The spring load adjustment method for six-axle high-power locomotives

The stability and comfort of locomotives need to be guaranteed by load adjustment technology. Considering the defects of the traditional two-step adjustment method for locomotive load distribution, including its lack of efficiency and error accumulation, a new technical approach is developed here under entire locomotive conditions, simulating the load adjustment test via the application of shimming under the treads. In the case of high-power locomotives, a complete theoretical model is established based on the classical two-suspension model. According to the difference between shimming on the treads and on primary suspension positions, a transformation matrix is established with which to describe the conversion relationship between the shim quantity on the primary supporting positions and on the treads. Considering that locomotive load regulation is a nonlinear problem characterised by nonlinearity, parametric uncertainty and multiple optimisation objectives, this paper proposes QAGA, an optimisation algorithm for entire locomotive load adjustment based on an adaptive genetic algorithm and a quantum-behaved particle swarm optimisation algorithm, to carry out simulations using data from an HXD1D-type electric locomotive. Analysis of the simulation results proves that the proposed approach can significantly improve the efficiency, accuracy and feasibility of the entire locomotive load adjustment process.