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A Real-Time Layer-Adaptive Wavelet Transform Energy Distribution Strategy in a Hybrid Energy Storage System of EVs

Author

Listed:
  • Jun Peng

    (School of Information Science and Engineering, Central South University, Changsha 410083, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410083, China)

  • Rui Wang

    (School of Information Science and Engineering, Central South University, Changsha 410083, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410083, China)

  • Hongtao Liao

    (School of Information Science and Engineering, Central South University, Changsha 410083, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410083, China)

  • Yanhui Zhou

    (School of Information Science and Engineering, Central South University, Changsha 410083, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410083, China)

  • Heng Li

    (School of Information Science and Engineering, Central South University, Changsha 410083, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410083, China)

  • Yue Wu

    (School of Information Science and Engineering, Central South University, Changsha 410083, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410083, China)

  • Zhiwu Huang

    (School of Information Science and Engineering, Central South University, Changsha 410083, China
    Hunan Engineering Laboratory of Rail Vehicles Braking Technology, Changsha 410083, China)

Abstract

In this paper, a real-time energy distribution strategy is designed by a layer-adaptive wavelet transform algorithm and proposed to meet the load power demand while distributing the high-frequency component to supercapacitors and the low-frequency component to batteries in a hybrid energy storage system. In the proposed method, the number of decomposition layers of wavelet transform corresponding to the load power is adaptively determined by dividing the operation zone of supercapacitors into eight cases to respectively distribute the low frequency component to batteries and the remaining high frequency component to supercapacitors. Firstly, since the state of charge of supercapacitors decreases faster as the decomposition layers increases, the state of charge of supercapacitors is divided into eight cases of operation zones. Secondly, since supercapacitors act as the peak power buffer unit, the corresponding number of decomposition layers is finally adaptively determined according to the operation zone of supercapacitors. An experiment testbed is built to verify the effectiveness of the proposed method. Extensive experiment results show that the proposed method provides a better real-time energy sharing between supercapacitors and batteries when compared with the conditional method.

Suggested Citation

  • Jun Peng & Rui Wang & Hongtao Liao & Yanhui Zhou & Heng Li & Yue Wu & Zhiwu Huang, 2019. "A Real-Time Layer-Adaptive Wavelet Transform Energy Distribution Strategy in a Hybrid Energy Storage System of EVs," Energies, MDPI, vol. 12(3), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:440-:d:202050
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    References listed on IDEAS

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    Cited by:

    1. Wu, Yue & Huang, Zhiwu & Liao, Hongtao & Chen, Bin & Zhang, Xiaoyong & Zhou, Yanhui & Liu, Yongjie & Li, Heng & Peng, Jun, 2020. "Adaptive power allocation using artificial potential field with compensator for hybrid energy storage systems in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    2. 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.
    3. Tengda Hu & Yunwu Li & Zhi Zhang & Ying Zhao & Dexiong Liu, 2021. "Energy Management Strategy of Hybrid Energy Storage System Based on Road Slope Information," Energies, MDPI, vol. 14(9), pages 1-18, April.
    4. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    5. Abdeldjalil Djouahi & Belkhir Negrou & Boubakeur Rouabah & Abdelbasset Mahboub & Mohamed Mahmoud Samy, 2023. "Optimal Sizing of Battery and Super-Capacitor Based on the MOPSO Technique via a New FC-HEV Application," Energies, MDPI, vol. 16(9), pages 1-18, May.
    6. Tao, Fazhan & Fu, Zhigao & Gong, Huixian & Ji, Baofeng & Zhou, Yao, 2023. "Twin delayed deep deterministic policy gradient based energy management strategy for fuel cell/battery/ultracapacitor hybrid electric vehicles considering predicted terrain information," Energy, Elsevier, vol. 283(C).

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