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Optimization and Application for Hydraulic Electric Hybrid Vehicle

Author

Listed:
  • Hsiu-Ying Hwang

    (Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Tian-Syung Lan

    (College of Mechatronic Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China)

  • Jia-Shiun Chen

    (Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

Targeting the application of medium and heavy vehicles, a hydraulic electric hybrid vehicle (HEHV) was designed, and its energy management control strategy is discussed in this paper. Matlab/Simulink was applied to establish the pure electric vehicle and HEHV models, and backward simulation was adopted for the simulation, to get the variation of torque and battery state of charge ( SOC ) through New York City Cycle of the US Environmental Protection Agency (EPA NYCC). Based on the simulation, the energy management strategy was designed. In this research, the rule-based control strategy was implemented as the energy distribution management strategy first, and then the genetic algorithm was utilized to conduct global optimization strategy analysis. The results from the genetic algorithm were employed to modify the rule-based control strategy to improve the electricity economic performance of the vehicle. The simulation results show that the electricity economic performance of the designed hydraulic hybrid vehicle was improved by 36.51% compared to that of a pure electric vehicle. The performance of energy consumption after genetic algorithm optimization was improved by 43.65%.

Suggested Citation

  • Hsiu-Ying Hwang & Tian-Syung Lan & Jia-Shiun Chen, 2020. "Optimization and Application for Hydraulic Electric Hybrid Vehicle," Energies, MDPI, vol. 13(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:322-:d:306773
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    References listed on IDEAS

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    1. Yuping Zeng & Yang Cai & Guiyue Kou & Wei Gao & Datong Qin, 2018. "Energy Management for Plug-In Hybrid Electric Vehicle Based on Adaptive Simplified-ECMS," Sustainability, MDPI, vol. 10(6), pages 1-24, June.
    2. Ximing Wang & Hongwen He & Fengchun Sun & Jieli Zhang, 2015. "Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 8(4), pages 1-20, April.
    3. Jia-Shiun Chen, 2015. "Energy Efficiency Comparison between Hydraulic Hybrid and Hybrid Electric Vehicles," Energies, MDPI, vol. 8(6), pages 1-27, May.
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    Cited by:

    1. Muataz Abotabik & Richard T. Meyer, 2021. "Switched Optimal Control of a Heavy-Duty Hybrid Vehicle," Energies, MDPI, vol. 14(20), pages 1-20, October.
    2. Teen-Hang Meen & Wenbing Zhao & Cheng-Fu Yang, 2020. "Special Issue on Selected Papers from IEEE ICKII 2019," Energies, MDPI, vol. 13(8), pages 1-5, April.
    3. Jian Yang & Tiezhu Zhang & Hongxin Zhang & Jichao Hong & Zewen Meng, 2020. "Research on the Starting Acceleration Characteristics of a New Mechanical–Electric–Hydraulic Power Coupling Electric Vehicle," Energies, MDPI, vol. 13(23), pages 1-20, November.
    4. Hsiu-Ying Hwang & Jia-Shiun Chen, 2020. "Optimized Fuel Economy Control of Power-Split Hybrid Electric Vehicle with Particle Swarm Optimization," Energies, MDPI, vol. 13(9), pages 1-18, May.
    5. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).

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