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Optimal power split in a hybrid electric vehicle using direct transcription of an optimal control problem

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  • Pérez, Laura V.
  • Pilotta, Elvio A.

Abstract

To efficiently operate electromechanical systems powered by two energy sources, it is necessary to determine the instantaneous power split between sources in order to minimize the energy consumption of the whole system. In this work, this problem is posed as a nonlinear finite horizon optimal control problem with control and state constraints and is solved using a direct transcription approach. The problem is fully discretized in time and the resulting finite dimensional optimization problem is solved using a nonlinear programming code. This paper describes the application of direct transcription to the case of the hybrid electric vehicle (HEV) being developed in the Applied Electronics Group (GEA) at the University of Río Cuarto. The statement and discretization of the control problem, the setting for using the nonlinear programming code and several examples and comparisons with those obtained by other approaches are described.

Suggested Citation

  • Pérez, Laura V. & Pilotta, Elvio A., 2009. "Optimal power split in a hybrid electric vehicle using direct transcription of an optimal control problem," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(6), pages 1959-1970.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:6:p:1959-1970
    DOI: 10.1016/j.matcom.2007.03.006
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    References listed on IDEAS

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    1. Pérez, Laura V. & Bossio, Guillermo R. & Moitre, Diego & García, Guillermo O., 2006. "Optimization of power management in an hybrid electric vehicle using dynamic programming," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(1), pages 244-254.
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    Cited by:

    1. Enang, Wisdom & Bannister, Chris, 2017. "Modelling and control of hybrid electric vehicles (A comprehensive review)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1210-1239.
    2. Vasallo, Manuel Jesús & Bravo, José Manuel & Andújar, José Manuel, 2013. "Optimal sizing for UPS systems based on batteries and/or fuel cell," Applied Energy, Elsevier, vol. 105(C), pages 170-181.
    3. Chen, Zeyu & Xiong, Rui & Wang, Chun & Cao, Jiayi, 2017. "An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle," Applied Energy, Elsevier, vol. 185(P2), pages 1663-1672.
    4. Duhr, Pol & Christodoulou, Grigorios & Balerna, Camillo & Salazar, Mauro & Cerofolini, Alberto & Onder, Christopher H., 2021. "Time-optimal gearshift and energy management strategies for a hybrid electric race car," Applied Energy, Elsevier, vol. 282(PA).
    5. Balerna, Camillo & Lanzetti, Nicolas & Salazar, Mauro & Cerofolini, Alberto & Onder, Christopher, 2020. "Optimal low-level control strategies for a high-performance hybrid electric power unit," Applied Energy, Elsevier, vol. 276(C).
    6. Sandra Ulrich Ngueveu & Stéphane Caux & Frédéric Messine & Mouloud Guemri, 2017. "Heuristics and lower bounds for minimizing fuel consumption in hybrid-electrical vehicles," 4OR, Springer, vol. 15(4), pages 407-430, December.
    7. Shabbir, Wassif & Evangelou, Simos A., 2014. "Real-time control strategy to maximize hybrid electric vehicle powertrain efficiency," Applied Energy, Elsevier, vol. 135(C), pages 512-522.
    8. Shabbir, Wassif & Evangelou, Simos A., 2019. "Threshold-changing control strategy for series hybrid electric vehicles," Applied Energy, Elsevier, vol. 235(C), pages 761-775.
    9. Camillo Balerna & Marc-Philippe Neumann & Nicolò Robuschi & Pol Duhr & Alberto Cerofolini & Vittorio Ravaglioli & Christopher Onder, 2020. "Time-Optimal Low-Level Control and Gearshift Strategies for the Formula 1 Hybrid Electric Powertrain," Energies, MDPI, vol. 14(1), pages 1-30, December.
    10. Wei, Shouyang & Zou, Yuan & Sun, Fengchun & Christopher, Onder, 2017. "A pseudospectral method for solving optimal control problem of a hybrid tracked vehicle," Applied Energy, Elsevier, vol. 194(C), pages 588-595.

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