cathode. Here we apply density functional theory calculations to examine four criteria of oxidative stability (oxidation potential, hydrogen removal energies, and initial reactivity on two types of oxide facets) using four different solvent/additive molecules (ethylene carbonate, fluoroethylene carbonate, 1,3-dioxolane, and dimethyl ether). The ranking of molecular stability differs with each criterion. Surprisingly, the all-oxygen-terminated basal planes of layered oxides exhibit lower reaction barriers than spinel surface facets with exposed transition-metal cations, especially for ether solvents; the calculations also suggest basal planes contribute to the dissolution of transition-metal ions. Further, the structure–degradation relation complexity underscores the challenge of understanding the function of the CEI but also offers a guide to future degradation-mitigation strategies including facet engineering. Our predictions and models help establish a framework for future studies relevant to high-voltage conditions.

25 ENERGY STORAGE↗
DOE OSTI2021

Demonstration of resonant tunneling effects in metal-double-insulator-metal (MI2M) diodes

Abstract Although the effect of resonant tunneling in metal-double-insulator-metal (MI 2 M) diodes has been predicted for over two decades, no experimental demonstrations have been reported at the low voltages needed for energy harvesting rectenna applications. Using quantum-well engineering, we demonstrate the effects of resonant tunneling in a Ni/NiO/Al 2 O 3 /Cr/Au MI 2 M structures and achieve the usually mutually exclusive desired characteristics of low resistance ( $${R}_{0}^{DC} \sim$$ R 0 D C ~ 13 kΩ for 0.035 μm 2 ) and high responsivity ( β 0 = 0.5 A W −1 ) simultaneously. By varying the thickness of insulators to modify the depth and width of the MI 2 M quantum well, we show that resonant quasi-bound states can be reached at near zero-bias, where diodes self-bias when driven by antennas illuminated at 30 THz. We present an improvement in energy conversion efficiency by more than a factor of 100 over the current state-of-the-art, offering the possibility of engineering efficient energy harvesting rectennas.

42 ENGINEERING↗