Research on the global energy optimization of multi-source and multi-actuator hydraulic systems based on dynamic programming and improved adaptive genetic algorithm

Multi-source and multi-actuator hydraulic systems (MSAHSs) are widely used in high-power energy transmission and construction machinery. However, individual control of each component without considering the overall power matching leads the system to the low-efficiency zone, results in environmental pollution and huge economic loss. Therefore, it is highly desirable to find a way of obtaining energy-saving green MSAHSs. In this paper, the power consumption model of closed MSAHSs is established firstly to analyze theoretical factors affecting the component efficiency and find that the hydraulic pressure is the key factor. On this basis, a multi-algorithm integration global power matching method is then proposed, which consist of back propagation (BP) neural network, dynamic programming (DP) and improved adaptive genetic algorithm (IAGA). BP is used to construct efficiency prediction models for power elements (pumps, motors and engines) respectively, DP is used for elements’ high efficiency zone preliminary search, and IAGA is used to realize the global power matching of the multiple power units with energy conversion and transfer finally through optimal control parameters precise searching. Experiment is conducted on the closed MSAHS in a hydraulic fracturing vehicle. Results demonstrate that the MSAHS applied with multi-algorithm integration method improves the overall efficiency to a highest fuel savings of 35.5 % under normal conditions compared with local power matching control.