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Automated Model Generation for Hybrid Vehicles Optimization and Control

N. Verdonck A. Chasse 1 P. Pognant-Gros 1 A. Sciarretta 1, * 
* Corresponding author
Abstract : Systematic optimization of modern powertrains, and hybrids in particular, requires the representation of the system by means of Backward Quasistatic Models (BQM). In contrast, the models used in realistic powertrain simulators are often of the Forward Dynamic Model (FDM) type. The paper presents a methodology to derive BQM’s of modern powertrain components, as parametric, steady-state limits of their FDM counterparts. The parametric nature of this procedure implies that changing the system modeled does not imply relaunching a simulation campaign, but only adjusting the corresponding parameters in the BQM. The approach is illustrated with examples concerning turbocharged engines, electric motors, and electrochemical batteries, and the influence of a change in parameters on the supervisory control of an hybrid vehicle is then studied offline, in co-simulation and on an HiL test bench adapted to hybrid vehicles (HyHiL).
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N. Verdonck, A. Chasse, P. Pognant-Gros, A. Sciarretta. Automated Model Generation for Hybrid Vehicles Optimization and Control. Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles, 2010, 65 (1), pp.115-132. ⟨10.2516/ogst/2009064⟩. ⟨hal-01937494⟩



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