Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
권호 표지
DOI QR코드

DOI QR Code

Prediction of Remaining Useful Life of Lithium-ion Battery based on Multi-kernel Support Vector Machine with Particle Swarm Optimization

  • Gao, Dong (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology) ;
  • Huang, Miaohua (Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology)
  • Received : 2017.03.02
  • Accepted : 2017.06.20
  • Published : 2017.09.20
Download PDF
⟨ Previous Next ⟩

Abstract

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

Keywords

References

  1. X. Chen, W. Shen, and T. T. Vo, "An overview of lithium-ion batteries for electric vehicles," IPEC, 2012 Conference on Power & Energy. IEEE, pp. 230-235, 2012.
  2. Y. Xing, W. M. Ma, and K. L. Tsui, "Battery management systems in electric and hybrid vehicles," Energies, Vol. 4, No. 11, pp. 1840-1857, Oct. 2011. https://doi.org/10.3390/en4111840
  3. X. S. Si, W. Wang, and C. H. Hu, "Remaining useful life estimation-A review on the statistical data driven approaches," European Journal of Operational Research, Vol. 213, No. 1, pp. 1-14, Aug. 2011. https://doi.org/10.1016/j.ejor.2010.11.018
  4. J. C. A. Anton, P. J. G. Nieto, and F. J. D. C. Juez, "Battery state-of-charge estimator using the SVM technique," Applied Mathematical Modelling, Vol. 37, No. 9, pp. 6244-6253, May 2013. https://doi.org/10.1016/j.apm.2013.01.024
  5. V. Klass, M. Behm, and G. Lindbergh, "A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation," Journal of Power Sources, Vol. 270, pp. 262-272, Dec. 2014. https://doi.org/10.1016/j.jpowsour.2014.07.116
  6. M. Galeotti, C. Giammanco, and L. Cina, "Synthetic methods for the evaluation of the State of Health (SOH) of nickel-metal hydride (NiMH) batteries," Energy Conversion and Management, Vol. 92, pp. 1-9, Mar. 2015. https://doi.org/10.1016/j.enconman.2014.12.040
  7. S. Buller, "Impedance-based simulation models for energy storage devices in advanced automotive applications,"[D]. Ph. D. Dissertation, RWTH Aachen, Aachen, Germany, 2003.
  8. G. L. Plett, "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification," Journal of Power Sources, Vol. 134, No. 2, pp. 262-276, Aug. 2004. https://doi.org/10.1016/j.jpowsour.2004.02.032
  9. Z. Xu, S. Gao, and S. Yang, "LiFePO4 battery state of charge estimation based on the improved Thevenin equivalent circuit model and Kalman filtering," Journal of Renewable & Sustainable Energy, Vol. 8, No. 2, pp. 376-378, Feb. 2016.
  10. K. Goebel, B. Saha, and A. Saxena, "Prognostics in battery health management," IEEE Instrum. Meas. Mag., Vol. 11, No. 4, pp. 33-40, Aug. 2008. https://doi.org/10.1109/MIM.2008.4579269
  11. A. Guha, A. Patra, and K. V. Vaisakh, "Remaining useful life estimation of lithium-ion batteries based on the internal resistance growth model," Control Conference. IEEE, pp. 33-38, 2017.
  12. F. Yang, D. Wang, and Y. Xing, "Prognostics of Li(NiMnCo)O2 -based lithium-ion batteries using a novel battery degradation model," Microelectronics Reliability, Vol. 70, pp. 70-78, Mar. 2017. https://doi.org/10.1016/j.microrel.2017.02.002
  13. B. Long, W. Xian, and L. Jiang, "An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries," Microelectronics Reliability, Vol. 53, No. 6, pp. 821-831, Jun. 2013. https://doi.org/10.1016/j.microrel.2013.01.006
  14. Y. Zhou and M. Huang, "Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model," Microelectronics Reliability, Vol. 65, pp. 265-273, Oct. 2016. https://doi.org/10.1016/j.microrel.2016.07.151
  15. X. Zheng and H. Fang, "An integrated unscented Kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction," Reliability Engineering & System Safety, Vol. 144, pp. 74-82, Dec. 2015. https://doi.org/10.1016/j.ress.2015.07.013
  16. A. Widodo, M. C. Shim, and W. Caesarendra, "Intelligent prognostics for battery health monitoring based on sample entropy," Expert Systems with Applications, Vol. 38, No. 9, pp. 11763-11769, Sep. 2011. https://doi.org/10.1016/j.eswa.2011.03.063
  17. E. Walker, S. Rayman, and R. E. White, "Comparison of a particle filter and other state estimation methods for prognostics of lithium-ion batteries," Journal of Power Sources, Vol. 287, pp. 1-12, Aug. 2015. https://doi.org/10.1016/j.jpowsour.2015.04.020
  18. H. Li, D. Pan, and C. L. P. Chen, "Intelligent prognostics for battery health monitoring using the mean entropy and relevance vector machine," IEEE Trans. Syst., Man, Cybern., Syst., Vol. 44, No. 7, pp. 851-862, Jul. 2014. https://doi.org/10.1109/TSMC.2013.2296276
  19. D. Liu, J. Zhou, and H. Liao, "A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics," IEEE Trans. Syst., Man, Cybern., Syst., Vol. 45, No. 6, pp. 915-928, Jun. 2015. https://doi.org/10.1109/TSMC.2015.2389757
  20. M. A. Patil, P. Tagade, and K. S. Hariharan, "A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation," Applied Energy, Vol. 159, pp. 285-297, Dec. 2015. https://doi.org/10.1016/j.apenergy.2015.08.119
  21. H. Dong, X. Jin, and Y. Lou, "Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter," Journal of Power Sources, Vol. 271, pp. 114-123, Dec. 2014. https://doi.org/10.1016/j.jpowsour.2014.07.176
  22. J. Wu, C. Zhang, and Z. Chen, "An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks," Applied Energy, Vol. 173, pp. 134-140, Jul. 2016. https://doi.org/10.1016/j.apenergy.2016.04.057
  23. L. Li, P. Wang, and K. H. Chao, "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture," Plos One, Vol. 11, No. 9, pp. e0163004 Sep. 2016. https://doi.org/10.1371/journal.pone.0163004
  24. B. Mo, J. Yu, and D. Tang, "A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter," IEEE International Conference on Prognostics and Health Management. IEEE, pp. 1-5, 2016.
  25. X. Zhang, Q. Miao and Z. Liu, "Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC," Microelectronics Reliability, Mar. 2017.
  26. X. Su, S. Wang, and M. Pecht, "Interacting multiple model particle filter for prognostics of lithium-ion batteries," Microelectronics Reliability, Vol. 70, pp. 59-69, Mar. 2017. https://doi.org/10.1016/j.microrel.2017.02.003
  27. A. Nuhic, T. Terzimehic, and T. Soczka-Guth, "Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods," Journal of Power Sources, Vol. 239, pp. 680-688, Oct. 2013. https://doi.org/10.1016/j.jpowsour.2012.11.146
  28. D. Li, R. M. Mersereau, and S. Simske, "Blind image deconvolution through support vector regression," IEEE Trans. Neural Netw., Vol. 18, No. 3, pp. 931-5, may 2007. https://doi.org/10.1109/TNN.2007.891622
  29. V. Klass, M. Behm, and G. Lindbergh, "A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation," Journal of Power Sources, Vol. 270, pp. 262-272, Dec. 2014. https://doi.org/10.1016/j.jpowsour.2014.07.116
  30. C. Weng, Y. Cui, and J. Sun, "On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression," Journal of Power Sources, Vol. 235, pp. 36-44, Aug. 2013. https://doi.org/10.1016/j.jpowsour.2013.02.012
  31. N. Chen, W. Lu, and J. Yang, "Support Vector Machine," Machine Learning Models and Algorithms for Big Data Classification. Springer US, pp.24-52, 2016.
  32. C. Y. Yeh, W. P. Su, and S. J. Lee, "Employing multiple-kernel support vector machines for counterfeit banknote recognition," Applied Soft Computing, Vol. 11, No. 1, pp. 1439-1447, Jan. 2011. https://doi.org/10.1016/j.asoc.2010.04.015
  33. M. Gonen and E. Alpaydin, "Cost-conscious multiple kernel learning," Pattern Recognition Letters, Vol. 31, No. 9, pp. 959-965, Jul. 2010. https://doi.org/10.1016/j.patrec.2009.12.027
  34. S. Yuan and F. Chu, "Fault diagnosis based on support vector machines with parameter optimisation by artificial immunisation algorithm," Mechanical Systems and Signal Processing, Vol. 21, No. 3, pp. 1318-1330, Apr. 2007. https://doi.org/10.1016/j.ymssp.2006.06.006
  35. G. F. Smits and E. M. Jordaan, "Improved SVM regression using mixtures of kernels," International Joint Conference on Neural Networks IEEE Xplore, pp. 2785-2790, 2002.
  36. C. W. Hsu and C. J. Lin, "A simple decomposition method for support vector machines," Machine Learning, Vol. 46, No. 1, pp. 291-314, Jan. 2002. https://doi.org/10.1023/A:1012427100071
  37. C. L. Huang and C. J. Wang, "A GA-based feature selection and parameters optimization for support vector machines," Expert Systems with applications, Vol. 31, No. 2, pp. 231-240, Aug. 2006. https://doi.org/10.1016/j.eswa.2005.09.024
  38. R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," International Symposium on MICRO Machine and Human Science IEEE, pp. 39-43, 2002.
  39. J. Kennedy and R. Eberhart, "Particle swarm optimization," IEEE International Conference on Neural Networks, 1995. Proceedings. IEEE, pp. 1942-1948, 2002.
  40. S. W. Lin, K. C. Ying, and S. C. Chen, "Particle swarm optimization for parameter determination and feature selection of support vector machines," Expert systems with applications, Vol. 35, No. 4, pp. 1817-1824, Nov. 2008. https://doi.org/10.1016/j.eswa.2007.08.088
  41. M. Rezvani, S. Lee, and J. Lee, "A comparative analysis of techniques for electric vehicle battery prognostics and health management (PHM)," SAE Technical Paper, Sep. 2011.
  42. B. Saha, K. Goebel, and S. Poll, "Prognostics methods for battery health monitoring using a Bayesian framework," IEEE Trans. Instrum. Meas., Vol. 58, No. 2, pp. 291-296, Feb. 2009. https://doi.org/10.1109/TIM.2008.2005965
  43. B. Saha and K. Goebel (2007). "Battery Data Set," NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA.