Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling

Adarsh Dave, Jared Mitchell, Sven Burke, Hongyi Lin, Jay Whitacre () and Venkatasubramanian Viswanathan ()
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Adarsh Dave: Carnegie Mellon University
Jared Mitchell: Carnegie Mellon University
Sven Burke: Carnegie Mellon University
Hongyi Lin: Carnegie Mellon University
Jay Whitacre: Carnegie Mellon University
Venkatasubramanian Viswanathan: Carnegie Mellon University

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Developing high-energy and efficient battery technologies is a crucial aspect of advancing the electrification of transportation and aviation. However, battery innovations can take years to deliver. In the case of non-aqueous battery electrolyte solutions, the many design variables in selecting multiple solvents, salts and their relative ratios make electrolyte optimization time-consuming and laborious. To overcome these issues, we propose in this work an experimental design that couples robotics (a custom-built automated experiment named "Clio”) to machine-learning (a Bayesian optimization-based experiment planner named "Dragonfly”). An autonomous optimization of the electrolyte conductivity over a single-salt and ternary solvent design space identifies six fast-charging non-aqueous electrolyte solutions in two work-days and forty-two experiments. This result represents a six-fold time acceleration compared to a random search performed by the same automated experiment. To validate the practical use of these electrolytes, we tested them in a 220 mAh graphite∣∣LiNi0.5Mn0.3Co0.2O2 pouch cell configuration. All the pouch cells containing the robot-developed electrolytes demonstrate improved fast-charging capability against a baseline experiment that uses a non-aqueous electrolyte solution selected a priori from the design space.

Date: 2022
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DOI: 10.1038/s41467-022-32938-1

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