Energy Consumption and Running Time for Trains: Modelling of Running Resistance and Driver Behaviour Based on Full Scale Testing

Lukaszewicz, Piotr · 2001 · ROSA P / Royal Institute of Technology. Department of Vehicle Engineering

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Summary

This doctoral thesis by Piotr Lukaszewicz addresses the critical need for accurate computer simulations of train energy consumption and running times. The research is motivated by the significant operational costs associated with railway energy use and the limitations of existing simulation tools, which often lack validated models for running resistance and driver behavior. The study aims to develop and verify empirical models for train dynamics and driver operations based on full-scale testing, specifically focusing on Swedish freight trains hauled by SJ Rc4 locomotives, as well as high-speed and conventional passenger trains. The methodology combines experimental full-scale testing with theoretical modeling. Lukaszewicz introduces the "Energy Coasting Method," a simplified technique for determining running resistance that minimizes measurement errors compared to traditional tractive effort or dynamometer methods. This method involves measuring speed decay during coasting runs to calculate resistance forces. The study systematically parameterizes running resistance across various train types, analyzing the influence of speed, axle load, train length, track type, and wind conditions. A specific model was developed to account for the ground boundary layer’s effect on head and tail wind drag. Additionally, empirical models for traction, braking, and mechanical efficiency (including wheel slippage) were derived for the SJ Rc4 locomotive. Driver behavior was analyzed through observations of real freight train operations, leading to the development of a general driver model in Matlab that simulates acceleration, speed holding, coasting, and braking decisions. Key findings indicate that the Energy Coasting Method is both simple and accurate, applicable to any train and track configuration. The research revealed that mechanical running resistance is less than proportional to actual axle load, contradicting some previous assumptions, while air drag increases approximately linearly with train length. The study also demonstrated that conventional methods for calculating wind effects on air drag are inadequate unless the ground boundary layer is considered. The synthesized train and driver models, when implemented in a Matlab simulator, showed good agreement with measured energy consumption and running times. Crucially, the results highlighted that omitting a driver model in simulations leads to significant overestimations of energy consumption, particularly for freight trains. The significance of this work lies in providing a validated, comprehensive framework for simulating train performance. By integrating accurate running resistance data with realistic driver behavior models, the thesis offers a tool that improves the precision of energy and time predictions for railway operators. The findings emphasize the importance of including driver behavior in simulations to avoid biased results, thereby supporting more effective timetable design, energy management, and infrastructure planning. The empirical data and models presented contribute to updating running resistance formulas for modern train configurations and provide a basis for future research into energy-saving driving strategies.

Key finding

Simulations using a developed driver model for freight trains show good agreement with measurements, whereas omitting the driver model likely leads to over-estimated energy consumption.

Methodology

field_study

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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