IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i11p3185-d183400.html
   My bibliography  Save this article

Development of a Multi-Architecture and Multi-Application Hybrid Vehicle Design and Management Tool

Author

Listed:
  • Shiyu Gan

    (DRIVE EA 1859, Univ. Bourgogne Franche Comté, F58027 Nevers, France)

  • Daniela Chrenko

    (Femto-ST, UMR 6174, CNRS, Univ. Bourgogne Franche-Comte, F90010 Belfort, France)

  • Alan Kéromnès

    (DRIVE EA 1859, Univ. Bourgogne Franche Comté, F58027 Nevers, France)

  • Luis Le Moyne

    (DRIVE EA 1859, Univ. Bourgogne Franche Comté, F58027 Nevers, France)

Abstract

Hybrid electric vehicles (HEVs) are very promising sustainable mobility solutions. Series, parallel and series-parallel (SP) seem to be three most promising architectures among the multitude of hybrid architectures, and it is possible to find them in a multi-applications such as the motorcycles, family-cars, hybrid city busses and sport cars. It is import to have a well configured model in order to develop the different control strategies (CsTs) for each application. Therefore, a multi-architecture/multi-application (MAMA) approach capable of identifying the most energy efficient hybrid architecture considering both the dimensions of key components: electric motor (EM), battery, internal combustion engine (ICE) and the optimal control is presented. Basis of the model is the energetic macroscopic representation (EMR), which has been combined with object oriented programming (OOP) in order to enhance its modularity and reuse capabilities. The obtained results show, that different hybrid architectures are most adapted for different applications. Moreover, the robustness of the results using real time control algorithms are studied, showing that CsT matters. The obtained results contribute to simplify and harmonize the design of hybrid solutions for multiple applications.

Suggested Citation

  • Shiyu Gan & Daniela Chrenko & Alan Kéromnès & Luis Le Moyne, 2018. "Development of a Multi-Architecture and Multi-Application Hybrid Vehicle Design and Management Tool," Energies, MDPI, vol. 11(11), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3185-:d:183400
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/11/3185/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/11/3185/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Finesso, Roberto & Spessa, Ezio & Venditti, Mattia, 2014. "Layout design and energetic analysis of a complex diesel parallel hybrid electric vehicle," Applied Energy, Elsevier, vol. 134(C), pages 573-588.
    3. Cipek, Mihael & Pavković, Danijel & Petrić, Joško, 2013. "A control-oriented simulation model of a power-split hybrid electric vehicle," Applied Energy, Elsevier, vol. 101(C), pages 121-133.
    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. García, Antonio & Monsalve-Serrano, Javier & Martinez-Boggio, Santiago & Gaillard, Patrick, 2021. "Impact of the hybrid electric architecture on the performance and emissions of a delivery truck with a dual-fuel RCCI engine," Applied Energy, Elsevier, vol. 301(C).
    2. García, Antonio & Carlucci, Paolo & Monsalve-Serrano, Javier & Valletta, Andrea & Martínez-Boggio, Santiago, 2021. "Energy management optimization for a power-split hybrid in a dual-mode RCCI-CDC engine," Applied Energy, Elsevier, vol. 302(C).
    3. Javier Solano & Diego Jimenez & Adrian Ilinca, 2020. "A Modular Simulation Testbed for Energy Management in AC/DC Microgrids," Energies, MDPI, vol. 13(16), pages 1-23, August.

    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. Shi, Dehua & Liu, Sheng & Cai, Yingfeng & Wang, Shaohua & Li, Haoran & Chen, Long, 2021. "Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information," Applied Energy, Elsevier, vol. 286(C).
    2. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    3. Danijel Pavković & Mihael Cipek & Zdenko Kljaić & Tomislav Josip Mlinarić & Mario Hrgetić & Davor Zorc, 2018. "Damping Optimum-Based Design of Control Strategy Suitable for Battery/Ultracapacitor Electric Vehicles," Energies, MDPI, vol. 11(10), pages 1-26, October.
    4. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    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. Massimiliano Passalacqua & Mauro Carpita & Serge Gavin & Mario Marchesoni & Matteo Repetto & Luis Vaccaro & Sébastien Wasterlain, 2019. "Supercapacitor Storage Sizing Analysis for a Series Hybrid Vehicle," Energies, MDPI, vol. 12(9), pages 1-15, May.
    7. García, Antonio & Monsalve-Serrano, Javier & Martinez-Boggio, Santiago & Gaillard, Patrick, 2021. "Emissions reduction by using e-components in 48 V mild hybrid trucks under dual-mode dual-fuel combustion," Applied Energy, Elsevier, vol. 299(C).
    8. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    9. Matija Krznar & Petar Piljek & Denis Kotarski & Danijel Pavković, 2021. "Modeling, Control System Design and Preliminary Experimental Verification of a Hybrid Power Unit Suitable for Multirotor UAVs," Energies, MDPI, vol. 14(9), pages 1-24, May.
    10. He, Hongwen & Han, Mo & Liu, Wei & Cao, Jianfei & Shi, Man & Zhou, Nana, 2022. "MPC-based longitudinal control strategy considering energy consumption for a dual-motor electric vehicle," Energy, Elsevier, vol. 253(C).
    11. López-Ibarra, Jon Ander & Gaztañaga, Haizea & Saez-de-Ibarra, Andoni & Camblong, Haritza, 2020. "Plug-in hybrid electric buses total cost of ownership optimization at fleet level based on battery aging," Applied Energy, Elsevier, vol. 280(C).
    12. Yi, Chenyu & Epureanu, Bogdan I. & Hong, Sung-Kwon & Ge, Tony & Yang, Xiao Guang, 2016. "Modeling, control, and performance of a novel architecture of hybrid electric powertrain system," Applied Energy, Elsevier, vol. 178(C), pages 454-467.
    13. Danijel Pavković & Mihael Cipek & Filip Plavac & Juraj Karlušić & Matija Krznar, 2022. "Internal Combustion Engine Starting and Torque Boosting Control System Design with Vibration Active Damping Features for a P0 Mild Hybrid Vehicle Configuration," Energies, MDPI, vol. 15(4), pages 1-24, February.
    14. Mayyas, Abdel Ra'ouf & Kumar, Sushil & Pisu, Pierluigi & Rios, Jacqueline & Jethani, Puneet, 2017. "Model-based design validation for advanced energy management strategies for electrified hybrid power trains using innovative vehicle hardware in the loop (VHIL) approach," Applied Energy, Elsevier, vol. 204(C), pages 287-302.
    15. Chaoying Xia & Cong Zhang, 2015. "Power Management Strategy of Hybrid Electric Vehicles Based on Quadratic Performance Index," Energies, MDPI, vol. 8(11), pages 1-16, November.
    16. Ouyang, Minggao & Zhang, Weilin & Wang, Enhua & Yang, Fuyuan & Li, Jianqiu & Li, Zhongyan & Yu, Ping & Ye, Xiao, 2015. "Performance analysis of a novel coaxial power-split hybrid powertrain using a CNG engine and supercapacitors," Applied Energy, Elsevier, vol. 157(C), pages 595-606.
    17. Shi, Dehua & Pisu, Pierluigi & Chen, Long & Wang, Shaohua & Wang, Renguang, 2016. "Control design and fuel economy investigation of power split HEV with energy regeneration of suspension," Applied Energy, Elsevier, vol. 182(C), pages 576-589.
    18. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    19. S. N. Shivappriya & S. Karthikeyan & S. Prabu & R. Pérez de Prado & B. D. Parameshachari, 2020. "A Modified ABC-SQP-Based Combined Approach for the Optimization of a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 13(17), pages 1-22, September.
    20. Peng, Hujun & Li, Jianxiang & Löwenstein, Lars & Hameyer, Kay, 2020. "A scalable, causal, adaptive energy management strategy based on optimal control theory for a fuel cell hybrid railway vehicle," Applied Energy, Elsevier, vol. 267(C).

    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:11:p:3185-:d:183400. 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.