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A Single-Degree-of-Freedom Energy Optimization Strategy for Power-Split Hybrid Electric Vehicles

Author

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  • Chaoying Xia

    (School of Electrical and Information Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China)

  • Zhiming DU

    (School of Electrical and Information Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China)

  • Cong Zhang

    (School of Electrical and Information Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China)

Abstract

This paper presents a single-degree-of-freedom energy optimization strategy to solve the energy management problem existing in power-split hybrid electric vehicles (HEVs). The proposed strategy is based on a quadratic performance index, which is innovatively designed to simultaneously restrict the fluctuation of battery state of charge (SOC) and reduce fuel consumption. An extended quadratic optimal control problem is formulated by approximating the fuel consumption rate as a quadratic polynomial of engine power. The approximated optimal control law is obtained by utilizing the solution properties of the Riccati equation and adjoint equation. It is easy to implement in real-time and the engineering significance is explained in details. In order to validate the effectiveness of the proposed strategy, the forward-facing vehicle simulation model is established based on the ADVISOR software (Version 2002, National Renewable Energy Laboratory, Golden, CO, USA). The simulation results show that there is only a little fuel consumption difference between the proposed strategy and the Pontryagin’s minimum principle (PMP)-based global optimal strategy, and the proposed strategy also exhibits good adaptability under different initial battery SOC, cargo mass and road slope conditions.

Suggested Citation

  • Chaoying Xia & Zhiming DU & Cong Zhang, 2017. "A Single-Degree-of-Freedom Energy Optimization Strategy for Power-Split Hybrid Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-23, July.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:896-:d:103315
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    References listed on IDEAS

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    1. Tian, He & Lu, Ziwang & Wang, Xu & Zhang, Xinlong & Huang, Yong & Tian, Guangyu, 2016. "A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus," Applied Energy, Elsevier, vol. 177(C), pages 71-80.
    2. Hou, Cong & Ouyang, Minggao & Xu, Liangfei & Wang, Hewu, 2014. "Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 115(C), pages 174-189.
    3. Khayyam, Hamid & Bab-Hadiashar, Alireza, 2014. "Adaptive intelligent energy management system of plug-in hybrid electric vehicle," Energy, Elsevier, vol. 69(C), pages 319-335.
    4. Shabbir, Wassif & Evangelou, Simos A., 2014. "Real-time control strategy to maximize hybrid electric vehicle powertrain efficiency," Applied Energy, Elsevier, vol. 135(C), pages 512-522.
    5. Cipek, Mihael & Pavković, Danijel & Petrić, Joško, 2013. "A control-oriented simulation model of a power-split hybrid electric vehicle," Applied Energy, Elsevier, vol. 101(C), pages 121-133.
    6. Chaoying Xia & Cong Zhang, 2015. "Power Management Strategy of Hybrid Electric Vehicles Based on Quadratic Performance Index," Energies, MDPI, vol. 8(11), pages 1-16, November.
    7. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    8. Yue Hu & Weimin Li & Hui Xu & Guoqing Xu, 2015. "An Online Learning Control Strategy for Hybrid Electric Vehicle Based on Fuzzy Q-Learning," Energies, MDPI, vol. 8(10), pages 1-20, October.
    9. Chen, Syuan-Yi & Hung, Yi-Hsuan & Wu, Chien-Hsun & Huang, Siang-Ting, 2015. "Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization," Applied Energy, Elsevier, vol. 160(C), pages 132-145.
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    Cited by:

    1. Keun-Young Yoon & Soo-Whang Baek, 2019. "Robust Design Optimization with Penalty Function for Electric Oil Pumps with BLDC Motors," Energies, MDPI, vol. 12(1), pages 1-14, January.
    2. Andrea Bonfiglio & Damiano Lanzarotto & Mario Marchesoni & Massimiliano Passalacqua & Renato Procopio & Matteo Repetto, 2017. "Electrical-Loss Analysis of Power-Split Hybrid Electric Vehicles," Energies, MDPI, vol. 10(12), pages 1-17, December.

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