Abstract
Aiming at the parameters of the different displacements and related components of the variable-displacement asymmetric axial piston pump (VAPP) required by the energy-recovery system of excavator booms of different tonnages, a rapid multi-process parallel optimization method of complex hydraulic products based on a multi-core CPU was proposed for parameter matching. The parameter matching was used to reasonably select relevant parameters so that the excavator’s boom energy-recovery and utilization system can improve operational efficiency and energy-saving efficiency under the premise of satisfying the normal working conditions of the working mechanism, and achieving the purpose of serializing VAPP products. A multi-objective optimization model was put forward according to energy-saving efficiency and operational efficiency. First, the accuracy of the acceleration method of the CVODE, a solver for stiff and non-stiff ordinary differential equation (ODE) systems, was verified by a physical prototype test. The results showed that the test and simulation results were in good agreement. A particle swarm optimization algorithm (PSO) was used to optimize the main parameters of the boom energy-recovery system to obtain the appropriate energy-saving efficiency and obtain the VAPP displacement and related component parameters required by the energy-recovery system of excavator booms of different tonnages. The simulation results showed that a motor working condition was necessary in the guaranteed descending stage, and the process of lifting−descending−lifting was completed under the condition that the total time did not exceed a certain value. The energy-saving rates of the 7-ton (7T), 12-ton (12T), 20-ton (20T), and 30-ton (30T) excavator boom energy-recovery systems reached 29.8%, 35.3%, 31.25%, and 27.88%, respectively. In the eight-core CPU workstation under the simulation conditions, compared with the Simulation X platform simulation method, the simulation efficiency of the multi-core CPU parallel method was improved by more than 80 times.