mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A driver advisory system with dynamic losses for passenger electric multiple units
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0001-8697-855x
RISE SICS, Västerås, Sweden.
Bombardier Transportation, Västerås, Swede.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-7233-6916
Show others and affiliations
2017 (English)In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 85, p. 111-130Article in journal (Refereed) Published
Abstract [en]

Driver advisory systems, instructing the driver how to control the train in an energy efficient manner, is one the main tools for minimizing energy consumption in the railway sector. There are many driver advisory systems already available in the market, together with significant literature on the mathematical formulation of the problem. However, much less is published on the development of such mathematical formulations, their implementation in real systems, and on the empirical data from their deployment. Moreover, nearly all the designed driver advisory systems are designed as an additional hardware to be added in drivers’ cabin. This paper discusses the design of a mathematical formulation and optimization approach for such a system, together with its implementation into an Android-based prototype, the results from on-board practical experiments, and experiences from the implementation. The system is based on a more realistic train model where energy calculations take into account dynamic losses in different components of the propulsion system, contrary to previous approaches. The experimental evaluation shows a significant increase in accuracy, as compared to a previous approach. Tests on a double-track section of the Mälaren line in Sweden demonstrates a significant potential for energy saving.

Place, publisher, year, edition, pages
Elsevier Ltd , 2017. Vol. 85, p. 111-130
Keywords [en]
Driver advisory system, Electric multiple unit, Energy efficiency, Driver training, Electric losses, Electric railroads, Electric traction, Energy conservation, Energy utilization, Propulsion, Railroad transportation, Rapid transit, Advisory systems, Double-track section, Energy calculation, Experimental evaluation, Mathematical formulation, Minimizing energy, Optimization approach, control system, electric vehicle, energy use, optimization, railway transport, train, Sweden
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-37410DOI: 10.1016/j.trc.2017.09.010ISI: 000423006600006Scopus ID: 2-s2.0-85034098198OAI: oai:DiVA.org:mdh-37410DiVA, id: diva2:1163620
Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2018-10-06Bibliographically approved
In thesis
1. Increasing Energy Efficiency in Electric Trains Operation: Driver Advisory Systems and Energy Storage
Open this publication in new window or tab >>Increasing Energy Efficiency in Electric Trains Operation: Driver Advisory Systems and Energy Storage
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Electric traction is the most efficient traction system in the railway transportation. However, due to the expensive infrastructure and high power demand from the grid, the share of electric trains in the railway transportation is still lower than other trains. Two of the possible solutions to increase the share of electric trains are: optimal train operation to minimize energy consumption, the use of batteries as the energy source for driving electric trains on non-electrified lines. This thesis aims to extend the knowledge in the field of energy optimal operation of electric trains and battery-driven electric trains.

Energy optimal operation of electric trains is supervised using a driver advisory system (DAS), which instructs the driver to operate the train in an energy-efficient manner. This thesis contributes to DAS technology under two topics: the increase of energy efficiency and the design of DAS.

This thesis presents a complete procedure of designing a DAS from the mathematical formulation to application on the train. The designed DAS is in the form of an Android application and is based on a dynamic programming approach. The computational performance of the approach is enhanced using heuristic state reducing rules based on the physical constraints of the system. The application of the DAS shows a potential reduction of 28% in energy consumption.

This thesis considers the detailed energy losses in the whole propulsion system using a regression model that is generated from validated physical models. The application of the regression model instead of a previous constant efficiency factor model results in 2.3% reduction in energy consumption of the optimum speed profiles.

Based on the solution for the normal electric trains, a solution is also offered for the optimal operation of battery-driven electric trains, in which the characteristics of the battery as one of the main components are considered using an electrical model. The solution presented in this thesis, is to combine the popular single mass point train model with an electrical circuit battery model.

Furthermore, this thesis evaluates the performance of the optimization approaches and validates the models against the measurements from actual drives of a real-life battery train. The results show a potential of around 30% reduction in the charge consumption of the battery.

The results of this thesis (algorithms and the Android application) are provided as open source for further research in the field of energy efficient train control.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2018
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 275
Keywords
Driver Advisory System, Catenary-free Operation, Electric Train, Electric Multiple Unit, Battery, Energy Efficiency, Energy Storage, Speed Profile Optimization
National Category
Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-41136 (URN)978-91-7485-409-1 (ISBN)
Public defence
2018-11-16, Pi, Mälardalens högskola, Västerås, 13:00 (English)
Opponent
Supervisors
Projects
STREAM
Funder
VINNOVA, 2014-04319VINNOVA, 2012-01277
Available from: 2018-10-08 Created: 2018-10-06 Last updated: 2018-10-16Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Ghaviha, NimaDahlquist, Erik

Search in DiVA

By author/editor
Ghaviha, NimaDahlquist, ErikSkoglund, RobertJonasson, Daniel
By organisation
Future Energy Center
In the same journal
Transportation Research Part C: Emerging Technologies
Energy Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 44 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf