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Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles

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  • Wang, Bin
  • Xu, Jun
  • Cao, Binggang
  • Ning, Bo

Abstract

This paper proposes an adaptive mode switch strategy (AMSS) based on simulated annealing (SA) optimization of a multi-mode hybrid energy storage system (HESS) for electric vehicles. The proposed SA-AMSS is derived from a rule-based strategy to achieve the adaptive mode selection and energy management optimization. To improve the overall system efficiency of the multi-mode HESS, the state of charge (SOC) level of the supercapacitor (SC), the power level and the component efficiencies are discussed. On this basis, the objective function for the AMSS is established, focusing on selecting the most suitable mode. Furthermore, to accomplish a global energy management optimization based on the driving cycles, the SA approach is introduced into the optimizations of the reference SC SOC and battery power, rather than the direct power distribution optimization between the battery and SC. The AMSS is implemented based on the SA optimization. Simulations and experiments are presented to verify the effectiveness of the SA-AMSS for the multi-mode HESS. Results show that the SA-AMSS can not only reduce the frequency of the mode switching, but also avoid the sudden excessive power output of the battery. The SC can respond to all peak power demands and absorb all the braking energy. So the SA-AMSS is very flexible and effective, and the battery safety can be guaranteed. Compared with the rule-based strategy, the overall system efficiency of the multi-mode HESS is significantly improved.

Suggested Citation

  • Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    • Handle: RePEc:eee:appene:v:194:y:2017:i:c:p:596-608
    DOI: 10.1016/j.apenergy.2016.05.030
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    1. 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.
    2. Ren, Guizhou & Ma, Guoqing & Cong, Ning, 2015. "Review of electrical energy storage system for vehicular applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 225-236.
    3. Herrera, Victor & Milo, Aitor & Gaztañaga, Haizea & Etxeberria-Otadui, Ion & Villarreal, Igor & Camblong, Haritza, 2016. "Adaptive energy management strategy and optimal sizing applied on a battery-supercapacitor based tramway," Applied Energy, Elsevier, vol. 169(C), pages 831-845.
    4. Castaings, Ali & Lhomme, Walter & Trigui, Rochdi & Bouscayrol, Alain, 2016. "Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints," Applied Energy, Elsevier, vol. 163(C), pages 190-200.
    5. 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.
    6. Yanzi Wang & Weida Wang & Yulong Zhao & Lei Yang & Wenjun Chen, 2016. "A Fuzzy-Logic Power Management Strategy Based on Markov Random Prediction for Hybrid Energy Storage Systems," Energies, MDPI, vol. 9(1), pages 1-20, January.
    7. Kuperman, Alon & Aharon, Ilan, 2011. "Battery-ultracapacitor hybrids for pulsed current loads: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 981-992, February.
    8. M. Sabri, M.F. & Danapalasingam, K.A. & Rahmat, M.F., 2016. "A review on hybrid electric vehicles architecture and energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1433-1442.
    9. Sharafi, Masoud & ELMekkawy, Tarek Y., 2014. "Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach," Renewable Energy, Elsevier, vol. 68(C), pages 67-79.
    10. He, Hongwen & Xiong, Rui & Zhao, Kai & Liu, Zhentong, 2013. "Energy management strategy research on a hybrid power system by hardware-in-loop experiments," Applied Energy, Elsevier, vol. 112(C), pages 1311-1317.
    11. 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.
    12. 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.
    13. Song, Ziyou & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Lu, Languang & Ouyang, Minggao & Hofmann, Heath, 2014. "Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 135(C), pages 212-224.
    14. Chia, Yen Yee & Lee, Lam Hong & Shafiabady, Niusha & Isa, Dino, 2015. "A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the Support Vector Machine," Applied Energy, Elsevier, vol. 137(C), pages 588-602.
    15. Yang, Yalian & Hu, Xiaosong & Pei, Huanxin & Peng, Zhiyuan, 2016. "Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach," Applied Energy, Elsevier, vol. 168(C), pages 683-690.
    16. Tie, Siang Fui & Tan, Chee Wei, 2013. "A review of energy sources and energy management system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 82-102.
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