Effect of carbon blacks on electrical conduction and conductive binder domain of next-generation lithium-ion batteries

Lu, Xuesong; Lian, Guo J.; Parker, James; Ge, Ruihuan; Sadan, Milan K.; Smith, Rachel M.; Cumming, Denis

doi:10.1016/j.jpowsour.2023.233916

Journal of Power Sources

2024

DOI: 10.1016/j.jpowsour.2023.233916

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Effect of carbon blacks on electrical conduction and conductive binder domain of next-generation lithium-ion batteries

Xuesong Lu,

Guo J. Lian,

James Parker

et al.

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2024

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Cited by 4 publications

References 65 publications

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Optimized NMC622 electrodes with a high content of the active material: A comprehensive study

Hamed,

Obrezkov,

Huotari

et al. 2024

Journal of Power Sources

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Optimized NMC622 electrodes with a high content of the active material: A comprehensive study

Hamed,

Obrezkov,

Huotari

et al. 2024

Journal of Power Sources

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Optimization of mixing speed and time to disperse the composite conductive agent composed of carbon black and graphene in lithium-ion battery slurry

Wang,

Tu,

et al. 2024

Particuology

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Swift Prediction of Battery Performance: Applying Machine Learning Models on Microstructural Electrode Images for Lithium-Ion Batteries

Deeg,

Weisenberger,

Oehm

et al. 2024

Batteries

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In this study, we investigate the use of artificial neural networks as a potentially efficient method to determine the rate capability of electrodes for lithium-ion batteries with different porosities. The performance of a lithium-ion battery is, to a large extent, determined by the microstructure (i.e., layer thickness and porosity) of its electrodes. Tailoring the microstructure to a specific application is a crucial process in battery development. However, unravelling the complex correlations between microstructure and rate performance using either experiments or simulations is time-consuming and costly. Our approach provides a swift method for predicting the rate capability of battery electrodes by using machine learning on microstructural images of electrode cross-sections. We train multiple models in order to predict the specific capacity based on the batteries’ microstructure and investigate the decisive parts of the microstructure through the use of explainable artificial intelligence (XAI) methods. Our study shows that even comparably small neural network architectures are capable of providing state-of-the-art prediction results. In addition to this, our XAI studies demonstrate that the models are using understandable human features while ignoring present artefacts.

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Effect of carbon blacks on electrical conduction and conductive binder domain of next-generation lithium-ion batteries

Cited by 4 publications

References 65 publications

Optimized NMC622 electrodes with a high content of the active material: A comprehensive study

Optimized NMC622 electrodes with a high content of the active material: A comprehensive study

Optimization of mixing speed and time to disperse the composite conductive agent composed of carbon black and graphene in lithium-ion battery slurry

Swift Prediction of Battery Performance: Applying Machine Learning Models on Microstructural Electrode Images for Lithium-Ion Batteries

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