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A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition

Author

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  • Zhenzhen Lei

    (State Key Laboratory of Mechanical Transmissions & School of Automotive Engineering, Chongqing University, Chongqing 400044, China
    Key Laboratory of Advanced Manufacture Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China)

  • Dong Cheng

    (State Key Laboratory of Mechanical Transmissions & School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

  • Yonggang Liu

    (State Key Laboratory of Mechanical Transmissions & School of Automotive Engineering, Chongqing University, Chongqing 400044, China
    Key Laboratory of Advanced Manufacture Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China)

  • Datong Qin

    (State Key Laboratory of Mechanical Transmissions & School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

  • Yi Zhang

    (Department of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Qingbo Xie

    (State Key Laboratory of Mechanical Transmissions & School of Automotive Engineering, Chongqing University, Chongqing 400044, China)

Abstract

The driving pattern has an important influence on the parameter optimization of the energy management strategy (EMS) for hybrid electric vehicles (HEVs). A new algorithm using simulated annealing particle swarm optimization (SA-PSO) is proposed for parameter optimization of both the power system and control strategy of HEVs based on multiple driving cycles in order to realize the minimum fuel consumption without impairing the dynamic performance. Furthermore, taking the unknown of the actual driving cycle into consideration, an optimization method of the dynamic EMS based on driving pattern recognition is proposed in this paper. The simulation verifications for the optimized EMS based on multiple driving cycles and driving pattern recognition are carried out using Matlab/Simulink platform. The results show that compared with the original EMS, the former strategy reduces the fuel consumption by 4.36% and the latter one reduces the fuel consumption by 11.68%. A road test on the prototype vehicle is conducted and the effectiveness of the proposed EMS is validated by the test data.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:1:p:54-:d:86933
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    References listed on IDEAS

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    Cited by:

    1. Ahmed M. Ali & Dirk Söffker, 2018. "Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions," Energies, MDPI, vol. 11(3), pages 1-24, February.
    2. Zou, Runnan & Fan, Likang & Dong, Yanrui & Zheng, Siyu & Hu, Chenxing, 2021. "DQL energy management: An online-updated algorithm and its application in fix-line hybrid electric vehicle," Energy, Elsevier, vol. 225(C).
    3. Hu, Jie & Liu, Di & Du, Changqing & Yan, Fuwu & Lv, Chen, 2020. "Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition," Energy, Elsevier, vol. 198(C).
    4. Yongliang Zheng & Feng He & Xinze Shen & Xuesheng Jiang, 2020. "Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine," Energies, MDPI, vol. 13(2), pages 1-18, January.
    5. Xu, Bin & Wang, Hanchen, 2023. "A comparative analysis of adaptive energy management for a hybrid electric vehicle via five driving condition recognition methods," Energy, Elsevier, vol. 269(C).
    6. Weiyi Lin & Han Zhao & Bingzhan Zhang & Ye Wang & Yan Xiao & Kang Xu & Rui Zhao, 2022. "Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast," Energies, MDPI, vol. 15(20), pages 1-27, October.
    7. Carmen Raga & Andres Barrado & Henry Miniguano & Antonio Lazaro & Isabel Quesada & Alberto Martin-Lozano, 2018. "Analysis and Sizing of Power Distribution Architectures Applied to Fuel Cell Based Vehicles," Energies, MDPI, vol. 11(10), pages 1-30, September.
    8. Haeseong Jeoung & Kiwook Lee & Namwook Kim, 2019. "Methodology for Finding Maximum Performance and Improvement Possibility of Rule-Based Control for Parallel Type-2 Hybrid Electric Vehicles," Energies, MDPI, vol. 12(10), pages 1-17, May.
    9. Rui Xiong & Hailong Li & Xuan Zhou, 2017. "Advanced Energy Storage Technologies and Their Applications (AESA2017)," Energies, MDPI, vol. 10(9), pages 1-3, September.
    10. Louback, Eduardo & Biswas, Atriya & Machado, Fabricio & Emadi, Ali, 2024. "A review of the design process of energy management systems for dual-motor battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    11. Wahl, Alexander & Wellmann, Christoph & Monissen, Christian & Andert, Jakob, 2023. "Active temperature control of electric drivetrains for efficiency increase," Applied Energy, Elsevier, vol. 338(C).
    12. Sina Shojaei & Andrew McGordon & Simon Robinson & James Marco, 2017. "Improving the Performance Attributes of Plug-in Hybrid Electric Vehicles in Hot Climates through Key-Off Battery Cooling," Energies, MDPI, vol. 10(12), pages 1-28, December.
    13. Xianbin Wang & Yuqi Zhao & Weifeng Li, 2023. "Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model," Sustainability, MDPI, vol. 15(3), pages 1-21, February.

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