Study on the influence and optimization of blend braking strategy of train at gradient change point

Abstract

Due to the difference in gradient resistance between the front and rear vehicles, the long marshalling train will produce a larger longitudinal impulse when it passes through the gradient change point for blend braking. The conventional strategy of simultaneous application of dynamic and air braking and average distribution of dynamic braking is not necessarily optimal, it is significant to optimize in-train longitudinal forces under the target braking performance through the different blend braking strategy for heavy haul train. Firstly, mathematical models of in-train longitudinal forces are developed for longitudinal train dynamic simulations including the air braking system sub-model with the brake delay time and multi-stages nonlinear charging characteristics and the friction draft gear sub-model with the viscous resistance characteristics. Besides, longitudinal dynamic simulation is conducted to study and analyze the dynamic braking matching strategy in a typical case when “1+1+1” 20,000-ton train passes through the gradient change point of “-12‰ downhill-straight track” for blend braking. The influence of different dynamic braking distribution and asynchronous application strategy on the longitudinal impulse of the train is analyzed. Finally, a particle swarm optimization algorithm is proposed to achieve rapid optimization of the optimal locomotive dynamic braking distribution. This work provides valuable insights into simulating in-train longitudinal forces to realize the safe and efficient operation of heavy haul trains in the future.

Keywords

Heavy haul train longitudinal train dynamic gradient change point blend braking

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