IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1311-d747440.html
   My bibliography  Save this article

Internal Combustion Engine Starting and Torque Boosting Control System Design with Vibration Active Damping Features for a P0 Mild Hybrid Vehicle Configuration

Author

Listed:
  • Danijel Pavković

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10000 Zagreb, Croatia)

  • Mihael Cipek

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10000 Zagreb, Croatia)

  • Filip Plavac

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10000 Zagreb, Croatia)

  • Juraj Karlušić

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10000 Zagreb, Croatia)

  • Matija Krznar

    (Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10000 Zagreb, Croatia)

Abstract

In order to meet the increasingly stricter emissions’ regulations, road vehicles require additional technologies aimed at the reduction of emissions from the internal combustion engine (ICE). A favorable solution from the standpoint of costs and simplicity of integration is a 48-V electrical architecture utilizing a low-voltage/high-power induction machine, which operates as the so-called engine belt starter generator (BSG) coupled via a timing belt with the ICE crankshaft within a P0 mild hybrid power train and used for starting up and boosting of the ICE power output, as well as for recuperating kinetic energy during vehicle deceleration. The aim of this work was to design a vibration damping system for the belt transmission within the so-called front end accessory drive (FEAD), which couples the BSG with the ICE crankshaft and to test the control system by means of simulations for realistic operating regimes of the P0 mild hybrid power train in order to show the functionality of the proposed approach in terms of mild hybrid vehicle performance improvement. Simulation results have pointed out effective attenuation of belt compliance-related vibrations using the proposed active damping control, with vibration magnitude reduced between three and five times compared to the default case during engine start-up phase. They have indicated the realistic belt slippage effects during engine start-up phase and have illustrated the effectiveness of the FEAD torque boosting capability with 30% gain in acceleration during vehicle launch.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1311-:d:747440
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1311/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/4/1311/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyeonjik Lee & Kihyung Lee, 2020. "Comparative Evaluation of the Effect of Vehicle Parameters on Fuel Consumption under NEDC and WLTP," Energies, MDPI, vol. 13(16), pages 1-19, August.
    2. Olivier Bethoux, 2020. "Hydrogen Fuel Cell Road Vehicles: State of the Art and Perspectives," Energies, MDPI, vol. 13(21), pages 1-28, November.
    3. Yeau-Jian Gene Liao & Allen M. Quail, 2011. "Experiment and Simulation of Medium-Duty Tactical Truck for Fuel Economy Improvement," Energies, MDPI, vol. 4(2), pages 1-18, February.
    4. Shima Nazari & Jason Siegel & Robert Middleton & Anna Stefanopoulou, 2020. "Power Split Supercharging: A Mild Hybrid Approach to Boost Fuel Economy," Energies, MDPI, vol. 13(24), pages 1-17, December.
    5. 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.
    6. 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).
    7. 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.
    8. Federico Millo & Francesco Accurso & Alessandro Zanelli & Luciano Rolando, 2019. "Numerical Investigation of 48 V Electrification Potential in Terms of Fuel Economy and Vehicle Performance for a Lambda-1 Gasoline Passenger Car," Energies, MDPI, vol. 12(15), pages 1-21, August.
    9. Qicheng Xue & Xin Zhang & Teng Teng & Jibao Zhang & Zhiyuan Feng & Qinyang Lv, 2020. "A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-30, October.
    Full references (including those not matched with items on IDEAS)

    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. Pierpaolo Polverino & Ivan Arsie & Cesare Pianese, 2021. "Optimal Energy Management for Hybrid Electric Vehicles Based on Dynamic Programming and Receding Horizon," Energies, MDPI, vol. 14(12), pages 1-11, June.
    3. Shantanu Pardhi & Sajib Chakraborty & Dai-Duong Tran & Mohamed El Baghdadi & Steven Wilkins & Omar Hegazy, 2022. "A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions," Energies, MDPI, vol. 15(24), pages 1-55, December.
    4. Józef Drewniak & Tomasz Kądziołka & Jacek Rysiński & Konrad Stańco, 2023. "Power Flow in Coupled Three-Row Series-Parallel Planetary Gear System, Part I: Without Power Losses," Energies, MDPI, vol. 16(21), pages 1-37, October.
    5. Saiteja, Pemmareddy & Ashok, B., 2022. "Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    6. Sara Luciani & Andrea Tonoli, 2022. "Control Strategy Assessment for Improving PEM Fuel Cell System Efficiency in Fuel Cell Hybrid Vehicles," Energies, MDPI, vol. 15(6), pages 1-17, March.
    7. Matija Krznar & Danijel Pavković & Mihael Cipek & Juraj Benić, 2021. "Modeling, Controller Design and Simulation Groundwork on Multirotor Unmanned Aerial Vehicle Hybrid Power Unit," Energies, MDPI, vol. 14(21), pages 1-26, November.
    8. Hong, Sanghyun & Kim, Eunsung & Jeong, Saerok, 2023. "Evaluating the sustainability of the hydrogen economy using multi-criteria decision-making analysis in Korea," Renewable Energy, Elsevier, vol. 204(C), pages 485-492.
    9. Yi Zhang & Qiang Guo & Jie Song, 2023. "Internet-Distributed Hardware-in-the-Loop Simulation Platform for Plug-In Fuel Cell Hybrid Vehicles," Energies, MDPI, vol. 16(18), pages 1-17, September.
    10. Yuan, Xinmei & Zhang, Chuanpu & Hong, Guokai & Huang, Xueqi & Li, Lili, 2017. "Method for evaluating the real-world driving energy consumptions of electric vehicles," Energy, Elsevier, vol. 141(C), pages 1955-1968.
    11. 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.
    12. Barouch Giechaskiel & Dimitrios Komnos & Georgios Fontaras, 2021. "Impacts of Extreme Ambient Temperatures and Road Gradient on Energy Consumption and CO 2 Emissions of a Euro 6d-Temp Gasoline Vehicle," Energies, MDPI, vol. 14(19), pages 1-20, September.
    13. Jiaming Zhou & Chunxiao Feng & Qingqing Su & Shangfeng Jiang & Zhixian Fan & Jiageng Ruan & Shikai Sun & Leli Hu, 2022. "The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    14. Gianmarco Gottardo & Andrea Basso Peressut & Silvia Colnago & Saverio Latorrata & Luigi Piegari & Giovanni Dotelli, 2023. "LCA of a Proton Exchange Membrane Fuel Cell Electric Vehicle Considering Different Power System Architectures," Energies, MDPI, vol. 16(19), pages 1-19, September.
    15. Hyungkwan Jang & Hyunwoo Kim & Huai-Cong Liu & Ho-Joon Lee & Ju Lee, 2021. "Investigation on the Torque Ripple Reduction Method of a Hybrid Electric Vehicle Motor," Energies, MDPI, vol. 14(5), pages 1-13, March.
    16. Singh, Somendra Pratap & Hanif, Athar & Ahmed, Qadeer & Meijer, Maarten & Lahti, John, 2022. "Optimal management of electric hotel loads in mild hybrid heavy duty truck," Applied Energy, Elsevier, vol. 326(C).
    17. Sascha Krysmon & Frank Dorscheidt & Johannes Claßen & Marc Düzgün & Stefan Pischinger, 2021. "Real Driving Emissions—Conception of a Data-Driven Calibration Methodology for Hybrid Powertrains Combining Statistical Analysis and Virtual Calibration Platforms," Energies, MDPI, vol. 14(16), pages 1-27, August.
    18. Jacek Pielecha & Kinga Skobiej & Przemyslaw Kubiak & Marek Wozniak & Krzysztof Siczek, 2022. "Exhaust Emissions from Plug-in and HEV Vehicles in Type-Approval Tests and Real Driving Cycles," Energies, MDPI, vol. 15(7), pages 1-38, March.
    19. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    20. Chen, Jiaxin & Shu, Hong & Tang, Xiaolin & Liu, Teng & Wang, Weida, 2022. "Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment," Energy, Elsevier, vol. 239(PC).

    More about this item

    Keywords

    P0 mild hybrid power train; belt starter generator; vibration control; simulations; realistic driving conditions; MATLAB/Simulink; AVL ECXITE/CRUISE M;
    All these keywords.

    JEL classification:

    • P0 - Political Economy and Comparative Economic Systems - - General

    Statistics

    Access and download statistics

    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:15:y:2022:i:4:p:1311-:d:747440. 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.