Optimising power management strategies for railway traction systems

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