Design of Experiments for Optimizing the Calendering Process in Li-Ion Battery Manufacturing

Hidalgo, Marc Francis V.; Apachitei, Geanina; Dogaru, Daniela; Niri, Mona Faraji; Lain, Michael; Copley, Mark; Marco, James

doi:10.2139/ssrn.4305950

Cited by 5 publications

(4 citation statements)

References 48 publications

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“…Figure 11 shows the ALE plots, focusing on the most influential parameters impacting the discharge capacity at a high C‐rate and the drying temperature of the second dryer as an example. A high mass loading resulted in a pronounced loss of capacity at a high C‐rate (c f. 11 (a)), which is in line with the findings in the literature [43] . In the case of the porosity, an inverse trend was observed, with the lowest porosity corresponding to the most significant capacity loss.…”

Section: Prediction Of Mechanical and Electrochemical Properties Usin...supporting

confidence: 89%

Interdependencies in Electrode Manufacturing: A Comprehensive Study Based on Design Augmentation and Explainable Machine Learning

Haghi,

Chen,

Molzberger

et al. 2024

Batteries & Supercaps

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Electrode manufacturing, as the core of battery cell production, is a complex process chain with a large number of interrelated parameters. An in‐depth understanding of the processes, their relevant parameters, and the resulting effects on intermediate and final product properties can accelerate the transition toward quality‐oriented, efficient battery cell production. Given the complexity of the process chain, data‐driven models have emerged as promising solutions for analyzing the existing interdependencies. The accuracy and effectiveness of these models significantly depend on the quality and comprehensiveness of the underlying data. With a low‐quality dataset, there is an increased risk of drawing inaccurate conclusions or generating misleading results. This article aimed to demonstrate a use case for the evaluation and enhancement of historical datasets to provide a statistically robust foundation for the development of machine learning models. The study was based on pilot‐scale anode manufacturing and covered variations in the coating, drying, and calendering processes. The key intermediate product and process parameters were used to predict two primary target variables: adhesion strength and discharge capacity at different C‑rates. To gain a better understanding of the analyzed interdependencies, explainable machine learning methods were adopted.

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Section: Prediction Of Mechanical and Electrochemical Properties Usin...supporting

confidence: 89%

Interdependencies in Electrode Manufacturing: A Comprehensive Study Based on Design Augmentation and Explainable Machine Learning

Haghi,

Chen,

Molzberger

et al. 2024

Batteries & Supercaps

View full text Add to dashboard Cite

show abstract

“…A previous DoE had investigated different calendering conditions on a graphite anode and an NMC-622 cathode. 45 The formulations for these two coatings are summarised in Table S2, † as anode #1 and cathode #1. The graphite particles had a D 50 = 15.6 mm and were irregularly shaped i.e.…”

Section: Experimental Measurementsmentioning

confidence: 99%

Measurement of anisotropic volumetric resistivity in lithium ion electrodes

Lain,

Apachitei,

Dogaru

et al. 2023

RSC Adv.

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“…The systematic and model‐based manufacturing for rechargeable energy storage devices and particularly lithium‐ion batteries has been a new topic to the field. The data driven models for capturing the dependency of the mixing, [7] coating process, [8,9] calendering, [10–12] drying [13,14] and electrolyte injection [15] are shown to perform well in laboratory and pilot scale manufacturing. These studies try to quantify the effect of some of the critical decision factors, such as coating speed, coating gap, calendering pressure, calendering roll temperature, drying air speed and temperature, on the responses (such as coating porosity, coating thickness) at the end of each step and investigate that further towards the cell characterisations 9such as capacity, internal resistance).…”

Section: Introductionmentioning

confidence: 99%

Impact of Formulation and Slurry Properties on Lithium‐ion Electrode Manufacturing

Reynolds,

Faraji Niri,

Hidalgo

et al. 2023

Batteries & Supercaps

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The characteristics and performance of lithium‐ion batteries typically rely on the precise combination of materials in their component electrodes. Understanding the impact of this formulation and the interdependencies between each component is critical in optimising cell performance. Such optimisation is difficult as the cost and effort for the myriad of possible combinations is too high. This problem is addressed by combining a design of experiments (DoE) and advanced statistical machine learning approach with comprehensive experimental characterisation of electrode slurries and coatings. An industry relevant graphite anode system is used, and with the aid of DoE, less than 30 experiments are defined to map impact of different weight fractions of active material (80–96 wt%), conductive additive (Carbon Black at 1–10 wt%) and a two‐component binder system (Carboxymethyl Cellulose (CMC) at 1–3 wt% and Styrene Butadiene Rubber (SBR), at 1–7 wt%). Using Explainable Machine Learning (XML) methods, correlations between the formulation, slurry weight percentage (30–50 wt% in water) and coating speed (1–15 m/min) are quantified. Slurry viscosity, while known to depend on the CMC concentration, is also heavily influenced by carbon black and SBR when at high concentration, as is common in research. Viscosity increasing components also improve adhesion, by improving dispersion and hindering binder migration. Conductivity of the coating on current collector is sensitive to the current collector‐coating interface, which makes it a highly useful probe. Improvements in cell capacity are observed with higher viscosity formulations (High weight percentage, CMC content), attributed to reduction in migration and slumping of the slurry on the current collector. SBR had a negative impact at any concentration due to its insulating nature, and carbon black reduces gravimetric capacity when included at high concentrations. The insights from this study facilitate the formulation optimisation of electrodes providing improved slurry design rules for future high performance electrode manufacturing.

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Design of Experiments for Optimizing the Calendering Process in Li-Ion Battery Manufacturing

Cited by 5 publications

References 48 publications

Interdependencies in Electrode Manufacturing: A Comprehensive Study Based on Design Augmentation and Explainable Machine Learning

Interdependencies in Electrode Manufacturing: A Comprehensive Study Based on Design Augmentation and Explainable Machine Learning

Measurement of anisotropic volumetric resistivity in lithium ion electrodes

Impact of Formulation and Slurry Properties on Lithium‐ion Electrode Manufacturing

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