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The electric traction system used in trains is the most energy efficient traction system in the transportation sector. Moreover, it has the least NOx and CO2 emissions in comparison to other transportation systems (e.g. busses, passenger cars, airplanes, etc.). On the other hand, they are extremely expensive, mainly due to high installation and maintenance cost of the catenary system, including e.g. overhead lines and substations. Consequently, the share of electrified lines is only slightly higher than non-electrified lines. For instance in Europe, 60% of the railway networks are electrified, and the percentage is much lower in other continents. Battery driven trains are a new generation of electric trains that can overcome such high costs while keeping CO2 emissions and energy consumption low.At the moment, there are only two battery driven electric trains developed and both of the trains are passenger electric multiple units (EMUs). An EMU is an electric train with a traction system in more than one wagon, in contrast to loco-haul electric trains which have a traction system in one wagon only. Energy management during the operation of battery driven trains is a crucial task, as energy optimal operation of trains considering the optimal use of batteries can increase both the operating time and the lifetime of batteries. Energy efficient train operation is realized using driver advisory systems (DAS) that instructs drivers on how to drive trains for minimum energy consumption. The aim of this research is to propose an algorithm for speed profile optimization of both EMUs and battery driven EMUs. The desired algorithm should be suitable as a core component for an online DAS with short response time.Several approaches are proposed in the literature for speed profile optimization of electric trains, and a few of these have been proposed for speed profile optimization of battery driven electric trains. The trains modeled in almost all of the approaches are trains using a notch system for controlling tractive effort. The proposed solution in this research project is to use discrete dynamic programming (DP) to find the optimum speed profile. The application of DP is studied for speed profile optimization of EMUs with a notch system as well as EMUs with a smooth gliding handle for controlling tractive effort. The problem is solved for both normal EMUs and battery driven EMUs.The results of this research show that DP can provide accurate results in a reasonably short time. Moreover, the proposed algorithm can be used as a base for a DAS with fast response time (real-time).

Elektriska traktionssystem i tåg är det mest energieffektiva alternativet inom transportsektorn, och dessutom har det lägst NOx- och CO2-utsläpp i jämförelse med andra transportsystem (exempelvis bussar, personbilar, flygplan, etc.). Å andra sidan är de relativt dyra, främst på grund av höga installations- och underhållskostnader för kontaktledningssystem, inklusive t.ex. luftledningar och transformatorstationer. Följaktligen är andelen elektrifierade linjer något högre än andelen icke-elektrifierade linjer. I Europa är endast 60 % av järnvägsnäten elektrifierade, och andelen är till och med mycket lägre i andra världsdelar. Batteridrivna tåg representerar en ny generation av eltåg som kan nå rimliga kostnader samtidigt med låga CO2-utsläpp och låg energiförbrukning.För närvarande finns det bara två batteridrivna elektriska tåg utvecklade och båda tågen är passagerartåg med elektriska multipla enheter (EMUs). En EMU är ett elektriskt tåg med drivsystem i mer än en vagn, i motsats till lokomotiveltåg som har framdrivningssystemet centrerat till en enda vagn. Energihantering under driften av batteridrivna tåg är en viktig uppgift, och vid en energioptimal drift av tåget tillsammans med en optimerad användning av batterier ökar både driftstiden och livscykeln för batterierna. Energioptimal drift tillämpas i tågdrift med hjälp av ett system som kallas förarrådgivning (eng. Driver Advisory Support, DAS). DAS är ett system som instruerar tågföraren om hur man kör tåget med minimal energiförbrukning.Syftet med denna forskning är att föreslå en algoritm för hastighetsprofilsoptimering av både vanliga EMU:er samt motsvarande batteridrivna. Den önskade algoritmen skall vara lämpad att användas som en bas för ett online-DAS med kort svarstid.Olika metoder föreslås i litteraturen för hastighetsprofilsoptimering av eltåg, och några även för hastighetsprofilsoptimering av batteridrivna elektriska tåg. De tågmodeller som används har oftast ett så kallat “notch”-system för kontrollering av dragkraft. Den föreslagna lösningen i detta forskningsprojekt är att använda diskret dynamisk programmering (DP) för att hitta den optimala hastighetsprofilen. Tillämpning av DP studeras för hastighetsprofilsoptimering av EMU:er både med ”notch”-system samt EMU:er med ett kontinuerligt glidhandtag för styrning av dragkraft. Problemet löses för både normala EMU:er och batteridrivna EMU:er.Resultaten av denna forskning visar att DP kan ge korrekta resultat inom rimlig tid. Vidare kan den föreslagna algoritmen användas som en bas för en DAS med snabb svarstid (realtid).

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.