A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

Zhile Yang, Kang Li, Aoife Foley, Cheng Zhang

Research output: Contribution to conferencePaperpeer-review

15 Citations (Scopus)


One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.
Original languageEnglish
Number of pages7
Publication statusPublished - 11 Jul 2014
EventIEEE World Congress on Computational Intelligence (WCCI) - Beijing, China
Duration: 06 Jul 201411 Jul 2014


ConferenceIEEE World Congress on Computational Intelligence (WCCI)
OtherIncluding the IEEE Congress on Evolutionary Computation (CEC)

Bibliographical note

Evolutionary Computation (CEC), 2014 IEEE Congress on


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