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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

C3PU:Real-Time Predictive Charge Control Software for Battery Management Systems (Code for Charge Control and Predictive Unit) [SWR-21-48]

C3PU sets up a model predictive control formulation for the optimal charging control of battery packs and can be deployed for real-time operation on an embedded microprocessor. C3PU uses electrochemical and thermal models of battery packs to predict the charging trajectories of the battery pack over a time horizon under different operating conditions. It then selects the optimal charging trajectory such that a pre-defined objective is minimized, which in the present state of the code is to minimize the charging duration of the battery pack. The optimal charging trajectory is obtained by solving an underlying mathematical problem. However, C3PU is flexible to incorporate other charging objectives. The underlying battery models can be swapped as well, as long as they follow certain mathematical properties. Mathematically, optimal control problems (in this case, optimally controlling the charging current of the battery pack) are computationally expensive. C3PU implements advanced numerical techniques, namely pseudo-spectral optimization, to reduce the computational burden of the underlying problem to solve. This allows for the problem to be solved in real-time in an embedded system. Once the optimal charging trajectory is computed over a time horizon, C3PU can package such data and send it out via appropriate communication protocols.

Mishra, Partha↗
DOE OSTI2020

Lifetime estimation of a BWR core shroud in terms of IGSCC

The continued operation of aging Boiling Water Reactors (BWRs) worldwide requires gradually increasing cost of inspection, maintenance, and repair. Intergranular Stress Corrosion Cracking (IGSCC) in sensitized austenitic stainless steel piping first became a major issue for BWRs in the 1980s, resulting in recognition of the susceptibility of reactor internals to IGSCC. Shroud cracking identified in 1993–1994 confirmed that IGSCC of internals is a significant issue for BWRs. IGSCC is a time-dependent, material degradation process, which is caused and accelerated by the presence of residual stresses, material sensitization, irradiation, cold work, elevated temperature, and corrosive environments. This paper emphasizes the importance of accounting for corrosive environments, or more exactly, electrochemical phenomena in modelling IGSCC and predicting the service life of BWR in-vessel components, and stresses the necessity of performing such modeling not just for a single state point under full power conditions, but for the whole operating history of the reactor, including startups and shutdowns. Overall, this paper demonstrates that ignoring electrochemical considerations may result in underestimating component lifetime and lead to unnecessary expenses for inspection and repair.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Growth of Well-Defined Model Catalysts for Electrochemistry: From Surface Science Studies to Electrocatalytic CO2 Conversion

We combined ultrahigh vacuum (UHV) surface science techniques, electrochemical measurements, and computational modeling to investigate electrocatalytic systems that are crucial components of the carbon management effort, including oxygen evolution reactions (OER) and CO2 reduction reactions (CO2RR). Well-defined Model catalysts were grown on substrates in the UHV chamber and characterized with X-ray photoelectron spectroscopy (XPS) and scanning tunneling microscopy (STM). Selected model electrocatalysts were then tested in electrochemical cells to establish the structure-property relationships in OER and CO2RR. Our results showed that the edge sites of Fe2O3 grown on Au(111) were the most active toward OER and incorporation of Ni at the edge sites (NiFeOx) further boosted their OER activity. We also resolved the size-dependent electrocatalytic CO2-to-CO conversion of the Ag nanoparticle electrocatalysts with average particle diameter between 2 to 6 nm: smaller diameter (< 3 nm) particles favored H2 evolution reaction (HER) due to a high population of Ag edge sites, whereas larger diameter particles favored CO2RR as the population of Ag(100) surface sites grew. We further discovered that electronic interactions between small diameter Ag particles and highly defective carbon supports could break the size-dependent CO2RR selectivity, resulting in highly selective (CO Faradaic Efficiency > 90%) and active Ag nanoparticle electrocatalysts with sizes < 2 nm diameter.

Deng, Xingyi↗
DOE OSTI2023

A Dicationic fac -Re(bpy)(CO) 3 Cl for CO 2 Electroreduction at a Reduced Overpotential

Here, a dicationic Re bipyridine-type complex, fac-Re(6,6'-(2-((trimethylammonio)-methyl)phenyl)-2,2'-bipyridine)(CO) 3 Cl hexafluorophosphate (1 2+ ), has been synthesized and its electrochemical behavior under Ar and CO 2 has been investigated. The presence of pendent tetra-alkylammonium cations induces an anodic shift in the electrocatalytic potential for CO 2 reduction relative to structurally similar model complexes. The electrochemical mechanisms in anhydrous CH 3 CN and in the presence of weak acids (water or trifluoroethanol) have been analyzed using cyclic voltammetry assisted by infrared spectroelectrochemistry and theoretical calculations. The dication enables catalysis at a diminished potential through coulombic stabilization of the doubly reduced pentacoordinate species, its CO 2 adduct, the hydroxide anion, and the conjugate base formed during acid-assisted C-OH bond cleavage of the metallocarboxylic acid to the metallocarbonyl and H 2 O. The major reduction product is CO, but in the presence of trifluoroethanol, formate is also produced with 14% Faradaic efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Electrical Conductivity of Clayey Rocks and Soils: A Non-Linear Model

It is well-accepted that Archie's law is only applicable to “clean” rocks and soils but fails in “dirty” ones where clay minerals possess an additional component of surface conductivity. Although several models, for example, Waxman-Smits model, were presented to account for this phenomenon, surface conductivity is always inappropriately treated as constant, which actually only holds at high salinities. The essential non-linear characteristic differing over fluid salinities has not been physically or mathematically explained well in those models. Here, we scrutinize the conduction mechanism of clayey rocks and soils and ascribe this non-linear feature to (a) variation of the electrical double layer and (b) the intrinsic clay-and-water conduction pattern. With effective medium theory, we develop an easy-to-use non-linear model that both reflects electrochemical theories and explains the measurement data well. Our model can be used to produce more accurate results for laboratory- and field-scale petrophysical parameter evaluations than the previous models.

58 GEOSCIENCES↗
DOE OSTI2024

Revisiting the electrochemical reduction of CO 2 on Au 25 (SR)$\overline{_{18}}$ nanocluster

Experiments show that Au25(SR)$\overline{_{18}}$ is a potential electrocatalyst for the electrochemical reduction of CO 2 , but elucidation of the reaction mechanisms remain challenging experimentally. We evaluate key steps in the active site formation, CO 2 reduction, and H 2 evolution on the nanocluster employing density functional theory coupled with an explicit solvent model of the electrochemical interface to calculate activation barriers. The predicted preferred pathway for activating the nanocluster involves dethiolation, resulting in an exposed Au site that is both active and selective for CO 2 reduction. Finally, the alternative S site is found to be kinetically prohibitive and does not facilitate CO 2 reduction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems

Widespread adoption of high-temperature electrochemical systems such as polymer electrolyte membrane fuel cells (HT-PEMFCs) requires models and computational tools for accurate optimization and guiding new materials for enhancing fuel cell performance and durability. Furthermore, while robust and better suited for extrapolation, knowledge-based modeling has limitations as it is time-consuming and requires information about the system that is not always available (e.g., material properties and interfacial behavior between different materials). Data-driven modeling, on the other hand, is easier to implement but often necessitates large datasets that could be difficult to obtain. In this contribution, knowledge-based modeling and data-driven modeling are combined by implementing a few-shot learning (FSL) approach. A knowledge-based model originally developed for a HT-PEMFCs was used to generate simulated data (887,735 points) and used to pretrain a neural network source model tuned via a genetic algorithm-based AutoML. Then, experimental datasets from HT-PEMFCs with different materials and operating conditions (~50 points each) were used to train six target models via FSL. Models for the unseen data reached high accuracies in all cases (rRMSE < 10%).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Extended Finite Element Based Approach in Additive Manufacturing Modeling for Optimizing Highly Complex Manifold in Protonic Ceramic Electrochemical Cells

The objective of this project is to develop a novel numerical approach based on the extended finite element method (XFEM) for modeling moving boundaries of material deposited in additive manufacturing (AM) process with improved accuracy and reduced computational cost. One of the major challenges in simulating AM processes is that the boundaries of the computational domain need to change as material is deposited. Previous approaches have achieved this by activating new finite elements, but this requires a high level of mesh refinement to capture small movement of the boundary. XFEM allows the solution boundary to move independently of the mesh, permitting smooth representation of the evolution of the boundary as material is deposited. As a result, this new method will significantly improve the simulation accuracy while reducing the computational cost compared with existing approaches. Furthermore, this project will bring significant improvements for the design and AM process optimization in an iterative fashion among numerical simuation, parameter optimization, and experimental validation. Specifically, this project will support the protonic ceramic electrochemical cells (PCEC) stack development at INL by providing insights into the density, residual stress, thermal and mechanical properties of the PCEC manifold and interconnect, which is critical in enhancing the system lifetime and reducing the overall cost. Upon the success of proposed development and validation of the proposed approach, simulation will be applied to determine an optimal set of AM process parameters for the PCEC manifold production. This project will consolidate Idaho National Laboratory (INL)’s simulation capabilities provided by the MOOSE framework and the Valhalla AM simulation application, with encouraging expansion to emerging PCEC applications and AM technologies at INL and beyond.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Lifetime studies of high power rhodium/tungsten and molybdenum electrodes for application to AMTEC (alkali metal thermal-to-electric converter)

A detailed and fundamental model for the electrochemical behavior of AMTEC electrodes is developed which can aid in interpreting the processes which occur during prolonged operation of these electrodes. Because the sintering and grain growth of metal particles is also a well-understood phenomenon, the changes in electrode performance which accompany its morphological evolution may be anticipated and modeled. The grain growth rate observed for porous Mo AMTEC electrodes is significantly higher than that predicted from surface diffusion data obtained at higher temperatures and incorporated into the grain growth model. The grain growth observed under AMTEC conditions is also somewhat higher than that measured for Mo films on BASE (beta-alumina solid electrolyte) substrates in vacuum or at similar temperatures. Results of modeling indicate that thin Mo electrodes may show significant performance degradation for extended operation (greater than 10,000 h) at higher operating temperatures (greater than 1150 K), whereas W/Rh and W/Pt electrodes are expected to show adequate performance at 1200 K for lifetimes greater than 10,000 h. It is pointed out that current collection grids and leads must consist of refractory metals such as Mo and W which do not accelerate sintering or metal migration.

Williams, R. M.↗
DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗
DOE OSTI2021

Advanced battery modeling for interfacial phenomena and optimal charging

Lithium ion batteries are one of the most promising energy storage systems for portable devices, transportation, and renewable grids. To meet the increasing requirements of these applications, higher energy density and areal capacity, long cycle life, fast charging rate and enhanced safety for lithium ion battery (LIBs) are urgently needed. To solve these challenges, the relevant physics at different length scale need to be understood. However, experimental study is time consuming and limited in small scale’s study. Modeling techniques provide us powerful tools to get a deep understanding of the relevant physics and find optimal solutions. This work focuses on studying the mechanism in advanced battery engineering techniques and developing a new charging algorithm by model-based optimization. The research topics are divided into six topics and each topic is reported as a form of journal publication. Paper Ⅰ provides a new aspect of how ALD coating improves the lithium ion diffusion at electrode particles. Paper Ⅱ explains the mechanisms by which 3D electrodes enhance battery performance and reveals guidelines for optimized 3D electrode designs by a 3D electrochemical-mechanical battery model. Paper Ⅲ investigates the electrolyte concentration impact on SEI layer growth and Li plating, especially under high charge rates. Paper Ⅳ proposes an optimized charging protocol for fast charging for reducing the charging time with minimal degradation. Paper Ⅴ reports a comprehensive degradation model for degradation estimation and life predication of energy storage system (ESS). Paper Ⅵ is a study of temperature-dependent state of charge (SOC) estimation for battery pack.

25 ENERGY STORAGE↗
DOE OSTI2023

Analysis of bipolar membranes for electrochemical CO 2 capture from air and oceanwater

Carbon dioxide (CO 2 ) must be removed from the atmosphere to mitigate the negative effects of climate change. However, the most scalable methods for removing CO 2 from the air require heat from fossil-fuel combustion to produce pure CO 2 and continuously regenerate the sorbent. Bipolar-membrane electrodialysis (BPM-ED) is a promising technology that uses renewable electricity to dissociate water into acid and base to regenerate bicarbonate-based CO 2 capture solutions, such as those used in chemical loops of direct-air-capture (DAC) processes, and in direct-ocean capture (DOC) to promote atmospheric CO 2 drawdown via decarbonization of the shallow ocean. In this study, we develop an experimentally validated 1D model for the electrochemical regeneration of CO 2 from bicarbonate-based carbon capture solutions and seawater using BPM-ED. For DAC, our experimental and computational results demonstrate that pH swings induced by BPM water dissociation drive the formation of CO 2 at the cation-exchange layer|catholyte interface with energy-intensities of less than 150 kJ mol -1 . However, high rates of bubble formation increase energy intensity at current densities >100 mA cm -2 . Correspondingly, accelerating water dissociation catalysis and enacting bubble removal could enable CO 2 recovery at energy intensities <100 kJ mol -1 and current densities >100 mA cm -2 . For DOC, mass transport limitations associated with low carbon concentrations in oceanwater suggest that DOC is best suited for clean production of acid and base usable in downstream processes. Further, these results provide design principles for industrial-scale CO 2 recovery using BPM-ED.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Technoeconomic Analysis of the Electrochemically Mediated Amine Regeneration CO 2 Capture Process

The electrochemically mediated amine regeneration (EMAR) process presents an alternative route to the conventional thermal amine regeneration for carbon capture from a flue gas source. In this study, we conducted an economic analysis on the EMAR system for postcombustion CO2 capture from a 550 MWe power plant capturing 3.1 MtCO2 annually and from a mini steel mill with annual capture close to 170 ktCO2. We followed the recommendation of the National Energy Technology Laboratory (NETL) 2010 report to estimate the cost of CO2 avoided ([$/tCO2]). Detailed cost modeling of the electrochemical separation stage was conducted. This is integrated with the entire process flowsheet (e.g., including absorber, compressor, pumps, and other auxiliary equipment). Here, we identified the membrane cost as the dominant capital cost for the electrochemical separation train. At a membrane unit price of less than 10/m2, the CO2 capture cost can be reduced to below 50/tCO2 with optimized process conditions (e.g., desorption pressure and utilization of waste heat). Improvements in process design, cell construction, and solvent formulation may lead to additional reductions in the CO2 capture cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Transport and mechanical behavior in PEO-LLZO composite electrolytes

Abstract Composite solid electrolytes (CEs), wherein ion-conducting polymer and ceramic/glass is mixed, are promising candidates for all-solid-state batteries due to their promise of acceptable ionic conductivity and mechanical properties compared to their individual constituents. While numerous studies have focused on improving the performance of CEs, it is still unclear what the material targets are that can result in improved macroscopic performance especially in light of the coupled needs for high transport and high mechanical strength in these materials. In this study, a two-dimensional (2D) mathematical model is developed to investigate electrochemical and mechanical characteristics of CEs. The model is compared to CEs consisting of poly-ethylene-oxide (PEO) polymer and lithium lanthanum zirconium oxide (LLZO) ceramic material with examination of the impact of varying LLZO volume fractions. The potential drop at the PEO-LLZO interface is evaluated using the junction potential theory. Using experimental data from the literature, the model estimates the ionic conductivity, effective transference number, and mechanical stiffness of the CEs. While the mechanical stiffness improves with increasing volume fraction of LLZO, the impact on conductivity and transference number depends on interfacial resistance at the interface. Finally, the study reports CE’s potential to enhance Li-ion transport and mechanical properties to inhibit lithium (Li) dendrite growth.

25 ENERGY STORAGE↗
DOE OSTI2026

Early calendar life and health prediction of silicon batteries via machine learning with uncertainty quantification

Lithium-ion batteries with silicon anodes promise high energy density but are limited by calendar lifetime. Reducing the long iteration time to obtain experimental results requires predicting calendar lifetime early in a cell's life. In this study, we demonstrate that lightweight machine learning models with feature engineering can provide calendar lifetime estimates from early electrochemical signals. After 1 month of electrochemical aging, the best models achieve 10% error in calendar-life prediction and can separate "bad" from "good" lifetime cells with a mean F1 score of 0.857. As battery systems exhibit inherent variability, four methods for uncertainty quantification are compared, and confidence intervals are demonstrated with an uncertainty of +-3.6 months in lifetime prediction. A feature importance analysis indicates that early patterns in voltage decay are the strongest indicators of calendar lifetime. Finally, this modeling approach has high error when generalizing to new electrode chemistries or testing conditions but with appropriately low confidence.

25 ENERGY STORAGE↗
DOE OSTI2024

Controlling Ion Uptake in Carboxylated Mixed Conductors

Organic mixed ionic‐electronic conductors (OMIECs) have garnered significant attention due to their capacity to transport both ions and electrons, making them ideal for applications in energy storage, neuromorphics, and bioelectronics. However, charge compensation mechanisms during the polymer redox process remain poorly understood, and are often oversimplified as single‐ion injection with little attention to counterion effects. To advance understanding and design strategies toward next‐generation OMIEC systems, a series of p‐channel carboxylated mixed conductors is investigated. Varying side‐chain functionality, distinctive swelling character is uncovered during electrochemical doping/dedoping with model chao‐/kosmotropic electrolytes. Carboxylic acid functionalized polymers demonstrate strong deswelling and mass reduction during doping, indicating cation expulsion, while ethoxycarbonyl counterparts exhibit prominent mass increase, pointing to an anion‐driven doping mechanism. By employing operando grazing incidence X‐ray fluorescence (GIXRF), it is revealed that the carboxyl functionalized polymer engages in robust cation interaction, whereas ester functionalization shifts the mechanism towards no cation involvement. It is demonstrated that cations are pivotal in mitigating swelling by counterbalancing anions, enabling efficient anion uptake without compromising performance. These findings underscore the transformative influence of functionality‐driven factors and side‐chain chemistry in governing ion dynamics and conduction, providing new frameworks for designing OMIECs with enhanced performance and reduced swelling.

carboxylated polythiophenes↗