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Most hydraulic excavators use passive power control to adjust the engine speed according to the load torque to achieve power matching, which has very limited effectiveness in energy saving. In this paper, an active power optimization control of speed closed-loop engine is proposed to improve its tracking performance for the dynamic energy-saving operating points. The inertia movement of the engine during variable speed control is analyzed to determine the kinetic energy requirements for different start and end operating points. An RBF-based neural network controller is designed based on power optimization to actively compensate the strong hysteresis of the engine speed relative to the optimal torque. The simulation and the experimental results show that the proposed control method has a much faster response for the energy-saving operating points, which reduces the energy consumption by 9.28% and 5.56% without adding any energy storage devices to the hydraulic excavator. [ABSTRACT FROM AUTHOR]