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Modelling of supercapacitors based on SVM and PSO algorithms

Modelling of supercapacitors based on SVM and PSO algorithms

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Supercapacitors present an attractive energy storage alternative for high-performance applications due to their compact size and high-power density. Therefore, the supercapacitors have broad prospects for the development in the field of electric vehicles and renewable energy. To describe the output characteristics of the supercapacitors with high accuracy for the simulation research and practical application, a dynamic modelling method is proposed for the supercapacitors based on support vector machine (SVM) and particle swarm optimisation (PSO) algorithm. In this study, the SVM is used to predict the output voltage of the supercapacitors with the key parameters (temperature, current and initial voltage). The PSO algorithm is adopted to optimise the parameters of the SVM to improve the performance of the dynamic modelling. An experimental platform is established, where an electric machine drive system powered by the supercapacitors is controlled to operate at frequent acceleration and deceleration modes, thus leading to the frequent charging and discharging of the supercapacitors. The experimental data is collected to validate the effectiveness of the proposed method. The results show that the proposed method can effectively predict the output voltage of the supercapacitors.

References

    1. 1)
      • 1. Jayalakshmi, M., Balasubramanian, K.: ‘Simple capacitors to supercapacitors – an overview’, Int. J. Electrochem. Sci., 2008, 3, pp. 11961217.
    2. 2)
      • 2. Machado, F., Trovao, J.P.F., Henggeler Antunes, C.: ‘Effectiveness of supercapacitors in pure electric vehicles using a hybrid metaheuristic approach’, IEEE Trans. Veh. Technol., 2016, 65, (1), pp. 2936.
    3. 3)
      • 3. Carter, R., Cruden, A., Hall, P.J.: ‘Optimizing for efficiency or battery life in a battery/supercapacitor electric vehicle’, IEEE Trans. Veh. Technol., 2012, 61, (4), pp. 12561533.
    4. 4)
      • 4. Parisa, G., Nasser, L.A.: ‘Real-time nonlinear model predictive control of a battery-supercapacitor hybrid energy storage system in electric vehicles’, IEEE Trans. Veh. Technol., 2017, 66, (11), pp. 96789688.
    5. 5)
      • 5. Cheng, M., Zhu, Y.: ‘The state of the art of wind energy conversion systems and technologies: a review’, Energy Convers. Manage., 2014, 88, pp. 332347.
    6. 6)
      • 6. Ujjal, M., Narsa, R.T., Sathish, K., et al: ‘Validation of faster joint control strategy for battery and supercapacitor based energy storage system’, IEEE Trans. Ind. Electron., 2018, 65, (4), pp. 32863295.
    7. 7)
      • 7. Kollimalla, S.K., Mishra, M.K., Narasamma, N.L.: ‘Design and analysis of novel control strategy for battery and supercapacitor storage system’, IEEE Trans. Sustain. Energy, 2014, 5, (4), pp. 11371144.
    8. 8)
      • 8. Wang, W., Cheng, M., Wang, Y., et al: ‘A novel energy management strategy of onboard supercapacitor for subway applications with permanent-magnet traction system’, IEEE Trans. Veh. Technol., 2014, 63, (6), pp. 25782588.
    9. 9)
      • 9. Odeim, F., Roes, J., Heinzel, A.: ‘Power management optimization of a fuel cell/battery/supercapacitor hybrid system for transit bus applications’, IEEE Trans. Veh. Technol., 2016, 65, (7), pp. 57835788.
    10. 10)
      • 10. Belhachemi, F., Rael, S., Davat, B.: ‘A physical based model of power electric double-layer supercapacitors’. IEEE Ind. Appl. Conf., 2000, pp. 30693076.
    11. 11)
      • 11. Spyker, R.L., Nelms, R.M.: ‘Classical equivalent circuit parameters for a double-layer capacitor’, IEEE Trans. Aerosp. Electron. Syst., 2000, 36, (3), pp. 829836.
    12. 12)
      • 12. Nelms, R.M., Cahela, D.R., Tatarchuk, B.J.: ‘Modeling double-layer capacitor behavior using ladder circuits’, IEEE Trans. Aerosp. Electron. Syst., 2003, 39, (2), pp. 430438.
    13. 13)
      • 13. Zubieta, L., Bonert, R.: ‘Characterization of double-layer capacitors for power electronics applications’, IEEE Trans. Ind. Appl., 2000, 36, (1), pp. 199205.
    14. 14)
      • 14. Rafik, F., Gualous, H., Gallay, R., et al: ‘Frequency, thermal and voltage supercapacitor characterization and modelling’, J. Power Sources, 2007, 165, (2), pp. 928934.
    15. 15)
      • 15. Farsi, H., Gobal, F.: ‘Artificial neural network simulator for supercapacitor performance prediction’, Comput. Mater. Sci., 2007, 39, pp. 678683.
    16. 16)
      • 16. Marie-Francoise, J.N., Gualous, H., Berthon, A.: ‘Supercapacitor thermal- and electrical-behaviour modelling using ANN’, Proc. Inst. Electr. Eng., Electr. Power Appl., 2006, 153, (2), pp. 255262.
    17. 17)
      • 17. Huang, J., Hu, X.G., Fang, F.: ‘Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker’, Measurement, 2011, 44, (4), pp. 10181027.
    18. 18)
      • 18. Huang, J., Hu, X.G., Geng, X.: ‘An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine’, Electr. Power Syst. Res., 2011, 82, (2), pp. 400407.
    19. 19)
      • 19. Cortes, C., Vapnik, V.N.: ‘Support vector networks’, Mach. Learn., 1995, 20, (3), pp. 273297.
    20. 20)
      • 20. Yuan, S.F., Chu, F.L.: ‘Fault diagnostics based on particle swarm optimisation and support vector machines’, Mech. Syst. Signal Process., 2007, 21, (4), pp. 17871798.
    21. 21)
      • 21. Liu, Z.W., Cao, H.R., Chen, X.F., et al: ‘Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings’, Neurocomputing, 2013, 99, (1), pp. 399410.
    22. 22)
      • 22. Huang, C.L., Dun, J.F.: ‘A distributed PSO–SVM hybrid system with feature selection and parameter optimization’, Appl. Soft Comput., 2008, 8, (4), pp. 13811391.
    23. 23)
      • 23. Kennedy, J., Eberhart, R.: ‘Particle swarm optimization’. IEEE Int. Conf. Neural Networks, 1995, pp. 19421948.
    24. 24)
      • 24. Ganguly, S., Sahoo, N.C., Das, D.: ‘Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation’, Fuzzy Set Syst., 2013, 213, pp. 4773.
    25. 25)
      • 25. Tsekouras, G.E., Tsimikas, J.: ‘On training RBF neural networks using input–output fuzzy clustering and particle swarm optimization’, Fuzzy Set Syst., 2013, 221, pp. 6589.
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