STUDY OF THE POWER CONSUMPTION OF ELECTRIC ROLLING STOCK OPERATING WITH REPEATED SHORT-TIME TRACTION CYCLES

The article is devoted to the issues of optimization of energy consumption in rail transport systems. This problem is complicated by uncertainty about the operating conditions and characteristics of electric rolling stock operating in a repeated-short-term traction mode. Given the significant impact of gradient and variability of it on energy consumption, the study presents a method for synthesizing generic gradients for modelling and optimization purposes. By applying the k-means clustering algorithm to real data on metro gradients, three ‘generic’ track gradients were derived, corresponding to light, medium and heavy operating conditions. Each generic gradient represents key statistical indicators (such as maximum gradient, average segment length, and total track section length), which summaries the diversity of gradient characteristics of urban rail systems. Building upon the established generic gradient profiles, the research develops a dynamic programming-based optimization model to determine the optimal train trajectory. The model simultaneously incorporates the train’s dynamic behavior and its control strategy. By discretizing the route into fixed-length segments, the model sequentially determines the optimal traction or braking force at each segment. This decision-making process is framed as a series of interconnected subproblems, with a cost function that balances energy expenditure and travel time. The optimization procedure utilizes a gradient descent method to iteratively adjust a Lagrange multiplier, ensuring that the final trajectory satisfies both the prescribed schedule and energy minimization objectives. Based on the results of the study, it is proposed to use the added energy consumption coefficient (AECC) for a holistic assessment of the energy efficiency of a rail transport system on a particular section. This coefficient quantifies the additional energy required by the train when operating on real-world gradient profiles compared to a baseline profile with a constant gradient. By standardizing energy performance metrics across different track conditions, the AECC provides a robust tool for assessing and comparing the energy efficiency of various rolling stock configurations and operational strategies.