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.

Minimum-fuel Engine On/Off Control for the Energy Management of a Hybrid Electric Vehicle via Iterative Linear Programming

Robuschi N.;Braghin F.;
2019-01-01

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.
2019
IFAC-PapersOnLine
convex optimization; energy management; Hybrid vehicles; linear programming; mixed-integer optimal control; supervisory control
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S240589631930641X-main.pdf

accesso aperto

Descrizione: paper
: Publisher’s version
Dimensione 1.01 MB
Formato Adobe PDF
1.01 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1128127
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 20
  • ???jsp.display-item.citation.isi??? 15
social impact