Title: Shift control of vehicle automatic transmission based on traffic congestion identification

Authors: Guang Xia; You Zheng; Xiwen Tang; Baoqun Sun; Shaojie Wang

Addresses: School of Automotive and Traffic Engineering, Hefei University of Technology, Hefei, Anhui 230009, China ' School of Automotive and Traffic Engineering, Hefei University of Technology, Hefei, Anhui 230009, China ' School of Radar Confrontation, National University of Defense Technology, Hefei, Anhui 230037, China ' Institute of Automotive Engineering, Hefei University of Technology, Hefei, Anhui 230039, China ' Institute of Automotive Engineering, Hefei University of Technology, Hefei, Anhui 230039, China

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.

Keywords: traffic congestion identification; T-S fuzzy neural network; shift correction; layered control; real vehicle test.

DOI: 10.1504/IJVAS.2020.108433

International Journal of Vehicle Autonomous Systems, 2020 Vol.15 No.2, pp.131 - 151

Accepted: 05 Jan 2020
Published online: 13 Jul 2020 *

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