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Research on a new power distribution control strategy of hybrid energy storage system for hybrid electric vehicles based on the subtractive clustering and adaptive fuzzy neural network

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Abstract

In order to give full play to the advantages of power battery and super-capacitor in the hybrid energy storage system (HESS) of hybrid electric vehicles (HEV), a new control strategy based on the subtractive clustering (SC) and adaptive fuzzy neural network (AFNN) was proposed to solve the problem of power distribution between the two energy sources when the driving schedule changes. Firstly, we used the SC to determine the structure of AFNN. Secondly, in order to improve the learning efficiency of AFNN, the back-propagation hybrid least square algorithm was applied to optimize the antecedent and conclusion parameters of network. Finally, the fuzzy membership function and rule set automatically generated by the neural network were used to the power distribution control of HESS in HEV. We verified the SC and AFNN control strategy by simulation and experiment based on the ADVISOR 2002 simulation software and the experimental platform, and the results show that the proposed control strategy can give full play to the advantages of HESS, and improve the energy storage performance of HEV.

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Data availability

The datasets generated during the current study are available in the [Replication Data for: Cluster Computing] repository, [https://doi.org/10.7910/DVN/WBL9IE].

References

  1. Zhou, M.L., Wang, J.M., Chen, Q.L.: Research on vehicle controller and control strategy for pure electric vehicle with composite power. Int J Electric and Hybrid Vehicles 2, 170–193 (2019)

    Article  Google Scholar 

  2. Gao, J.W., Li, M., Hu, Y.F., Chen, H., et al.: Challenges and developments of automotive fuel cell hybrid power system and control. SCIENCE CHINA Inf. Sci. 62, 51201–51225 (2019)

    Article  Google Scholar 

  3. Marx, N., Hissel, D., Gustin, F., Boulon, L., et al.: On the sizing and energy management of an hybrid multistack fuel cell–battery system for automotive applications. Int J Hydrogen Energy. 42, 1518–1526 (2017)

    Article  Google Scholar 

  4. Bizon, N.: Real-time optimization strategy for fuel cell hybrid power sources with load-following control of the fuel or air flow. Energy Convers Manage. 157, 13–27 (2018)

    Article  Google Scholar 

  5. Tahri, A., Fadil, H.E., Belhaj, F.Z., et al.: Management of fuel cell power and supercapacitor state-of-charge for electric vehicles. Electric Power Syst Res. 160, 89–98 (2018)

    Article  Google Scholar 

  6. Hong, Z.H., Li, Q., Han, Y., et al.: An energy management strategy based on dynamic power factor for fuel cell/battery hybrid locomotive. Int J Hydrogen Energy. 43, 3261–3272 (2018)

    Article  Google Scholar 

  7. Liu, Y.W., Zhu, Y.X., Lin, Z.Y., et al.: Energy management strategy optimization of hybrid energy storage system based on Radau Pseudo-spectral Method. Automot. Eng. 41, 625–633 (2019)

    Google Scholar 

  8. Zhou, M.L., Liu, Z.H., Guo, J.M.: Control strategy of hybrid electric vehicle. J Harbin Univ Sci Technol. 24, 41–47 (2019)

    Google Scholar 

  9. Boumediene, A., Khadidja, A., Brahim, M.: Energy management of PEM fuel cell/supercapacitor hybrid power sources for an electric vehicle. Int J Hydrogen Energy. 42, 21158–21166 (2016)

    Google Scholar 

  10. Jeongwon, S., Myoungho, S., Kyunghan, M., et al.: Power management strategy for the 48 V mild hybrid electric vehicle based on the Charge-Sustaining control. Int J Automotive Technol. 20, 37–49 (2017)

    Google Scholar 

  11. Singh, A., Pattnaik, S.: Design of an efficient power sharing strategy for a battery-ultracapacitor hybrid energy storage system. 2016 IEEE 1st International Conference on Power Electronics (ICPEICES). Delhi, India, 4–6 July. 1–5(2016).

  12. Hu, Y., Li, W.M., Xu, K., et al.: Energy management strategy for a hybrid electric vehicle based on deep reinforcement learning. Appl. Sci. 8, 1–15 (2018)

    Google Scholar 

  13. Shi, L., Zheng, M.G., Li, F.Y.: The energy management strategy for parallel hybrid electric vehicles based on MNN. Multimedia Tools App. 79, 5321–5333 (2020)

    Article  Google Scholar 

  14. Mohamad, F.M., Kumeresan, A.D., Mohd, F.R.: Improved fuel economy of through-the-road hybrid electric vehicle with fuzzy logic-based energy management strategy. Int J Fuzzy Syst. 20, 2677–2692 (2018)

    Article  Google Scholar 

  15. Masoud, M., Masoud, D.: A novel power distribution system employing state of available power estimation for a hybrid energy storage system. Appl. Energy 228, 2478–2489 (2018)

    Google Scholar 

  16. Chen, H., Lin, C.X., R.: Online adaptive energy management strategy for a hybrid energy storage system in electric vehicles. Trans China Electrotech Soc. 35, 644–651 (2020)

    Google Scholar 

  17. Sun, Z.Q., Deng, Z.D., Zhang, Z.X.: The theory and technology of Intelligent control. Tsinghua University Press, Beijing (2011)

    Google Scholar 

  18. Chandar, S.K.: Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach. Clust. Comput. 22, 13159–13166 (2019)

    Article  Google Scholar 

  19. Khalil, B., Ali, C.: Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theoret. Appl. Climatol. 137, 31–43 (2019)

    Article  Google Scholar 

  20. Kokkinos, Y., Margaritis, K.G.: Kernel averaged gradient descent subtractive clustering for exemplar selection. Evol. Syst. 9, 285–297 (2018)

    Article  Google Scholar 

  21. Fei, J.T., Wang, T.T.: Adaptive fuzzy-neural-network based on RBFNN control for active power filter. Int J Machine Learning and Cybernetics. 10, 1139–1150 (2019)

    Article  Google Scholar 

  22. Dinibel, P.B., María, P.N., Amalia, M.: Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting. Neural Comput. Appl. 31, 8411–8422 (2019)

    Article  Google Scholar 

  23. Zhang, Q., Hu, L., Gow, J.: Output feedback stabilization for MIMO semi-linear stochastic systems with transient optimisation. Int. J. Autom. Comput. 17, 83–95 (2020)

    Article  Google Scholar 

  24. Zhang, Q., Zhou, J., Wang, H., Chai, T.: Output feedback stabilization for a class of multi-variable bilinear stochastic systems with stochastic coupling attenuation. IEEE Trans. Autom. Control 62, 2936–2942 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  25. Zhang, X.P., Zhang, X.H., Li, D., et al.: Adaptive synchronization for a class of fractional order time-delay uncertain chaotic systems via fuzzy fractional order neural network. Int J Control Autom Syst. 17, 1209–1220 (2019)

    Article  Google Scholar 

  26. Wang, R.M., Zhang, Y.N., Chen, Y.Q., et al.: Fuzzy neural network-based chaos synchronization for a class of fractional-order chaotic systems: an adaptive sliding mode control approach. Nonlinear Dyn. 100, 1275–1287 (2020)

    Article  MATH  Google Scholar 

  27. Li, C.I., Chen, G.D., Sung, T.Y., et al.: Novel adaptive kalman filter with fuzzy neural network for trajectory estimation system. Int J Fuzzy Syst. 21, 1649–1660 (2019)

    Article  Google Scholar 

  28. Senthilkumar, C., Gnanamurthy, R.K.: A Fuzzy clustering-based MRI brain image segmentation using back propagation neural networks. Clust. Comput. 22, 12305–12312 (2019)

    Article  Google Scholar 

  29. Huang, Y.J., Li, Z., Ye, H.C., et al.: Mapping soil electrical conductivity using ordinary kriging combined with back-propagation network. Chin. Geogra. Sci. 29, 270–282 (2019)

    Article  Google Scholar 

  30. Zou, W.D., Xia, Y.Q.: Back propagation bidirectional extreme learning machine for traffic flow time series prediction. Neural Comput. Appl. 31, 7401–7414 (2019)

    Article  Google Scholar 

  31. Chao, Z., Kim, H.J.: Brain image segmentation based on the hybrid of back propagation neural network and Ada-boost system. J Sign Process Syst. 92, 289–298 (2020)

    Article  Google Scholar 

  32. Liu, J., Wang, Y., Wu, Y.J., et al.: Research and implementation of new-type supercapacitor and battery hybrid energy storage system. J Power Electron. 20, 1395–1404 (2020)

    Article  Google Scholar 

  33. Zhang, Q., Li, G.: A predictive energy management system for hybrid energy storage systems in electric vehicles. Electr. Eng. 101, 759–770 (2019)

    Article  Google Scholar 

  34. Wang, Z.S., Wang, P., Bi, H.K., et al.: A bidirectional DC/DC converter with wide-voltage gain range and low-voltage stress for hybrid-energy storage systems in electric vehicles. J Power Electron. 2(20), 76–86 (2020)

    Article  Google Scholar 

  35. Masoud, M.T., Mohammad, R.H., Vahid, E., et al.: Wavelet-based power management for hybrid energy storage system. J Modern Power Syst Clean Energy. 7, 779–790 (2019)

    Article  Google Scholar 

  36. Rajesh, A.V.: Hybrid energy storage system for electric vehicle using battery and ultracapacitor. Adv Electrical Comput Technol. 672, 1203–1214 (2020)

    Article  Google Scholar 

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Acknowledgements

The research described in this paper was financially supported by Changzhou key research plan (Applied Basic Research) project (Grant No. CJ20200044) and the basic science (Natural Science) research project of colleges and universities in Jiangsu Province (Grant No. 22KJD470002).

Funding

The research described in this paper was financially supported by Changzhou key research plan (Applied Basic Research) project (Grant No. CJ20200044) and the basic science (Natural Science) research project of colleges and universities in Jiangsu Province (Grant No. 22KJD470002).

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China

    Qi Wang & Yinsheng Luo

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  1. Qi Wang
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  2. Yinsheng Luo
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by QW, and YL. The first draft of the manuscript was written by QW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Qi Wang.

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Wang, Q., Luo, Y. Research on a new power distribution control strategy of hybrid energy storage system for hybrid electric vehicles based on the subtractive clustering and adaptive fuzzy neural network. Cluster Comput 25, 4413–4422 (2022). https://doi.org/10.1007/s10586-022-03687-z

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