Journal of Power Electronics

  • 제13권4호
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  • Pages.516-527
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  • 2013
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  • 1598-2092(pISSN)
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  • 2093-4718(eISSN)
전력전자학회 (The Korean Institute of Power Electronics)
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Adaptive On-line State-of-available-power Prediction of Lithium-ion Batteries

  • Fleischer, Christian (Electrochemical Energy Conversion and Storage Systems Group, Institute of Power Electronics and Electrical Drives (ISEA), RWTH Aachen University) ;
  • Waag, Wladislaw (Electrochemical Energy Conversion and Storage Systems Group, Institute of Power Electronics and Electrical Drives (ISEA), RWTH Aachen University) ;
  • Bai, Ziou (Electrochemical Energy Conversion and Storage Systems Group, Institute of Power Electronics and Electrical Drives (ISEA), RWTH Aachen University) ;
  • Sauer, Dirk Uwe (Electrochemical Energy Conversion and Storage Systems Group, Institute of Power Electronics and Electrical Drives (ISEA), RWTH Aachen University)
  • 투고 : 2013.01.24
  • 발행 : 2013.07.20
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초록

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

키워드

참고문헌

  1. W. Waag, C. Fleischer, C. Schaper, J. Berger, and D. U. Sauer, "Self-adapting on-board diagnostic algorithms for lithium-ion batteries," Advanced Battery Development for Automotive and Utility Applications and their Electric Power Grid Integration, Aachen/Germany, Mar. 2011.
  2. G. Ascheid and H. Meyr, "Systemtheorie I+II, 6th edition," Druck und Verlagshaus Mainz GmbH Aachen, Aachen/Germany, Mar. 2007.
  3. D. Simon, Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, Wiley-Interscience, 2006.
  4. M. T. Hagan and M. Menhaj, "Training feedforward networks with marquardt algorithm," IEEE Trans. Neural Netw., Vol. 5, No. 6, pp.989-993, Nov. 1994. https://doi.org/10.1109/72.329697
  5. G. Plett, "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1: Background," Journal of Power Sources, Vol. 134, No. 2, pp. 252-261, Aug. 2004. https://doi.org/10.1016/j.jpowsour.2004.02.031
  6. R. E. Kalman. "A new approach to linear filtering and prediction problems," Transactions of the ASME-Journal of Basic Engineering, Vol. 82, pp. 35-45, 1960. https://doi.org/10.1115/1.3662552
  7. D. Nauck, C. Borgelt, F. Klawonn, and R. Kruse, "Neuro-fuzzy-systeme: von den grundlagen künstlicher neuronaler netze zur kopplung mit fuzzy-systemen," Computational Intelligence, Vieweg, 2003.
  8. J.-S. R. Jang, "Neuro-fuzzy modeling: architectures, analyses and applications," Ph.D. Thesis, University of California, Berkeley, 1992.
  9. J.-S. R. Jang, C.-T. Sun, and E. Mizutani, "Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence," Prentice Hall, 2007.
  10. J.-S. R. Jang, "Input selection for ANFIS learning," In: Proc. Fifth IEEE Int Fuzzy Systems Conf., Vol. 2, pp. 1493-1499, 1996.
  11. J.-S. R. Jang and E. Mizutani, "Levenberg-marquardt method for ANFIS learning," In: Proc. NAFIPS Fuzzy Information Processing Society, Biennial Conference of the North American, pp. 87-91, Jun. 1996.
  12. D. U. Sauer, O. Bohlen, T. Sanders, W. Waag, R. Schmidt, and J. B. Gerschler, "Batteriezustanderkennung: mögliche verfahrens- und algorithmenansatze, grenzen der batteriezustandserkennung," Energiemanagement und Bordnetze II, Hrsg. Matthias Schollmann, Expert-Verlag, pp. 1-30, 2007.
  13. J.-S. R. Jang and S. Chuen-Tsai, "Neuro-fuzzy modeling and control," Proceedings of the IEEE, Vol. 83, No. 3, pp. 378-406, Mar. 1995. https://doi.org/10.1109/5.364486
  14. K. Xiong, H. Zhang, and L. Liu, "Adaptive robust extended Kalman filter for nonlinear stochastic systems," IET Control Theory Applications, Vol. 2, No. 3, pp. 239-250, Mar. 2008. https://doi.org/10.1049/iet-cta:20070096
  15. D. Abel and A. Bollig, "Rapid Control Prototyping: Methoden und Anwendungen," Springer Verlag Heidelberg, 2006.
  16. G. Welch and G. Bishop, "An introduction to the kalman filter," In: Department of Computer Science, University of North Carolina, Chapel Hill, 2006.
  17. O. Bohlen, J. B. Gerschler, D. U. Sauer, P. Birke, M. Keller, "Robust algorithms for a reliable battery diagnosis - managing batteries in hybrid electric vehicles," 22nd Electric Vehicle Symposium (EVS22), Yokohama, Japan, 2006.
  18. PNGV battery test manual, INEEL, DOE/ID-10597, Rev. 3, 2001.
  19. Advanced Technology Development Program For Lithium-Ion Batteries. Battery Technology Life Verification. Test Manual., INEEL/EXT-04-01986, 2005.
  20. Battery Test Manual For Plug-In Hybrid Electric Vehicles, U.S. Department of Energy, INL/EXT-07-12536, 2010.
  21. N. Nieto, M. Ecker, S. Käbitz, J. Münnix, and D. U. Sauer, "Detailed calendar and cycle life studies of NMC-based 18650 automotive lithium-ion batteries," 16th International Meeting on Lithium Batteries (IMLB), Korea, 2012.
  22. M. Broussely, Ph. Biensan, F. Bonhomme, Ph. Blanchard, S. Herreyre, K. Nechev, R. J. Staniewicz, "Main aging mechanisms in Li ion batteries," Journal of Power Sources, Vol. 146, No. 1-2, pp. 90-96, Aug. 2005. https://doi.org/10.1016/j.jpowsour.2005.03.172
  23. D. P. Abraham, J. L. Knuth, D. W. Dees, I. Bloom, and J. P. Christophersen, "Performance degradation of high-power lithium-ion cells electrochemistry of harvested electrodes," Journal of Power Sources, Vol. 170, No. 2, pp. 465-475, Jul. 2007. https://doi.org/10.1016/j.jpowsour.2007.03.071
  24. M. Safari and C. Delacourt, "Aging of a commercial Graphite/LiFePO4 cell," Journal of The Electrochemical Society, Vol. 158, No. 10, pp. 1123-1135, Aug. 2011. https://doi.org/10.1149/1.3614529
  25. R. G. Jungst, G. Nagasubramanian, H. L. Case, B. Y. Liaw, A. Urbina, T. L. Paez, and D. H. Doughty, "Accelerated calendar and pulse life analysis of lithium-ion cells," Journal of Power Sources, Vol. 119-121, pp. 870-873, Jun. 2003. https://doi.org/10.1016/S0378-7753(03)00193-9
  26. D. Y. Kim and D. Y. Jung, US 7518375, 2009.
  27. G. L. Plett, "High-performance battery-pack power estimation using a dynamic cell model," IEEE Trans. Veh. Technol., Vol. 53, No. 5, pp. 1586-1593, Sep. 2004. https://doi.org/10.1109/TVT.2004.832408
  28. G.L. Plett, WO 2005050810 A1, 2005.
  29. O. Bohlen and M. Roscher, "Method for determining and/or predicting the maximum power capacity of a battery," US 20120215517 A1, 2012.
  30. M. Roscher, "Verfahren zur Bestimmung und/oder Vorhersage der Hochstrombelastbarkeit einer Batterie," DE 102009049320 A1, 2011.
  31. R. Xiong, H. He, F. Sun, and K. Zhao, "Estimation of peak power capability of li-ion batteries in electric vehicles by a hardware-in-loop approach," Energies, Vol. 5, pp. 1455-1469, May 2012. https://doi.org/10.3390/en5051455
  32. F. Sun, R. Xiong, H. He, W. Li, and J. E. E. Aussems, "Model-based dynamic multi-parameter method for peak power estimation of lithiumion batteries," Applied Energy, Vol. 96, pp 378-386, Aug. 2012. https://doi.org/10.1016/j.apenergy.2012.02.061
  33. S. Wang, M. Verbrugge, J. S. Wang, and P. Liu, "Power prediction from a battery state estimator that incorporates diffusion resistance," Journal of Power Sources, Vol. 214, pp. 399-406, Sep. 2012. https://doi.org/10.1016/j.jpowsour.2012.04.070
  34. D. Yumoto and H. Nakamura, "Estimating apparatus and method of input and output enabling powers for secondary cell," US 7009402, 2006.
  35. D. Yumoto and H. Nakamura, "Available input-output power estimating device for secondary battery," US 7486079, 2009.
  36. R. Schmidt, O. Bohlen, and D. U. Sauer, "Passive Impedanzmessung zur Batteriediagnose in Kraftfahr- zeugen," Bunsenkolloquium, Dresden, Germany, 2007.
  37. C. Hu , B.D. Youn, J. Chung, "A multiscale framework with extended kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Vol. 92, pp. 694-704, Apr. 2012. https://doi.org/10.1016/j.apenergy.2011.08.002
  38. J. Lee, O. Nam, and B. H. Cho, "Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering," Journal of Power Sources, Vol. 174, No. 1, pp. 9-15, Nov. 2007. https://doi.org/10.1016/j.jpowsour.2007.03.072
  39. G. L. Plett, "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation," Journal of Power Sources, Vol. 134, No. 2, pp. 277-292, Aug. 2004. https://doi.org/10.1016/j.jpowsour.2004.02.033
  40. M. A. Roscher, "Zustandserkennung von LiFePO4- Batterien für Hybrid- und Elektrofahrzeuge," RWTH Aachen University, Ph.D. Thesis, 2010.
  41. M. A. Roscher, O. S. Bohlen, and D. U. Sauer, "Reliable state estimation of multicell lithium-ion battery systems," IEEE Trans. Energy Convers., Vol. 26, No. 3, pp. 737-743, Sep. 2011. https://doi.org/10.1109/TEC.2011.2155657
  42. M. Verbrugge, "Adaptive, multi-parameter battery state estimator with optimized time-weighting factors," Journal of Applied Electrochemistry, Vol. 37, pp. 605-616, Feb. 2007. https://doi.org/10.1007/s10800-007-9291-7
  43. S. Wang, M. Verbrugge, J. S. Wang, and P. Liu, "Multi-parameter battery state estimator based on the adaptive and direct solution of the governing differential equations," Journal of Power Sources, Vol. 196, No. 20, pp. 8735-8741, Oct. 2011. https://doi.org/10.1016/j.jpowsour.2011.06.078

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