Study of influence of imprecision of primary information on energy consumption of rolling stock
Keywords:
speed trajectory optimization, urban rail transport, energy efficiency, dynamic programmingAbstract
The energy efficiency of urban rail transportation systems is a crucial indicator, as traction energy consumption typically accounts for 40-60% of the total energy consumption of the transportation system. This study examines the sensitivity of energy consumption to deviations from nominal conditions under the implementation of pre-calculated optimized trajectories for electric rolling stock, considering rolling stock with operation modes typical for suburban and urban transport. To determine globally optimal control strategies that minimize energy consumption while complying with operational constraints, the study uses dynamic programming based on Bellman's optimality principle. The optimization model divides the track section into discrete segments and uses the backward induction method to establish optimal control laws, producing speed trajectories as functions of the train's current coordinates on a given gradient profile. The trade-off between energy and time is represented by an indefinite Lagrange multiplier to ensure adherence to the timetable. Sensitivity analysis is performed by simulating inaccuracies in the estimates of the train's current coordinates and variations in its passenger load. Modelling of a targeted braking system has been implemented so as to ensure stopping accuracy in the event of measurement inaccuracies. Modelling was performed using three typical gradient profiles, characteristic primarily of underground railways; for comparison, modelling was also performed on a conditional section with a negligible gradient. The research methodology allows for a quantitative assessment of the degree of energy overconsumption that may be caused by deviations in train passenger load factors and errors in the estimation of the position of rolling stock (±25 meters), which provides information for assessing the effectiveness of pre-calculated optimized trajectories in real operating conditions.
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