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Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms

Author

Listed:
  • Lincun Fang

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China)

  • Shiyin Qin

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing, China)

  • Gang Xu

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China)

  • Tianli Li

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China)

  • Kemin Zhu

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China)

Abstract

Compared to conventional vehicles Hybrid Electric Vehicles (HEVs) provide fairly high fuel economy with lower emissions. To enhance HEV performance in terms of fuel economy and emissions, and ensure user satisfaction with driving performance, the need for simultaneous optimization for the main parameters of powertrain components and control system is inevitable. However, this problem is challenging due to the large amount of coupling design parameters, conflicting design objectives and nonlinear constraints. Considering the defect of the methods which convert multi-objective optimization problems into single-objective ones, a comprehensive methodology based on the non-dominated sorting genetic algorithms II (NSGA II) to achieve parameter optimization for powertrain components and control system simultaneously and successfully find the Pareto-optimal solutions set is presented in this paper. A case simulation is carried out and simulated by ADVISOR, The simulation results show that this method can produce many Pareto-optimal solutions and a satisfactory solution can be selected by decision-makers according to their requirements. The results demonstrate the effectiveness of the algorithms proposed in this paper.

Suggested Citation

  • 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, vol. 4(3), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:4:y:2011:i:3:p:532-544:d:11743
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    Cited by:

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    2. Mohammad Ali Karbaschian & Dirk Söffker, 2014. "Review and Comparison of Power Management Approaches for Hybrid Vehicles with Focus on Hydraulic Drives," Energies, MDPI, vol. 7(6), pages 1-25, May.
    3. Antonio Galvagno & Umberto Previti & Fabio Famoso & Sebastian Brusca, 2021. "An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles," Energies, MDPI, vol. 14(6), pages 1-16, March.
    4. Jianlei Lang & Shuiyuan Cheng & Ying Zhou & Beibei Zhao & Haiyan Wang & Shujing Zhang, 2013. "Energy and Environmental Implications of Hybrid and Electric Vehicles in China," Energies, MDPI, vol. 6(5), pages 1-23, May.
    5. Ganesh Mohan & Francis Assadian & Stefano Longo, 2013. "An Optimization Framework for Comparative Analysis of Multiple Vehicle Powertrains," Energies, MDPI, vol. 6(10), pages 1-31, October.
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    8. Tran, Dai-Duong & Vafaeipour, Majid & El Baghdadi, Mohamed & Barrero, Ricardo & Van Mierlo, Joeri & Hegazy, Omar, 2020. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
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    10. Zhaobo Qin & Yugong Luo & Keqiang Li & Huei Peng, 2017. "Optimal Design of a Novel Hybrid Electric Powertrain for Tracked Vehicles," Energies, MDPI, vol. 10(12), pages 1-25, December.
    11. 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, vol. 9(12), pages 1-24, November.
    12. Farouk Odeim & Jürgen Roes & Angelika Heinzel, 2015. "Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System," Energies, MDPI, vol. 8(7), pages 1-26, June.
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    14. Zhang, Pei & Yan, Fuwu & Du, Changqing, 2015. "A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 88-104.
    15. Caiyang Wei & Theo Hofman & Esin Ilhan Caarls & Rokus van Iperen, 2020. "A Review of the Integrated Design and Control of Electrified Vehicles," Energies, MDPI, vol. 13(20), pages 1, October.
    16. 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).
    17. Diju Gao & Xuyang Wang & Tianzhen Wang & Yide Wang & Xiaobin Xu, 2018. "An Energy Optimization Strategy for Hybrid Power Ships under Load Uncertainty Based on Load Power Prediction and Improved NSGA-II Algorithm," Energies, MDPI, vol. 11(7), pages 1-14, July.
    18. 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, vol. 10(1), pages 1-20, January.
    19. Hassam Muazzam & Mohamad Khairi Ishak & Athar Hanif & Ali Arshad Uppal & AI Bhatti & Nor Ashidi Mat Isa, 2022. "Virtual Sensor Using a Super Twisting Algorithm Based Uniform Robust Exact Differentiator for Electric Vehicles," Energies, MDPI, vol. 15(5), pages 1-18, February.
    20. Hyo Seon Park & Bongkeun Kwon & Yunah Shin & Yousok Kim & Taehoon Hong & Se Woon Choi, 2013. "Cost and CO 2 Emission Optimization of Steel Reinforced Concrete Columns in High-Rise Buildings," Energies, MDPI, vol. 6(11), pages 1-16, October.
    21. Jianyun, Zhu & Li, Chen & Lijuan, Xia & Bin, Wang, 2019. "Bi-objective optimal design of plug-in hybrid electric propulsion system for ships," Energy, Elsevier, vol. 177(C), pages 247-261.
    22. Mahmoud Abdelsalam & Hatem Y. Diab, 2019. "Optimal Coordination of DOC Relays Incorporated into a Distributed Generation-Based Micro-Grid Using a Meta-Heuristic MVO Algorithm," Energies, MDPI, vol. 12(21), pages 1-16, October.

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