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An Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Model for the Temperature Prediction of Lithium-Ion Power Batteries

Published August 14, 2018 by SAE International in United States
An Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Model for the Temperature Prediction of Lithium-Ion Power Batteries
Sector:
Citation: Fan, B., Lin, C., Wang, F., Liu, S. et al., "An Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Model for the Temperature Prediction of Lithium-Ion Power Batteries," SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 12(1):5-11, 2019, https://doi.org/10.4271/07-12-01-0001.
Language: English

References

  1. Darcovich, K., Henquin, E.R., Kenney, B., and Beausoleil-Morrison, I. , “Higher-Capacity Lithium Ion Battery Chemistries for Improved Residential Energy Storage with Micro-Cogeneration,” Applied Energy 111:853-861, 2013.
  2. Selman, J.R., Al-Hallaj, S., Uchida, I., and Hirano, Y. , “Cooperative Research on Safety Fundamentals of Lithium Batteries,” Journal of Power Sources 97-98:726-732, 2001.
  3. Bandhauer, T.M., Garimella, S., and Fuller, T.F. , “A Critical Review of Thermal Issues in Lithium-Ion Batteries,” Journal of the Electrochemical Society 158:R1-R25, 2011.
  4. Lin, C.J., Xu, S.C., Li, Z., and Liu, J. , “Thermal Analysis of Large-Capacity LiFePO4 Power Batteries for Electric Vehicles,” Journal of Power Sources 294:633-642, 2015.
  5. Xu, M., Zhang, Z., Wang, X., Jia, L. et al. , “A Pseudo Three-Dimensional Electrochemical-Thermal Model of a Prismatic LiFePO4 Battery during Discharge Process,” Energy 80:303-317, 2015.
  6. Dai, H., Guo, P., Wei, X., Sun, Z.C. et al. , “ANFIS (Adaptive Neuro-Fuzzy Inference System) Based Online SOC (State of Charge) Correction Considering Cell Divergence for the EV (Electric Vehicle) Traction Batteries,” Energy 80:350-360, 2015.
  7. Kang, L.W., Zhao, X., and Ma, J. , “A New Neural Network Model for the State-of-Charge Estimation in the Battery Degradation Process,” Applied Energy 5:20-27, 2014.
  8. He, W., Williard, N., Chen, C., and Pecht, M. , “State of Charge Estimation for Li-Ion Batteries Using Neural Network Modeling and Unscented Kalman Filter-Based Error Cancellation,” International Journal of Electrical Power & Energy Systems 62:783-791, 2014.
  9. Parthiban, T., Ravi, R., and Kalaiselvi, N. , “Exploration of Artificial Neural Network [ANN] to Predict the Electrochemical Characteristics of Lithium-Ion Cells,” Electrochimica Acta 53:1877-1882, 2008.
  10. Fang, K., Mu, D., Chen, S., Wu, B. et al. , “A Prediction Model Based on Artificial Neural Network for Surface Temperature Simulation of Nickel-Metal Hydride Battery during Charging,” Journal of Power Sources 208:378-382, 2012.
  11. Liu, Z. and Li, H.X. , “Integrated Modeling for Intelligent Battery Thermal Management,” 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, U.K., vol. 8215, 2522-2527, 2013.
  12. Kim, T.J., Youn, B.D., and Kim, H.J. , “Battery Pack Temperature Estimation Model for EVs and its Semi-Transient Case Study,” IEEE Conference on Prognostics and System Health Management, Beijing, China, 955-960, 2013.
  13. Mellit, A. and Kalogirou, S.A. , “ANFIS-Based Modelling for Photovoltaic Power Supply System: A Case Study,” Renewable Energy 36:250-258, 2011.
  14. Tian, Z., Qian, C., Gu, B., Yang, L. et al. , “Electric Vehicle Air Conditioning System Performance Prediction Based on Artificial Neural Network,” Applied Thermal Engineering 89:101-114, 2015.
  15. Cavdar, S. and Aydin, A. , “An Empirical Analysis for the Prediction of a Financial Crisis in Turkey through the Use of Forecast Error Measures,” Journal of Risk & Financial Management 8:337-354, 2015.
  16. Lin, C., Xu, S., Chang, G., and Liu, J. , “Experiment and Simulation of a LiFePO4, Battery Pack with a Passive Thermal Management System Using Composite Phase Change Material and Graphite Sheets,” Journal of Power Sources 275:742-749, 2015.

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