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

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