Energy-efficient Train Control: Online Train Control Considering Train-TPSS Integration

This paper presents an online train control convex model based on the shrinking horizon model predictive control (SHMPC) framework, which integrates train-network coupling characteristics to enable real-time prediction and updating of the train’s future states and traction power supply system (TPSS) network topology. The study aims to reduce traction energy consumption at the TPSS level. The results indicate that the minimum single iteration optimization time of the proposed model is 0.109 s, making it suitable for application in automatic train operation (ATO) systems. Furthermore, while the proposed model achieves the minimum TPSS traction energy consumption, this optimal solution does not equate to minimizing the train’s mechanical energy consumption. When targeting minimum TPSS traction energy consumption, the proposed iterative optimization model can achieve a traction energy saving rate of 8.95% and 8.23%.