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127 results on '"Weddle, Peter J."'

1. Generating multi-scale NMC particles with radial grain architectures using spatial stochastics and GANs

Author

Fuchs, Lukas, Furat, Orkun, Finegan, Donal P., Allen, Jeffery, Usseglio-Viretta, Francois L. E., Ozdogru, Bertan, Weddle, Peter J., Smith, Kandler, and Schmidt, Volker

Subjects
Physics - Applied Physics, Computer Science - Artificial Intelligence
Abstract

Understanding structure-property relationships of Li-ion battery cathodes is crucial for optimizing rate-performance and cycle-life resilience. However, correlating the morphology of cathode particles, such as in NMC811, and their inner grain architecture with electrode performance is challenging, particularly, due to the significant length-scale difference between grain and particle sizes. Experimentally, it is currently not feasible to image such a high number of particles with full granular detail to achieve representivity. A second challenge is that sufficiently high-resolution 3D imaging techniques remain expensive and are sparsely available at research institutions. To address these challenges, a stereological generative adversarial network (GAN)-based model fitting approach is presented that can generate representative 3D information from 2D data, enabling characterization of materials in 3D using cost-effective 2D data. Once calibrated, this multi-scale model is able to rapidly generate virtual cathode particles that are statistically similar to experimental data, and thus is suitable for virtual characterization and materials testing through numerical simulations. A large dataset of simulated particles with inner grain architecture has been made publicly available.

Published
2024

2. Solubilities of Ethylene and Carbon Dioxide Gases in Lithium-Ion Battery Electrolyte

Author

Soto, Mel, Fink, Kae, Zweifel, Christof, Weddle, Peter J, Spotte-Smith, Evan Walter Clark, Veith, Gabriel M, Persson, Kristin A, Colclasure, Andrew M, and de Villers, Bertrand J Tremolet

Subjects
Engineering, Chemical Sciences, Physical Chemistry, Chemical Engineering, Physical chemistry, Chemical engineering
Abstract

During Li-ion battery operation, (electro)chemical side reactions occur within the cell that can promote or degrade performance. These complex reactions produce byproducts in the solid, liquid, and gas phases. Studying byproducts in these three phases can help optimize battery lifetimes. To relate the measured gas-phase byproducts to species dissolved in the liquid-phase, equilibrium proprieties such as the Henry's law constants are required. The present work implements a pressure decay experiment to determine the thermodynamic equilibrium concentrations between the gas and liquid phases for ethylene (C2H4) and carbon dioxide (CO2), which are two gases commonly produced in Li-ion batteries, with an electrolyte of 1.2 M LiPF6 in 3:7 wt/wt ethylene carbonate/ethyl methyl carbonate and 3 wt % fluoroethylene carbonate (15:25:57:3 wt % total composition). The experimentally measured pressure decay curve is fit to an analytical dissolution model and extrapolated to predict the final pressure at equilibrium. The relationship between the partial pressures and concentration of dissolved gas in electrolyte at equilibrium is then used to determine Henry's law constants of 2.0 × 104 kPa for C2H4 and k CO2 = 1.1 × 104 kPa for CO2. These values are compared to Henry's law constants predicted from density functional theory and show good agreement within a factor of 3.

Published
2024

3. PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model

Author

Hassanaly, Malik, Weddle, Peter J., King, Ryan N., De, Subhayan, Doostan, Alireza, Randall, Corey R., Dufek, Eric J., Colclasure, Andrew M., and Smith, Kandler

Subjects
Computer Science - Machine Learning, Physics - Applied Physics
Abstract

Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be easily repeated for multiple cycles, multiple operating conditions, or multiple replicate cells. To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates. A physics-informed neural network (PINN) is developed as a surrogate for the pseudo-2D (P2D) battery model calibration. For the P2D surrogate, additional training regularization was needed as compared to the PINN single-particle model (SPM) developed in Part I. Both the PINN SPM and P2D surrogate models are exercised for parameter inference and compared to data obtained from a direct numerical solution of the governing equations. A parameter inference study highlights the ability to use these PINNs to calibrate scaling parameters for the cathode Li diffusion and the anode exchange current density. By realizing computational speed-ups of 2250x for the P2D model, as compared to using standard integrating methods, the PINN surrogates enable rapid state-of-health diagnostics. In the low-data availability scenario, the testing error was estimated to 2mV for the SPM surrogate and 10mV for the P2D surrogate which could be mitigated with additional data.

Published
2023

4. PINN surrogate of Li-ion battery models for parameter inference. Part I: Implementation and multi-fidelity hierarchies for the single-particle model

Author

Hassanaly, Malik, Weddle, Peter J., King, Ryan N., De, Subhayan, Doostan, Alireza, Randall, Corey R., Dufek, Eric J., Colclasure, Andrew M., and Smith, Kandler

Subjects
Computer Science - Machine Learning, Physics - Applied Physics
Abstract

To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapidly. This study seeks to reduce the computational resources needed to determine a battery's internal states by replacing physics-based Li-ion battery models -- such as the single-particle model (SPM) and the pseudo-2D (P2D) model -- with a physics-informed neural network (PINN) surrogate. The surrogate model makes high-throughput techniques, such as Bayesian calibration, tractable to determine battery internal parameters from voltage responses. This manuscript is the first of a two-part series that introduces PINN surrogates of Li-ion battery models for parameter inference (i.e., state-of-health diagnostics). In this first part, a method is presented for constructing a PINN surrogate of the SPM. A multi-fidelity hierarchical training, where several neural nets are trained with multiple physics-loss fidelities is shown to significantly improve the surrogate accuracy when only training on the governing equation residuals. The implementation is made available in a companion repository (https://github.com/NREL/pinnstripes). The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.

Published
2023

5. Voltage-Based Strategies for Preventing Battery Degradation under Diverse Fast-Charging Conditions

Author

Konz, Zachary M, Weddle, Peter J, Gasper, Paul, McCloskey, Bryan D, and Colclasure, Andrew M

Subjects
Engineering, Materials Engineering, Chemical Sciences, Affordable and Clean Energy, Chemical sciences
Abstract

Maintaining safe operating conditions is a key challenge for high-performance lithium-ion battery applications. The lithium-plating reaction remains a risk during charging, but limited studies consider the highly variable charging conditions possible in commercial cells. Here we combine pseudo-2D electrochemical modeling with data visualization methods to reveal important relationships between the measurable cell voltage and difficult-to-predict Li-plating onset criteria. An extensively validated model is used to compute Li plating for thousands of multistep charging conditions spanning diverse rates, temperatures, states-of-charge, and cell aging. We observe an empirical cell operating voltage limit below which plating does not occur across all conditions, and this limit varies with the battery state-of-charge and aging. A model sensitivity analysis also indicates that, when comparing two charging voltage profiles, the capacity difference at 4.0 V correlates well with the difference in the plating onset capacity. These results encourage simple strategies for Li-plating prevention that are complementary to existing battery controls.

Published
2023

6. Cell-format-dependent mechanical damage in silicon anodes

Author

Rodrigues, Marco-Tulio F., Rajendran, Sathish, Trask, Stephen E., Dunlop, Alison R., Singh, Avtar, Allen, Jeffery M., Weddle, Peter J., Colclasure, Andrew M., and Jansen, Andrew N.

Subjects
Physics - Chemical Physics
Abstract

It is generally believed that silicon-based anodes for Li-ion batteries would benefit from stronger binders, as cyclic volume changes would not disrupt the cohesion of the composite electrode. Here, we put this belief to the proof by testing electrodes containing SiOx particles and an aromatic polyimide binder. We observe that the electrodes can stretch laterally by as much as 6% during the first cycle, indicating that internal stresses are high enough to induce plastic deformation on the copper current collector. Remarkably, no coating delamination is observed. Additional consequences were size-dependent: while pouch-cell-sized electrodes developed wrinkles, coin-cell-sized ones remained mostly smooth. We demonstrate that wrinkling of the current collector damages the electrode coating, inactivating SiOx domains and accelerating capacity fade. This size-dependent performance decay indicates that, in extreme cases, testing outcomes are highly dependent on scale. Novel battery materials may require testing at larger cell formats for complete validation.

Published
2023
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24. Dynamic In‐Plane heterogeneous and Inverted Response of Graphite to Fast Charging and Discharging Conditions in Lithium‐Ion Pouch Cells

Author

Monasterial, Abigale P., primary, Weddle, Peter J., additional, Atkinson, Kristen, additional, Wragg, David S., additional, Colclasure, Andrew M., additional, Usseglio-Viretta, Francois L. E., additional, Seitzman, Natalie, additional, Park, Jun-Sang, additional, Almer, Jonathan, additional, Smith, Kandler, additional, and Finegan, Donal P., additional

Published
2023
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27. Continuum-Level Modeling of Li-Ion Battery Sei by Upscaling Atomistically Informed Reaction Mechanisms

Author

Weddle, Peter J., primary, Spotte-Smith, Evan Walter Clark, additional, Verma, Ankit, additional, Patel, Hetal D., additional, Fink, Kae, additional, de Villers, Bertrand J. Tremolet, additional, Schulze, Maxwell C., additional, Blau, Samuel M., additional, Smith, Kandler, additional, Persson, Kristin, additional, and Colclasure, Andrew, additional

Published
2023
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28. Enabling Extreme Fast‐Charging: Challenges at the Cathode and Mitigation Strategies

Author

Tanim, Tanvir R., primary, Weddle, Peter J., additional, Yang, Zhenzhen, additional, Colclasure, Andrew M., additional, Charalambous, Harry, additional, Finegan, Donal P., additional, Lu, Yanying, additional, Preefer, Molleigh, additional, Kim, Sangwook, additional, Allen, Jeffery M., additional, Usseglio‐Viretta, Francois L. E., additional, Chinnam, Parameswara R., additional, Bloom, Ira, additional, Dufek, Eric J., additional, Smith, Kandler, additional, Chen, Guoying, additional, Wiaderek, Kamila M., additional, Weker, Johanna Nelson, additional, and Ren, Yang, additional

Published
2022
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37. A Comprehensive Understanding of the Aging Effects of Extreme Fast Charging on High Ni NMC Cathode

Author

Tanim, Tanvir R., primary, Yang, Zhenzhen, additional, Finegan, Donal P., additional, Chinnam, Parameswara R., additional, Lin, Yulin, additional, Weddle, Peter J., additional, Bloom, Ira, additional, Colclasure, Andrew M., additional, Dufek, Eric J., additional, Wen, Jianguo, additional, Tsai, Yifen, additional, Evans, Michael C., additional, Smith, Kandler, additional, Allen, Jeffery M., additional, Dickerson, Charles C., additional, Quinn, Alexander H., additional, Dunlop, Alison R., additional, Trask, Stephen E., additional, and Jansen, Andrew N., additional

Published
2022
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45. (Invited) Calendar Life Analysis of Si-Containing Anodes for Li-Ion Batteries.

Author

Tomich, Anton, Verma, Ankit, Gupta, Abhay, Rodrigues, Marco-Tulio F, Jansen, Andrew N., Abraham, Daniel P., Carroll, Gerard Michael, Schulze, Maxwell C, Burrell, Anthony, Colclasure, Andrew M., Veith, Gabriel M., Dufek, Eric J., Weddle, Peter J, Prakash, Nina, Harrison, Katharine L., Neale, Nathan R., Vaughey, John T., McBrayer, Josey D,, and Johnson, Christopher S.

Published
2024
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48. Extracting and Interpreting Electrochemical Impedance Spectra (EIS) from Physics-Based Models of Lithium-Ion Batteries

Author

Zhu, Huayang, Evans, Tyler A. P., Weddle, Peter J., Colclasure, Andrew M., Chen, Bor-Rong, Tanim, Tanvir R., Vincent, Tyrone L., and Kee, Robert J.

Abstract

This paper implements a highly efficient algorithm to extract electrochemical impedance spectra (EIS) from physics-based battery models (e.g., a P2D model). The mathematical approach is different from how EIS is practiced experimentally. Experimentally, the voltage (current) is harmonically perturbed over a wide range of frequencies and the amplitude and phase shift of the corresponding current (voltage) is measured. The experimental approach can be implemented in simulation software, but is computationally expensive. The approach here is to determine locally linear state-space models from the full physical model. The four Jacobian matrices that are the basis of the state-space models can be derived by numerical differentiation of the physical model. The EIS is then extracted from the state-space model using computationally efficient matrix-manipulation techniques. The algorithm can evaluate the full EIS at an instant in time during a transient, independent of whether the battery is in a stationary state. The approach is also able to separate the full-cell impedance to evaluate partial EIS, such as for a battery anode alone. Although such partial EIS is difficult to measure experimentally, the partial EIS provides valuable insights in interpreting the full-cell EIS.

Published
2024
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