DRNN-based shift decision for automatic transmission

Abstract

In research on intelligent shift for automatic transmission, the neural network selected has no feedback and lacks an associative memory function. Thus, its adaptability needs to be improved. To achieve this, an automatic shift strategy based on a deep recurrent neural network (DRNN) is proposed. First, a neural network framework was designed in combination with an eight-speed gearbox that matches a particular type of vehicle. Then, the working principle of the DRNN was applied to the shifting process of an automatic gearbox, and the implementation model of the shift logic was established in MATLAB/Stateflow. A data sample obtained from the model was used to train the DRNN. Training and evaluation of the DRNN were accomplished in Python. Finally, a simulation comparison of the DRNN with a back-propagation (BP) neural network proved that after the epochs have been increased, the DRNN has higher precision and adaptation than a BP neural network. This research provides a theoretical basis and technical support for intelligent control of automatic transmission.