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Electrochemical impedance spectroscopy image transformation-based convolutional neural network for diagnosis of external environment classification affecting abnormal aging of Li-ion batteries

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  • Han, Dongho
  • Kwon, Sanguk
  • Lee, Miyoung
  • Kim, Jonghoon
  • Yoo, Kisoo

Abstract

Various electric mobilities are being developed that use Lithium (Li)-ion batteries as the primary power source for target applications. Especially, with the widespread adoption of electric vehicle (EV), the significance of battery management system (BMS) which are closely related to the stable operation of Li-ion batteries, is also increasing. Diagnosing the external environment is essential because the aging pattern of the battery varies depending on the environment condition in which the battery is exposed and can cause abnormal failures. This study presents an approach for classifying the external environment using electrochemical impedance spectroscopy (EIS) as an indicator, which is closely related to the internal chemical characteristics of the batteries. Recurrence plot (RP) algorithm is applied to improve the performance of convolution neural network (CNN) used as classification model as well as original EIS image. According to the frequency at which the main parameters of the Randles circuit model are derived, an additional dataset is constructed based on the selected 3-points. This paper presents the results of the classification from the external environment based on the three methods and derives a statistical evaluation metric to prove the stability and performance of the model. Moreover, it is also suggested that the appropriate method can be selected based on the desired cost by deriving the detailed model loss and accuracy for the training epoch, which is a trade-off relationship for each method.

Suggested Citation

  • Han, Dongho & Kwon, Sanguk & Lee, Miyoung & Kim, Jonghoon & Yoo, Kisoo, 2023. "Electrochemical impedance spectroscopy image transformation-based convolutional neural network for diagnosis of external environment classification affecting abnormal aging of Li-ion batteries," Applied Energy, Elsevier, vol. 345(C).
    • Handle: RePEc:eee:appene:v:345:y:2023:i:c:s0306261923007006
    DOI: 10.1016/j.apenergy.2023.121336
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    References listed on IDEAS

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    1. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Aging abnormality detection of lithium-ion batteries combining feature engineering and deep learning," Energy, Elsevier, vol. 297(C).

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