bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging Molecular Dynamics to Improve Porous Electrode Theory Modeling Predictions of Lithium-Ion Battery Cells
Authors: Dix Sean T. , Lowe Jeffrey S. , Rashvand Avei Mehdi , Garrick Taylor R. 
Published: September 2023

Lithium-ion battery cell modeling using physics-based approaches such as porous electrode theory is a powerful tool for battery design and analysis. Performance metrics such as cell power and resistance can be quickly calculated in a pseudo-two-dimensional (P2D) framework and when coupled with thermal models, component level detail of heating and temperature rise can be evaluated1. For engineering of electric vehicle batteries, speed and fidelity of electrochemical models is paramount in a competitive landscape. Physics-based models allow for high fidelity but require detailed knowledge of the cell component material properties. Acquiring these material characteristics typically requires time-consuming and expensive experiments limiting the ability to quickly screen through cell designs. Therefore, engineering assumptions of the materials properties must be made based on prior tests or literature sources necessarily limiting model accuracy. One approach to improve model accuracy and circumvent costly experiments is to use atomistic modeling to calculate battery cell material properties such as kinetics, OCVs, and transport properties without the need of experiments2–4.
In this contribution we use molecular dynamics to calculate electrolyte conductivity and lithium-ion diffusion coefficients for a commercial electrolyte. We first validate our MD modeling approach by reproducing experimental measures of electrolyte conductivity for several test systems. We then construct a porous electrode theory model using the computed electrolyte transport values for the commercial system. Using this model, cell resistance and power is calculated and compared to experimental values measured from small-format pouch cells using the same electrolyte. We show how the variability in the MD modeling impacts cell performance metrics and demonstrate the robustness of this approach to improve cell-level model accuracy.
References:
(1) Song, M.; Hu, Y.; Choe, S.-Y.; Garrick, T. R. Analysis of the Heat Generation Rate of Lithium-Ion Battery Using an Electrochemical Thermal Model. J. Electrochem. Soc.
2020, 167 (12), 120503. https://doi.org/10.1149/1945-7111/aba96b.
(2) Liu, Z.; Li, Y.; Ji, Y.; Zhang, Q.; Xiao, X.; Yao, Y.; Chen, L.-Q.; Qi, Y. Dendrite-Free Lithium Based on Lessons Learned from Lithium and Magnesium Electrodeposition Morphology Simulations. Cell Rep. Phys. Sci.
2021, 2 (1), 100294. https://doi.org/10.1016/j.xcrp.2020.100294.
(3) Pande, V.; Viswanathan, V. Robust High-Fidelity DFT Study of the Lithium-Graphite Phase Diagram. Phys. Rev. Mater.
2018, 2 (12), 125401. https://doi.org/10.1103/PhysRevMaterials.2.125401.
(4) Wei, Y.; Zheng, J.; Cui, S.; Song, X.; Su, Y.; Deng, W.; Wu, Z.; Wang, X.; Wang, W.; Rao, M.; Lin, Y.; Wang, C.; Amine, K.; Pan, F. Kinetics Tuning of Li-Ion Diffusion in Layered Li(NixMnyCoz)O2. J. Am. Chem. Soc.
2015, 137 (26), 8364–8367. https://doi.org/10.1021/jacs.5b04040.

Figure 1
Modeling, Model Calibration, and Characterization of Graphite Anodes from Coal Derived Carbon
Authors: Paul Abigail , Magee Regan , Wichert Nathan , Wilczewski Warren , Mollah Rafid , Jones Matthew , Trembly Jason , Staser John A. , Garrick Taylor R. 
Published: September 2023

Most current production electric vehicles (EVs) contain cells with a battery pack or module in order to maintain electrical conductivity, prevent fatigue due to vibrations, and provide efficient cooling. Various module hardware has to be designed in order to facilitate the efficient rejection of heat and contain volume change due to operation and battery cell aging. As we see a shift towards EVs both globally and domestically, the time from concept development to vehicle production needs to rapidly increase. The use of model-based engineering is critical to driving rapid design and virtual prototyping of electric vehicles to ensure that battery module components meet EV requirements.
Currently, empirical data is used to characterize and parameterize battery models that are used to drive design decisions at the battery cell and battery module level. Typical vehicle level use profiles such as the Hybrid Pulse Power Characterization (HPPC) profile, constant rate discharging, constant rate charging, and US06 drive cycles are traditionally used to parameterize battery models. In this work, we discuss select cell level testing profiles, evaluate the electrochemical transport and reaction parameters that are estimated using a porous electrode model, and provide insight and recommendations based on data from coal derived graphite for battery applications.
A Reduced Order Modeling Approach to Capture Transport within a Three-Electrode System Towards Fast Charge Applications
Authors: Mollah Rafid , Garrick Taylor R. , Gao Jing , Zeng Yangbing , Koch Brian 
Published: September 2023

In support of GM's traction battery efforts, we derive and implement a reduced order modeling method to describe the electrochemical performance of a battery cell through the combination of a modified Newman Pseudo-2-Dimensional model and a three-electrode experimental apparatus. To assess the capability of the method, we compare the modeling results with experimental data for a lithiated graphite electrode and a lithiated nickel rich electrode system. The model is applied to simulate the electrochemical and transport processes within the battery cell to predict the negative electrode potential and positive electrode potential with respect to a lithium iron phosphate reference electrode, as well as the terminal voltage.
The manufacture and set up of the three electrode system, model development and reduction approaches, as well as a discussion of applicability to fast charge considerations to avoid lithium plating at the anode during a charging event will be presented.
References:
1. T. R. Garrick, J. Gao, X. Yang, and B. J. Koch. 2021. J. Electrochem. Soc. 168 010530
2. T. R. Garrick, J. Gao, X. Yang, and B. Koch. 2021, Meet. Abstr. MA2021-02 485
3. T. R. Garrick, J. Gao, and B. J. Koch. 2022. Meet. Abstr. MA2022-02 1077
Microstructure Model to Predict Mechanical Behavior of Lithium-Ion Battery Active Material Under Compressive Load
Authors: Teel Hunter , Lopata Joseph Steven , Garrick Taylor R. , Wang Fengkun , Zeng Yangbing , Shimpalee Sirivatch 
Published: September 2023

A three-dimensional microstructure-based modeling method (3DMS) is developed to predict the mechanical behaviors of porous electrode that includes both active material (AM) and binders inside lithium-ion battery during compression. The geometry of both AM and binder shown in Fig. 1a is generated stochastically to create microstructures by using a modified MATBOX opensource program with our in-house code. The advanced technique of volume element generation is also established to overcome the interfacial error due to complexity of binder distribution as illustrated in Fig. 1b. The Finite Element Analysis (FEA) based solid-stress model is applied to predict local mechanical activities including stress-strain relationship and microstructure evolution.
The predictions of anisotropic behavior in the porous electrode under compressive loads will be presented and the insights into the microstructure deformation will be discussed. Further, the analysis results will be validated using experimental testing results acquired at various length scales. This work can be used for the model-based FEA platform to predict the impact of volume change under charge/discharge cycles, volume change of a jelly-roll during formation, and the response of the active material in typical pouch cells during battery module assembly.

Figure 1
Using Multiscale Direct Simulation Approach to Understanding Transports Behavior Inside PEM Water Electrolyzer
Authors: Sepe Mitchell , Lopata Joseph Steven , Madkour Sherif , Mayerhoefer Britta , Ciesielski Adrian , Shimpalee Sirivatch 
Published: September 2023

In this work, numerical modeling techniques were developed to predict oxygen bubble evolution through complex pore geometries to provide information of gas transport pathways inside saturated porous transport layers (PTL). Each PTL undergoes an image processing technique that uses X-ray CT images to generate accurate 3D microstructures. An example of a generated PTL structure is shown in Figure 1.
Historically, meshing of the complex geometry was tedious and non-automated. Due to the random particle size across the geometry, a constant base size for meshing resulted in an inadequate mesh for accurate predictions. The Lattice-Boltzmann method is therefore more commonly used because a lattice is easier than a finite volume mesh to generate. Although sufficiently fine lattices adhered to the topology of the physical structure, simulations required a timestep between 1e-8 and 1e-10 seconds, resulting in long computational time.
Meshing techniques have improved and can now be applied to complex geometries. Using the finite volume method accompanied with an adaptive mesh and adaptive time scale, provides an accurate representation of the PTL structure and larger timesteps. A volume of fluid (VoF) method was used to model two phase flow, using liquid water and gaseous oxygen, through different PTL structures. Evolution, growth, size, velocity, and surface interaction of oxygen bubbles through microstructures were studied as also shown in Figure 2. Findings from the work will provide better understanding of oxygen evolution through saturated PTLs

Figure 1
Multiscale Modeling of Radio Frequency Reactor Coupled Catalyst Heating for Ethylene Production
Authors: Teel Hunter , Mercado Hector-Colon , Ganesan Prabhu , Craps Matthew , Gaillard Jay B , Ciesielski Peter , Shimpalee Sirivatch 
Published: September 2023

Ethylene is the petrochemical compound with the highest production worldwide. Whether reactors are heated by direct fired heating or indirect heating via steam, manufacturers rely on economies of scale to overcome inherent thermodynamic inefficiencies while burning fossil fuels. New approaches to supply energy to chemical reactor systems can reduce energy waste created by traditional techniques while enabling large scale facilities to use other sources of raw materials and energy. Electromagnetic (EM) induction heating is one potential solution for providing energy efficiently to reactor systems. By taking advantage of the nature of radio frequency (RF) waves, heterogeneous-catalyst can be precisely targeted for heating inside the reactors. Site-selective heating can significantly reduce the energy requirements of the process by providing heat at reaction sites and reducing unnecessary heat transfer elsewhere. Other advantages to an EM enhanced system include rapid volumetric heating, broader turn-down capacity, and reduced process footprint.
The purpose of this work is to establish a multiscale computational fluid dynamics (CFD) model that can be used to emulate the proposed process mechanics at the macroscale and microscale. At the macroscale, coil geometry (coil diameter, gap between coils, distance between coil and susceptors) is investigated to elucidate coil design and effects on heating rates. In the microscale, the oxidative (CO2) dehydrogenation (ODH) of ethane is explored using different catalysts and possible catalyst/susceptor configurations with heat supplied by an EM susceptor. This multiscale method can also be applied to electrochemical systems.
Modeling and Simulation of a High Temperature Proton Exchange Membrane Fuel Cell
Authors: Aryal Utsav Raj , Zhu Gaohua , Banerjee Debasish 
Published: September 2023

High temperature (HT) (around 120-200°C) PEM FCs are predicted to be the next generation of PEMFCs particularly for hydrogen-powered automobiles and combined heat and power (CHP) systems because the water management can be simplified at such temperatures as only a single phase of water vapor needs to be considered. Additionally, the cooling system can be streamlined due to an increase in temperature gradient between the coolant and the FC stack. This will also allow easy recovery of waste heat that can be used as a practical heat source. Furthermore, the CO tolerance improves dramatically at high temperatures thereby allowing HT PEMFCs to utilize reformed and impure hydrogen.
A single phase three-dimensional, steady-state, isothermal model for a single 5 HT PEM fuel cell with serpentine flow channels is implemented in COMSOL to investigate the effect of various operating conditions like temperature, back pressure and cathode flow rate and design parameters like catalyst layer loading, cathode GDL porosity, and flow field channels including parallel and interdigitated channels. Different performance indicators in terms of polarization curve, loss mechanisms, oxygen molar concentration, and anode and cathode pressure are represented for a complete overall analysis. In fact, the breakdown of different overpotential loss mechanism for HT PEMFC presented here is unique that not only quantifies these losses but also provides an accurate comparison with corresponding low temperature counterpart. The result from the computational model follows the experimental result very closely, thus, validating our model. The simulations stipulates that the performance of a HT PEMFC improves with increasing temperature, back pressure, and air flow rate. Increasing catalyst layer loading improves the performance up to a point after which it starts to drop at low voltages because of hindered gas diffusion. Similarly, a comparative study among different flow field channel is also presented indicating improved performance when going from parallel to interdigitated and serpentine channels. Furthermore, this study provides guidelines to optimize HT PEMFC performance through comprehensive parametric study.
In-Situ Analysis of Chronopotentiometry Data to Predict Electrodeposition Rates
Authors: St John Christopher , Arrington Christian L. , Oglesby Skyler , Gucik Mary Louise , Finnegan Patrick Sean , Weatherred Scott Edward , Carr Stephen M 
Published: September 2023

Often in MEMS or NEMS microfabrication R&D, many single layer “test” samples are created prior to committing more complex multiple layer “device” samples which require more time and effort to create. Ideally, much of the R&D can focus on less critical test samples and the experimental results can then be applied to more valuable device wafers. We investigated tin electroforming into patterned polymer molds creating Meissner-Effect Transition-Edge-Sensor (ME-TES) devices1 and found multiple variables changed between the test and device samples making it difficult to target a desired tin metal thickness. These variables were analyzed and distilled down to two: (I) area and (II) the measured voltage at constant current 30 seconds after plating started. In addition to the underlying analysis, a Graphical User Interface (GUI)2 was developed in R to quickly calculate and predict in-situ tin electroplating rates regardless of uncontrolled variables in the sample.
ME-TES devices required a final tin plating feature height of 8-12 microns for 50-micron diameter discs coupled with patterned niobium electrical traces3. During chronopotentiometry experiments, two samples were plated for 1 minute and 18 minutes, respectively – both achieving the target height of 10 microns. This extreme variability in rate caused the height to undershoot or overshoot within just a few minutes of plating. Many variables were collected into a database and analyzed including wafer design layout, electrochemical methods, chemistry make-up, electrochemical setup, the use of a reference electrode, current density, voltage, mounting technique, height, device size, and surface roughness across the substrate. The resulting GUI resulted in fewer necessary test samples, reduced processing time, and improved accuracy in plating thickness.
(1) Finnegan, P. Packaging & Solid-state Materials and Fabrication Processes. In Packaging & Solid-state Materials and Fabrication Processes, June 2023.
(2) St. John, C. “R Shiny User Interface for Electrochemical Development.” In CoDA, Santa Fe NM, March 2023.
(3) Arrington, C.L. Optimizing Tin Electroplating for MEMS Meissner-Effect Transition Edge Sensors. In 243rd ECS Meeting with SOFC-XVIII, Boston, MA, 2023.
Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
SAND2022-10287A
The Use of Model-Predicted Internal States from Locally Linear, Gain-Scheduled, Reduced-Order Physics-Based Models in Model-Predictive Control Strategies
Authors: Evans Tyler A. P. , Vincent Tyrone , Kee Robert J. 
Published: September 2023

The objective of the research is to develop reduced-order state-space models that can predict internal states that are not directly observable with existing sensors. In doing so, battery management systems (BMS) can make real-time control decisions based upon sensor inferences. For example, estimating local electrode Li and electrolyte Li-ion concentrations or local electrostatic potential differences between electrode and electrolyte phases offer valuable knowledge in predicting behaviors such as Li plating, dendrite growth, or electrode fracture. Such indirect predictive capabilities, for example, can enable fast-charging protocols that avoid damage mechanisms.
This talk explores the mathematical feasibility of estimating local Li concentrations and electrostatic potentials using observed current-voltage sequences. The approach for determining internal battery states begins with an estimator that uses observed input-output data from a physics-based reduced-order state-space model. If the model satisfies the property of observability, a sufficiently long window of input-output data can predict unique internal-state trajectories that are compatible with the observable data. The research considers understanding the effects of sampling time and state of charge on internal-state observability.
Efficient estimation algorithms must incorporate battery physics and chemistry yet be sufficiently simple as to run in real-time on a battery management system (BMS). The present research first develops a physics-based pseudo-two-dimensional (P2D) model, which describes battery thermodynamics, transport, and kinetics, results in a system of nonlinear, coupled, differential-algebraic equations that can be solved computationally, but too slow for real-time implementation. The P2D model is then reduced to a gain-scheduled, locally linear, state-space model that can be run in real-time. Both models are implemented within the MATLAB/Simulink framework that facilitates development and evaluation of control strategies.
On the Unique Parameterization of a Single Particle Model Using Electrochemical Impedance Spectroscopy (EIS) and Hybrid Power Pulse Characterization (HPPC) Tests
Authors: Olide Everardo , Pannala Sravan , Tran Vivian , Movavahedi Hamidreza , Siegel Jason , Stefanopoulou Anna 
Published: September 2023

Prior work has shown that equivalent circuit models (ECM) fitted to match frequency data (EIS) do not necessarily match time data (HPPC). Depending on the fitting methodology, the quality of data, and the ECM order, the parameter mismatch is manageable and can be justified by the simplification of the physical phenomena in an ECM. An investigation of the fitting methods and the sensitivity to noise will be shown using synthetic data of an OCV-R2RC model.
The investigation will be expanded in fitting EIS and HPPC synthetic data from an SPMe and an SPM model. The sensitivity of the SPM parameters to the measured EIS and HPPC will clarify long-standing confusion of the uniqueness and even the magnitude of physically-relevant parameters in SPM models.
The learnings from the synthetic data will then be demonstrated with EIS and HPPC data obtained from several Graphite/NMC battery pouch cells at different states of charge (SOC) and states of health (SOH) after cycling at a variety of accelerated aging protocols that include c-rates, temperatures, SOC swing, and externally applied pressure.
Figure: Voltage Profile of Characterization Protocols(HPPC and EIS) for a single cell at different SOC.
Bottom right, Nyquist plot corresponding to first characterization scheme of a fresh and aged cell.

Figure 1
(Digital Presentation) Temporal Evolution of Kinetic Energy in Two Different Plasma Systems
Authors: Ostovarpour Farzaneh , Abbassi Shanbehbazari Mohammad Sadegh , Hajisharifi Kamal , Mehdian Hassan 
Published: September 2023

During the last few decades, simulation has emerged as a powerful new tool. Numerous branches of science and engineering have been revolutionized by computer simulations. Through the use of particle-in-cell simulation systems comprising many species, including electrons, ions, neutrals, molecules, dust particles, etc., are studied by the plasma research community. The current work attempts to investigate the kinetic energy behavior in two various electrostatic plasma systems using a 1D PIC simulation. Two homogeneous and non-collisional systems are considered. In the first system, just cold electrons are present. Along with cold electrons, the second system also contains electrons with a thermal velocity of 0.4 ωpe/K . In other words, the second system includes two kinds of electrons (cold and warm electrons). The same initial perturbation is applied to both systems at t=0 s. Ions are assumed to be infinitely massive and only serve as a neutralizing background for electrons.
Diagrams of I, II, and III represent the kinetic energy of cold electrons, kinetic energy of electrons with a thermal velocity of 0.4 ωpe/K, and the total kinetic energy, respectively. It is worth noting that kinetic energy of cold electrons is equal to total kinetic energy in the first system. From the comparison of Figs. 1(a) and (b), it can be understood that when there is a cold type of electron in the system, the particles receive energy from the wave, but in a system that contains two types of cold and warms electrons, only warm electrons will receive energy from the wave and the kinetic energy of these warm particles increases. In contrast to Fig. 1(a), cold electrons give energy to the system. Additionally, in both cases, the total kinetic energy increases and as time goes on, the system approximates to stability and fluctuations decline. This result indicates that not only all the initial energy is not absorbed by the environment, but also a portion of it will remain in the medium.

Figure 1