Energy-Efficient Train Control: Online Train Operation Considering Dc Traction Network

Abstract

The network efficiency and real-time power of the urban rail transit traction power supply system (TPSS) are closely linked to train speed and output power. Collaborative optimization of energy-efficient train control (EETC) and railway systems can reduce TPSS-level energy consumption. This paper proposes a high-accuracy, high-efficiency EETC model integrated with TPSS-train integration to minimize TPSS energy, utilizing a shrinking horizon model predictive control (SHMPC) framework. The proposed iterative model uses spatial-to-temporal domain conversion to update the train’s future operational states and network topology. By optimizing spatial discretization and iteration count, the model reduces solution deviations caused by speed limit and time discrepancies, enhancing its accuracy. With a minimum operation time of 0.09s per iteration. The result reveals that less mechanical energy does not necessarily equate to less traction energy sourced from the TPSS. Compared to the distance-based EETC model, the proposed model achieves an 11.74% savings rate in traction energy from the TPSS. Within this, the proportion of traction energy loss is 8.53%, indicating less traction energy loss than the total energy loss incurred by the model without considering TPSSs-train integeration.

Keywords: Shrinking horizon Model Predictive ControlOnline Energy-efficient Train Control Network-TPSS-Train IntegrationSpatial-to-temporal Conversion