Energy-efficient design and power flow analysis of electro-hydraulic steering systems for heavy-duty wheeled vehicles via parameter identification

Jun Xu, Heng Du, Shizhao Zhou, Lingtao Wei, Peiyang Chen and Yulan Zheng

Energy, 2025, vol. 322, issue C

Abstract: The growing demand for energy efficiency, environmental protection in the heavy transportation sector, particularly in large-scale projects, highlights the importance of improving steering systems for vehicles. A pump-controlled electro-hydraulic steering system is proposed, offering significant advantages in energy efficiency under high power. However, it leading to soft speed-load characteristics, reduced circuit stiffness, and compromised performance. To address challenges, an improved back-pressure-controllable BPC-PC-EHSS is introduced, the dynamic and power flow models are established. But it increases power loss, conflicting with the energy-saving objectives. Therefore, back-pressure parameter identification that balances both high performance and low energy-consumption is crucial. The energy-saving boundary is analyzed using the hydraulic conductivity factor, a parallel-input multilayer neural network (PIM-NN) is designed for nonlinear system back-pressure identification. Experimental results show that the proposed system significantly improves steering performance and energy-efficiency with minimal change in pump peak pressure and reduced pressure-vibrations. Specifically, under 6 tons load the error is 1°,which is improved by 55.6 % compared to the non-identification. Compared with valve-controlled and pump-valve systems under same-typical-conditions, significant energy-saving advantages and steering economy are demonstrated. Additionally, the real-world driving hardware environment is reconstructed, it is validated that the total steering input energy is reduced by 76.19 % on the experimental road.

Keywords: Heavy-duty multi-axle wheeled vehicle; Electro-hydraulic steering system; Variable-speed pump control; Energy-efficient parameter identification; Multi-layer neural network; Realistic driving simulation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:322:y:2025:i:c:s0360544225012046

DOI: 10.1016/j.energy.2025.135562

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