IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i1p54-d86933.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/1/54/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/1/54/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Shuo & Xiong, Rui & Cao, Jiayi, 2016. "Battery durability and longevity based power management for plug-in hybrid electric vehicle with hybrid energy storage system," Applied Energy, Elsevier, vol. 179(C), pages 316-328.
    2. 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.
    3. Chen, Zeyu & Xiong, Rui & Cao, Jiayi, 2016. "Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions," Energy, Elsevier, vol. 96(C), pages 197-208.
    4. 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, vol. 8(5), pages 1-18, April.
    5. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Wahl, Alexander & Wellmann, Christoph & Monissen, Christian & Andert, Jakob, 2023. "Active temperature control of electric drivetrains for efficiency increase," Applied Energy, Elsevier, vol. 338(C).
    11. 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.
    12. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).
    3. 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).
    4. Konrad Prajwowski & Wawrzyniec Golebiewski & Maciej Lisowski & Karol F. Abramek & Dominik Galdynski, 2020. "Modeling of Working Machines Synergy in the Process of the Hybrid Electric Vehicle Acceleration," Energies, MDPI, vol. 13(21), pages 1-20, November.
    5. Dafen Chen & Jiuchun Jiang & Xue Li & Zhanguo Wang & Weige Zhang, 2016. "Modeling of a Pouch Lithium Ion Battery Using a Distributed Parameter Equivalent Circuit for Internal Non-Uniformity Analysis," Energies, MDPI, vol. 9(11), pages 1-18, October.
    6. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei & Guo, Qiuyi, 2018. "Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction," Energy, Elsevier, vol. 155(C), pages 838-852.
    7. He, Hongwen & Guo, Xiaoguang, 2018. "Multi-objective optimization research on the start condition for a parallel hybrid electric vehicle," Applied Energy, Elsevier, vol. 227(C), pages 294-303.
    8. Wang, Chun & Xiong, Rui & He, Hongwen & Ding, Xiaofeng & Shen, Weixiang, 2016. "Efficiency analysis of a bidirectional DC/DC converter in a hybrid energy storage system for plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 612-622.
    9. Li, Jianwei & Wang, Xudong & Zhang, Zhenyu & Le Blond, Simon & Yang, Qingqing & Zhang, Min & Yuan, Weijia, 2017. "Analysis of a new design of the hybrid energy storage system used in the residential m-CHP systems," Applied Energy, Elsevier, vol. 187(C), pages 169-179.
    10. Chen, Zeyu & Xiong, Rui & Cao, Jiayi, 2016. "Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions," Energy, Elsevier, vol. 96(C), pages 197-208.
    11. Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
    12. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei, 2017. "Particle swarm optimization of driving torque demand decision based on fuel economy for plug-in hybrid electric vehicle," Energy, Elsevier, vol. 123(C), pages 89-107.
    13. Wang, Chun & He, Hongwen & Zhang, Yongzhi & Mu, Hao, 2017. "A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures," Applied Energy, Elsevier, vol. 196(C), pages 268-278.
    14. 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).
    15. Jing Sun & Guojing Xing & Chenghui Zhang, 2017. "Data-Driven Predictive Torque Coordination Control during Mode Transition Process of Hybrid Electric Vehicles," Energies, MDPI, vol. 10(4), pages 1-21, April.
    16. 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.
    17. 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.
    18. Ding, Xiaofeng & Chen, Feida & Du, Min & Guo, Hong & Ren, Suping, 2017. "Effects of silicon carbide MOSFETs on the efficiency and power quality of a microgrid-connected inverter," Applied Energy, Elsevier, vol. 201(C), pages 270-283.
    19. Yonggang Liu & Jie Li & Ming Ye & Datong Qin & Yi Zhang & Zhenzhen Lei, 2017. "Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information," Energies, MDPI, vol. 10(4), pages 1-20, March.
    20. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:1:p:54-:d:86933. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.