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M. A. C. Valdez, J. A. O. Valera and M. J. O. Arteaga, “Estimating Soc in Lead-Acid Batteries Using Neural Networks in a Microcontroller-Based Charge-Controller,” International Joint Conference on Neural Network, Vancouver, 30 October 2006, pp. 2713-2719.

has been cited by the following article:

• TITLE: The SOC Estimation of Power Li-Ion Battery Based on ANFIS Model

  AUTHORS: Tiezhou Wu, Mingyue Wang, Qing Xiao, Xieyang Wang

  KEYWORDS: State of Charge; ANFIS; Estimation Method; Li-Ion Battery

  JOURNAL NAME: Smart Grid and Renewable Energy, Vol.3 No.1, February 29, 2012

  ABSTRACT: On basis of traditional battery performance model, paper analyzed the advantage and disadvantage of SOC estimation methods, introduced Adaptive Neuro-Fuzzy Inference Systems which integrated artificial neural network and fuzzy logic have predicted SOC of battery. It’s a battery residual capacity model with more generalization ability, adaptability and high precision. By analyzing the battery charge and discharge process, the key parameters of SOC are determined and the experimental model is modified in MATLAB platform.Experimental results show that the difference of SOC prediction and actual SOC is below 3%.The model can reflect the characteristics curve of the battery. SOC estimation algorithm can meet the requirements for precision. The results have a high practical value.