Measurement accuracy has always been the main factor affecting the effective reliability of vehicle dynamic weighing systems.Aiming at the problem of low measurement accuracy of vehicle dynamic weighing system,a dynamic weighing model based on back propagation neural network and hybrid optimization of whale optimization algorithm(WOA)and simulated annealing(SA)algorithm is proposed.Firstly,the structure and principle of the dynamic weighing system are briefly introduced.Then,the sampling signal of the dy-namic weighing system is filtered and reconstructed by using wavelet transform,and the calculated dynamic vehicle weight,vehicle speed and number of axles are used as the input parameters of the BP neural network model.Secondly,a BP neural network optimized by the WOSA algorithm is established to predict the actual gross vehicle weight and axle load.Finally,the prediction ability of BP neural net-work model optimized by WOSA algorithm is compared and a conclusion is drawn.The simulation results show that the WOSA-BP vehi-cle dynamic weighing model has fast convergence speed and high accuracy,the relative error of the maximum gross weight is 0.58%,and the relative error of the maximum axle weight is 6.73%.
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