Optimal Traction Control for Heavy-haul Train Using Dynamic Response Identification Model

The Automatic Train Operation (ATO) equipment is specifically designed to ensure optimal traction for heavy-haul trains operating under challenging conditions. Onboard controllers are installed on locomotives to enhance freight transportation capacity, reduce costs, and alleviate driver workload. These controllers can be engaged or disengaged to switch between two distinct operating modes: the inertial response mode characterized by a longer time delay, and the open mode which offers better anti-interference capability. This paper proposes an optimal traction control strategy for heavy-haul trains utilizing dynamic response identification technology. The strategy compares three ATO control algorithms based on the same identified vehicle model that considers time delay; however, each algorithm relies on output signals from different sensors. By simulating with real section data from the "Shenchi-Nan-Ningwuxi" heavy haul railway of "Shuohuang," this paper verifies the control effect of the proposed optimal traction control strategy for heavy haul trains in the presence of disturbance. Simulation results based on real line data and HXD1 electric locomotive running data demonstrate significant improvement in speed tracking ability achieved through our approach.