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State-of-charge estimation based on theory of evidence and interval analysis with differential evolution optimization

  • Suradej DuangpummetEmail author
  • Jessada Karnjana
  • Waree Kongprawechnon
S.I.: Integrated Uncertainty in Knowledge Modelling & Decision Making 2018
  • 14 Downloads

Abstract

In this paper, we propose a new method for estimating the state-of-charge (SoC) of a lithium battery. There are remaining drawbacks of the existing methods, such as inaccurate estimation, high computation, and the need for massive datasets or an expensive sensor. Hence, the technique that we use to resolve such problems is derived from the theory of evidence (Dempster–Shafer theory) in which the degree of belief, namely mass, based on prior knowledge has to be assigned to each source of a variable. The proposed method is based on bounded-error state estimation with forward-backward propagation. However, instead of describing each variable error that propagates in the propagation process by a single error bound or a single interval, we define it by sets of intervals. In addition, to determine the optimal masses assigned to the variables, differential evolution is applied. To evaluate the proposed method, we carried out the experiments that used two different current sensors and two different voltage sensors to represent those variables with varying levels of uncertainty. Experimental results show that the root-mean-square errors of the proposed method are slightly better than those of a Kalman-filtering-based approach. The optimum masses also show the best performance, compared with masses assigned randomly. The results suggest that the proposed method can correctly estimate the SoC of a lithium battery.

Keywords

Theory of evidence Dempster–Shafer theory Interval analysis State-of-Charge estimation Data fusion Differential evolution 

Notes

References

  1. Chaoui, H., & Ibe-Ekeocha, C. C. (2017). State of charge and state of health estimation for lithium batteries using recurrent neural networks. IEEE Transactions on Vehicular Technology, 66(10), 8773–8783.CrossRefGoogle Scholar
  2. Charkhgard, M., & Farrokhi, M. (2010). State-of-charge estimation for lithium-ion batteries using neural networks and ekf. IEEE Transactions on Industrial Electronics, 57(12), 4178–4187.CrossRefGoogle Scholar
  3. Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A. (2017). Long short-term memory networks for accurate state-of-charge estimation of li-ion batteries. IEEE Transactions on Industrial Electronics, 65(8), 6730–6739.CrossRefGoogle Scholar
  4. Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics, 38(2), 325–399.CrossRefGoogle Scholar
  5. Denœux, T., & Masson, M.-H. (2012). Evidential reasoning in large partially ordered sets. Annals of Operations Research, 195(1), 135–161.CrossRefGoogle Scholar
  6. He, W., Williard, N., Osterman, M., & Pecht, M. (2011). Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the bayesian monte carlo method. Journal of Power Sources, 196(23), 10314–10321.CrossRefGoogle Scholar
  7. Jaulin, L. (2002). Nonlinear bounded-error state estimation of continuous-time systems. Automatica, 38(6), 1079–1082.CrossRefGoogle Scholar
  8. Jaulin, L., Kieffer, M., Didrit, O., & Walter, E. (2001). Interval analysis. Berlin: Springer.CrossRefGoogle Scholar
  9. Karnjana, J., Aimmanee, P., Unoki, M., & Wutiwiwatchai, C. (2015). An audio watermarking scheme based on automatic parameterized singular-spectrum analysis using differential evolution. In Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 543–551.Google Scholar
  10. Lieckens, K., & Vandaele, N. (2016). Differential evolution to solve the lot size problem in stochastic supply chain management systems. Annals of Operations Research, 242(2), 239–263.CrossRefGoogle Scholar
  11. Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013). A review on the key issues for lithium-ion battery management in electric vehicles. Journal of power sources, 226, 272–288.CrossRefGoogle Scholar
  12. Mamun, A., Narayanan, I., Wang, D., Sivasubramaniam, A., & Fathy, H. K. (2016). Multi-objective optimization of demand response in a datacenter with lithium-ion battery storage. Journal of Energy Storage, 7, 258–269.CrossRefGoogle Scholar
  13. Moo, C., Ng, K., Chen, Y., & Hsieh, Y. (2007). State-of-charge estimation with open-circuit-voltage for lead-acid batteries. In Power Conversion Conference-Nagoya, pp. 758–762. IEEE.Google Scholar
  14. Nassreddine, G., Abdallah, F., & Denoux, T. (2009). State estimation using interval analysis and belief-function theory: Application to dynamic vehicle localization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40(5), 1205–1218.CrossRefGoogle Scholar
  15. Plett, G . L. (2004). Extended kalman filtering for battery management systems of lipb-based hev battery packs: Part 3. State and parameter estimation. Journal of Power sources, 134(2), 277–292.CrossRefGoogle Scholar
  16. Puig, V. (2010). Fault diagnosis and fault tolerant control using set-membership approaches: Application to real case studies. Journal of Applied Mathematics and Computer Science, 20(4), 619–635.Google Scholar
  17. Reihani, E., Sepasi, S., Roose, L. R., & Matsuura, M. (2016). Energy management at the distribution grid using a battery energy storage system (bess). Journal of Electrical Power and Energy Systems, 77, 337–344.CrossRefGoogle Scholar
  18. Shafer, G. (1976). A mathematical theory of evidence (Vol. 42). Princeton: Princeton University Press.Google Scholar
  19. Shafer, G. (2016). Dempster’s rule of combination. International Journal of Approximate Reasoning, 79, 26–40.CrossRefGoogle Scholar
  20. Storn, R., & Price, K. (1997). Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.CrossRefGoogle Scholar
  21. Tulsyan, A., Tsai, Y., Gopaluni, R. B., & Braatz, R. D. (2016). State-of-charge estimation in lithium-ion batteries: A particle filter approach. Journal of Power Sources, 331, 208–223.CrossRefGoogle Scholar
  22. Van, A. W., Lopez, I. D., Frangioni, A., Lacalandra, F., & Tahanan, M. (2018). Large-scale unit commitment under uncertainty: An updated literature survey. Annals of Operations Research, 271(1), 11–85.CrossRefGoogle Scholar
  23. Waag, W., Fleischer, C., & Sauer, D. U. (2014). Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. Journal of Power Sources, 258, 321–339.CrossRefGoogle Scholar
  24. Wei, Z., Meng, S., Xiong, B., Ji, D., & Tseng, K. J. (2016). Enhanced online model identification and state of charge estimation for lithium-ion battery with a fbcrls based observer. Applied Energy, 181, 332–341.CrossRefGoogle Scholar
  25. Xu, D.-L. (2012). An introduction and survey of the evidential reasoning approach for multiple criteria decision analysis. Annals of Operations Research, 195(1), 163–187.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.NECTEC, National Science and Technology Development AgencyKlong LuangThailand
  2. 2.Sirindhorn International Institute of TechnologyThammasat UniversityMuangThailand

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