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A novel battery abnormality detection method using interpretable Autoencoder

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  • Zhang, Xiang
  • Liu, Peng
  • Lin, Ni
  • Zhang, Zhaosheng
  • Wang, Zhenpo

Abstract

The abnormality detection of lithium-ion battery pack is crucial to ensure the safety of electric vehicles (EVs). However, the dynamic and complex operating conditions of EVs making it challenging for algorithms designed under laboratory conditions to perform properly. In this study, a novel data-driven framework for abnormality detection is developed through establishment of a neural network with interpretable modules on top of an Autoencoder using data from real EVs to recognize abnormality while charging. The encoding guide matrix proposed in this method greatly accelerates the training speed, which also helps retains the learning ability of the neural network with consideration of the influence from each feature to provide supplementary information. The proposed algorithm is validated with data from real EVs. The results show that, compared with most existing algorithms, evidently higher accuracy can be achieved with shorter training time and lower computational cost, where the accuracy remains above 94% for all tested sample and the average root mean square error (RMSE) is as small as 0.03913. The proposed method can be utilized for both cloud-based and vehicle-based battery fault diagnoses.

Suggested Citation

  • Zhang, Xiang & Liu, Peng & Lin, Ni & Zhang, Zhaosheng & Wang, Zhenpo, 2023. "A novel battery abnormality detection method using interpretable Autoencoder," Applied Energy, Elsevier, vol. 330(PB).
    • Handle: RePEc:eee:appene:v:330:y:2023:i:pb:s0306261922015690
    DOI: 10.1016/j.apenergy.2022.120312
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    References listed on IDEAS

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    1. Shen, Dongxu & Lyu, Chao & Yang, Dazhi & Hinds, Gareth & Wang, Lixin, 2023. "Connection fault diagnosis for lithium-ion battery packs in electric vehicles based on mechanical vibration signals and broad belief network," Energy, Elsevier, vol. 274(C).

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