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Numerical Analysis of Optimal Hybridization in Parallel Hybrid Electric Powertrains for Tracked Vehicles

Author

Listed:
  • Stefan Milićević

    (Faculty of Mechanical Engineering, University of Belgrade, Kraljice Marije 16, 11120 Belgrade, Serbia)

  • Ivan Blagojević

    (Faculty of Mechanical Engineering, University of Belgrade, Kraljice Marije 16, 11120 Belgrade, Serbia)

  • Saša Milojević

    (Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia)

  • Milan Bukvić

    (Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia)

  • Blaža Stojanović

    (Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia)

Abstract

Tracked vehicles are integral for maneuvering diverse terrains, with hybrid propulsion systems offering potential benefits in terms of fuel efficiency and performance. However, research in hybrid electric tracked vehicles remains limited, thus necessitating a comprehensive analysis to maximize their advantages. This study presents a numerical analysis focusing on optimizing hybridization in speed-coupled parallel hybrid electric powertrains for tracked vehicles. A dynamic programming algorithm and custom drive cycle are utilized to determine optimal hybridization factors and assess parameter sensitivities. The study reveals that a hybridization factor of 0.48 is optimal for speed-coupled parallel configurations. Furthermore, the sensitivity analysis underscores the substantial impact of factors such as the engine displacement and bore-to-stroke ratio on the fuel economy, with a 10% change in these parameters potentially influencing the fuel economy by up to 2%, thus emphasizing the importance of thorough consideration during powertrain sizing. Parallel hybrid configurations exhibit considerable potential for tracked vehicles, thus highlighting the viability of choosing them over series configurations.

Suggested Citation

  • Stefan Milićević & Ivan Blagojević & Saša Milojević & Milan Bukvić & Blaža Stojanović, 2024. "Numerical Analysis of Optimal Hybridization in Parallel Hybrid Electric Powertrains for Tracked Vehicles," Energies, MDPI, vol. 17(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3531-:d:1437926
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    References listed on IDEAS

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    Cited by:

    1. Jingxian Tang & Bolan Liu & Wenhao Fan & Dawei Zhong & Liang Liu, 2024. "Degradation Diagnosis and Control Strategy for a Diesel Hybrid Powertrain Considering State of Health," Energies, MDPI, vol. 17(21), pages 1-26, October.

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