会议论文详细信息
2018 2nd International Conference on Artificial Intelligence Applications and Technologies
Research on Fault Diagnosis Method of Electric Vehicle Battery System Based on Wavelet-RBF Neural Network
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
PDF
【 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.

【 预 览 】
附件列表
Files Size Format View
Research on Fault Diagnosis Method of Electric Vehicle Battery System Based on Wavelet-RBF Neural Network 480KB PDF download
  文献评价指标  
  下载次数:7次 浏览次数:32次