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The optimization of the electrode manufacturing process is important forupscaling the application of Lithium Ion Batteries (LIBs) to cater for growingenergy demand. In particular, LIB manufacturing is very important to beoptimized because it determines the practical performance of the cells when thelatter are being used in applications such as electric vehicles. In this study,we tackled the issue of high-performance electrodes for desired batteryapplication conditions by proposing a powerful data-driven approach supportedby a deterministic machine learning (ML)-assisted pipeline for bi-objectiveoptimization of the electrochemical performance. This ML pipeline allows theinverse design of the process parameters to adopt in order to manufactureelectrodes for energy or power applications. The latter work is an analogy toour previous work that supported the optimization of the electrodemicrostructures for kinetic, ionic, and electronic transport propertiesimprovement. An electrochemical pseudo-two-dimensional model is fed with theelectrode properties characterizing the electrode microstructures generated bymanufacturing simulations and used to simulate the electrochemicalperformances. Secondly, the resulting dataset was used to train a deterministicML model to implement fast bi-objective optimizations to identify optimalelectrodes. Our results suggested a high amount of active material, combinedwith intermediate values of solid content in the slurry and calendering degree,to achieve the optimal electrodes.

Toward High-Performance Energy and Power Battery Cells with Machine Learning-based Optimization of Electrode Manufacturing