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

Deeg, Patrick; Weisenberger, Christian; Oehm, Jonas; Schmidt, Denny; Csiszar, Orsolya; Knoblauch, Volker

doi:10.3390/batteries10030099

Open AccessArticle

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

by

Patrick Deeg

^*

,

Christian Weisenberger

,

Jonas Oehm

,

Denny Schmidt

,

Orsolya Csiszar

and

Volker Knoblauch

^*

Materials Research Institute Aalen (IMFAA), Aalen University, Beethovenstr. 1, 73430 Aalen, Germany

^*

Authors to whom correspondence should be addressed.

Batteries 2024, 10(3), 99; https://doi.org/10.3390/batteries10030099

Submission received: 19 January 2024 / Revised: 4 March 2024 / Accepted: 8 March 2024 / Published: 12 March 2024

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Abstract

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.

Keywords: CNN; deep learning; machine learning; image regression; lithium-ion batteries

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MDPI and ACS Style

Deeg, P.; Weisenberger, C.; Oehm, J.; Schmidt, D.; Csiszar, O.; Knoblauch, V. Swift Prediction of Battery Performance: Applying Machine Learning Models on Microstructural Electrode Images for Lithium-Ion Batteries. Batteries 2024, 10, 99. https://doi.org/10.3390/batteries10030099

AMA Style

Deeg P, Weisenberger C, Oehm J, Schmidt D, Csiszar O, Knoblauch V. Swift Prediction of Battery Performance: Applying Machine Learning Models on Microstructural Electrode Images for Lithium-Ion Batteries. Batteries. 2024; 10(3):99. https://doi.org/10.3390/batteries10030099

Chicago/Turabian Style

Deeg, Patrick, Christian Weisenberger, Jonas Oehm, Denny Schmidt, Orsolya Csiszar, and Volker Knoblauch. 2024. "Swift Prediction of Battery Performance: Applying Machine Learning Models on Microstructural Electrode Images for Lithium-Ion Batteries" Batteries 10, no. 3: 99. https://doi.org/10.3390/batteries10030099

APA Style

Deeg, P., Weisenberger, C., Oehm, J., Schmidt, D., Csiszar, O., & Knoblauch, V. (2024). Swift Prediction of Battery Performance: Applying Machine Learning Models on Microstructural Electrode Images for Lithium-Ion Batteries. Batteries, 10(3), 99. https://doi.org/10.3390/batteries10030099

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

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