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Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition

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  • Hu, Jie
  • Liu, Di
  • Du, Changqing
  • Yan, Fuwu
  • Lv, Chen

Abstract

To achieve optimal power distribution of hybrid energy storage system composed of batteries and supercapacitors in electric vehicles, an adaptive wavelet transform-fuzzy logic control energy management strategy based on driving pattern recognition (DPR) is proposed in view of the fact that driving cycle greatly affects the performance of EMS. The DPR uses cluster analysis to classify driving cycles into different patterns according to the features extracted from the historical driving data sampling window and utilizes pattern recognition to identify real-time driving patterns. After recognition results are obtained, an adaptive wavelet transform is employed to allocate the high frequency components of power demand to supercapacitor which contains transient power and rapid variations, while the low frequency components are distributed to battery accordingly. The use of fuzzy logic control is to maintain the SOC of supercapacitor within desired level. The simulation results indicate that the proposed control strategy can effectively decrease the maximum charge/discharge current of battery by 58.2%, and improve the battery lifetime by 6.16% and the vehicle endurance range by 11.06% compared with conventional control strategies. Further demonstrate the advantage of hybrid energy storage system and the presented energy management strategy.

Suggested Citation

  • Hu, Jie & Liu, Di & Du, Changqing & Yan, Fuwu & Lv, Chen, 2020. "Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition," Energy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220304059
    DOI: 10.1016/j.energy.2020.117298
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    References listed on IDEAS

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    1. Kegang Zhao & Zhihao Liang & Yanjun Huang & Hong Wang & Amir Khajepour & Yuke Zhen, 2017. "Research on a Novel Hydraulic/Electric Synergy Bus," Energies, MDPI, vol. 11(1), pages 1-18, December.
    2. Ettihir, K. & Boulon, L. & Agbossou, K., 2016. "Optimization-based energy management strategy for a fuel cell/battery hybrid power system," Applied Energy, Elsevier, vol. 163(C), pages 142-153.
    3. Wang, Yujie & Sun, Zhendong & Chen, Zonghai, 2019. "Development of energy management system based on a rule-based power distribution strategy for hybrid power sources," Energy, Elsevier, vol. 175(C), pages 1055-1066.
    4. Liu, Chang & Wang, Yujie & Chen, Zonghai, 2019. "Degradation model and cycle life prediction for lithium-ion battery used in hybrid energy storage system," Energy, Elsevier, vol. 166(C), pages 796-806.
    5. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    6. Farouk Odeim & Jürgen Roes & Angelika Heinzel, 2015. "Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System," Energies, MDPI, vol. 8(7), pages 1-26, June.
    7. Trovão, João P. & Pereirinha, Paulo G. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2013. "A multi-level energy management system for multi-source electric vehicles – An integrated rule-based meta-heuristic approach," Applied Energy, Elsevier, vol. 105(C), pages 304-318.
    8. Zhenzhen Lei & Dong Cheng & Yonggang Liu & Datong Qin & Yi Zhang & Qingbo Xie, 2017. "A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition," Energies, MDPI, vol. 10(1), pages 1-20, January.
    9. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Hou, Jun & Han, Xuebing & Ouyang, Minggao, 2014. "Energy management strategies comparison for electric vehicles with hybrid energy storage system," Applied Energy, Elsevier, vol. 134(C), pages 321-331.
    10. Hu, Xiaosong & Johannesson, Lars & Murgovski, Nikolce & Egardt, Bo, 2015. "Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus," Applied Energy, Elsevier, vol. 137(C), pages 913-924.
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