IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i10p2838-d177147.html
   My bibliography  Save this article

Optimization of Control Variables and Design of Management Strategy for Hybrid Hydraulic Vehicle

Author

Listed:
  • Branimir Škugor

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, 10000 Code, Croatia)

  • Joško Petrić

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, 10000 Code, Croatia)

Abstract

The article deals with optimization of control variables and design of management strategy for a hybrid hydraulic vehicle in parallel configuration. Conventionally driven delivery truck with experimentally verified data from the previous research is taken as a starting base and benchmark for comparison of the benefits of hybridization. Optimization of control variables is carried out using dynamic programming (DP) algorithm to gain insight into optimum operation of the driveline and minimum possible fuel consumption for five different driving cycles. Two rule based management strategies are given and compared, one of which is improved and innovative, based on the knowledge gained from DP results. Hybrid driveline can reduce fuel consumption from 5% to 30% depending on the driving cycle. More dynamic cycles with lot of "stop-and-go" events score greater reduction. Innovative management strategy has achieved a similar distribution of internal combustion engine (ICE) operating points as DP optimization but this did not result in a consistent reduction of fuel consumption compared to basic management strategy for all cycles. That is explained by the state of charge ( SoC ) behaviour and reducing the potential for recovery of regenerative braking energy.

Suggested Citation

  • Branimir Škugor & Joško Petrić, 2018. "Optimization of Control Variables and Design of Management Strategy for Hybrid Hydraulic Vehicle," Energies, MDPI, vol. 11(10), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2838-:d:177147
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/10/2838/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/10/2838/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyunhwa Kim & Junbeom Wi & Jiho Yoo & Hanho Son & Chiman Park & Hyunsoo Kim, 2018. "A Study on the Fuel Economy Potential of Parallel and Power Split Type Hybrid Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-19, August.
    2. Guoqing Xu & Weimin Li & Kun Xu & Zhibin Song, 2011. "An Intelligent Regenerative Braking Strategy for Electric Vehicles," Energies, MDPI, vol. 4(9), pages 1-17, September.
    3. 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.
    4. Feiyan Qin & Guoqing Xu & Yue Hu & Kun Xu & Weimin Li, 2017. "Stochastic Optimal Control of Parallel Hybrid Electric Vehicles," Energies, MDPI, vol. 10(2), pages 1-16, February.
    5. A. Pfeffer & T. Glück & W. Kemmetmüller & A. Kugi, 2016. "Mathematical modelling of a hydraulic accumulator for hydraulic hybrid drives," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 22(5), pages 397-411, September.
    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. Chien-Hsun Wu & Yong-Xiang Xu, 2019. "The Optimal Control of Fuel Consumption for a Heavy-Duty Motorcycle with Three Power Sources Using Hardware-in-the-Loop Simulation," Energies, MDPI, vol. 13(1), pages 1-16, December.

    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. Bảo-Huy Nguyễn & João Pedro F. Trovão & Ronan German & Alain Bouscayrol, 2020. "Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation," Energies, MDPI, vol. 13(21), pages 1-19, October.
    2. Zhang, Wei & Wang, Jixin & Liu, Yong & Gao, Guangzong & Liang, Siwen & Ma, Hongfeng, 2020. "Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery," Applied Energy, Elsevier, vol. 275(C).
    3. 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.
    4. Emilia M. Szumska & Rafał S. Jurecki, 2021. "Parameters Influencing on Electric Vehicle Range," Energies, MDPI, vol. 14(16), pages 1-23, August.
    5. Bedatri Moulik & Dirk Söffker, 2015. "Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review," Energies, MDPI, vol. 8(9), pages 1-15, August.
    6. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    7. Md Ragib Ahssan & Mehran Ektesabi & Saman Gorji, 2020. "Gear Ratio Optimization along with a Novel Gearshift Scheduling Strategy for a Two-Speed Transmission System in Electric Vehicle," Energies, MDPI, vol. 13(19), pages 1-24, September.
    8. Duo Zhang & Guohai Liu & Wenxiang Zhao & Penghu Miao & Yan Jiang & Huawei Zhou, 2014. "A Neural Network Combined Inverse Controller for a Two-Rear-Wheel Independently Driven Electric Vehicle," Energies, MDPI, vol. 7(7), pages 1-15, July.
    9. Jingang Guo & Xiaoping Jian & Guangyu Lin, 2014. "Performance Evaluation of an Anti-Lock Braking System for Electric Vehicles with a Fuzzy Sliding Mode Controller," Energies, MDPI, vol. 7(10), pages 1-18, October.
    10. Wang, Chun & Yang, Ruixin & Yu, Quanqing, 2019. "Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty," Applied Energy, Elsevier, vol. 256(C).
    11. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.
    12. Jinhyun Park & In Gyu Jang & Sung-Ho Hwang, 2018. "Torque Distribution Algorithm for an Independently Driven Electric Vehicle Using a Fuzzy Control Method: Driving Stability and Efficiency," Energies, MDPI, vol. 11(12), pages 1-22, December.
    13. Petronilla Fragiacomo & Francesco Piraino & Matteo Genovese & Lorenzo Flaccomio Nardi Dei & Daria Donati & Michele Vincenzo Migliarese Caputi & Domenico Borello, 2022. "Sizing and Performance Analysis of Hydrogen- and Battery-Based Powertrains, Integrated into a Passenger Train for a Regional Track, Located in Calabria (Italy)," Energies, MDPI, vol. 15(16), pages 1-20, August.
    14. Jinhyun Park & Houn Jeong & In Gyu Jang & Sung-Ho Hwang, 2015. "Torque Distribution Algorithm for an Independently Driven Electric Vehicle Using a Fuzzy Control Method," Energies, MDPI, vol. 8(8), pages 1-25, August.
    15. Vadym SAMORODOV & Anatoliy BONDARENKO & Igor TARAN & Iryna KLYMENKO, 2020. "Power Flows In A Hydrostatic-Mechanical Transmission Of A Mining Locomotive During The Braking Process," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 15(3), pages 17-28, September.
    16. L. Viktor Larsson & Liselott Ericson & Karl Uebel & Petter Krus, 2019. "Low-Level Control of Hybrid Hydromechanical Transmissions for Heavy Mobile Working Machines," Energies, MDPI, vol. 12(9), pages 1-20, May.
    17. Yang Yang & Xiaolong He & Yi Zhang & Datong Qin, 2018. "Regenerative Braking Compensatory Control Strategy Considering CVT Power Loss for Hybrid Electric Vehicles," Energies, MDPI, vol. 11(3), pages 1-15, February.
    18. Wu, Fuliang & Bektaş, Tolga & Dong, Ming & Ye, Hongbo & Zhang, Dali, 2021. "Optimal driving for vehicle fuel economy under traffic speed uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 175-206.
    19. Ryszard Dindorf & Jakub Takosoglu & Piotr Wos, 2023. "Review of Hydro-Pneumatic Accumulator Models for the Study of the Energy Efficiency of Hydraulic Systems," Energies, MDPI, vol. 16(18), pages 1-45, September.
    20. Baogang Li & Dongye Sun & Minghui Hu & Junlong Liu, 2018. "Research on Economic Comprehensive Control Strategies of Tractor-Planter Combinations in Planting, Including Gear-Shift and Cruise Control," Energies, MDPI, vol. 11(3), pages 1-21, March.

    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:11:y:2018:i:10:p:2838-:d:177147. 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.