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A Fuzzy-Logic Power Management Strategy Based on Markov Random Prediction for Hybrid Energy Storage Systems

Author

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  • Yanzi Wang

    (National Key Laboratory of Vehicle Transmission, Beijing Institute of Technology, Beijing 100081, China)

  • Weida Wang

    (National Key Laboratory of Vehicle Transmission, Beijing Institute of Technology, Beijing 100081, China)

  • Yulong Zhao

    (National Key Laboratory of Vehicle Transmission, Beijing Institute of Technology, Beijing 100081, China)

  • Lei Yang

    (Transmission System Section, Powertrain Department, Shanghai Automotive Industry Corporation Motor Commercial Vehicle Technical Center, Shanghai 200432, China)

  • Wenjun Chen

    (The Forth Branch Company, Inner Mongolia First Machinery Group Co. Ltd., Baotou 014032, China)

Abstract

Over the last few years; issues regarding the use of hybrid energy storage systems (HESSs) in hybrid electric vehicles have been highlighted by the industry and in academic fields. This paper proposes a fuzzy-logic power management strategy based on Markov random prediction for an active parallel battery-UC HESS. The proposed power management strategy; the inputs for which are the vehicle speed; the current electric power demand and the predicted electric power demand; is used to distribute the electrical power between the battery bank and the UC bank. In this way; the battery bank power is limited to a certain range; and the peak and average charge/discharge power of the battery bank and overall loss incurred by the whole HESS are also reduced. Simulations and scaled-down experimental platforms are constructed to verify the proposed power management strategy. The simulations and experimental results demonstrate the advantages; feasibility and effectiveness of the fuzzy-logic power management strategy based on Markov random prediction.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:1:p:25-:d:61673
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    References listed on IDEAS

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    1. 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.
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    1. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    2. Xingyue Jiang & Jianjun Hu & Meixia Jia & Yong Zheng, 2018. "Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, July.
    3. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    4. Hoai-Linh T. Nguyen & Bảo-Huy Nguyễn & Thanh Vo-Duy & João Pedro F. Trovão, 2021. "A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles," Energies, MDPI, vol. 14(12), pages 1-23, June.
    5. Tran, Dai-Duong & Vafaeipour, Majid & El Baghdadi, Mohamed & Barrero, Ricardo & Van Mierlo, Joeri & Hegazy, Omar, 2020. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    6. Jiajun Liu & Tianxu Jin & Li Liu & Yajue Chen & Kun Yuan, 2017. "Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    7. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    8. 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.
    9. Yupeng Yuan & Tianding Zhang & Boyang Shen & Xinping Yan & Teng Long, 2018. "A Fuzzy Logic Energy Management Strategy for a Photovoltaic/Diesel/Battery Hybrid Ship Based on Experimental Database," Energies, MDPI, vol. 11(9), pages 1-15, August.
    10. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    11. 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.
    12. João Faria & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2019. "Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System," Energies, MDPI, vol. 12(5), pages 1-24, March.

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