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Implementation methodology of powertrain for series-hybrid military vehicles applications equipped with hybrid energy storage

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  • Lee, Seongjun
  • Kim, Jonghoon

Abstract

This paper investigates a component-sizing method and a power-control algorithm for series-hybrid military vehicles equipped with hybrid energy storages that comprise batteries and super-capacitors. Component sizing of the powertrain is determined by the performance specification that is related to mission profiles and power-flow control methods. In order to minimize the effects of mission profiles and power-flow control methods, the linear programming (LP) technique is employed. The LP problem for minimizing the output energy from the engine under different conditions of driving cycles and capacities of the energy storage system (ESS) is solved to eliminate the effect of the power distribution. Through analyzing the effects of different power and energy ratings of the ESS, the optimal values of power and energy capacities of the ESS are determined. The design approaches are extensively verified with simulations and experimental results of a reduced-scale per-unit equivalent system of the 10-ton series-hybrid electric vehicle (SHEV).

Suggested Citation

  • Lee, Seongjun & Kim, Jonghoon, 2017. "Implementation methodology of powertrain for series-hybrid military vehicles applications equipped with hybrid energy storage," Energy, Elsevier, vol. 120(C), pages 229-240.
    • Handle: RePEc:eee:energy:v:120:y:2017:i:c:p:229-240
    DOI: 10.1016/j.energy.2016.11.109
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    References listed on IDEAS

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    1. Hou, Cong & Ouyang, Minggao & Xu, Liangfei & Wang, Hewu, 2014. "Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 115(C), pages 174-189.
    2. Khayyam, Hamid & Bab-Hadiashar, Alireza, 2014. "Adaptive intelligent energy management system of plug-in hybrid electric vehicle," Energy, Elsevier, vol. 69(C), pages 319-335.
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    Cited by:

    1. Loïc Joud & Rui Da Silva & Daniela Chrenko & Alan Kéromnès & Luis Le Moyne, 2020. "Smart Energy Management for Series Hybrid Electric Vehicles Based on Driver Habits Recognition and Prediction," Energies, MDPI, vol. 13(11), pages 1-17, June.
    2. Sun, Li & Walker, Paul & Feng, Kaiwu & Zhang, Nong, 2018. "Multi-objective component sizing for a battery-supercapacitor power supply considering the use of a power converter," Energy, Elsevier, vol. 142(C), pages 436-446.
    3. Pinto, Cláudio & Barreras, Jorge V. & de Castro, Ricardo & Araújo, Rui Esteves & Schaltz, Erik, 2017. "Study on the combined influence of battery models and sizing strategy for hybrid and battery-based electric vehicles," Energy, Elsevier, vol. 137(C), pages 272-284.
    4. Piotr Wróblewski & Wojciech Drożdż & Wojciech Lewicki & Paweł Miązek, 2021. "Methodology for Assessing the Impact of Aperiodic Phenomena on the Energy Balance of Propulsion Engines in Vehicle Electromobility Systems for Given Areas," Energies, MDPI, vol. 14(8), pages 1-24, April.
    5. Chen, Syuan-Yi & Wu, Chien-Hsun & Hung, Yi-Hsuan & Chung, Cheng-Ta, 2018. "Optimal strategies of energy management integrated with transmission control for a hybrid electric vehicle using dynamic particle swarm optimization," Energy, Elsevier, vol. 160(C), pages 154-170.

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