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Neural network and numerical analysis methods are studied, and applied to create the models of fuel economy for CFA6470 parallel hybrid electric vehicle (CFA6470PHEV) engine. Based on optimally designing configuration and parameters of the models, the optimal models of fuel economy are achieved. With numerical analysis methods, the engine cycles having optimal fuel economy has been determined, along with the relationship of throttle angle and fuel economy of the engine determined too. The optimal fuel economy of the engine can be controlled with adjusting the throttle angle.