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Ghaviha, N., Bohlin, M., Holmberg, C. & Dahlquist, E. (2019). Speed Profile Optimization of Catenary-free Electric Trains with Lithium-ion Batteries. Journal of Modern Transportation, 3(sept), 153-168
Open this publication in new window or tab >>Speed Profile Optimization of Catenary-free Electric Trains with Lithium-ion Batteries
2019 (English)In: Journal of Modern Transportation, ISSN 2095-087X, Vol. 3, no sept, p. 153-168Article in journal (Other academic) Published
Abstract [en]

Catenary-free operated electric trains, as one of the recent technologies in railwaytransportation, has opened a new field of research: speed profile optimization and energy optimaloperation of catenary-free operated electric trains. A well-formulated solution for this problem shouldconsider the characteristics of the energy storage device using a validated model and method. This paper,discusses the consideration of the battery behavior in the problem of speed profile optimization ofcatenary-free operated electric trains. We combine the single mass point train model with an electricalbattery model and apply a dynamic programming approach to minimize the charge taken from thebattery during the catenary-free operation. The models and the method are validated and evaluatedagainst experimental data gathered from the test runs of an actual battery driven train tested in Essex,UK. The results show a significant potential in energy saving. Moreover, we show that the optimumspeed profiles generated using our approach consume less charge from the battery compared to theprevious approaches.

Keywords
Electric Trains, Catenary-free Operation, Speed Profile Optimization, Energy Efficiency
National Category
Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-41132 (URN)10.1007/s40534-018-0181-y (DOI)2-s2.0-85071607452 (Scopus ID)
Funder
Vinnova, 2014-04319Vinnova, 2012-01277
Available from: 2018-10-04 Created: 2018-10-04 Last updated: 2020-02-20Bibliographically approved
Ghaviha, N. (2018). Increasing Energy Efficiency in Electric Trains Operation: Driver Advisory Systems and Energy Storage. (Doctoral dissertation). Västerås: Mälardalen University
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
Ghaviha, N., Bohlin, M., Holmberg, C., Dahlquist, E., Skoglund, R. & Jonasson, D. (2017). A driver advisory system with dynamic losses for passenger electric multiple units. Transportation Research Part C: Emerging Technologies, 85, 111-130
Open this publication in new window or tab >>A driver advisory system with dynamic losses for passenger electric multiple units
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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
Keywords
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:nbn:se:mdh:diva-37410 (URN)10.1016/j.trc.2017.09.010 (DOI)000423006600006 ()2-s2.0-85034098198 (Scopus ID)
Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2018-10-06Bibliographically approved
Ghaviha, N., Holmberg, C., Bohlin, M. & Dahlquist, E. (2017). Modeling of Losses in the Motor Converter Module of Electric Multiple Units for Dynamic Simulation Purposes. Energy Procedia, 142, 2303-2309
Open this publication in new window or tab >>Modeling of Losses in the Motor Converter Module of Electric Multiple Units for Dynamic Simulation Purposes
2017 (English)In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 142, p. 2303-2309Article in journal (Refereed) Published
Abstract [en]

Simulation of power consumption in electric trains is categorized in two categories: electrical power simulation and mechanical power simulation. The mechanical power is calculated as speed times tractive effort and it gives an overall view on the total energy consumption of the train during different driving cycles. Detailed calculation of losses in different components in the propulsion system is however done using complex electrical models. In this paper, we introduce a nonlinear regression model generated from validated electrical equations for the calculation of the power loss in the motor converter module of electric trains. The function can be used instead of efficiency maps to evaluate the trains’ performance during the operation or dynamic simulations.

Place, publisher, year, edition, pages
Elsevier Ltd, 2017
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-38717 (URN)10.1016/j.egypro.2017.12.633 (DOI)000452901602070 ()2-s2.0-85041531667 (Scopus ID)
Available from: 2018-03-01 Created: 2018-03-01 Last updated: 2019-01-03Bibliographically approved
Campillo, J., Dahlquist, E., Danilov, D. L., Ghaviha, N., Notten, P. H. & Zimmerman, N. (2016). Battery technologies for transportation applications. In: Technologies and Applications for Smart Charging of Electric and Plug-in Hybrid Vehicles: (pp. 151-206). Springer International Publishing
Open this publication in new window or tab >>Battery technologies for transportation applications
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2016 (English)In: Technologies and Applications for Smart Charging of Electric and Plug-in Hybrid Vehicles, Springer International Publishing , 2016, p. 151-206Chapter in book (Other academic)
Abstract [en]

More than a fifth of the greenhouse emissions produced worldwide come from the transport sector. Several initiatives have been developed over the last few decades, aiming at improving vehicles’ energy conversion efficiency and improve mileage per liter of fuel. Most recently, electric vehicles have been brought back into the market as real competitors of conventional vehicles. Electric vehicle technology offers higher conversion efficiencies, reduced greenhouse emissions, low noise, etc. There are, however, several challenges to overcome, for instance: improving batteries’ energy density to increase the driving range, fast recharging, and initial cost. These issues are addressed on this chapter by looking in depth into both conventional and non-conventional storage technologies in different transportation applications. 

Place, publisher, year, edition, pages
Springer International Publishing, 2016
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-36207 (URN)10.1007/978-3-319-43651-7_5 (DOI)2-s2.0-85026527662 (Scopus ID)9783319436517 (ISBN)9783319436494 (ISBN)
Available from: 2017-08-10 Created: 2017-08-10 Last updated: 2017-08-10Bibliographically approved
Ghaviha, N., Bohlin, M., Wallin, F. & Dahlquist, E. (2015). Optimal Control of an EMU Using Dynamic Programming. Paper presented at 7th International Conference on Applied Energy (ICAE), MAR 28-31, 2015, Abu Dhabi, U ARAB EMIRATES. Energy Procedia, 75, 1913-1919
Open this publication in new window or tab >>Optimal Control of an EMU Using Dynamic Programming
2015 (English)In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 75, p. 1913-1919Article in journal (Refereed) Published
Abstract [en]

A model is developed for minimizing the energy consumption of an electric multiple unit through optimized driving style, based on Hamilton-Jacobi-Bellman equation and Bellman's backward approach. Included are the speed limits, track profile (elevations), different driving modes and the train load. This paper includes aspects like the power loss in the auxiliary systems, time management, validation of the model regarding energy calculations and a study on discretization and the accuracy of the model. The model will be used as a base for a new driver advisory system. 

National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-29330 (URN)10.1016/j.egypro.2015.07.184 (DOI)000361030003026 ()2-s2.0-84947087187 (Scopus ID)
Conference
7th International Conference on Applied Energy (ICAE), MAR 28-31, 2015, Abu Dhabi, U ARAB EMIRATES
Available from: 2015-10-15 Created: 2015-10-15 Last updated: 2018-02-16Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8697-855x

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