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Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode Microstructure

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  • Mona Faraji Niri

    (Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UK
    The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot OX11 0RA, UK)

  • Jimiama Mafeni Mase

    (Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UK
    The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot OX11 0RA, UK)

  • James Marco

    (Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UK
    The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot OX11 0RA, UK)

Abstract

Li-ion batteries play a critical role in the transition to a net-zero future. The discovery of new materials and the design of novel microstructures for battery electrodes is necessary for the acceleration of this transition. The battery electrode microstructure can potentially reveal the cells’ electrochemical characteristics in great detail. However, revealing this relation is very challenging due to the high dimensionality of the problem and the large number of microstructure features. In fact, it cannot be achieved via the traditional trial-and-error approaches, which are associated with significant cost, time, and resource waste. In search for a systematic microstructure analysis and design method, this paper aims at quantifying the Li-ion battery electrode structural characteristics via deep learning models. Deliberately, here, a methodology and framework are developed to reveal the hidden microstructure characteristics via 2D and 3D images through dimensionality reduction. The framework is based on an auto-encoder decoder for microstructure reconstruction and feature extraction. Unlike most of the existing studies that focus on a limited number of features extracted from images, this study concentrates directly on the images and has the potential to define the number of features to be extracted. The proposed methodology and model are computationally effective and have been tested on a real open-source dataset where the results show the efficiency of reconstruction and feature extraction based on the training and validation mean squared errors between 0.068 and 0.111 and from 0.071 to 0.110, respectively. This study is believed to guide Li-ion battery scientists and manufacturers in the design and production of next generation Li-ion cells in a systematic way by correlating the extracted features at the microstructure level and the cell’s electrochemical characteristics.

Suggested Citation

  • Mona Faraji Niri & Jimiama Mafeni Mase & James Marco, 2022. "Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode Microstructure," Energies, MDPI, vol. 15(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4489-:d:843250
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    References listed on IDEAS

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    1. Ma’d El-Dalahmeh & Maher Al-Greer & Mo’ath El-Dalahmeh & Michael Short, 2020. "Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 13(20), pages 1-19, October.
    2. Yang Zhang & Bo Guo, 2015. "Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine," Energies, MDPI, vol. 8(11), pages 1-19, November.
    3. Phattara Khumprom & Nita Yodo, 2019. "A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm," Energies, MDPI, vol. 12(4), pages 1-21, February.
    4. Sungwoo Jo & Sunkyu Jung & Taemoon Roh, 2021. "Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge," Energies, MDPI, vol. 14(21), pages 1-16, November.
    5. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    6. Jikai Bi & Jae-Cheon Lee & Hao Liu, 2022. "Performance Comparison of Long Short-Term Memory and a Temporal Convolutional Network for State of Health Estimation of a Lithium-Ion Battery using Its Charging Characteristics," Energies, MDPI, vol. 15(7), pages 1-24, March.
    7. Yi Wu & Saurabh Saxena & Yinjiao Xing & Youren Wang & Chuan Li & Winco K. C. Yung & Michael Pecht, 2018. "Analysis of Manufacturing-Induced Defects and Structural Deformations in Lithium-Ion Batteries Using Computed Tomography," Energies, MDPI, vol. 11(4), pages 1-22, April.
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    Cited by:

    1. Mona Faraji Niri & Koorosh Aslansefat & Sajedeh Haghi & Mojgan Hashemian & Rüdiger Daub & James Marco, 2023. "A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation," Energies, MDPI, vol. 16(17), pages 1-38, September.

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