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High-Precision Fault Detection for Electric Vehicle Battery System Based on Bayesian Optimization SVDD

Author

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  • Jiong Yang

    (Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Anhui Polytechnic University, Wuhu 241000, China)

  • Fanyong Cheng

    (Anhui Province Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China)

  • Maxwell Duodu

    (Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Anhui Polytechnic University, Wuhu 241000, China)

  • Miao Li

    (Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Anhui Polytechnic University, Wuhu 241000, China)

  • Chao Han

    (Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Anhui Polytechnic University, Wuhu 241000, China)

Abstract

Fault detection of the electric vehicle battery system is vital for safe driving, energy economy, and lifetime extension. This paper proposes a data-driven method to achieve early and accurate battery system fault detection to realize rapid early warning. The method first adopts the support vector data description model mapping the feature of unlabeled voltage and temperature into a minimum volume hypersphere in high-dimensional space. When the feature is located outside the hypersphere, it is judged to be faulty. Then, to overcome the problem of hyperparameters selection, Bayesian optimization and a small amount of label data are used to iteratively train the model. This step can greatly improve the fault detection ability of the model, which is conducive to mining early and minor faults. Finally, the proposed model is compared with three unsupervised fault detection models, principal component analysis, kernel principal component analysis, and support vector data description to validate the performance of fault detection and robustness, respectively. The experimental results show that: 1. the proposed model has high detection accuracy in all four fault datasets, especially in the highly concealed cumulative short-circuit fault, which is substantially ahead of the other three models; and 2. The proposed model has higher and more stable accuracy than the other three models even in the case of a large range of signal-to-noise ratio.

Suggested Citation

  • Jiong Yang & Fanyong Cheng & Maxwell Duodu & Miao Li & Chao Han, 2022. "High-Precision Fault Detection for Electric Vehicle Battery System Based on Bayesian Optimization SVDD," Energies, MDPI, vol. 15(22), pages 1-20, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8331-:d:966334
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    References listed on IDEAS

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