A03 - Li-ion Modeling and Simulations

Sunday, 9 October 2022: 08:00-12:00
Galleria 1 (The Hilton Atlanta)
08:20
Modeling Onset of Lithium Plating on Porous Graphite Anode in Fast Charging
H. Lian and M. Bazant (Massachusetts Institute of Technology)
09:00
Physics-Informed Neural Network Modeling of Li-Ion Batteries
M. Hassanaly, P. J. Weddle, K. Smith (National Renewable Energy Laboratory), S. De, A. Doostan (University of Colorado Boulder), and R. King (National Renewable Energy Laboratory)
09:20
Lithium Ion Battery Electrode Manufacturing Model Accounting for 3D Realistic Shapes of Active Material Particles: Exploring the Effect of Processing Parameters on Electrode Heterogeneity
J. Xu, A. C. Ngandjong, A. Demortiere, and A. A. Franco (Laboratoire de Réactivité et Chimie des Solides (LRCS), Réseau sur le Stockage Electrochimique de l’Energie (RS2E))
09:40
coffee break
10:00
Data Driven Model for Lithium-Ion Battery Electrode Microstructure Property Estimation
V. Kabra (Purdue University), I. Kamboj, V. Augustyn (North Carolina State University), and P. P. Mukherjee (Purdue University)
10:20
A Thermal Tanks-in-Series Model for Capacity Fade Validation Studies in Lithium-Ion Batteries
R. S. Thiagarajan, A. Subramaniam, S. Kolluri, M. Uppaluri (University of Texas at Austin), Y. Preger (Sandia National Laboratories), and V. R. Subramanian (University of Texas at Austin)
10:40
An Efficient Electrochemical State of Health Model for Lithium-Ion Batteries
J. H. Lim (LG Energy Solution. Ltd, University of Texas at Austin), M. Uppaluri, A. Subramaniam, and V. R. Subramanian (University of Texas at Austin)
11:00
Enhancing Lithium-Ion Battery Aging Simulations By Coupling a High-Resolution, 3D, Grain-Scale Electromechanical Model to a Single Particle Model
J. M. Allen, P. J. Weddle, F. L. E. Usseglio-Viretta, A. Verma, A. M. Colclasure, and K. Smith (National Renewable Energy Laboratory)
11:20
Impact of Data Window on Prediction of Battery Aging and Swelling
S. Pannala, J. Siegel, and A. Stefanopoulou (University of Michigan)
11:40
A Battery Aging Mode Identification Framework: Encompassing Multiple Cell Chemistries, Electrode Designs, and Use Conditions
B. R. Chen, C. M. Walker, M. R. Kunz, T. R. Tanim, and E. J. Dufek (Idaho National Laboratory)