Energy-efficient Train Control Considering Energy Storage Devices and Traction Power Network using a Model Predictive Control Framework

The optimization of the train speed trajectory and the traction power supply system (TPSS) with hybrid energy storage devices (HESDs) has significant potential to reduce electrical energy consumption (EEC). However, some existing studies have focused predominantly on optimizing these components independently and have ignored the goal of achieving systematic optimality from the standpoint of both electric systems and train control. This paper aims to establish a comprehensive coupled model integrating the train control, DC traction power supply, and stationary HESDs to reach the minimum EEC within the integrated system. The original non-convex and time-varying model is initially relaxed and reformulated as a convex program that can be solved quickly. On this basis, a model predictive control (MPC) framework is proposed to derive specifications in the space-domain-based model and overcome the drawbacks of the time-domain-based model. The designed controller solves the optimization problem for the remaining journey through time sampling, guaranteeing real-time and closed-loop performance. The numerical experiments present five case studies based on the real-world scenario i.e. Guangzhou Metro Line No.7. The results demonstrate that the proposed integrated convex model without stationary HESDs can reduce the accumulated EEC by up to 27.99% compared to the existing field test results. In addition, compared to mixed integer linear programming (MILP) method, the convex program proposed in this work obtains the highest energy savings rate (48. 71%) and significant computational efficiency, ranging from milliseconds (0.03 s) to seconds (4.20 s) in the TPSS with stationary HESDs. Additionally, the convex model features satisfactory modeling accuracy by invoking the nonlinear solver to simulate the power flow of the integrated system and recalculate the EEC.