A control benchmark on the energy management of a plug-in hybrid electric vehicle.
ELBERT, P.
Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich]
Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich]
LAVEAU, A.
Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich]
Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich]
NÜESCH, T.
Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich]
< Leer menos
Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich]
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en
Article de revue
Este ítem está publicado en
Control Engineering Practice. 2014-08-01, vol. 29, n° August, p. 287-298
Elsevier
Resumen en inglés
A benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy ...Leer más >
A benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy management of a plug-in hybrid-electric vehicle was to be developed by the benchmark participants. The simulator, provided by the benchmark organizers, implements a model of the GM Voltec powertrain. Each solution was evaluated according to several metrics, comprising of energy and fuel economy on two driving profiles unknown to the participants, acceleration and braking performance, computational performance. The nine solutions received are analyzed in terms of the control technique adopted (heuristic rule-based energy management vs. equivalent consumption minimization strategies, ECMS), battery discharge strategy (charge depleting-charge sustaining vs. blended mode), ECMS implementation (vector-based vs. map-based), ways to improve the implementation and improve the computational performance. The solution having achieved the best combined score is compared with a global optimal solution calculated offline using the Pontryagin's minimum principle-derived optimization tool HOT.< Leer menos
Palabras clave en inglés
supervisory control
plug-in hybride lectricvehicles
energy management
optimal control
rule-based control
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