  Bayesian Optimization is a useful tool for experiment design. Unfortunately,
the classical, sequential setting of Bayesian Optimization does not translate
well into laboratory experiments, for instance battery design, where
measurements may come from different sources and their evaluations may require
significant waiting times. Multi-fidelity Bayesian Optimization addresses the
setting with measurements from different sources. Asynchronous batch Bayesian
Optimization provides a framework to select new experiments before the results
of the prior experiments are revealed. This paper proposes an algorithm
combining multi-fidelity and asynchronous batch methods. We empirically study
the algorithm behavior, and show it can outperform single-fidelity batch
methods and multi-fidelity sequential methods. As an application, we consider
designing electrode materials for optimal performance in pouch cells using
experiments with coin cells to approximate battery performance.
