Monitoring energy efficiency of heavy haul freight trains with energy meter data
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 use...
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KTH, Spårfordon
2021
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ftkthstockholm:oai:DiVA.org:kth-299421 2023-05-15T17:45:12+02:00 Monitoring energy efficiency of heavy haul freight trains with energy meter data Uppföljning av energieffektiviteten för tunga godståg med hjälp av elmätardata Geiberger, Philipp 2021 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299421 eng eng KTH, Spårfordon TRITA-SCI-GRU 2021:242 http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299421 info:eu-repo/semantics/openAccess Energy consumption Energy meter Energy monitoring Heavy haul freight train Key performance indicator Multi-particle model Simulation Energiförbrukning Elmätare Energiuppföljning Tungt godståg Nyckeltal Flerpartikelmodell Simulering Vehicle Engineering Farkostteknik Student thesis info:eu-repo/semantics/bachelorThesis text 2021 ftkthstockholm 2022-08-11T12:34:21Z 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 ... Bachelor Thesis Northern Sweden Royal Institute of Technology, Stockholm: KTHs Publication Database DiVA Tunga ENVELOPE(8.683,8.683,62.698,62.698) |
institution |
Open Polar |
collection |
Royal Institute of Technology, Stockholm: KTHs Publication Database DiVA |
op_collection_id |
ftkthstockholm |
language |
English |
topic |
Energy consumption Energy meter Energy monitoring Heavy haul freight train Key performance indicator Multi-particle model Simulation Energiförbrukning Elmätare Energiuppföljning Tungt godståg Nyckeltal Flerpartikelmodell Simulering Vehicle Engineering Farkostteknik |
spellingShingle |
Energy consumption Energy meter Energy monitoring Heavy haul freight train Key performance indicator Multi-particle model Simulation Energiförbrukning Elmätare Energiuppföljning Tungt godståg Nyckeltal Flerpartikelmodell Simulering Vehicle Engineering Farkostteknik Geiberger, Philipp Monitoring energy efficiency of heavy haul freight trains with energy meter data |
topic_facet |
Energy consumption Energy meter Energy monitoring Heavy haul freight train Key performance indicator Multi-particle model Simulation Energiförbrukning Elmätare Energiuppföljning Tungt godståg Nyckeltal Flerpartikelmodell Simulering Vehicle Engineering Farkostteknik |
description |
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 ... |
format |
Bachelor Thesis |
author |
Geiberger, Philipp |
author_facet |
Geiberger, Philipp |
author_sort |
Geiberger, Philipp |
title |
Monitoring energy efficiency of heavy haul freight trains with energy meter data |
title_short |
Monitoring energy efficiency of heavy haul freight trains with energy meter data |
title_full |
Monitoring energy efficiency of heavy haul freight trains with energy meter data |
title_fullStr |
Monitoring energy efficiency of heavy haul freight trains with energy meter data |
title_full_unstemmed |
Monitoring energy efficiency of heavy haul freight trains with energy meter data |
title_sort |
monitoring energy efficiency of heavy haul freight trains with energy meter data |
publisher |
KTH, Spårfordon |
publishDate |
2021 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299421 |
long_lat |
ENVELOPE(8.683,8.683,62.698,62.698) |
geographic |
Tunga |
geographic_facet |
Tunga |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_relation |
TRITA-SCI-GRU 2021:242 http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299421 |
op_rights |
info:eu-repo/semantics/openAccess |
_version_ |
1766148012785008640 |