Polymer coatings and high mechanical pressure are promising solutions for improving interfacial contact in all-solid-state lithium metal batteries. However, design guidelines for polymer type, thickness, and stack pressure are still missing. In this study, we present a model for mechanics at the interface of polymer-coated solid-state electrolytes in contact with a lithium metal anode, considering lithium creep, polymer viscoelasticity, and pressure-driven electrochemistry. We cover various common polymer coatings, eventually highlighting the dependence of interfacial resistance on stack pressure and coating thickness. A machine learning algorithm with high-throughput calculations is used to optimize the combination of pressure and coating thicknesses. Numerical results are in good agreement with existing experimental evidence. A transition map is derived, which may serve as design guideline in predicting the values of current density, stack pressure, and polymeric thickness able to ensure a steady performance over time.
Pressure and polymer selections for solid-state batteries investigated with high-throughput simulations / Zhang, X.; Luo, C.; Menga, N.; Zhang, H.; Li, Y.; Zhu, S. -P.. - In: CELL REPORTS PHYSICAL SCIENCE. - ISSN 2666-3864. - 4:3(2023), p. 101328.101328. [10.1016/j.xcrp.2023.101328]
Pressure and polymer selections for solid-state batteries investigated with high-throughput simulations
Menga N.;Li Y.;
2023-01-01
Abstract
Polymer coatings and high mechanical pressure are promising solutions for improving interfacial contact in all-solid-state lithium metal batteries. However, design guidelines for polymer type, thickness, and stack pressure are still missing. In this study, we present a model for mechanics at the interface of polymer-coated solid-state electrolytes in contact with a lithium metal anode, considering lithium creep, polymer viscoelasticity, and pressure-driven electrochemistry. We cover various common polymer coatings, eventually highlighting the dependence of interfacial resistance on stack pressure and coating thickness. A machine learning algorithm with high-throughput calculations is used to optimize the combination of pressure and coating thicknesses. Numerical results are in good agreement with existing experimental evidence. A transition map is derived, which may serve as design guideline in predicting the values of current density, stack pressure, and polymeric thickness able to ensure a steady performance over time.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.