Monitoring energy efficiency of heavy haul freight trains with energy meter data

Abstract: In this MSc thesis, it is investigated what parameters are relevant for describing energy consumption of heavy haul freight trains and how these can be used to develop key performance indicators (KPIs) for energy efficiency. The possible set of KPI is bounded by data available from energy meters used in electric IORE class locomotives hauling iron ore trains in northern Sweden. Furthermore, the analysis is only concerned with energy efficiency at the rolling stock level, excluding losses in the electric power supply network. Based on a literature study, parameters of interest describing driver, operations and rolling stock energy efficiency have been identified. By means of simulation, a parametric study is performed, simulating a 30 ton axle load iron ore train with 68 wagons. Train modelling input is obtained from technical documentation or estimated through measurements and statistical analysis. A multi-particle representation of the train is used to calculate gradient resistance for the simulation, which is also applied to determine the curve resistance.  Results show that the motion resistance is simulated quite accurately, while the lack of a driver model in the simulation tool leads to overestimation of energy consumption. Taking this into account, the importance of the driver for energy efficiency can still clearly be showcased in the parametric study. Especially on long steep downhill sections, prioritising the electric brakes over mechanical brakes is demonstrated to have a huge influence on net energy consumption, as has the amount of coasting applied. With the same driver behaviour in all simulations, the savings in specific energy from increasing axle load to 32.5 tons is estimated. Moreover, a comparison of increased train length and axle load points towards higher savings for the latter. In the end, parametric study results are used to recommend a structure for a monitoring system of energy efficiency based on a set of KPIs. With a sufficiently high sampling rate of energy meter data, it is adequate for calculating driver related KPIs and some additional KPIs. More KPIs can be tracked with access to additional data, e.g. cargo load.