Analysis and prediction of the discharge characteristics of the lithium–ion battery based on the Grey system theory

Lin Chen; Binbin Tian; Weilong Lin; Bing Ji; Junzi Li; Haihong Pan

Analysis and prediction of the discharge characteristics of the lithium–ion battery based on the Grey system theory

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Author(s): Lin Chen ¹ ; Binbin Tian ¹ ; Weilong Lin ¹ ; Bing Ji ² ; Junzi Li ¹ ; Haihong Pan ¹
- Affiliations: 1: Department of Mechatronics Engineering, College of Mechanical Engineering, Guangxi University, Nanning, People's Republic of China ;
  2: Department of Engineering, University of Leicester, Leicester LE1 7RH, UK
Source: Volume 8, Issue 12, December 2015, p. 2361 – 2369
DOI: 10.1049/iet-pel.2015.0182 , Print ISSN 1755-4535, Online ISSN 1755-4543

Received 13/03/2015, Accepted 19/05/2015, Revised 10/05/2015, Published 01/12/2015

The capacity/state-of-charge (SoC) and voltage of lithium–ion batteries are of prime importance in electric vehicles (EVs), so their condition-monitoring techniques are extensively studied. This study focuses on the application of the grey system theory to the parameters analysing and predicting behaviour during the discharge/charge cycles of the battery. First, Grey relation analysis is applied to study and analyse the relationship between capacity/SoC and various influencing factors. Second, the segment Grey prediction model is proposed in order to test and improve the accuracy of the capacity/SoC prediction. Finally, based on the ageing data from the National Aeronautics and Space Administration Prognostics Data Repository, the effects of different Grey theory models, such as the GM(1,1), the Verhulst model and the segment Grey prediction model, are investigated. The results show that: (i) the GRA is efficient in figuring out the relationship between the capacity/SoC and various influencing factors; (ii) the segment Grey prediction model is an effective mode of prediction for EV batteries, because its accuracy is more reliable than other two Grey models; and (iii) the segment Grey prediction model is suitable for predicting the capacity/SoC of batteries under various loading conditions.

References

1. 1)
  - 30. Charkhgard, M., Farrokhi, M.: ‘State-of-charge estimation for lithium-ion batteries using neural networks and Ekf’, IEEE Trans. Ind. Electron., 2010, 57, (12), pp. 4178–4187 (doi: 10.1109/TIE.2010.2043035).
2. 2)
  - 30. Zhang, W., Li, H.: ‘The prediction for Shanghai business climate index by Grey model’, Res. J. Appl. Sci. Eng. Technol., 2014, 7, (14), pp. 2976–2980.
3. 3)
  - 15. Zhang, S., Liu, S., Zhai, R.: ‘An extended GRA method for MCDM with interval-valued triangular fuzzy assessments and unknown weights’, Comput. Ind. Eng., 2011, 61, (4), pp. 1336–1341 (doi: 10.1016/j.cie.2011.08.008).
4. 4)
  - 28. Cheng, K.W.E., Divakar, B.P., Hongjie, W., et al: ‘Battery-management system (BMS) and SOC development for electrical vehicles’, IEEE Trans. Veh. Technol., 2011, 60, (1), pp. 76–88 (doi: 10.1109/TVT.2010.2089647).
5. 5)
  - 23. INL.: ‘Advanced technology development program for lithium–ion batteries: Gen 2 performance evaluation final report’ (Idaho, Idaho National Laboratory, 2006).
6. 6)
  - 19. Chang, T.C., Lin, S.J.: ‘Grey relation analysis of carbon dioxide emissions from industrial production and energy uses in Taiwan’, J. Environ. Manage., 1999, 56, (4), pp. 247–257 (doi: 10.1006/jema.1999.0288).
7. 7)
  - 2. United States Advanced Battery Consortium.: ‘Electric vehicle battery test procedures manual’ (1996, 2nd edn.).
8. 8)
  - 14. Deng, J.L.: ‘The primer methods of Grey system theory’ (Wuhan, Huazhong University of Science and Technology Press, 2005, 2nd edn.).
9. 9)
  - 12. Liu, S., Forrest, J., Yang, Y.: ‘A brief introduction to Grey systems theory’, Grey Syst. Theory Appl., 2012, 2, (2), pp. 89–104 (doi: 10.1108/20439371211260081).
10. 10)
  - 7. Singh, P., Vinjamuri, R., Wang, X., et al: ‘Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators’, J. Power Sour., 2006, 162, (2), pp. 829–836 (doi: 10.1016/j.jpowsour.2005.04.039).
11. 11)
  - 22. Bhaskar, S., Kai, G.: ‘Battery data set’ (California, NASA Ames Prognostics Data Repository, 2007).
12. 12)
  - 13. Lu, I.J., Lin, S.J., Lewis, C.: ‘Grey relation analysis of motor vehicular energy consumption in Taiwan’, Energy Policy, 2008, 36, (7), pp. 2556–2561 (doi: 10.1016/j.enpol.2008.03.015).
13. 13)
  - 14. Huang, S.J., Huang, B.G., Pai, F.S.: ‘An approach to measurements of electrical characteristics of lithium-ion battery with open-circuit voltage function’, IET Power Electron., 2012, 5, (9), pp. 1968–1975 (doi: 10.1049/iet-pel.2012.0070).
14. 14)
  - 21. Xu, J., Tan, T., Tu, M., et al: ‘Improvement of Grey models by least squares’, Expert Syst. Appl., 2011, 38, (11), pp. 13961–13966.
15. 15)
  - 11. Deng, J.L.: ‘Control problems of Grey systems’, Syst. Control Lett., 1982, 1, (5), pp. 288–294 (doi: 10.1016/S0167-6911(82)80025-X).
16. 16)
  - 7. Ng, K.S., Moo, C.-S., Chen, Y.-P., Hsieh, Y.-C.: ‘Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries’, Appl. Energy, 2009, 86, pp. 1506–1511 (doi: 10.1016/j.apenergy.2008.11.021).
17. 17)
  - 27. Choi, S.S., Lim, H.S.: ‘Factors that affect cycle-life and possible degradation mechanisms of a Li-ion cell based on LiCoO2’, J. Power Sour., 2002, 111, (1), pp. 130–136 (doi: 10.1016/S0378-7753(02)00305-1).
18. 18)
  - 1. Zhao, R., Zhang, S., Gu, J., et al: ‘An experimental study of lithium ion battery thermal management using flexible hydrogel films’, J. Power Sour., 2014, 255, (0), pp. 29–36 (doi: 10.1016/j.jpowsour.2013.12.138).
19. 19)
  - 16. Pandey, R.K., Panda, S.S.: ‘Optimization of multiple quality characteristics in bone drilling using Grey relational analysis’, J. Orthopaedics, 2015, 12, (1), pp. 39–45 (doi: 10.1016/j.jor.2014.06.003).
20. 20)
  - 29. Kayacan, E., Ulutas, B., Kaynak, O.: ‘Grey system theory-based models in time series prediction’, Expert Syst. Appl., 2010, 37, (2), pp. 1784–1789 (doi: 10.1016/j.eswa.2009.07.064).
21. 21)
  - 18. Deng, J.L.: ‘Introduction to Grey system theory’, J. Grey Syst., 1989, 1, (1), pp. 1–24.
22. 22)
  - 31. Gholizade-Narm, H., Charkhgard, M.: ‘Lithium-ion battery state of charge estimation based on square-root unscented Kalman filter’, IET Power Electron., 2013, 6, (9), pp. 1833–1841 (doi: 10.1049/iet-pel.2012.0706).
23. 23)
  - 26. Zhang, S.S., Xu, K., Jow, T.R.: ‘Charge and discharge characteristics of a commercial LiCoO2-based 18650 Li-ion battery’, J. Power Sour., 2006, 160, (2), pp. 1403–1409 (doi: 10.1016/j.jpowsour.2006.03.037).
24. 24)
  - 20. Evans, M.: ‘An alternative approach to estimating the parameters of a generalised Grey Verhulst model: an application to steel intensity of use in the UK’, Expert Syst. Appl., 2014, 41, (4), pp. 1236–1244 (doi: 10.1016/j.eswa.2013.08.006).
25. 25)
  - 11. Lee, S., Kim, J., Lee, J., Cho, B.H.: ‘State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge’, J. Power Sources, 2008, 185, pp. 1367–1373 (doi: 10.1016/j.jpowsour.2008.08.103).
26. 26)
  - 25. Zhao, S., Wu, F., Yang, L., et al: ‘A measurement method for determination of dc internal resistance of batteries and supercapacitors’, Electrochem. Commun., 2010, 12, (2), pp. 242–245 (doi: 10.1016/j.elecom.2009.12.004).
27. 27)
  - 24. Goebel, K., Saha, B., Saxena, A., et al: ‘Prognostics in battery health management’, IEEE Instrum. Meas. Mag., 2008, 11, (4), pp. 33–40 (doi: 10.1109/MIM.2008.4579269).
28. 28)
  - 17. Wei, G.: ‘GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting’, Knowl.-Based Syst., 2010, 23, (3), pp. 243–247 (doi: 10.1016/j.knosys.2010.01.003).
29. 29)
  - 6. Hu, C., Youn, B.D., Chung, J.: ‘A multiscale framework with extended Kalman filter for lithium–ion battery SOC and capacity estimation’, Appl. Energy, 2012, 92, (0), pp. 694–704 (doi: 10.1016/j.apenergy.2011.08.002).
30. 30)
  - 10. Hansen, T., Wang, C.: ‘Support vector based battery state of charge estimator’, J. Power Sour., 2005, 141, (2), pp. 351–358 (doi: 10.1016/j.jpowsour.2004.09.020).

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Analysis and prediction of the discharge characteristics of the lithium–ion battery based on the Grey system theory

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