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.

Advances of LiCoO2 in Cathode of Aqueous Lithium‐Ion Batteries

“…The introduction of machine learning methods can effectively overcome these limitations and provide a faster, efficient and accurate optimization strategy. [ 208,209 ]…”

Machine Learning in Lithium‐Ion Battery Cell Production: A Comprehensive Mapping Study

“…Analysis of the slurry formulation and electrode manufacturing parameters and their influence on the cell performance with a focus on the graphite-based anode [23] Faraji Niri et al, 2021 Investigating the effects of coating control parameters on the electrode properties and cell characteristics using different ML models [26] Faraji Niri et al, 2022a Quantifying the effect of the N : P ratio on energy capacity and gravimetric capacity at different C-rates [35] Faraji Niri et al, 2022b Analysis of the calendering control variables on the cell impedance and capacity fading using explainable machine learning [36] Faraji Niri et al, 2022c Quantification of the contribution of coating control parameters to predict electrode and cell properties [24] Faraji Niri et al, 2022d Analysis of the slurry properties in combination with different coating parameters and their impact on final cell characteristics using explainable machine learning To a certain degree, the identified studies coincide with the conventional use cases of the ML in the production domain, [61] such as quality control. Given the complexity of battery production, the majority of studies focus on establishing a foundation for process understanding by analyzing the interdependencies between process parameters and (intermediate) product properties.…”

A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation

“…According to research, even capturing data points from a mere 18 experiments in battery cell manufacturing can take up to six months in a pilot-line manufacturing assembly [52]. These experiments can involve various stages of testing and trials, each generating its own unique data which must be processed and interpreted.…”