Energy Consumption Optimization of Single-Action Operation in Distributed Independent Pump-Controlled Excavator Based on Pump Speed Distribution and GOA-SQP Closed-Loop Algorithm

Distributed independent pump-controlled systems enhance excavator energy efficiency by reducing throttling losses, but sustained high-load and high-speed operation often forces a single pump into low-efficiency regions. To overcome this limitation, this study proposes a dual-pump load-sharing architecture that enables load sharing and dynamic cooperative operation of two pumps within the high-efficiency region. An adaptive closed-loop GOA-SQP optimization strategy is also developed for real-time pump speed distribution. The proposed strategy combines the global exploration capability of the Grasshopper Optimization Algorithm (GOA) with the rapid local convergence characteristics of Sequential Quadratic Programming (SQP). Unlike conventional serial hybrid optimization methods, a bidirectional efficiency-feedback mechanism is introduced to dynamically coordinate global exploration and local refinement processes in real time. Furthermore, a local perturbation and re-explosion mechanism is incorporated to suppress premature convergence, enhance population diversity, and reduce redundant iterations. Experimental validation on an excavator test platform under four-quadrant conditions shows that the proposed system improves mechanical, volumetric, and overall pump efficiencies by 14.22, 4.57, and 18.79 percentage points, respectively, and reduces total energy consumption by 8.83%. Compared with GOA, SQP, GA-SQP, and PSO-SQP, the proposed GOA-SQP algorithm reduces solution time by 39.15% and improves optimization accuracy by 0.5%. The proposed architecture and optimization strategy offer a novel and effective solution for further improving the energy efficiency of distributed independent pump-controlled excavator systems.