Minimum-fuel engine on/off control for the energy management of a hybrid electric vehicle via iterative linear programming

Nicolò Robuschi, Mauro Salazar, Pol Duhr, Francesco Braghin, Christopher Onder

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)
2 Downloads (Pure)

Abstract

In this paper we present models and optimization algorithms to rapidly compute the fuel-optimal energy management strategies of a hybrid electric powertrain for a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear problem and demonstrate its effectiveness by assessing the impact of different battery charge targets on the achievable fuel consumption. Numerical results show that the proposed algorithm can assess fuel-optimal control strategies in a few seconds, paving the way for extensive parameter studies and real-time implementations.
Original languageEnglish
Pages (from-to)134-140
Number of pages7
JournalIFAC-PapersOnLine
Volume52
Issue number5
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event9th IFAC Symposium on Advances in Automotive Control, (AAC2019) - Orleans, France
Duration: 24 Jun 201927 Jun 2019
http://www.ifac-control.org/events/advances-in-automotive-control-9th-aac-2019

Bibliographical note

Part of special issue:
9th IFAC Symposium on Advances in Automotive Control AAC 2019: Orléans, France, 23–27 June 2019

Keywords

  • Hybrid vehicles
  • convex optimization
  • energy management
  • linear programming
  • mixed-integer optimal control
  • supervisory control

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