The optimization of the electrode manufacturing process is important for
upscaling the application of Lithium Ion Batteries (LIBs) to cater for growing
energy demand. In particular, LIB manufacturing is very 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
application conditions by proposing a powerful data-driven approach supported
by a deterministic machine learning (ML)-assisted pipeline for bi-objective
optimization of the electrochemical performance. This ML pipeline allows the
inverse design of the process parameters to adopt in order to manufacture
electrodes for energy or power applications. The latter 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 pseudo-two-dimensional 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
ML model to implement fast bi-objective 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.

本研究通过提出一种动力学机器学习辅助流程的数据驱动方法，优化了生产参数的逆向设计，从而实现了可用于能量或功率应用的电极的制造。