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Development of supervisory control strategy for optimized fuel consumption of the compound hybrid excavator
This paper describes a hybrid supervisory control strategy for fuel optimization of a compound hybrid excavator that incorporates an engine-assist motor, an electric swing motor and a super capacitor in the original drive train. The main features of the compound hybrid excavator are that the electrically-propelled swing motor increases the number of energy paths and also incurs many constraints related to the power balance of the hybrid drive train. The dynamic programming technique has been applied to the constrained nonlinear fuel optimization problem over representative excavation cycles. Then, by imitating the behaviour of the dynamic programming optimization scheme, a rule-based controller has been designed. The rule-based controller determines the diesel engine set speed and the engine-assist motor power to operate the engine in an efficient region and to minimize the energy loss in the hybrid drive train. The performance of the rule-based controller has been compared to that of the thermostat controller, which determines the energy distribution, only based on the state of charge of the applied super capacitor. Simulation results indicate that the compound hybrid excavator can improve overall fuel efficiency by about 20% compared to the conventional excavator for representative excavation cycles, and the proposed rule-based controller further enhances the fuel economy by about 2-3% compared to the simple thermostat controller.
Development of supervisory control strategy for optimized fuel consumption of the compound hybrid excavator
This paper describes a hybrid supervisory control strategy for fuel optimization of a compound hybrid excavator that incorporates an engine-assist motor, an electric swing motor and a super capacitor in the original drive train. The main features of the compound hybrid excavator are that the electrically-propelled swing motor increases the number of energy paths and also incurs many constraints related to the power balance of the hybrid drive train. The dynamic programming technique has been applied to the constrained nonlinear fuel optimization problem over representative excavation cycles. Then, by imitating the behaviour of the dynamic programming optimization scheme, a rule-based controller has been designed. The rule-based controller determines the diesel engine set speed and the engine-assist motor power to operate the engine in an efficient region and to minimize the energy loss in the hybrid drive train. The performance of the rule-based controller has been compared to that of the thermostat controller, which determines the energy distribution, only based on the state of charge of the applied super capacitor. Simulation results indicate that the compound hybrid excavator can improve overall fuel efficiency by about 20% compared to the conventional excavator for representative excavation cycles, and the proposed rule-based controller further enhances the fuel economy by about 2-3% compared to the simple thermostat controller.
Development of supervisory control strategy for optimized fuel consumption of the compound hybrid excavator
Kim, Hakgu (Autor:in) / Choi, Jaewoong (Autor:in) / Yi, Kyongsu (Autor:in)
2012
15 Seiten, 24 Bilder, 3 Tabellen, 15 Quellen
Aufsatz (Zeitschrift)
Englisch
Bagger , Hybridantrieb , Hilfsmotor , Elektromotor , Doppelschichtkondensator , dynamische Programmierung , nichtlineare Optimierung , Kraftstoffverbrauch , Steuerung und Regelung , regelorientiertes System , Dieselmotor , Temperaturregler , Energieverteilung , Batteriekontrolle , Ladezustand , numerische Simulation
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