Research on optimization and evaluation method of heavy-haul train cyclic braking manipulation

The study aims to build a high-precision longitudinal dynamics model for heavy-haul trains and validate it with line test data, present an optimization method for multi-stage cyclic brakes based on the model and conduct a multi-objective detailed evaluation of the driver’s manipulation during cyclic braking.

The high-precision longitudinal train dynamics model was established and verified by the cyclic braking test data of the 20,000 t heavy-haul combination train on the long and steep downgrade. Then the genetic algorithm is employed for optimization subsequent to decoupling multiple cyclic braking procedures, with due consideration of driver operation rules. For evaluation, key manipulation assessments in the scenario are prioritized, supplemented by multi-objective evaluation requirements, and the computational model is employed for detailed evaluation analysis.

Based on the model, experimental data reveal that the probability of longitudinal force error being less than 64.6 kN is approximately 68%, 95% for less than 129.2 kN and 99.7% for less than 193.8 kN. Upon optimizing manipulations during the cyclic braking, the maximum reduction in coupler force spans from 21% ~ 23.9%.And the evaluation scores imply that a proper elevation of the releasing speed favors safety.A high electric braking force, although beneficial to some extent for energy-saving, is detrimental to reducing coupler force.

The results will provide a theoretical basis and practical guidance for further ensuring the safety and energy-efficient operation of heavy haul trains on long downhill sections and improving the operational quality of heavy-haul trains.