Multi-Objective Predictive Energy Management Framework for Hybrid Electric Powertrains: An Online Optimization Approach

Autoren

Raja Sangili Vadamalu MSc., Prof. Dr.techn. Christian Beidl,
Institut für Verbrennungskraftmaschinen und Fahrzeugantriebe, Technische Universität Darmstadt;
Sebastian Barth MSc., Dipl.-Ing. Florian Rass,
Honda R&D Europe (Deutschland) GmbH, Offenbach am Main

Zusammenfassung

With the increasingly stringent CO₂ targets, hybridization of powertrains is gaining increased attention. Technological advances in the field of Connectivity enlarge the system boundary providing additional operative degrees-of-freedom, in addition to the
ones offered by the presence of multiple onboard energy converters of the hybridized powertrains. Further, a unimodal approach focusing only on fuel consumption is not desirable in view to the increasing technical complexity and customer requirements.
Exploiting the above trends, this paper presents a Multi-Objective Predictive Energy Management (EM) Framework for hybrid electric Powertrains using the solution of the optimal control problem computed online.

The core idea of the multi-objective implementation uses transient limits on the controlled variables in addition to the usually employed weighting of multiple objectives using scalarization approaches. With the increasing market share of GDI engines strategies to reduce particle number emissions are increasingly investigated. The developed generic framework was adapted to reduce particle emissions of a 1.5 liter GDI engine. Experimental validation at the engine test bench for the considered driving scenario demonstrated a reduction of particle emissions and fuel consumption using the
predictive EM strategy.

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