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Minimising Fuel Consumption of a Series Hybrid Electric Railway Vehicle Using Model Predictive Control

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Minimising Fuel Consumption of a Series Hybrid Electric Railway Vehicle Using Model Predictive Control Master of Science Thesis in Electrical Engineering Department of Electrical Engineering, Linköping University, 2017 Minimising Fuel Consumption of a Series Hybrid Electric Railway Vehicle Using Model Predictive Control Niklas Sundholm Master of Science Thesis in Electrical Engineering Minimising Fuel Consumption of a Series Hybrid Electric Railway Vehicle Using Model Predictive Control Niklas Sundholm LiTH-ISY-EX--17/5095--SE Supervisor: Måns Klingspor isy, Linköping University Keiichiro Kondo Department of Electrical and Electronic Engineering, Chiba University Examiner: Martin Enqvist isy, Linköping University Automatic Control Department of Electrical Engineering Linköping University SE-581 83 Linköping, Sweden Copyright © 2017 Niklas Sundholm Abstract With the increasing demands on making railway systems more environmentally friendly, diesel railcars have been replaced by hybrid electric railway vehicles. A hybrid system holds a number of advantages as it has the possibility of recuperat- ing energy and allows the internal combustion engine (ice) to be run at optimal efficiency. However, to fully utilise the advantages of a hybrid system the hybrid electric vehicle (hev) is highly dependent on the used energy management strat- egy (ems). In this thesis, the possibility of minimising the fuel consumption of the series hy- brid electric railway vehicle, Ki-Ha E200, has been studied. This has been done by replacing the currently used ems, based on heuristics, with a model predictive controller (mpc). The heuristic ems and the mpc have been evaluated by compar- ing the performance results from three different test cases. The performance of the implemented mpc seems promising as it yields more optimal operation of the ice and improved control of the battery state of charge (soc). iii Acknowledgments Firstly, I want to express my utmost gratitude to Shigetomo Shiraishi, Toshiba Corporation Railway Systems Division, for making this thesis work possible and Keiichiro Kondo, Chiba University, for accepting me to his laboratory. Further, I want to thank Martin Enqvist and Måns Klingspor for their valuable help and discussions through out this thesis work. Tokyo, June 2017 Niklas Sundholm v Contents Notation ix 1 Introduction 1 1.1 Background . 1 1.2 Purpose . 2 1.3 Objective . 2 1.4 Methodology . 3 1.5 Limitations . 3 1.6 Thesis Outline . 3 2 Series Hybrid Electric Railway Vehicle Modelling 5 2.1 Hybrid Electric Vehicle Powertrain . 5 2.2 Train Motion . 7 2.2.1 Train Resistances . 10 2.3 Internal Combustion Engine . 11 2.4 Battery . 12 2.5 Model Equation . 15 3 Controller Design 17 3.1 MPC Basics . 17 3.2 Hybrid MPC . 19 3.2.1 Piecewise Affine Model . 19 3.2.2 Mixed-Integer Programming . 21 3.2.3 Objective Function . 22 3.2.4 Constraints . 23 3.2.5 Softened Constraints . 25 3.2.6 Move-Blocking . 26 4 Results 29 4.1 Heuristic Controller . 29 4.2 Test Cases . 31 4.3 Simulator . 32 4.4 Simulation Results . 32 vii viii Contents 4.5 Discussion . 41 5 Conclusions and Future Work 45 5.1 Conclusions . 45 5.2 Future Work . 46 Bibliography 47 Notation Abbreviations Abbreviation Interpretation hev Hybrid Electric Vehicle ems Energy Management Strategy ice Internal Combustion Engine ac Alternating Current dc Direct Current soc State of Charge mpc Model Predictive Control pwa Piecewise Affine mip Mixed-Integer Programming qp Quadratic Programming miqp Mixed-Integer Quadratic Programming ix 1 Introduction This master’s thesis presents the results of the work that was conducted at Toshiba Corporation Railway Systems Division in cooperation with Chiba University, Ja- pan. 1.1 Background With the rise of environmental issues, due to global warming, emphasis has been put on making systems more environmentally friendly. In the case of the railway industry, different measures have been taken to reduce the total energy consump- tion and the environmental burden of trains. Among the measures that have been taken are weight saving, improved component efficiencies and utilisation of regenerative braking. Within the railway transportation system there are many non-electrified sections, mainly in rural areas, where diesel trains are operated. One of the major drawbacks of diesel trains, apart from their exhaust emissions, is their inability to recuperate energy through regenerative braking. In 2003, East Japan Railway Company addressed the environmental burden of diesel railway vehicles by developing the world’s first railcar, Ki-Ha E200, utilis- ing a hybrid system. A hybrid electric vehicle (hev) combines the conventional in- ternal combustion engine (ice) with an electric motor that will, depending on the hybrid configuration, fully or partially act as the main propulsion system. The presence of an electrochemical storage device is common in most modern hybrid systems. The railcar Ki-Ha E200 uses a diesel engine as ice and a lithium-ion bat- tery as storage device [6]. Ki-Ha E200 started operational services in 2007 [13]. A hev holds a number of advantages compared to a diesel railcar. A hybrid system 1 2 1 Introduction has the possibility of recuperating energy through regenerative braking, down- sizing of the engine, running the diesel engine at its optimal efficiency, reducing toxic substances in the exhaust emission and reducing noise levels when running inside stations. In order to fully utilise the advantages of a hybrid system, a supervisory energy management strategy (ems) is needed to determine the optimal combination of the available power sources. A great amount of research has been done in op- timal control of hevs, with the majority targeted towards automobiles [16]. As the computational power of modern computers has increased it has allowed the use of computationally heavy control methods such as model predictive control (mpc) [18], [17]. The principles used for automobiles can similarly be applied to locomotives as well. In [12] the optimal operation of railway vehicles, min- imising total energy consumption, is analysed by comparing the use of dynamic programming, the gradient method and sequential quadratic programming. It is stated that none of the aforementioned methods are applicable for real-time con- trol. However, mpc can be seen as an approximation of dynamic programming and could be realisable in railway applications if a control cycle of 1 second or less is achieved. 1.2 Purpose The series hev Ki-Ha E200 currently uses a heuristic ems, based on the hevs speed and the state of charge (soc) of the storage battery, to determine the opera- tion of the ice. The performance of the heuristic ems is highly dependent on the running pattern of the train and can therefore not guarantee optimal control. The purpose of this master’s thesis is to investigate the possibility of minimising the fuel consumption of the Ki-Ha E200 by replacing the current ems with a hybrid mpc instead. 1.3 Objective The objective of this master’s thesis can be divided into a number of sub-objectiv- es. Firstly, a basic model of the series hev Ki-Ha E200 should be developed. The model will be used to simulate the target hev and in the design of the hybrid mpc. The existing heuristic ems should be implemented and its performance should be used as a benchmark for the hybrid mpc. The performance of the two ems should be compared and analysed. 1.4 Methodology 3 1.4 Methodology A general study of the target railway system was conducted to determine the scope of the thesis work and to understand the basic principles. Secondly, a more specific literature study of emss used in the control of hevs was carried out, where the focus was specifically set on earlier applications of mpc. To imple- ment an mpc, with satisfactory performance, a precise model is required. Since no data was available for system identification, theoretical studies of the train components were carried out. To simplify the modelling of the hev certain com- ponents have been assumed to be static, while emphasising the modelling of the train motion, diesel engine and battery dynamics. Since previous modelling of the Ki-Ha E200 varied from the derived train model in this thesis work, the existing heuristic ems was implemented and used as a per- formance benchmark. Due to the non-linearities in the derived train model, the model was approximated by a number of piecewise affine (pwa) models instead that could be used as an internal model in the hybrid mpc. The performance of the hybrid mpc and heuristic emss was evaluated by applying them to predeter- mined test cases and comparing the acquired results. 1.5 Limitations The thesis is limited to the modelling of a series hybrid electric railway vehicle. The modelling is based on the train specifications and performance limitations of the railway vehicle Ki-Ha E200. Furthermore, only longitudinal motion of the train has been considered in this thesis, thus a straight track has been assumed in all of the test cases. All implementation and simulation is done using Mat- lab and Simulink. Due to performance restrictions in the platform used to run simulations, the mpc prediction horizon is limited to a maximum of 40 time steps. 1.6 Thesis Outline Chapter 2 introduces the target hev Ki-Ha E200 and gives a basic overview of its components. More detailed modelling of the train motion, ice and battery dy- namics is given. The results of the modelling is summarised in a model equation used to simulate the train dynamics and to develop the hybrid mpc. Continuing, Chapter 3 explains the basic theory of mpc. The basic mpc theory is expanded to describe hybrid mpc and miqp optimisation problems. After cov- ering the hybrid mpc prerequisite, the design and implementation of the hybrid mpc constraints and objective function is described. 4 1 Introduction In Chapter 4, the simulator implementation is presented. A short description of the heuristic controller is given followed by a presentation of the test cases that are used to analyse the performance of the emss.
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