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.
|Number of pages||7|
|Publication status||Published - 11 Jul 2014|
|Event||IEEE World Congress on Computational Intelligence (WCCI) - Beijing, China|
Duration: 06 Jul 2014 → 11 Jul 2014
|Conference||IEEE World Congress on Computational Intelligence (WCCI)|
|Period||06/07/2014 → 11/07/2014|
|Other||Including the IEEE Congress on Evolutionary Computation (CEC)|