Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approaches

This paper focuses on the energy optimization problem of traction substations. The paper addresses the difficulty of considering time-varying parameters and environmental characteristics of freight train in mechanism modeling. In response, a new optimization method called Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approachesis proposed. Firstly, a data-driven model considering the Train Track-Power grid(TTP) is constructed based on recorded data. The energy optimization problem is regarded as a finite Markov decision process. A dynamic programming (DP) algorithm is utilized to optimize the train's control force. The data-driven model is then used to solve for the train's speed curve, traction substation power, and contact network voltage. Secondly, a multi-input multi-output self-organizing fuzzy neural network (MIMO-SOFNN) model is designed during the modeling process, and an Levenberg-Marquardt algorithm with self-adaption damping factor (SA-LM) is proposed for model parameter learning. Finally, experimental analysis is conducted to validate the high accuracy of the MIMO-SOFNN model compared to five other models. The effectiveness of the SA-LM algorithm is also verified through a comparison with five other algorithms. In the energy optimization experiments, when compared with the actual operation data of a freight railway company in China, the proposed energy optimization method in this paper reduces the energy consumption of the traction substation by 34.8%.

Automated summary

This paper focuses on the energy optimization problem of traction substations and addresses the difficulty of considering time-varying parameters and environmental characteristics of freight train in mechanism modeling. A new optimization method called Modeling and Energy-Optimal Control for Freight Trains based on Data-Driven Approaches is proposed, which uses a data-driven model to solve for the train's speed curve, traction substation power, and contact network voltage. Experimental analysis validates the high accuracy of the proposed method in reducing energy consumption.