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A vehicle alarm network for high-temperature fault diagnosis of electric vehicles

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Abstract

Manual checking is necessary for electric vehicles because error diagnosis exist in the actual electric vehicle fault diagnosis process. To effectively perform detection tasks, we propose a VANet model to classify the true and the false third-level high-temperature fault of EVs of unbalanced samples. Firstly, we introduce an improved method which is called Weighted SMOTE to mitigate the imbalance between samples, that determines the number of positive and negative samples by calculating the weight of minority samples. Secondly, we design a two-Conv-one-Maxpool module to build the VANet network, using the GAP and the softmax as the output layer. And we add the dropout to reduce hidden layer neurons to avoid overfitting. Finally, we employ the least square method to optimize the custom loss function to deal with the problem of ignoring incorrect label features. The experimental results show that compared with other convolutional networks, our model can enhance the recognition ability of minority fault samples, and has a strong anti-interference ability against sample noise.

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Acknowledgements

his work was supported by the Industry-University-Research Innovation Fund of Science and Technology Development Center, MOE (2019ITA03029), Hubei Provincial Natural Science Foundation of China under Grant (2019CFB173),Science and Technology Development Funds of the State Administration of Market Regulation (2021MK-071), the Foundation of Hubei Provincial Key Laboratory of Intelligent Robot (HBIRL 202010) and the Twelfth Graduate Innovation Fund of Wuhan Institute of Technology (CX2020205, CX2020219).

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Authors and Affiliations

  1. Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, 430205, China

    Qing Hou, Jun Liu, Jianxing Zhang & Zihan Xu

  2. School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, 430205, China

    Qing Hou, Jun Liu, Jianxing Zhang & Zihan Xu

  3. Hubei Electronic Information Product Quality Supervision, Inspection Institute, Wuhan, 430061, China

    Xiao Chen & Peng Chen

Authors
  1. Qing Hou
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  2. Jun Liu
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  3. Jianxing Zhang
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  4. Zihan Xu
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  5. Xiao Chen
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  6. Peng Chen
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Correspondence to Jun Liu.

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Hou, Q., Liu, J., Zhang, J. et al. A vehicle alarm network for high-temperature fault diagnosis of electric vehicles. Appl Intell 53, 6230–6247 (2023). https://doi.org/10.1007/s10489-022-03615-z

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  • DOI: https://doi.org/10.1007/s10489-022-03615-z

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