Toward high-performance energy and power battery cells with machine learning-based optimization of electrode manufacturing

The optimization of the electrode manufacturing process is important for upscaling the application of Lithium-Ion Batteries (LIBs) to cater for growing energy demand. LIB manufacturing is important to be optimized because it determines the practical performance of the cells when the latter are being used in applications such as electric vehicles. In this study, we tackled the issue of high-performance electrodes for desired battery applications by proposing a data-driven approach supported by a deterministic machine learning (ML)-assisted pipeline for bi-objective optimization of the electrochemical performances. This pipeline allows the inverse design of the process parameters to adopt to manufacture electrodes for energy or power applications. This work is an analogy to our previous work that supported the optimization of the electrode microstructures for kinetic, ionic, and electronic transport properties improvement. An electrochemical model is fed with the electrode properties characterizing the electrode microstructures generated by manufacturing simulations and used to simulate the electrochemical performances. Secondly, the resulting dataset was used to train a deterministic model to implement fast optimizations to identify optimal electrodes. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes. [Display omitted] •Synthetic dataset generated by low-discrepancy sequences as inputs of the physics-based models.•Fast deterministic-assisted bi-objective optimization of the energy density and power density to evaluate the best set of manufacturing parameters.•Comparison of different optimization problems based on the battery application.