Research on the global energy optimization of multi-source and multi-actuator hydraulic systems based on dynamic...

Area of Science:

• Mechanical Engineering
• Hydraulic Systems
• Energy Efficiency

Background:

• Multi-source and multi-actuator hydraulic systems (MSAHSs) are crucial for high-power applications but often suffer from low efficiency due to uncoordinated component control.
• This inefficiency leads to environmental pollution and substantial economic losses, necessitating advanced energy-saving solutions.

Purpose of the Study:

• To develop and validate a global power matching method for closed MSAHSs to improve energy efficiency.
• To identify hydraulic pressure as a key factor influencing component efficiency in MSAHSs.

Main Methods:

• Established a power consumption model for closed MSAHSs to analyze efficiency factors.
• Proposed a multi-algorithm integration method combining back propagation (BP) neural networks, dynamic programming (DP), and an improved adaptive genetic algorithm (IAGA).
• Utilized BP for efficiency prediction, DP for preliminary high-efficiency zone search, and IAGA for global power matching and optimal control parameter searching.

Main Results:

• The multi-algorithm integration method was tested on a hydraulic fracturing vehicle's closed MSAHS.
• Significant improvements in overall system efficiency were observed.
• Achieved up to 35.5% fuel savings under normal operating conditions compared to traditional local power matching control.

Conclusions:

• The proposed multi-algorithm integration method effectively achieves global power matching in MSAHSs.
• This approach offers a viable strategy for developing energy-saving and environmentally friendly hydraulic systems.
• The findings demonstrate a substantial reduction in fuel consumption, highlighting the practical benefits of the developed method.