The Impact of Calendering Process Variables on the Impedance and Capacity Fade of Lithium-Ion Cells: An Explainable Machine Learning Approach

Abstract

Determining the calendering process variables during electrode manufacturing is critical to guarantee lithium-ion battery cell's performance; however, it is challenging due to the strong and unknown interdependencies. Herein, explainable machine learning (ML) techniques are used to uncover the impact of calendering process variables on the cells’ performance in terms of impedance and capacity fade. The study is based on experimental data from pilot-scale manufacturing line considering critical factors of calendering gap, calendering temperature, electrodes’ coating weight, and target porosity. It offers a hierarchical methodology based on designed experiment, data-oriented modeling via ML techniques, and model explainability technologies. The study reveals the relative importance of calendering control variables for cell impedance and capacity fade and quantifies the contribution of factors and the predictability of the cell's characteristics. The results show that the calendering factors affect cell's performance differently and are dominated by electrode features.