Control Strategy for Shift Schedule Correction Based on Driving Habits for Vehicles with Automatic Transmission

A shift schedule modification program is an intelligent system for automatic transmission. This program can adjust shift points to cater to drivers with different driving habits. An important prerequisite in designing a personalized shift schedule is identifying the driving habits of drivers. In this study, we developed an identification algorithm based on wavelet neural network and Bayesian fusion decision-making. First, a system for identifying driving styles was established based on the wavelet neural network. Second, the results were integrated by Bayesian fusion decision-making to obtain the driving habits. Finally, different correction coefficients were selected based on driving habits to satisfy the requirements of drivers. Experimental results show that the driving habits can be accurately identified based on wavelet neural network and Bayesian fusion decision-making, and the correction control strategy can rectify the shift schedule effectively. The correction control strategy satisfies the requirements of different drivers for vehicle performance and enhances the intelligence of automatic transmission.