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Optimal low-level control strategies for a high-performance hybrid electric power unit

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  • Balerna, Camillo
  • Lanzetti, Nicolas
  • Salazar, Mauro
  • Cerofolini, Alberto
  • Onder, Christopher

Abstract

In this paper we present models and optimization algorithms to compute the optimal low-level control strategies for hybrid electric powertrains. Specifically, we study the minimum-fuel operation of a turbocharged internal combustion engine coupled to an electrical energy recovery system, consisting of a battery and two motors connected to the turbocharger and to the wheels, respectively. First, we combine physics-based modeling approaches with neural networks to identify a piecewise affine model of the power unit accounting for the internal dynamics of the engine, and formulate the minimum-fuel control problem for a given driving cycle. Second, we parse the control problem to a mixed-integer linear program that can be solved with off-the-shelf optimization algorithms that guarantee global optimality of the solution. Finally, we validate our model against a high fidelity nonlinear simulator and showcase the presented framework with a case-study for racing applications. Our results show that cylinder deactivation and turbocharger electrification can decrease fuel consumption up to 4% and 8%, respectively.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920307601
    DOI: 10.1016/j.apenergy.2020.115248
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    References listed on IDEAS

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    1. Gölcü, Mustafa & Sekmen, Yakup & ErduranlI, Perihan & Sahir Salman, M., 2005. "Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine," Applied Energy, Elsevier, vol. 81(2), pages 187-197, June.
    2. Yu, Youhong & Chen, Lingen & Sun, Fengrui & Wu, Chih, 2007. "Neural-network based analysis and prediction of a compressor's characteristic performance map," Applied Energy, Elsevier, vol. 84(1), pages 48-55, January.
    3. Tobias Nüesch & Alberto Cerofolini & Giorgio Mancini & Nicolò Cavina & Christopher Onder & Lino Guzzella, 2014. "Equivalent Consumption Minimization Strategy for the Control of Real Driving NOx Emissions of a Diesel Hybrid Electric Vehicle," Energies, MDPI, vol. 7(5), pages 1-31, May.
    4. 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.
    5. Cortés, O. & Urquiza, G. & Hernández, J.A., 2009. "Optimization of operating conditions for compressor performance by means of neural network inverse," Applied Energy, Elsevier, vol. 86(11), pages 2487-2493, November.
    6. Tobias Nüesch & Philipp Elbert & Michael Flankl & Christopher Onder & Lino Guzzella, 2014. "Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs," Energies, MDPI, vol. 7(2), pages 1-23, February.
    7. Ghorbanian, K. & Gholamrezaei, M., 2009. "An artificial neural network approach to compressor performance prediction," Applied Energy, Elsevier, vol. 86(7-8), pages 1210-1221, July.
    8. Zammit, J.P. & McGhee, M.J. & Shayler, P.J. & Law, T. & Pegg, I., 2015. "The effects of early inlet valve closing and cylinder disablement on fuel economy and emissions of a direct injection diesel engine," Energy, Elsevier, vol. 79(C), pages 100-110.
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    Cited by:

    1. 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).
    2. Fridrichová, K. & Drápal, L. & Vopařil, J. & Dlugoš, J., 2021. "Overview of the potential and limitations of cylinder deactivation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    3. 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.

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