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Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty

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  • Wang, Chun
  • Yang, Ruixin
  • Yu, Quanqing

Abstract

In order to avoid the sharps and transients of power demand and extend the battery lifetime, three energy management strategies via wavelet transform (WT) considering temperature uncertainty for hybrid energy storage system (HESS) in the plug-in hybrid electric vehicle are proposed in this paper. The HESS consisting of batteries, ultracapacitors, along with two associated DC/DC converters is discussed and modeled in details. In addition, to further investigate the influence of temperature uncertainty, a random temperature variation and three-dimensional response surfaces are employed for modeling. To systematically compare the performances of WT-based (WTB) strategy, WT-and-rule-based (WTRB) strategy and WT-and fuzzy-logic-control-based (WTFLCB) strategy, an optimization scheme is presented directly. The simulation results demonstrate that the WTFLCB strategy shows better performance under temperature uncertainty. Moreover, a hardware in the loop experiment platform is set up to further verify the feasibility of the WTRB strategy for actual application. It is found that the battery SoC and ultracapacitor SoC estimation errors are less than 0.77% and 3.87%, respectively.

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  • Wang, Chun & Yang, Ruixin & Yu, Quanqing, 2019. "Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty," Applied Energy, Elsevier, vol. 256(C).
  • Handle: RePEc:eee:appene:v:256:y:2019:i:c:s0306261919316150
    DOI: 10.1016/j.apenergy.2019.113928
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    References listed on IDEAS

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    1. Wang, Chun & Xiong, Rui & He, Hongwen & Ding, Xiaofeng & Shen, Weixiang, 2016. "Efficiency analysis of a bidirectional DC/DC converter in a hybrid energy storage system for plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 612-622.
    2. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Hou, Jun & Zhang, Xiaowu & Ouyang, Minggao, 2015. "The optimization of a hybrid energy storage system at subzero temperatures: Energy management strategy design and battery heating requirement analysis," Applied Energy, Elsevier, vol. 159(C), pages 576-588.
    3. 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.
    4. Zhang, Shuo & Xiong, Rui & Zhang, Chengning, 2015. "Pontryagin’s Minimum Principle-based power management of a dual-motor-driven electric bus," Applied Energy, Elsevier, vol. 159(C), pages 370-380.
    5. Tian, Jinpeng & Xiong, Rui & Shen, Weixiang & Wang, Ju, 2019. "Frequency and time domain modelling and online state of charge monitoring for ultracapacitors," Energy, Elsevier, vol. 176(C), pages 874-887.
    6. 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.
    7. Wang, Chun & He, Hongwen & Zhang, Yongzhi & Mu, Hao, 2017. "A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures," Applied Energy, Elsevier, vol. 196(C), pages 268-278.
    8. Zou, Yuan & Liu, Teng & Liu, Dexing & Sun, Fengchun, 2016. "Reinforcement learning-based real-time energy management for a hybrid tracked vehicle," Applied Energy, Elsevier, vol. 171(C), pages 372-382.
    9. Feiyan Qin & Guoqing Xu & Yue Hu & Kun Xu & Weimin Li, 2017. "Stochastic Optimal Control of Parallel Hybrid Electric Vehicles," Energies, MDPI, vol. 10(2), pages 1-16, February.
    10. Chen, Zeyu & Xiong, Rui & Cao, Jiayi, 2016. "Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions," Energy, Elsevier, vol. 96(C), pages 197-208.
    11. Yang, Ruixin & Xiong, Rui & He, Hongwen & Mu, Hao & Wang, Chun, 2017. "A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles," Applied Energy, Elsevier, vol. 207(C), pages 336-345.
    12. Qiao Zhang & Weiwen Deng, 2016. "An Adaptive Energy Management System for Electric Vehicles Based on Driving Cycle Identification and Wavelet Transform," Energies, MDPI, vol. 9(5), pages 1-24, May.
    13. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
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    2. Sánchez, Marcelino & Delprat, Sébastien & Hofman, Theo, 2020. "Energy management of hybrid vehicles with state constraints: A penalty and implicit Hamiltonian minimization approach," Applied Energy, Elsevier, vol. 260(C).
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    6. Stefano Lodetti & Jorge Bruna & Julio J. Melero & José F. Sanz, 2019. "Wavelet Packet Decomposition for IEC Compliant Assessment of Harmonics under Stationary and Fluctuating Conditions," Energies, MDPI, vol. 12(22), pages 1-15, November.

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