Optimizing Discharge Capacity of Li-O-2 Batteries by Design of Air-Electrode Porous Structure: Multifidelity Modeling and Optimization

We develop a new mathematical framework to study the optimal design of air electrode microstructures for lithium-oxygen (Li-O-2) batteries. The design parameters to characterize an air-electrode microstructure include the porosity, surface-to-volume ratio, and parameters associated with the pore-size distribution. A surrogate model (also known as response surface) for discharge capacity is first constructed as a function of these design parameters. In particular, a Gaussian process regression method, co-kriging, is employed due to its accuracy and efficiency in predicting high-dimensional responses from a combination of multifidelity data. Specifically, a small sample of data from high-fidelity simulations are combined with a large sample of data obtained from computationally efficient low-fidelity simulations. The high-fidelity simulation is based on a multiscale modeling approach that couples the microscale (pore-scale) and macroscale (device-scale) models [Bao et al., J. Phys. Chem. C, 119, 14851 (2015)], while the low-fidelity simulation is based on an empirical macroscale model. The constructed response surface provides quantitative understanding and prediction about how air electrode microstructures affect the discharge capacity of Li-O2 batteries. The succeeding sensitivity analysis via Sobol indexes and optimization via genetic algorithm offer reliable guidance on the optimal design of air electrode microstructures. The proposed mathematical framework can be generalized to investigate other new energy storage techniques and materials. (C) The Author(s) 2017. Published by ECS. All rights reserved.