Formulation and manufacturing optimization of lithium-ion graphite-based electrodes via machine learning

Drakopoulos, Stavros X.; Gholamipour‐Shirazi, Azarmidokht; Macdonald, Paul M.; Parini, Robert; Reynolds, C. D.; Burnett, David L.; Pye, Ben; O’Regan, Kieran; Wang, Guanmei; Whitehead, Thomas M.; Conduit, Gareth; Cazacu, A.; Kendrick, Emma

doi:10.1016/j.xcrp.2021.100683

Cited by 35 publications

(41 citation statements)

References 53 publications

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“…Consequently, there is only a limited number of data-driven [3][4][5] studies in battery research on in-house assembled 6 i.e. non-commercially acquired cells.…”

Section: Introductionmentioning

confidence: 99%

“…Overall, there is very limited publicly available data on cycling behavior of cells, let alone their manufacturing or formation cycle. Typically, either large batches of commercial cells are tested that lack data on formation or data is published 5,14 on datasets containing less than 5-8 cells which are made manually 6 . A short literature review yields that some emblematic papers in the field of data driven battery research consist of 48 manually assembled cells for coating optimization 6 , and 45 commercial cells for early lifetime prediction 14 .…”

Section: Introductionmentioning

confidence: 99%

“…Typically, either large batches of commercial cells are tested that lack data on formation or data is published 5,14 on datasets containing less than 5-8 cells which are made manually 6 . A short literature review yields that some emblematic papers in the field of data driven battery research consist of 48 manually assembled cells for coating optimization 6 , and 45 commercial cells for early lifetime prediction 14 . The entire field seems to be only having publicly available data on less than 500 cells in total 5 .…”

Section: Introductionmentioning

confidence: 99%

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Robotic cell assembly to accelerate battery research

Zhang

Fischer

Sanin

et al. 2022

Preprint

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Manual cell assembly confounds with research digitalization and reproducibility. Both are however needed for data-driven optimization of cell chemistries and charging protocols. Therefore, we present herein an automatic battery assembly system (AutoBASS) that is capable of assembling batches of up to 64 CR2023 cells. AutoBASS allows us to acquire large datasets on in-house developed chemistries and is herein demonstrated with NMC811 and Si@Graphite electrodes with a focus on formation and manufacturing data. The large dataset enables us to gain insights into the formation process through dQ/dV analysis and assess cell to cell variability. Exact robotic electrode placement provides a baseline for laboratory-scale manufacturing and reproducibility towards the accelerated translation of findings from the laboratory to the pilot plant scale.

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“…Consequently, there is only a limited number of data-driven [3][4][5] studies in battery research on in-house assembled 6 i.e. non-commercially acquired cells.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robotic cell assembly to accelerate battery research

Zhang

Fischer

Sanin

et al. 2022

Preprint

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“…The force was recorded in a region where the axial force measurement was stable and peeling was visually consistent. 34 This was repeated on six samples for each coating to take an average and normalized by the width of the tape to give the force per unit length required to peel the coating from the substrate.…”

Section: Adhesion Testingmentioning

confidence: 99%

Applications of advanced metrology for understanding the effects of drying temperature in the lithium-ion battery electrode manufacturing process

et al. 2022

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“…several hours are needed for simulating an electrode slurry), hindering their usage to perform fast (in few seconds) electrode optimization. Such a fast optimization capabilities will be needed in digital twins collecting data through sensors, and giving instructions to the manufacturing machines through actuators for on the fly and autonomous optimization [12,23,24,25]. In the ARTISTIC project, we have demonstrated that ML can also constitute a powerful tool to unravel correlations between manufacturing process parameters and electrode properties [26,27,28,29].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations

Duquesnoy¹,

Liu²,

Dominguez³

et al. 2022

Preprint

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The optimization of the electrodes manufacturing process constitutes one of the most critical steps to ensure high-quality Lithium-Ion Battery (LIB) cells, in particular for automotive applications. Because LIB electrode manufacturing is a complex process involving multiple steps and interdependent parameters, we have shown in our previous works that 3D-resolved physics-based models constitute very useful tools to provide insights about the impact of the manufacturing process parameters on the textural and performance properties of the electrodes. However, their high-throughput application for electrode properties optimization and inverse design of manufacturing parameters is limited due to the high computational cost associated with this kind of model. In this work, we tackle this issue by proposing an innovative approach, supported by a deterministic machine learning (ML)-assisted pipeline for multi-objective optimization of LIB electrode properties and inverse design of its manufacturing process. Firstly, the pipeline generates a synthetic dataset from physics-based simulations with low discrepancy sequences, that allow to sufficiently represent the manufacturing parameters space. Secondly, the generated dataset is used to train deterministic ML models for the implementation of a fast multi-objective optimization, to identify an optimal electrode and the manufacturing parameters to adopt in order to fabricate it. Lastly, this electrode was successfully fabricated experimentally, proving that our modeling pipeline prediction is physical-relevant. Here, we demonstrate our pipeline for the simultaneous minimization of the electrode tortuosity factor and maximization of the effective electronic conductivity, the active surface area, and the density,

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Formulation and manufacturing optimization of lithium-ion graphite-based electrodes via machine learning

Cited by 35 publications

References 53 publications

Robotic cell assembly to accelerate battery research

Robotic cell assembly to accelerate battery research

Applications of advanced metrology for understanding the effects of drying temperature in the lithium-ion battery electrode manufacturing process

Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations

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