Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Model-based optimal parameter identification incorporating C-rate, state of charge and temperature effect for advance battery management system in electric vehicles

The battery management system in electrified transportation requires an accurate battery model for online state estimation of the battery. The parameters of the battery model depend upon state of charge, C-rate, and temperature. A detailed battery model defined by 31 polynomial coefficients is used for determination of battery parameters. The parameter estimation is formulated as an optimisation problem and six different meta-heuristic optimisation techniques are utilised for solving it. The efficiency of optimisation techniques is compared in terms of solution quality, computation efficiency, and convergence characteristics. Further, their performance is analysed statistically using parametric (t-test) and non-parametric tests (Wilcoxon test). The parameters values estimated by applying optimisation techniques are cross-validated with value of parameters extracted using standard constant-current pulse charge–discharge test to establish the effectiveness of the proposed approach.

References

    1. 1)
      • 1. Ambrose, H., Kendall, A.: ‘Effects of battery chemistry and performance on the life cycle greenhouse gas intensity of electric mobility’, Transp. Res. D Transp. Environ., 2016, 47, pp. 182194.
    2. 2)
      • 29. Goldberg, D.E., Holland, J.H.: ‘Genetic algorithms and machine learning’, Mach. Learn., 1988, 3, (2), pp. 9599.
    3. 3)
      • 7. 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, pp. 694704.
    4. 4)
      • 6. Plett, G.L.: ‘Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification’, J. Power Sources, 2004, 134, (2), pp. 262276.
    5. 5)
      • 17. Seaman, A., Dao, T.S., McPhee, J.: ‘A survey of mathematics-based equivalent circuit and electrochemical battery models for hybrid and electric vehicle simulation’, J. Power Sources, 2014, 256, pp. 410423.
    6. 6)
      • 11. Kumar, P., Bauer, P.: ‘Parameter extraction of battery models using multiobjective optimization genetic algorithms’. Proc. EPE-PEMC 2010: 14th Int. Power Electronics and Motion Control Conf., Ohrid, Macedonia, 2010..
    7. 7)
      • 4. Chen, S., Gooi, H.B., Xia, N., et al: ‘Modelling of lithium-ion battery for online energy management systems’, IET Electr. Syst. Transp., 2012, 2, (4), pp. 202210.
    8. 8)
      • 19. Lu, D., Tao, J., Yan, P., et al: ‘Formation of reversible solid electrolyte interface on graphite surface from concentrated electrolytes’, Nano Lett., 2017, 17, (3), pp. 16021609.
    9. 9)
      • 8. Yang, W.J., Yu, D.H., Kim, Y.B.: ‘Parameter estimation of lithium-ion batteries and noise reduction using an H∞ filter’, J. Mech. Sci. Technol., 2013, 27, (1), pp. 247256.
    10. 10)
      • 12. Thirugnanam, K., Reena, E., Joy, T., et al: ‘Mathematical modeling of li-ion battery using genetic algorithm approach for V2G applications’, IEEE Trans. Energy Convers., 2014, 29, (2), pp. 332343.
    11. 11)
      • 36. Zheng, F., Xing, Y., Jiang, J., et al: ‘Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries’, Appl. Energy, 2016, 183, pp. 513525.
    12. 12)
      • 33. Mirjalili, S., Mirjalili, S.M., Lewis, A.: ‘Grey wolf optimizer’, Adv. Eng. Softw., 2014, 69, pp. 4661.
    13. 13)
      • 23. Sangwan, V., Kumar, R., Rathore, A.: ‘Estimation of battery parameters of the equivalent circuit model using grey wolf optimization’. 2016 IEEE 6th Int. Conf. Power Systems (ICPS), New Delhi, India, 2016, pp. 16.
    14. 14)
      • 32. Rao, R., Savsani, V., Balic, J.: ‘Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems’, Eng. Optim., 2012, 44, (12), pp. 14471462.
    15. 15)
      • 28. Verbrugge, M.W., Conell, R.S.: ‘Electrochemical and thermal characterization of battery modules commensurate with electric vehicle integration’, J. Electrochem. Soc., 2002, 149, (1), pp. A45A53.
    16. 16)
      • 22. Hu, X., Li, S., Peng, H.: ‘A comparative study of equivalent circuit models for li-ion batteries’, J. Power Sources, 2012, 198, pp. 359367.
    17. 17)
      • 30. Eberhart, R.C., Kennedy, J.: ‘A new optimizer using particle swarm theory’. Proc. Sixth Int. Symp. Micro Machine and Human Science, vol. 1, New York, NY, 1995, pp. 3943.
    18. 18)
      • 35. Sharma, A., Sharma, A., Panigrahi, B., et al: ‘Ageist spider monkey optimization algorithm’, Swarm Evol. Comput., 2016, 28, pp. 5877.
    19. 19)
      • 25. Zhang, H., Chow, M.Y.: ‘Comprehensive dynamic battery modeling for PHEV applications’. IEEE PES General Meeting, Providence, RI, USA, 2010, pp. 16.
    20. 20)
      • 10. Kim, J., Lee, I., Tak, Y., et al: ‘State-of-health diagnosis based on hamming neural network using output voltage pattern recognition for a PEM fuel cell’, Int. J. Hydrog. Energy, 2012, 37, (5), pp. 42804289.
    21. 21)
      • 18. Lam, L.: ‘A practical circuit-based model for state of health estimation of li-ion battery cells in electric vehicles’. Master of Science thesis, University of Technology Delft, 2011, 10.
    22. 22)
      • 26. Bauer, P., Lam, L., Bhattacharya, S., et al: ‘A practical circuit-based model for state of health estimation of Li-ion battery cells in electric vehicles: report’ (University of Technology, Delft, Netherlands, 2012).
    23. 23)
      • 14. Brand, J., Zhang, Z., Agarwal, R.K.: ‘Extraction of battery parameters of the equivalent circuit model using a multi-objective genetic algorithm’, J. Power Sources, 2014, 247, pp. 729737.
    24. 24)
      • 20. Berzi, L., Delogu, M., Pierini, M.: ‘Development of driving cycles for electric vehicles in the context of the city of florence’, Transp. Res. D Transp. Environ., 2016, 47, pp. 299322.
    25. 25)
      • 13. Malik, A., Zhang, Z., Agarwal, R.K.: ‘Extraction of battery parameters using a multi-objective genetic algorithm with a non-linear circuit model’, J. Power Sources, 2014, 259, pp. 7686.
    26. 26)
      • 2. Rahimi-Eichi, H., Ojha, U., Baronti, F., et al: ‘Battery management system: an overview of its application in the smart grid and electric vehicles’, IEEE Ind. Electron. Mag., 2013, 7, (2), pp. 416.
    27. 27)
      • 15. Wolpert, D.H., Macready, W.G.: ‘No free lunch theorems for optimization’, IEEE Trans. Evol. Comput., 1997, 1, (1), pp. 6782.
    28. 28)
      • 38. Jackey, R., Saginaw, M., Sanghvi, P., et al: ‘Battery model parameter estimation using a layered technique: an example using a lithium iron phosphate cell’, SAE Technical Paper, 2013..
    29. 29)
      • 16. Mousavi, S.M., Nikdel, M.: ‘Various battery models for various simulation studies and applications’, Renew. Sust. Energy Rev., 2014, 32, pp. 477485.
    30. 30)
      • 31. Storn, R., Price, K.: ‘Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces’, J. Global Optim., 1997, 11, (4), pp. 341359.
    31. 31)
      • 21. Corti, A., Manzoni, V., Savaresi, S.M.: ‘Vehicle's energy estimation using low frequency speed signal’. 2012 15th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), Anchorage, AK, USA, 2012, pp. 626631.
    32. 32)
      • 3. Sangwan, V., Kumar, R., Rathore, A.K.: ‘State-of-charge estimation for li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF)’. 2017 IEEE Industry Applications Society Annual Meeting, Cincinnati, USA, 2017, pp. 16.
    33. 33)
      • 37. Ng, K.S., Moo, C.S., Chen, Y.P., et al: ‘Enhanced Coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries’, Appl. Energy, 2009, 86, (9), pp. 15061511.
    34. 34)
      • 9. Plett, G.L.: ‘Recursive approximate weighted total least squares estimation of battery cell total capacity’, J. Power Sources, 2011, 196, (4), pp. 23192331.
    35. 35)
      • 27. Chen, M., Rincón-Mora, G.: ‘Accurate electrical battery model capable of predicting runtime and I–V performance’, IEEE Trans. Energy Convers., 2006, 21, (2), pp. 504511.
    36. 36)
      • 24. Sangwan, V., Sharma, A., Kumar, R., et al: ‘Equivalent circuit model parameters estimation of li-ion battery: C-rate, SOC and temperature effects’. 2016 IEEE Int. Conf. Power Electronics, Drives and Energy Systems (PEDES), Trivandrum, India, 2016, pp. 16.
    37. 37)
      • 5. Feng, F., Lu, R., Wei, G., et al: ‘Identification and analysis of model parameters used for lifepo4 cells series battery pack at various ambient temperature’, IET Electr. Syst. Transp., 2016, 6, (2), pp. 5055.
    38. 38)
      • 34. Bansal, J.C., Sharma, H., Jadon, S.S., et al: ‘Spider monkey optimization algorithm for numerical optimization’, Memet. Comput., 2014, 6, (1), pp. 3147.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-est.2018.0003
Loading

Related content

content/journals/10.1049/iet-est.2018.0003
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address