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

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DOI: 10.4236/sgre.2012.31007    5,448 Downloads   10,752 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

T. Wu, M. Wang, Q. Xiao and X. Wang, "The SOC Estimation of Power Li-Ion Battery Based on ANFIS Model," Smart Grid and Renewable Energy, Vol. 3 No. 1, 2012, pp. 51-55. doi: 10.4236/sgre.2012.31007.

References

[1] Z. Bin, “Voltage Characteristics of Li-Ion Power Battery for EVs,” Chinese Battery Industry, Vol. 14, No. 6, 2009, pp. 398-404.
[2] S. Pang, J. Farrell and J. Du, “Battery State-of-Charge Estimation,” Proceedings of the American Control Conference, Arlington, 25-26 June 2001, pp. 1644-1649.
[3] J. Chiasson and B. Vairamohan, “Estimating the State of Charge of a Battery,” American Control Conference, 4-6 June 2003, pp. 2863-2868. doi:10.1109/TCST.2004.839571
[4] B. Zhang, C. T. Lin and Q. S. Chen, “Performance of LiFePO4/C Li-Ion Battery for Electric Vehicle,” Chinese Journal of Power Sources, Vol. 32, No. 2, 2008, pp. 9598.
[5] L. C. Tao, W. J. Ping and C. Q. Shi, “Methods for State of Charge Estimation of EV Batteries and Their Application,” Battery Bimonthly, Vol. 34, No. 5, 2004, pp. 376378.
[6] S. J. Lee, J. H. Kim, J. M. Lee and B. H. Cho, “The State and Parameter Estimation of an Li-Ion Battery Using a New OCV-SOC Concept,” Power Electronics Specialists Conference, Orlando, 17-27 June 2007, pp. 2799-2803. doi:10.1109/PESC.2007.4342462
[7] 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.
[8] D. H. Feng, W. X. Zhe and S. Z. Chang, “State and Parameter Estimation of a HEV Li-ion Battery Pack Using Adaptive Kalman Filter with a New SOC-OCV Concept,” International Conference on Measuring Technology and Mechatronics Automation, Zhangjiajie, 11-12 April 2009, pp. 375380.
[9] Q. Gang and C. Yong, “Neural Network Estimation of Battery Pack SOC for Electric Vehicles,” Journal of Liaoning Technical University, Vol. 25, No. 2, 2006, pp. 230-233.
[10] A. R. P. Robat and F. R. Salmasi, “State of Charge Estimation for Batteries in HEV Using Locally Linear Model Tree (LOLIMOT),” Proceeding of International Conference on Electrical Machines and Systems, Seoul, 8-11 October 2007, pp. 2041-2045.
[11] T. X. Hui, D. H. Nan, F. Bo and Q. Y. Peng, “Research on Estimation of Lithium-Ion Battery SOC for Electric Vehicle,” Chinese Journal of Power Sources, Vol. 134, No. 1, 2010, pp. 51-54.
[12] L. G. Hen, J. Hai and W. H. Ying, “The SOC Compute Model of Batteries Based on Fuzzy Neural Network,” Journal of Test and Measurement Technology, Vol. 21, No. 5, 2007, pp. 405-409.
[13] Z. H. Li, H, L. Ping and Z. Z. Hua, “Study of Intelligent Prediction of the SOC of MH/Ni Battery for Electric Vehicle,” Machinery & Electronics, No. 10, 2006, pp. 7-11.
[14] L. Y. Hong, “Sub Linearity of Generalized Sugeno Fuzzy Integrals,” Journal of Eastern Liaoning University (Natural Science), Vol. 17, No. 1, 2010, pp. 80-83.
[15] W. Tao, Q. Hao and C. Yang, “A New Method of Fuzzy Interpolative Reasoning Based on Gaussian-Type Membership Function,” Fourth International Conference on Innovative Computing Information and Control, Kaohsiung, 7-9 December 2009, pp. 966-969.

  
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