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  • 1.
    Campillo, Javier
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ghaviha, Nima
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zimmerman, Nathan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Flow batteries use potential in heavy vehicles2015In: Electrical Systems for Aircraft, Railway and Ship Propulsion, ESARS, 2015, p. Article number 7101496-Conference paper (Refereed)
    Abstract [en]

    Although batteries have been used in personal vehicles for more than a hundred years, the cost of the technology, limitation in range, absence of sufficient recharging infrastructure and rapid development of internal combustion engines during the mid-twentieth century limited its use to very niche applications. More recently, a global need for reducing CO2 emissions from fossil fuel usage and the great developments in power systems as well as in battery technology offers electric vehicles the possibility to return to the market, not just for personal use but also for a wide variety of transportation applications. In the present paper, a feasibility study for using flow batteries in heavy vehicles, more specifically, construction equipment is presented. The authors used measured energy demand profiles for different operation conditions of a wheel loader and developed a simulation model for a vanadium redox flow battery to test the performance of this vehicle using a flow battery. Additionally, the authors did a short theoretical analysis for the potential for flow batteries in train transportation, focusing on the requirements and limitations of the technology for this application.

  • 2.
    Ghaviha, Nima
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Energy Optimal Operation of Electric Trains: Development of a Driver Advisory System2016Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    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).

  • 3.
    Ghaviha, Nima
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Increasing Energy Efficiency in Electric Trains Operation: Driver Advisory Systems and Energy Storage2018Doctoral 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.

  • 4.
    Ghaviha, Nima
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bohlin, M.
    RISE SICS, Västerås, Sweden.
    Holmberg, C.
    Bombardier Transportation, Västerås, Swede.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Skoglund, Robert
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Jonasson, Daniel
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A driver advisory system with dynamic losses for passenger electric multiple units2017In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 85, p. 111-130Article in journal (Refereed)
    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.

  • 5.
    Ghaviha, Nima
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bohlin, Markus
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Speed profile optimization of an electric train with on-board energy storage and continuous tractive effort2016In: 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2016, 2016Conference paper (Refereed)
    Abstract [en]

    Electric traction system is the most energy efficient traction system in railways. Nevertheless, not all railway networks are electrified, which is due to high maintenance and setup cost of overhead lines. One solution to the problem is battery-driven trains, which can make the best use of the electric traction system while avoiding the high costs of the catenary system. Due to the high power consumption of electric trains, energy management of battery trains are crucial in order to get the best use of batteries. This paper suggests a general algorithm for speed profile optimization of an electric train with an on-board energy storage device, during catenary-free operation on a given line section. The approach is based on discrete dynamic programming, where the train model and the objective function are based on equations of motion rather than electrical equations. This makes the model compatible with all sorts of energy storage devices. Unlike previous approaches which consider trains with throttle levels for tractive effort, the new approach considers trains in which there are no throttles and tractive effort is controlled with a controller (smooth gliding handle with no discrete levels). Furthermore, unlike previous approaches, the control variable is the velocity change instead of the applied tractive effort. The accuracy and performance of the discretized approach is evaluated in comparison to the formal movement equations in a simulated experimented using train data from the Bombardier Electrostar series and track data from the UK.

  • 6.
    Ghaviha, Nima
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bohlin, Markus
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Optimal Control of a Battery Train Using Dynamic Programming2015Conference paper (Other academic)
    Abstract [en]

    Electric propulsion system in trains has the highest efficiency compared to other propulsion systems (i.e. steam and diesel). Still, electric trains are not used on all the routes, due to the high setup and maintenance cost of the catenary system. Energy storage technologies and the battery driven trains however, make it possible to have the electric trains on the non-electrified routes as well. High energy consumption of the electric trains, makes the energy management of such trains crucial to get the best use of the energy storage device. This paper suggests an algorithm for the optimal control of the catenary free operation of an electric train equipped with an onboard energy storage device. The algorithm is based on the discrete dynamic programming and Bellman’s backward approach. The objective function is to minimize the energy consumption, i.e. having the maximum battery level left at the end of the trip. The constraints are the trip time, battery capacity, local speed limits and limitations on the traction motor. Time is the independent variable and distance, velocity and battery level are the state variables. All of the four variables are discretized which results in some inaccuracy in the calculations, which is discussed in the paper. The train model and the algorithm are based on the equations of motion which makes the model adjustable for all sorts of electric trains and energy storage devices. Moreover, any type of electrical constraints such as the ones regarding the voltage output of the energy storage device or the power output can be enforced easily, due to the nature of the dynamic programming. 

  • 7.
    Ghaviha, Nima
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Mälardalen University.
    Bohlin, Markus
    Research Institutes of Sweden RISE SICS Västerås.
    Holmberg, Christer
    Bombardier Transportation.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Speed Profile Optimization of Catenary-free Electric Trains with Lithium-ion BatteriesManuscript (preprint) (Other academic)
    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.

  • 8.
    Ghaviha, Nima
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bohlin, Markus
    SICS - swedish institute of computer science, Sweden.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    AN ALGORITHM FOR OPTIMAL CONTROL OF AN ELECTRIC MULTIPLE UNIT2014In: Proceedings from The 55th Conference on Simulation and Modelling (SIMS 55),21-22 October, 2014. Aalborg, Denmark, Linköping: Linköping University Electronic Press, 2014Conference paper (Refereed)
    Abstract [en]

    This paper offers a solution for the optimal EMU train (Electric Multiple Unit) operation with the aim of minimizing the energy consumption. EMU is an electric train with traction motors in more than one carriage. The algorithm is based on dynamic programming and the Hamilton-Jacobi-Bellman equation. To model the train, real data has been used, which was provided by experts from Bombardier Transportation Västerås. To evaluate the model, some experiments have been done on the energy saving in exchange for the increase in the trip time. Moreover a simple accuracy factor is introduced to evaluate the accuracy of the model. The final goal is to use this approach as a base for a driver advisory system, therefore it is important to have the amount of calculations as minimum as possible. The paper also includes the studies done on the calculation time. The solution can be used for driverless trains as well as normal trains. It should be mentioned that this paper is a part of a research which is still in progress and the final model will also be used by Bombardier Transportation Västerås as an evaluation tool for the propulsions systems and trains.

  • 9.
    Ghaviha, Nima
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bohlin, Markus
    SICS Swedish ICT, Sweden.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Optimal Control of an EMU Using Dynamic Programming2015In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 75, p. 1913-1919Article in journal (Refereed)
    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. 

  • 10.
    Ghaviha, Nima
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bohlin, Markus
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Optimal Control of an EMU Using Dynamic Programming and Tractive Effort as the Control Variable2015In: Proceedings of the 56th SIMS, Linköping University Electronic Press, Linköpings universitet, 2015, p. 377-382Conference paper (Refereed)
    Abstract [en]

    Problem of optimal train control with the aim of minimizing energy consumption is one of the old optimal control problems. During last decades different solutions have been suggested based on different optimization techniques, each including a certain number of constraints or different train configurations, one being the control on the tractive effort available from traction motor. The problem is previously solved using dynamic programming for trains with continuous tractive effort, in which velocity was assumed to be the control variable. The paper at hand presents a solution based on dynamic programming for solving the problem for trains with discrete tractive effort. In this approach, tractive effort is assumed to be the control variable. Moreover a short comparison is made between two approaches regarding accuracy and ease of application in a driver advisory system.

  • 11.
    Ghaviha, Nima
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Campillo, Javier
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bohlin, Markus
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Review of Application of Energy Storage Devices in Railway Transportation2017In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 105, p. 4561-4568Article in journal (Refereed)
    Abstract [en]

    Regenerative braking is one of the main reasons behind the high levels of energy efficiency achieved in railway electric traction systems. During regenerative braking, the traction motor acts as a generator and restores part of the kinetic energy into electrical energy. To use this energy, it should be either fed back to the power grid or stored on an energy storage system for later use. This paper reviews the application of energy storage devices used in railway systems for increasing the effectiveness of regenerative brakes. Three main storage devices are reviewed in this paper: batteries, supercapacitors and flywheels. Furthermore, two main challenges in application of energy storage systems are briefly discussed. 

  • 12.
    Ghaviha, Nima
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Holmberg, C.
    Bombardier Transportation, Västerås, Sweden.
    Bohlin, M.
    RISE SICS, Västerås, Sweden.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Modeling of Losses in the Motor Converter Module of Electric Multiple Units for Dynamic Simulation Purposes2017In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 142, p. 2303-2309Article in journal (Refereed)
    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.

  • 13.
    Shashaj, A.
    et al.
    SICS Swedish, Sweden.
    Bohlin, M.
    SICS Swedish, Sweden.
    Ghaviha, Nima
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Joint optimization of multiple train speed profiles2016In: Proceedings - 2016 10th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2016, 2016, p. 478-483Conference paper (Refereed)
    Abstract [en]

    Energy efficiency of train operations is a critical issue for the electrical railway transportation, considering that one of the main benefits of this transportation mode is the lower environmental impact. A possible technology which decreases energy consumption is the reuse of the recovered energy from the braking system. Although have been made significant efforts to find efficient solutions in terms of driving strategies, timetable optimization etc, the potential of energy recovery from the braking system is still unexplored. In this paper, we consider the problem of joint optimization of multiple train speed profiles, which operate in the same power section, in order to increase the overall energy recovered from the braking system. We propose a Markov Decision Process formulation, which models the continuous space movements of the trains as stochastic transitions on discrete states, determined by train operations and electrical properties of the electrical network.

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