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Novel Energy Management Control Strategy for Improving Efficiency in Hybrid Powertrains

Author

Listed:
  • Alberto Broatch

    (CMT-Motores Térmicos, Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain)

  • Pablo Olmeda

    (CMT-Motores Térmicos, Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain)

  • Benjamín Plá

    (CMT-Motores Térmicos, Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain)

  • Amin Dreif

    (CMT-Motores Térmicos, Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain)

Abstract

Energy management in electrified vehicles is critical and directly impacts the global operating efficiency, durability, driveability, and safety of the vehicle powertrain. Given the multitude of components of these powertrains, the complexity of the proper control is significantly higher than the conventional internal combustion engine vehicle (ICEV). Hence, several control algorithms and numerical methods have been developed and implemented in order to optimize the operation of the hybrid powertrain while complying with the required boundary conditions. In this work, a model-based method is used for predicting the impacts of a set of possible control actions, choosing the one minimizing the associated costs. In particular, the energy management technique used in the present study is the equivalent consumption minimization strategy (ECMS). The novelty of this work consists of taking into account the thermal state of the ICE for optimization. This feature was implemented by means of an extensive experimental campaign at different coolant temperatures of the ICE to calibrate the additional fuel consumption due to operating the engine outside of its optimum temperature. The results showed significant gains in both WLTC and RDE cycles.

Suggested Citation

  • Alberto Broatch & Pablo Olmeda & Benjamín Plá & Amin Dreif, 2022. "Novel Energy Management Control Strategy for Improving Efficiency in Hybrid Powertrains," Energies, MDPI, vol. 16(1), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:107-:d:1011206
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    References listed on IDEAS

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    1. 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.
    2. Hsiu-Ying Hwang, 2020. "Developing Equivalent Consumption Minimization Strategy for Advanced Hybrid System-II Electric Vehicles," Energies, MDPI, vol. 13(8), pages 1-19, April.
    3. Zhang, Bo & Zhang, Jiangyan & Xu, Fuguo & Shen, Tielong, 2020. "Optimal control of power-split hybrid electric powertrains with minimization of energy consumption," Applied Energy, Elsevier, vol. 266(C).
    4. Tsiropoulos, Ioannis & Siskos, Pelopidas & Capros, Pantelis, 2022. "The cost of recharging infrastructure for electric vehicles in the EU in a climate neutrality context: Factors influencing investments in 2030 and 2050," Applied Energy, Elsevier, vol. 322(C).
    5. Vinay Simha Reddy Tappeta & Bhargav Appasani & Suprava Patnaik & Taha Selim Ustun, 2022. "A Review on Emerging Communication and Computational Technologies for Increased Use of Plug-In Electric Vehicles," Energies, MDPI, vol. 15(18), pages 1-26, September.
    6. 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.
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    Cited by:

    1. Kun He & Dongchen Qin & Jiangyi Chen & Tingting Wang & Hongxia Wu & Peizhuo Wang, 2023. "Adaptive Equivalent Consumption Minimization Strategy for Fuel Cell Buses Based on Driving Style Recognition," Sustainability, MDPI, vol. 15(10), pages 1-17, May.

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