Shift control of vehicle automatic transmission based on traffic congestion identification

Abstract

This work builds a T-S fuzzy neural network that identifies traffic congestion conditions by using average vehicle speed, average throttle opening and frequency of brake pedal actuation as evaluation factors. A strategy that controls the shift of vehicle automatic transmission based on the identified congestion conditions is also devised. This strategy divides the vehicle automatic transmission system into the upper identification and decision-making layer and the lower shift execution layer. Simulation and real vehicle tests are performed to verify the effectiveness of the proposed strategy. The results show that congestion conditions can be accurately identified by using the T-S fuzzy neural network and that the proposed layered correction shift control strategy can prevent the frequent changing of gears under congestion conditions, thereby reducing the wear of the shift execution parts and the braking system.