Knowledge-based gear-position decision

Abstract

Gear-position-decision (GPD) tactics strongly affect the performances of automatic transmissions (AT) and, therefore, the performance of the vehicle. Since the electronic control methods were introduced into ATs, many advanced techniques have been raised to make AT vehicles more human friendly and better in fuel economy and dynamic behaviors. As a type of emerging AT, the automated manual transmissions (AMT) are being researched and developed in all relevant technologies. In this paper, we proposed a driving knowledge-based GPD (KGPD) method for AMTs. The KGPD algorithm is composed of a driving environments and driver's intentions estimator, the shift schedules for each typical driving environment and driver's intention situations, and an inference logic to determine the most proper gear position for the present situation. The estimator identifies the driving environments and features of driver's intentions, which are divided into some typical patterns. Based on the identified results, the gear-position inference algorithm calculates the best gear position at the moment. In fact, the method just simulates the course of a driver's making gear-position decision when driving an automobile with manual transmission. The test results show that the AMT with the method gives less unnecessary shifting, conducts more proper gear positions, and behaves better in subjective assessment than that with the method that is directly based only on automotive state parameters.