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

Camillo Balerna (Corresponding author), Nicolas Lanzetti, Mauro Salazar, Alberto Cerofolini, Christopher H. Onder

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Article number115248
Number of pages17
JournalApplied Energy
Volume276
Early online date15 Jul 2020
DOIs
Publication statusPublished - 15 Oct 2020

Keywords

  • Hybrid electric vehicles
  • Mixed-integer optimization
  • Neural networks
  • Optimal control
  • Turbocharger

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