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

TL;DR

This paper presents a machine learning-based pipeline for optimizing electrode manufacturing parameters to produce high-performance lithium-ion battery electrodes tailored for energy or power applications, enhancing battery efficiency.

Contribution

It introduces a deterministic ML pipeline for inverse design of electrode manufacturing parameters, enabling targeted optimization for specific battery performance goals.

Findings

Optimal electrodes have high active material content and intermediate slurry solid content and calendering degree.

The ML pipeline enables fast bi-objective optimization of electrode properties.

The approach improves electrode design for energy and power applications.

Abstract

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.