ICME 2023: New & Emerging
Program Organizers: Charles Ward, AFRL/RXM; Heather Murdoch, U.S. Army Research Laboratory

Wednesday 9:40 AM
May 24, 2023
Room: Caribbean IV
Location: Caribe Royale

Session Chair: George Spanos, TMS

9:40 AM
A Quantitative Phase Field Tool for Lithium-metal Battery Design: Jin Zhang¹; Alexander Chadwick¹; Peter Voorhees¹; ¹Northwestern University
    Dendrite formation remains a critical safety issue for the success of next-generation lithium-metal-anode batteries. Quantitative computational tools can enable the design of new materials and structures to suppress dendrites. We employ a fully variational and thermodynamically-consistent phase field model to quantitatively model dendrite growth on lithium metal anodes during charging. The model can consider general nonlinear reaction kinetics and correctly captures the capillary effects. We use materials parameters calculated from first-principles simulations, including the temperature-dependent anisotropic interfacial energy and the temperature- and concentration-dependent diffusivity. Moreover, we developed a method to increase the computational grid spacing and time step by two to three orders of magnitude to enable efficient and quantitative simulations with realistic system properties. We study the effects of various factors such as applied voltage and current, nucleation density, and electrode structure and propose strategies to suppress dendrite formation.

10:00 AM
Influence Of Interfacial Voids And Grain Boundary Conductivity On Depletion Kinetics Of Sodium Metal Anodes In All-solid-state Batteries: Sourav Chatterjee¹; Michael Tonks¹; Will Gardner²; Marina Sessim²; ¹University of Florida; ²QuantumScape
    An electrochemical micro-scale phase-field model is developed to simulate the stripping and plating kinetics of a Na-metal anode in all-solid-state Na-ion batteries. In this model, in addition to the metallic anode and ceramic separator regions, a highly conductive but Na-poor region representing the free space is assumed to simulate the shrinking of the Na-anode during electro-stripping and its growth during plating. For a perfect anode/separator interface, we find that the growth and depletion kinetics are linear. However, the presence of multiple interfacial voids at the anode/separator interface causes deviation from this linear behaviour. Finally, we show that depletion kinetics is also proportional to the ratio of the grain boundary conductivity to that of the grain conductivity (σ_gb/σ_g) in the separator phase, either with or without interfacial voids.

10:20 AM
Machine Learning Driven Prediction of Capacity Fade in Lithium-ion Batteries: Abhinand Ayyaswamy¹; Bairav Sabarish Vishnugopi¹; Partha P Mukherjee¹; ¹Purdue University
    Lithium-ion batteries (LIB) continue to permeate numerous sectors including electric vehicles, medical devices, and portable electronics due to their high energy densities. However, predicting the cycle life of LIBs remain challenging due to various factors including operational variability and fast charging requirements. Early cycle life prediction helps lower the cost of batteries through optimization of manufacturing processes, and thereby enhances cell life. In this context, machine learning techniques that synergistically combine physics-based data and experimental measurements hold the potential to detect underlying trends in capacity degradation. While most data-driven approaches require the utilization of high-rate tests to induce accelerated degradation, low currents pose challenges to cycle life prediction due to slower degradation onsets and longer feedback times. In this work, we develop a machine learning model to deconvolute the degradation response by feeding memory information from electrochemical signatures, enabling accurate prediction of cycle life and capacity loss in LIBs.

10:40 AM
Molecular Modelling of Locally Concentrated Electrolytes for Lithium-ion Batteries: Mahesh Mynam¹; Saurav Chandel¹; Beena Rai¹; ¹TCS Research, Tata Consultancy Services Ltd.
    Electrolyte plays vital role in success of a battery technology. Current generation lithium-ion batteries (LIBs) use 1M LiPF6 salt in mixture of ethylene carbonate (EC) and dimethyl carbonate as electrolytic solution. To improve thermal stability, recently concentrated electrolytes (of concentration higher than 1M) are proposed. Formation of anion rich solid-electrolyte interface layer helps battery gain better stability. However, concentrated electrolytes offer significantly lower ionic conductivity to impact power performance. Locally concentrated electrolyte (LCE) formed by using a diluent that does not solvate lithium ions and of low viscosity is shown to be promising. LCEs enable one to reap benefits of both the worlds. However, molecular mechanism responsible for improved electrolytic properties of LCEs are not well understood. We study EC + Bis(2,2,2-trifluoroethyl) ether based LCE within the molecular dynamics method, and present simulation methodology, various properties of LCEs and the insights gained from the study that help one to design novel electrolytes.

11:00 AM
Multiscale Study of the Influence of Electrolyte on the System Level Performance of Na Ion Batteries: Saurav Chandel¹; Vamsi Krishna Garapati¹; Naga Neehar Dingari¹; Mahesh Mynam¹; Beena Rai¹; ¹Tata Consultancy Services (TCS) Research
    Battery electrolytes can have a significant influence on various macroscopic performance aspects of the battery such as internal heating, charging time and capacity fade. Since the molecular level electrolyte transport properties directly influence the system level performance, a multiscale study is necessary for optimal electrolyte selection. In this work, we conduct a multiscale comparative study of two commonly used Na ion battery electrolytes NaClO4 and NaPF6 in the non-aqueous solvent EC:DMC. Using a system level physics based model, combined with lower length-scale simulations, we compare system level performance metrics such as charging time and internal heat generation for both these electrolyte salts. Studies such as these can be very effective in identifying optimal electrolytes for next generation Na ion batteries.