Research on Fault Diagnosis Method of Electric Vehicle Battery System Based on Wavelet-RBF Neural Network

2018 2nd International Conference on Artificial Intelligence Applications and Technologies

计算机科学

Zhao, Jing-Bo^1 ; Wang, Zhong^1 ; Shen, Han-Wen^1 ; Liao, Peng-Hao^1

College of Information and Control Engineering, Qingdao University of Technology, Qingdao

266520, China^1

关键词: Data processing performance;    Electric vehicle batteries;    Fault diagnosis method;    Feature vector extraction;    Large data volumes;    Radial basis function neural networks;    Radial basis functions;    RBF Neural Network;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/435/1/012023/pdf
DOI  :  10.1088/1757-899X/435/1/012023

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Combining the good pattern recognition performance of neural network and the data processing performance of wavelet decomposition, this paper combines wavelet analysis with neural network and uses particle swarm optimization radial basis function neural network to diagnose the fault of electric vehicle battery. In this paper, for large data volume and redundant data, wavelet decomposition is used to process signal processing, including data noise reduction and feature vector extraction. In the fault diagnosis of electric vehicle battery system, a radial basis function RBF neural network based on particle swarm optimization (PSO) optimization is proposed. Finally, the reliability and feasibility of the proposed algorithm are proved by simulation.