Lu, Shaofeng (2011)
Ph.D. thesis, University of Birmingham.
Railway transportation is facing increasing pressure to reduce the energy demand of its vehicles due to increasing concern for environmental issues. This thesis presents studies based on improved power management strategies for railway traction systems and demonstrates that there is potential for improvements in the total system energy efficiency if optimised high-level supervisory power management strategies are applied. Optimised power management strategies utilise existing power systems in a more cooperative and energy-efficient manner in order to reduce the total energy demand. In this thesis, three case studies in different research scenarios have been conducted.
Under certain operational, geographic and physical constraints, the energy consumed by the train can be significantly reduced if improved control strategies are implemented. This thesis proposes a distance based model for train speed trajectory optimisation. Three optimisation algorithms, Ant Colony Optimisation (ACO), Genetic Algorithm (GA) and Dynamic Programming (DP), are applied to search for the optimal train speed trajectory, given a journey time constraint. The speed at each preset position along the journey is determined and optimised using these searching algorithms.
In a DC railway network, power peaks in a substation are not desirable as they could present safety risks and are not energy efficient. A power peak can be avoided if the control of multiple trains is coordinated. The allocation of inter-station journey time intrinsically affects both service quality and energy efficiency. By identifying an optimal journey time allocation, a multi-objective function targeting both energy efficiency and service quality can be used. In this study, a DC railway is modelled with two parallel railway tracks, five station stops and three DC electric substations. Regenerative braking is studied in this DC electric network using Nodal Analysis (NA) and the Load Flow (LF) method. This study demonstrates that within the neighbourhood of an electric railway network, the journey time allocation for inter-station journeys will affect the total service quality and the energy loss. A GA is applied to find the best inter-station journey time allocation.
Finally, this thesis explores the potential of applying advanced power management strategies to a Diesel Multiple Unit (DMU) train. DMU trains have multiple diesel engines which are commonly operated in a homogenous manner. The work presented in this thesis analyses the potential energy savings that may be obtained through the independent operation of the engines. Two widely investigated power management strategies which have been applied to the control of Hybrid Electric Vehicles are studied for a typical DMU railway vehicle. DP is applied to identify the optimal instant power distribution between engines. Based on the optimised results from DP, an adaptive rule-based online strategy is proposed using a non-linear programming optimisation algorithm.
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