Abstract:

The transmission system of construction vehicle is difficult to describe via the traditional mathematical model due to its high complexity and low transmission efficiency. In order to solve these problems, a typical transmission system of construction vehicle is modeled and analyzed, and a dynamic model is established. Based on the model, a four-parameter automatic shift strategy is presented. Moreover, in order to overcome the low convergence rate and the local minimum of the conventional back propagation （BP） neural network, the method of changing step length and the reverse transmission algorithm of the momentum gradient reduction are adopted to improve the BP neural network for automatic shift control. Some test data of shift control are used to train the improved BP algorithm and a simulation is finally performed. The results indicate that the proposed shift strategy improves the transmission efficiency, and that the improved BP neural network effectively shortens the training time and determines the optimal shift according to the driving condition of vehicle.

Key words: construction vehicle, automatic shift, improved back propagation algorithm, simulation