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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Energies

سال: 2022

ISSN: ['1996-1073']

DOI: https://doi.org/10.3390/en15228331