Multiphysics Simulation of Battery Electrode Drying

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Scaling Li-ion battery production to meet the demands of increasingly electrified grids and transportation systems will require higher throughput at all levels of the battery cell production process. One of the more energy intensive battery manufacturing steps is the necessary drying of solvents which persist in the electrodes after the initial slurry coating of their current collectors. It is critical to both optimize this drying process to minimize the time and resources required, as well as to understand the effect that the drying has on the electrode microstructure. As it is both expensive and time-intensive to iteratively test different temperatures on multiple initial electrode microstructures, it is necessary to have reliable, high-fidelity simulations which can model the evaporation process. Furthermore, it is easier to control microstructural features such as porosity, and particle size in a simulated environment, Indeed, by simulating evaporation, the design-of-experiments space can be minimized, and process conditions can be established to accelerate the production of high quality, lower cost electrodes. To that end, we develop a multiphysics model of solvent evaporation using Sandia’s in-house finite element software. By coupling the solvent equations of motion to species conservation equations for the carbon binder material and energy flux driven by evaporative cooling, we demonstrate good agreement between simulation and simple evaporation experiments. This model tackles several difficult aspects of evaporation, namely the evolution of free surfaces and gas-liquid phase-changes. We additionally extend our model to account for mesoscale features (i.e. particle size, porosity, binder distribution) and determine how different temperature profiles and microstructures affect the heating requirements for the electrode. These extensions present a key advantage over conventional 1D models, as these features can have a significant effect on the evaporation process and the final distribution of conductive additive and binder. We leverage this advantage to examine how several design parameters such as porosity, particle size distribution, and heating rate affect the resulting electrode structure and binder distribution. These results demonstrate a pathway to optimizing the electrode drying process and promise to inform the development of improved multiphysics models in the future.SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.