Modeling of electric vehicle batteries using RBF neural networks

Cheng Zhang, Zhile Yang, Kang Li

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

6 Citations (Scopus)

Abstract

Electric Vehicles (EVs) are promised to significantly reduce the consumption of conventional fossil fuels in the transport sector as well as to limit the overwhelming greenhouse gas emissions. An accurate battery model is indispensable for the design of charging and discharging control of EVs. A new Radial Basis Function (RBF) modelling approach, which combines the Levenberg-Marquardt method to tune the non-linear parameters and an input selection approach for confining the number of input variables is proposed to model the batteries of EVs. Experimental results on modelling Li-ion batteries show that the resultant models have achieved high accuracy on training data and desirable generalization performance on unseen data.
Original languageEnglish
Title of host publication2014 International Conference on Computing, Management and Telecommunications (ComManTel)
PublisherIEEE
Pages116-121
Number of pages6
ISBN (Print)978-1-4799-2903-0
DOIs
Publication statusPublished - 5 Jun 2014
Externally publishedYes

    Fingerprint

Keywords

  • Batteries
  • Computational modeling
  • Artificial neural networks
  • Training
  • Optimization
  • Data models

Cite this

Zhang, C., Yang, Z., & Li, K. (2014). Modeling of electric vehicle batteries using RBF neural networks. In 2014 International Conference on Computing, Management and Telecommunications (ComManTel) (pp. 116-121). IEEE. https://doi.org/10.1109/ComManTel.2014.6825590