Mesoscale Modeling and Simulation of Composition Manufacturing and Microstructure Effects on Electrical Conduction in Thermal Battery Cathodes.
Abstract not provided.
Reinholz, Emilee Lolita↗
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Results for “Battery Modeling”
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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Computer-Aided Engineering of Batteries for Designing Better Li-Ion Batteries (Presentation)
This presentation describes the current status of the DOE's Energy Storage R&D program, including modeling and design tools and the Computer-Aided Engineering for Automotive Batteries (CAEBAT) program.
25 ENERGY STORAGE↗
Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives
Abstract Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.
25 ENERGY STORAGE↗
R&D GREET Battery Carbon Footprint Calculator
The Battery Carbon Footprint (CF) Calculator was developed to help U.S. battery manufacturers meet the carbon footprint reporting requirements of the EU Battery Regulation (EU) 2023/1542. The calculator incorporates several major battery carbon footprint frameworks, including the Joint Research Centre's Rules for the Calculation of the Carbon Footprint of Electric Vehicle Batteries (CFB-EV), RECHARGE's Product Environmental Footprint Category Rules for High Specific Energy Rechargeable Batteries for Mobile Applications (PEFCR), the Catena-X Product Carbon Footprint Rulebook (CX-PCF Rules), Battery Pass's Battery Carbon Footprint: Rules for Calculating the Carbon Footprint of the "Distribution" and "End-of-Life and Recycling" Life Cycle Stages, the Global Battery Alliance's Greenhouse Gas Rulebook: Generic Rules, Version 2.1, and the Ministry of Economy, Trade and Industry's draft Carbon Footprint Calculation Method for Automotive Batteries. The tool pairs these frameworks with foreground data from Argonne's R&D GREET models and integrates user-supplied background data covering battery manufacturing and supply chain activities. By bringing multiple international methodologies together in a single platform, the calculator enables manufacturers to evaluate product carbon footprints, improve data consistency, and prepare for evolving regulatory compliance and global market reporting requirements.
Zhang, Jingyi↗
Neural Posterior Estimation for Scalable and Accurate Inverse Parameter Inference in Li-Ion Batteries
Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE can infer parameters equally or more accurately than Bayesian calibration, even if it leads to higher voltage reconstruction errors. We also demonstrate that the higher computational costs for data generation are tractable even in high-dimensional cases (ranging from 6 to 27 estimated parameters). The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (https://github.com/NatLabRockies/BatFIT).
25 ENERGY STORAGE↗
Northeastern Center for Chemical Energy Storage (NECCES)
The chemical reactions that occur in batteries are complex, spanning a wide range of time and length scales from atomic jumps to the entire battery structure. The NECCES team of experimentalists and theorists made use of, and developed new methodologies to determine how model compound electrodes function in real time, as batteries are cycled. The team determined that kinetic control of intercalation reactions (reactions in which the crystalline structure is maintained) can be achieved by control of the materials morphology and explains and allows for the high rates of many intercalation reactions where the fundamental properties might indicate poor behavior in a battery application. The small overvoltage required for kinetic control is technically effective and economically feasible. A wide range of state-of-the-art operando techniques was developed to study materials under realistic battery conditions, which are now available to the scientific community. The team also investigated the key reaction steps in conversion electrodes, where the crystal structure is destroyed on reaction with lithium and rebuilt on lithium removal. These so-called conversion reactions have in principle much higher capacities, but were found to form very reactive discharge products that reduce the overall energy efficiency on cycling. It was found that by mixing either the anion, as in FeOF, or the cation, as in Cu1-yFeyF2, the capacity on cycling could be improved. The fundamental understanding of the reactions occurring in electrode materials gained in this study will allow for the development of much improved battery systems for energy storage. This will benefit the public in longer lived electronics, higher electric vehicle ranges at lower costs, and improved grid storage that also enables renewable energy supplies such as wind and solar.
25 ENERGY STORAGE↗
Multidisciplinary Optimization of an Electric Quadrotor Urban Air Mobility Aircraft
Urban Air Mobility (UAM) vehicles have the potential to augment urban transportation systems, allowing passengers to skip the traffic below for a fee. This emerging market is opening up the design space for a new class of Urban Air Mobility (UAM) vehicles which could be powered by electric propulsion systems to be economical and environmentally friendly. However, development of these UAM concepts presents several additional challenges in the design process. First, these concept designs require including new disciplinary models for subsystems including the electric motors, cables, batteries and thermal management systems. Second, correctly designing and evaluating these various subsystems requires tight coupling between the discipline models to capture interactions. This paper presents the continued development of a multidisciplinary design optimization environment to aid in the development of these vehicle concepts. The multidisciplinary environment fully couples the various subsystem models allowing for the full vehicle to be designed and optimized simultaneously. In this research, the developed modeling approach is demonstrated in the analysis of a small, all-electric quadrotor UAM concept. Results from these studies show that numerous disciplines can be tightly coupled and producing improved overall vehicle designs.
Multidisciplinary Optimization↗
Modeling of Microgrid for Critical Data Center Applications
As part of continuing efforts to develop support, understanding, and infrastructure of data center integration onto the electric grid, our work aims to model and analyze the behavior of data center loads in a microgrid power system. Our model consists of renewable energy sources, batteries, and a nuclear reactor-steam Rankine cycle to power data center loads. The simulation studies investigate the electrical behavior of the microgrid system to assess its ability in supporting large data center electrical demands.
14 - SOLAR ENERGY↗
Models and Strategies for Optimal Demand Side Management in the Chemical Industries
Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed the U.S. electricity market. Economic and environmental benefits notwithstanding, the presence of renewables has increased variability and uncertainty on the supply side of the grid. Managing demand, rather than generation – a strategy referred to as “demand response (DR)” – is an attractive approach for mitigating this imbalance. DR efforts aim to reduce electricity usage during peak demand times, lessening stress on the grid. Industrial users are particularly attractive entities for DR participation since they present large, localized loads that can provide significant relief on grid demand and –unlike other large loads, such as buildings – are minimally dependent on human needs and preferences. In this project, we accomplished three main objectives. (1) We developed data-driven low-order DR scheduling-relevant dynamic models of chemical processes. Concurrently, we studied the formulation and solution of the associated optimal DR production scheduling problems. (a) A prototype air separation unit (ASU) model was used to generate simulated operating data for initial modeling efforts, which enabled the later use of industrial data for data-driven modeling. (b) We utilized Hammerstein-Wiener (HW) and Finite Step Response (FSR) models to represent nonlinear plant dynamics. (c) The HW models were linearized using exact linearization so they could potentially be embedded in power system models, which are formulated as mixed integer linear programs (MILPs). (d) We solved DR optimization problems under uncertainty and found that even naïve predictions of electricity price and product demand led to significant cost savings benefits. (2) Our DR scheduling optimization problem formulations are amenable to real-time solution. (a) We utilized Lagrangian Relaxation (LR) to efficiently solve the optimization problem by decoupling subproblems linked by complicating constraints. (b) We have achieved computation times for the 3-day DR scheduling problem of an ASU as low as 1.88 minutes. (3) Our representations of the DR behavior of chemical process as grid-level batteries were embedded in power system models. (a) For a small-scale grid, we found that incorporating the dynamics of the chemical plant in the optimal power flow calculations resulted in better resource management leading to up to 15% and 46% cost reduction for the grid and chemical plant operations, respectively, during periods of power line congestion. We have published several works dedicated to modeling and solving DR optimization problems from the user side. These were published in top peer-reviewed journals and are summarized in this report. The most recent work (and papers in preparation) considers DR scheduling from the grid side. Future efforts will consider networked plants (e.g., air separation units operating on a common pipeline) for DR participation, which is expected to amplify the capabilities of industrial DR participants to perform load-shifting. Our consideration of uncertainty in DR has inspired future directions in this area as well: we plan to develop multistage methods to fully account for the effects of uncertainty in DR scheduling.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Atomic layer deposition in porous electrodes: A pore-scale modeling study
Coating porous electrodes of Li-ion batteries with functional materials by atomic layer deposition (ALD) can enhance their capacity stability. Because of the complex microstructure of the porous electrodes, predicting the required exposure time of the precursors for achieving a desired coating and the coating characteristics for a given exposure time remains difficult. Here, a three-dimensional (3D) pore-scale lattice Boltzmann model is developed to investigate the reactive transport processes during the ALD of reconstructed electrodes and to assess the accuracy of one-dimensional (1D) mean-field models. The effects of the hierarchical structure of pores and their connectivity on the coating process of ALD are investigated and the detailed coating characteristics in the electrodes are resolved. Electrodes with smaller pore sizes requires a longer exposure time to achieve full coating. At the same depth within an electrode, the smaller pores are coated more slowly than the wider pores (especially at high Damkohler numbers) and the coating speed of well-connected pores are faster than that of poorly connected pores. Furthermore, simulations also reveal that the 1D mean-field model can capture the average coating characteristics reasonably well when pores in the electrodes are well connected but may perform poorly if the pores are poorly connected.
25 ENERGY STORAGE↗
Nonequilibrium Phase Transformation and Particle Shape Effect in LiFePO4 Materials for Li-Ion Batteries
The nonequilibrium phase transformation and particle shape effects in LiFePO4 materials of Li-ion batteries are explored in this work. A continuum model employing the mushy-zone (MZ) approach, accounting for sluggish Li diffusion across the two-phase boundary, has been developed to study the kinetically-induced nonequilibrium phenomenon in Li-ion batteries. A theoretical analysis is presented to show that the nonequilibrium miscibility gap expands and shifts to higher Li composition at high discharge rates, due to insufficient compositional readjustments at the two-phase boundary. Furthermore, critical effects of particle shape on nonequilibrium phase transformation and discharge capacity have been discovered by the model.
25 ENERGY STORAGE↗
On the Fundamental and Practical Aspects of Modeling Complex Electrochemical Kinetics and Transport
Numerous technologies, such as batteries and fuel cells, depend on electrochemical kinetics. In some cases, the responsible electrochemistry and charged-species transport is complex. However, to date, there are essentially no general-purpose modeling capabilities that facilitate the incorporation of thermodynamic, kinetic, and transport complexities into the simulation of electrochemical processes. A vast majority of the modeling literature uses only a few (often only one) global charge-transfer reactions, with the rates expressed using Butler-Volmer approximations. The objective of the present paper is to identify common aspects of electrochemistry, seeking a foundational basis for designing and implementing software with general applicability across a wide range of materials sets and applications. The development of new technologies should be accelerated and improved by enabling the incorporation of electrochemical complexity (e.g., multi-step, elementary charge-transfer reactions and as well as supporting ionic and electronic transport) into the analysis and interpretation of scientific results. The spirit of the approach is analogous to the role that Chemkin has played in homogeneous chemistry modeling, especially combustion. The Cantera software, which already has some electrochemistry capabilities, forms the foundation for future capabilities expansion.
30 DIRECT ENERGY CONVERSION↗
ICME for NASA Aerospace Applications: Batteries for Electric Aviation
NASA’s approach to computational materials modeling is detailed in the NASA Vision 2040 Roadmap for Multiscale Modeling and Simulation of Materials and Systems. This report is in the spirit of national initiatives such as the Material Genome Initiative (MGI), Integrated Computational Materials Engineering (ICME), and others. We utilize a combination of fundamental modeling, computational high-throughput screening, and data science methods, e.g., machine learning, are used to find innovative solutions to NASA or national technology challenges. Applications of interest are wide ranging from advanced alloys to batteries to coatings, among others. In this talk, we present three examples for recent work related to NASA applications. First, doping advanced sulfur battery cathodes with selenium boosts electrical conductivity important for electric aircraft applications. First principles calculations will be discussed that result in compositional design maps for these materials. Second, development of icephobic coatings is important to mitigate safety hazards associated with icing for aircraft. Molecular dynamics simulations are reported for ice-surface interfaces to understand adhesion mechanisms and help screen optimal ice-phobic coatings. Third, shape memory alloys have numerous applications as actuators, superelastic materials, etc. for aerospace. We report machine learning models that predict martensitic transition temperatures across a broad swath of compositional space.
John Lawson↗
Evolution of solid electrolyte interphase and active material in the silicon wafer model system
Alloying anode materials for lithium-ion batteries, such as silicon (Si), are an important focus of materials research due to the demand for increased energy density for electric vehicles and stationary grid storage. However, progress towards next generation anode materials has been hindered by poor capacity retention due to the instability of the Si active material and the solid electrolyte interphase (SEI). Herein, the evolution of the active Si material and the SEI are simultaneously investigated from the perspective of chemical, structural, morphological, and electronic evolution in the Si wafer model system through its cycling life, using time-of-flight secondary ion mass spectrometry (TOF-SIMS), scanning transmission electron microscopy (STEM), atomic force microscopy (AFM), and scanning spreading resistance microscopy (SSRM). The results illustrate a dynamic evolution of the SEI and active Si material through cycling. Through an improved understanding of evolution of SEI and Si active material within the Si wafer model system, mitigation strategies may be designed to extend the lifetime of Si anodes.
25 ENERGY STORAGE↗
Performance evaluation of underground thermal storage integrated dual-source heat pump systems
The increasing demand for electricity stresses the existing electric grids. Buildings consume 73% of all U.S. electricity and are responsible for 30% of U.S. greenhouse gas emissions. Integrating thermal energy storage (TES) in building heating/cooling systems, which consume considerable electricity, can mitigate the challenges to electric grids. Here, this study reports on a novel thermal energy storage device integrated heat pump system to reshape the building electricity demand profile while maintaining thermal comfort. The annual performance of the proposed system has been evaluated through a dynamic system simulation with high fidelity in the Modelica platform. The dynamic model of the novel hybrid component named ‘dual purpose underground thermal battery’ was developed and validated. It was then incorporated into the system model. Given a time-of-use tariff, a rule-based control strategy was designed to shift the electric demand and switch the heat pump source for a typical single-family house in different climate zones of the United States. The system performance of the new TES-integrated dual-source heat pump was compared with that of a conventional air-source heat pump system. The results indicate that the proposed system can reduce the annual HVAC electricity cost by up to 52% while saving 45.2% on electricity consumption. In the Northern areas, the annual peak load of the HVAC system can be reduced by 64.9%. However, this reduction is less in the Southern areas as the system’s higher efficiency in winter dominates the overall energy-saving potential.
25 ENERGY STORAGE↗
Automated and Accelerated Continuum Model Development for Electrochemical Systems (Abbreviated Report)
Despite the availability of computational resources and advancements in numerical computing capabilities, the multiscale models core to understanding, predicting the behaviors of, and designing energy and environmental systems involving porous media are still 1.) developed through by-hand derivations and 2.) limited by many methodological assumptions employed during model derivation. As a result, the advancement of effective media models for engineering DOE mission-critical systems (e.g., batteries, flow batteries, electrolyzers, geothermal systems, subsurface chemical storage systems, etc.) is slow (i.e., it takes years for models to traverse from stages of “development” to “practical utilization”), hindering our ability to effectively optimize such systems and stay at the cutting-edge of the energy frontier. In this work, we aimed to address these limitations by 1.) automating and accelerating multiscale model derivation via symbolic computing and 2.) develop a novel multiscale modeling methodology for flow and transport through porous media that avoids the typical assumptions hindering previous models. As a result of our efforts, we 1.) developed a hybrid symbolic-numeric code called Fouriera for fully-automating the implementation of multiphysical and phase-field models via the Fourier spectral method for materials science research, and 2.) advanced a multiscale modeling methodology called The Method of Finite Averages that rigorously predicts the behaviors of flow and transport through heterogeneous porous media under the influence of non-local effects and strong advection. Ultimately, these deliverables provide strong foundations from which further efforts can advance multiscale modeling tools and capabilities that do not intrinsically rely on 1.) the speed and mathematical capabilities of humans, nor 2.) the methodological assumptions limiting current models.
36 MATERIALS SCIENCE↗
Low-current field-assisted assembly of copper nanoparticles for current collectors
Current collectors are essential features of batteries and many other electronic devices being responsible for efficient charge transport to active electrode materials. Three-dimensional (3D), high surface area current collectors considerably improve the performance of cathodes and anodes in batteries, but their technological implementation is impeded by the complexity of their preparation, which needs to be simple, fast, and energy efficient. In this paper we demonstrate that field-stimulated assembly of ~3 nm copper nanoparticles (NPs) enables the preparation of porous Cu NP films. The use of NP dispersions enables 30× reduction of the deposition current for making functional 3D coatings. In addition to high surface area, lattice-to-lattice connectivity in the self-assembly of NPs in 3D structures enables fast charge transport. The mesoscale dimensions of out-of-plane features and the spacing between them in Cu films made by field-stimulated self-assembly of NPs provides promising morphology for current collection in lithium ion batteries (LIBs). Half-cell electrochemical models based on self-assembled films show improved specific capacity, total capacity, and cycling performance compared to traditional flat and other 3D current collectors. While integration of active electrode material into the 3D topography of the current collector needs to be improved, this study indicates that self-assembled NP films represent a viable manufacturing approach for 3D electrodes.
36 MATERIALS SCIENCE↗