Hybrid supervisory control algorithm for fuel consumption minimization of a compound-type hybrid excavator

Abstract

Recently, global warming and environmental concern with exhaustion of natural resources has stimulated the development of environmentally friendly vehicles. Especially, in the automobile industry, hybridization of conventional powertrain has been actively studied as a short-term solution of the emission problem. Various types of hybrid vehicles have been successfully marketed by major automobile manufacturers and these vehicles show a significant performance improvement in the fuel economy. Successful hybridization of conventional powertrain also stimulates the hybridization of heavy-duty vehicles. The hybridization of heavy-duty vehicles is expected to be profitable because fuel costs generally account for a large portion of the total operating cost. This dissertation has been focused on the hybridization of a conventional excavator among the various types of the heavy duty vehicles. This dissertation describes a hybrid supervisory control strategy for fuel consumption minimization of a hybrid excavator. The target hybrid excavatoris a compound-type hybrid excavator which replaces one of hydraulic actuator to an electric motor. The target hybrid excavator removes the hydraulic swing motor since its conversion efficiency is lower than other hydraulic actuators. The electrically propelled swing motor will increase the number of energy paths and it also incurs many constrains related to the power balance of the hybrid drive train. The compound-type hybrid excavator also incorporates an engine assist motor and super capacitor for the hybridization of original drive train. The dynamic programming technique (DP) has been applied to obtain the global optimal solutions of the constrained nonlinear fuel optimization problem over representative excavation cycles. The optimal control problem also has been applied to solve the same problem. The both method give insights to design the real-time hybrid supervisory control algorithm and can be a benchmark for evaluating the performance of the control algorithm. Based on the analysis of the DP results and optimal control problem, a realtime control algorithm has been designed. The algorithm contains a power management control algorithm to increase the overall efficiency and an engine set speed regulation for dragging engine operating points near the optimal Operating line. The power management algorithm is designed based on the equivalent fuel consumption minimization strategy (ECMS). The engine set speed regulator is based on the DP results. The designed hybrid supervisory control algorithm has been evaluated using a simulation model which is developed based on the Matlab/Simulink. The simulation results show that the hybrid supervisory control algorithm is near optimal compared to DP results and that is about 3 percent of improvement of fuel economy compared to a thermostat control algorithm which determines power distribution based on the state of charge of the super capacitor. Excellent charge-sustaining performance also has been achieved. Since the required power of electrically propelled swing motor should beprovided by the super capacitor, the charge sustaining performance should be a relevant consideration when the power management algorithm is designed. The performance of the developed algorithm has been verified through realworld operating tests, and about 30% of fuel economy has been improved compared to the conventional excavator.