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Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm

Author

Listed:
  • Jingxian Hao

    () (School of Automotive Studies, Tongji University, Shanghai 201804, China
    SAIC Motor Commercial Vehicle Technical Center, Shanghai 200438, China)

  • Zhuoping Yu

    () (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Zhiguo Zhao

    () (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Peihong Shen

    () (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Xiaowen Zhan

    () (School of Automotive Studies, Tongji University, Shanghai 201804, China)

Abstract

The rule-based logic threshold control strategy has been frequently used in energy management strategies for hybrid electric vehicles (HEVs) owing to its convenience in adjusting parameters, real-time performance, stability, and robustness. However, the logic threshold control parameters cannot usually ensure the best vehicle performance at different driving cycles and conditions. For this reason, the optimization of key parameters is important to improve the fuel economy, dynamic performance, and drivability. In principle, this is a multiparameter nonlinear optimization problem. The logic threshold energy management strategy for an all-wheel-drive HEV is comprehensively analyzed and developed in this study. Seven key parameters to be optimized are extracted. The optimization model of key parameters is proposed from the perspective of fuel economy. The global optimization method, DIRECT algorithm, which has good real-time performance, low computational burden, rapid convergence, is selected to optimize the extracted key parameters globally. The results show that with the optimized parameters, the engine operates more at the high efficiency range resulting into a fuel savings of 7% compared with non-optimized parameters. The proposed method can provide guidance for calibrating the parameters of the vehicle energy management strategy from the perspective of fuel economy.

Suggested Citation

  • Jingxian Hao & Zhuoping Yu & Zhiguo Zhao & Peihong Shen & Xiaowen Zhan, 2016. "Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm," Energies, MDPI, Open Access Journal, vol. 9(12), pages 1-24, November.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:997-:d:83817
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    References listed on IDEAS

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    1. Zeyu Chen & Rui Xiong & Kunyu Wang & Bin Jiao, 2015. "Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm," Energies, MDPI, Open Access Journal, vol. 8(5), pages 1-18, April.
    2. Jiankun Peng & Hao Fan & Hongwen He & Deng Pan, 2015. "A Rule-Based Energy Management Strategy for a Plug-in Hybrid School Bus Based on a Controller Area Network Bus," Energies, MDPI, Open Access Journal, vol. 8(6), pages 1-21, June.
    3. Lincun Fang & Shiyin Qin & Gang Xu & Tianli Li & Kemin Zhu, 2011. "Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms," Energies, MDPI, Open Access Journal, vol. 4(3), pages 1-13, March.
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    Citations

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    Cited by:

    1. repec:gam:jeners:v:11:y:2018:i:2:p:348-:d:130017 is not listed on IDEAS
    2. Zhenzhen Lei & Dong Cheng & Yonggang Liu & Datong Qin & Yi Zhang & Qingbo Xie, 2017. "A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition," Energies, MDPI, Open Access Journal, vol. 10(1), pages 1-20, January.
    3. Yu-Huei Cheng & Ching-Ming Lai, 2017. "Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm," Energies, MDPI, Open Access Journal, vol. 10(3), pages 1-21, March.
    4. repec:eee:energy:v:155:y:2018:i:c:p:838-852 is not listed on IDEAS

    More about this item

    Keywords

    fuel economy; hybrid electric vehicle; energy management strategy; logic threshold value; DIRECT; parameters optimization;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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