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February 8, 2026World Electric Vehicle Journal0 citationsOpen Access

Performance Optimization of Hydro-Pneumatic Suspension for Mining Dump Trucks Based on the Improved Multi-Objective Particle Swarm Optimization

LYLin YangTGTianli GaoMZMi Zhao

Key Points

  • The central aim is to optimize both ride comfort and wheel grounding performance for mining dump trucks under challenging road conditions.
  • Developed a dynamic model of hydro-pneumatic suspension incorporating valve and gas chamber characteristics.
  • Systematically analyzed the effect of key parameters on vertical dynamic performance.
  • Constructed a multi-objective optimization model targeting minimized RMS values of sprung mass acceleration and dynamic tire load.
  • Enhanced MOPSO algorithm using adaptive inertia weighting and dynamic parameter updates.
  • Achieved reductions of 37.6% in sprung mass acceleration and 15.8% in dynamic tire load on class-C roads.
  • Observed a 10.2% decrease in suspension rattle space.
  • Under transient bump conditions, peak-to-peak values dropped by 38.9% and 44.9% for sprung mass acceleration and dynamic tire load, respectively.
  • Demonstrated superior balance and stability compared to the NSGA-II algorithm.

Abstract

Aiming at the challenge of simultaneously optimizing ride comfort and wheel grounding performance for mining dump trucks under severe road conditions, this paper proposes a hydro-pneumatic suspension parameter design method based on an improved multi-objective particle swarm optimization (IMOPSO) algorithm. First, a dynamic model of the hydro-pneumatic suspension is established, incorporating the coupled nonlinear characteristics of the valve system and the gas chamber. The accuracy of the model is verified through bench tests. Subsequently, the influence of key parameters, including the damping orifice diameter, check valve seat hole diameter, and initial gas charging height, on the vertical dynamic performance of the vehicle, is systematically analyzed. On this basis, a multi-objective optimization model is constructed with the objective of minimizing the root mean square (RMS) values of both the sprung mass acceleration and the dynamic tire load. To enhance the global search capability and convergence performance of the MOPSO algorithm, adaptive inertia weighting, dynamic flight parameter update, and an enhanced mutation strategy are introduced. Simulation results demonstrate that the optimized suspension achieves significant improvements under various road conditions. On class-C roads, the RMS values of the sprung mass acceleration (SMA) and the dynamic tire load (DTL) are reduced by 37.6% and 15.8%, respectively, while the suspension rattle space (SRS) decreases by 10.2%. Under transient bump roads, the peak-to-peak (Pk-Pk) values of the same two indicators drop by 38.9% and 44.9%, respectively. Furthermore, compared to the NSGA-II algorithm, the proposed method demonstrates superior performance in terms of convergence stability and overall performance balance. These results indicate that the proposed design effectively balances ride comfort, wheel grounding performance, and driving safety. This study provides a theoretical foundation and an engineering-feasible method for the performance balancing and parameter co-design of suspension systems in heavy-duty engineering vehicles.

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Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/698828cb0fc35cd7a8848904https://doi.org/10.3390/wevj17020076
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