Load Estimation Algorithm Based on Extended Kalman Filter for Freight Train

Freight trains operating in various scenarios with severe track conditions require traction and braking systems characterized by large delays and strong constraints. Researchers usually build complex train multi-particle motion models and design control algorithms, seeking to establish accurate control in the control system of freight trains. However, in actual operation, due to flexible formation, operation schedule, and traction weight of freight trains, the cost for on-board equipment to obtain accurate model parameters is very high, which is also the main reason hindering the large-scale application of the automatic train operation (ATO) system on freight trains. This paper presents a load estimation algorithm based on extended Kalman filter (EKF) for freight trains, in response to their flexibility in traction weight. This algorithm is designed for real-time load estimation upon train starting under the control of the ATO system, based on inputs composed of ATO system control outputs and train movement status. The following corrections to the control system model and parameters can reduce the frequent operation of drivers by entering/confirming train data in the human-computer interface. The algorithm also improves the control accuracy and efficiency of the ATO system. Simulation results showed that the proposed algorithm generated more accurate load values, compared with relatively accurate load values entered by drivers. In the simulation experiment of 2,000-ton load, the estimation error ranged from 3.5% to 4%, demonstrating the proposed algorithm could effectively improve the accuracy of the ATO system, with lower requirements on multi-particle motion models.