Study On Operation Control And Energy Management Strategy For Hybrid Electric Train

Due to the rapid depletion of the Earth’s petroleum resources, air pollution, and greenhouse effect, people are enforced to quest for cleaner, low-carbon production and transportation. In recent years, electric vehicles (EVs), hybrid electric vehicles (HEVs) have emerged and have been typically proposed to replace conventional vehicles in the near future. The dissertation offers an overview of the hybrid electric train based on the "grid+ battery + ultracapacitors" conception, where the energy comes from electrical grid, and the energy storage devices are composed of batteries and ultralcapacitors. If the electric resource is derived from renewable and clean sources, zero well-to-wheel emissions are possible to be realized. The main work of this dissertation is to extend the hybrid propulsion systems to the field of rail transportation, and study operation control issues and energy management strategies for a new type of hybrid electric train.In this dissertation, the following subjects are studied:(1) The system structure and the mathematic model are built. The system structure of hybrid electric trains consisting of "grid + battery + ultracapacitor" is introduced, and the topology of HESS (Hybird Energy Storage System) is analyzed. For such trains, the vehicle dynamics model is analyzed, and the calculation methods of traction power are given. Further multi- particle model which is more accurate is discussed. For the hybrid power system, the model of HESS is built, where the battery model, the ultracapacitor model and the DC/DC convertor model are included. Finally, the coupling relationship between the dynamic behavior of trains and the hybrid power system is studied whilst the mechanical transmission and energy transfer model are also analyzed.(2) The automatic train control method for hybrid electric trains is studied and the generation algorithm of target speed profiles is proposed. The automatic train control system is able to generate target speed profiles according to the information of the railway line and the stations. Then the operations, like accelerating, cruising, braking, stopping and restarting of the train can be done in an automatic way. The train speed control system is analyzed, and controllers based on nonlinear control method are designed, which leads the hybrid electric train to track the target speed profiles in a good way. Constraints are given on the traction motors, DC/AC inverters, DC/DC converters and other electrical devices. The maximum output torque of motors at a certain train speed and position is obtained, which provides a basis for motor control.(3) A rule-based energy management strategy is proposed. When the electric grid is available, the grid supplies energy for the train; when the electric grid is not available, the HESS supplies energy for the train with the following strategy:under high power command, the ultracapacitors are the main energy source; under low power command, the batteries are the main energy source; under negative power command, regenerative energy is absorbed by the batteries and ultracapacitors. With this strategy two hybrid energy storage devices can be utilized in a mixed way to obtain their complementary advantages and acquire their relatively good specific power and specific energy. Consequently, the train performance is improved by such a strategy.(4) According to the known railway lines information, an energy management strategy based on energy estimate is proposed by pre-estimating the energy consumption in every sub section of the railway. The basic idea is described as follows. First, the target speed profile is converted to the target power profile. Second, the line is divided into sub-sections by power values. By estimating the energy of sub-sections, the energy range of every sub-section is obtained; the upper and lower limit of target power profiles in different power allocation strategies can be obtained by calculating forward and backward. Finally, two target power profiles are solved for the two energy storage devices respectively to acquire the optimal power allocation strategy by using the maximum performance satisfaction rate and the maximum energy absorption rate as the evaluation criteria.(5) Parameter matching methods of hybrid electric trains are studied. On one hand, considering the constraint of train performance indexes, motor performance requirements are obtained according to the maximum speed, the maximum acceleration and the maximum gradeability performance respectively. Then the motor parameters are matched with the previous requirements; on this basis, by analyzing the electrical power requirements of the power supply according to the power output of the motor, the methods to calculate minimum number of batteries and ultracapacitors are obtained; the best design of a HESS with batteries and ultracapacitors is given from the point of specific power and specific energy. On the other hand, under given parameters configuration, the calculation methods of train performance are given.(6) Consider multi-objective, such as energy saving, punctual arriving, accurate parking, safety and riding quality, some ideas to optimize the operation of hybrid electric trains are proposed according to the automatic operation control methods based on target speed profiles. The multi-objective optimization approach based on genetic algorithm studied in this paper can effectively improve the integrated indicators of target speed profiles. Simulation results show that the optimization method is possible to ensure punctual arriving, accurate parking, safety, riding quality while it reduces energy consumption of the train.(7) In order to evaluate the dynamic performance and simulate the operation process for hybrid electric trains under certain configurations and given railway lines, a simulation platform software is studied and developed to support their design and optimization. The overall design is first given. Then the demand analysis, the overall architecture and the modular decomposition are accomplished based on object-oriented analysis and modeling methods, which are described definitely by UML. After analyzing the mathematical models of each subsystem, the implementation methods of each subsystem are obtained. The software is developed in Visual Studio 2010 environment using C# as a programming language. A simulation is conducted in this platform to verify the effectiveness of the software.