Extended Data Fig. 8: Closed-loop performance under resource constraints.
From: Closed-loop optimization of fast-charging protocols for batteries with machine learning
Comparison of the closed loop with and without the Bayesian optimization algorithm (that is, with and without the explore/exploit component) as a function of number of channels and number of rounds in the 224-protocol space, using the first-principles simulator as the ground-truth source for cycle lives. Early prediction is not included. Each point represents the mean of 100 simulations; error bars represent the 95% confidence intervals (n = 100). Early prediction is not incorporated into these simulations. The complete closed loop (that is, with Bayesian optimization) consistently outperforms the closed loop without Bayesian optimization. Bayesian optimization offers the largest advantage when the number of channels is low relative to the number of protocols.
