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The SOC Estimation of Power Li-Ion Battery Based on ANFIS Model
School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan, China.
School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan, China.
School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan, China.
School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan, China.
- 1 School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan, China.
- 2 School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan, China.
- 3 School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan, China.
- 4 School of Electrical & Electronic Engineering, Hubei University of Technology, Wuhan, China.
Smart Grid and Renewable Energy·Volume 03 (2012)·Pages 51–55·Published 29 February 2012·DOI10.4236/sgre.2012.31007
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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.
KeywordsState of ChargeANFISEstimation MethodLi-Ion Battery
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