A Literature Review on Train Motion Model Calibration

Abstract

The dynamics of a moving train are usually described by means of a motion model based on Newton's second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict train dynamics accurately if the mentioned train characteristics are carefully calibrated. The model constitutes the core element of a broad variety of railway applications, from timetabling tools to Driver Advisory Systems and Automatic Train Operation. Among the existing train motion model calibration techniques, those that use operational data are of particular interest, as they benefit from on-board recorded data, capturing the train dynamics during operation. In this literature review article we provide an overview of the train motion model calibration techniques that have been published in the scientific literature between January 2000 and December 2021 and either use operational data or can be minimally adapted to use it. To this end, we present a critical overview of the existing train motion model calibration approaches, distinguishing online calibration that analyzes data on-the-go and offline calibration that analyzes historical data batchwise. We propose a research agenda and highlight some potential goals to be tackled in the near future: from devising accurate online calibrators for eco-driving applications to quantitizing the physical sources of parameter variation. Last, we discuss practical recommendations for practitioners and scholars inferred from the current state of the art.

A Low Power Analog Integrated Fractional Order Type-2 Fuzzy PID Controller

“…We observe that the circuit exhibits a robust behavior with respect to process and mismatch random variations. As further proof of concept for the proper operation of the proposed analog implementation of the control system, the circuit is tested on a 2nd order non-linear plant, modelling the dynamics of the motion of a train on a track [92]. The differential equation representing the plant system is as follows ÿ(t) = −k ẏ(t) − r ẏ(t)| ẏ(t)| + u(t) (12) Likewise the 1st plant, in this case both a software (Matlab/Simulink) and the hardwareequivalent (post-layout simulation) controller have been tested to account for the analog implementation's efficiency and accuracy.…”

Dynamic Simulation and Modeling of a Novel NeuRaiSya for Railway Monitoring System Using Petri Nets

“…For example, they are applied in capacity evaluations for current and prospective railway lines, determining track section occupancy and arrival times for input into timetabling tools. Simulators are also utilized for estimating energy consumption, aiding in infrastructure planning, testing new signaling systems, and providing training for drivers [20].…”

Two-Stage Optimal Trajectory Planning Based on Resilience Adjustment Model for Virtually Coupled Trains

“…In this paper, the model used to update the state of virtually coupled trains is based on longitudinal train dynamics, with the train viewed as a point mass with a single degree of freedom. According to Newton's second law, the train dynamics can be described by [28]:…”