The use of multiple power sources in railway applications is common on modern rolling stock. Prime movers are usually distributed along the length of the train, and are able to provide distributed traction. If the primary power for such vehicles comes from individual diesel engines, or if there are non-linear efficiency characteristics within the drive train, then the supervisory controllers for each motive unit may be decoupled and operated independently. This can potentially save energy. This paper investigates two global optimisation algorithms. These are the dynamic programming method, and the greedy algorithm. Both of these two graphic searching algorithms help to find the optimum power distribution between engines for a typical Diesel Multiple Unit (DMU) train. It is found that the application of Dijkstra's Algorithm, in association with a Fibonacci heap (F-heap) based advanced data structure, can significantly improve the computing efficiency while maintaining the same optimisation precision as the dynamic programming method. In an optimisation process with M power requirements and n possible engine power states for each requirement, the time complexity will be reduced from O(M*n 2 ) to O(M*(n log(n) + n)).
[1]
R. J. Hill.
Electric railway traction. I. Electric traction and DC traction motor drives
,
1994
.
[2]
Edsger W. Dijkstra,et al.
A note on two problems in connexion with graphs
,
1959,
Numerische Mathematik.
[3]
Clive Roberts,et al.
A Power-Management Strategy for Multiple-Unit Railroad Vehicles
,
2011,
IEEE Transactions on Vehicular Technology.
[4]
Shinji Wakao,et al.
Design Estimation of the Hybrid Power Source Railway Vehicle based on the Multiobjective Optimization by the Dynamic Programming
,
2008
.
[5]
S. Wakao,et al.
Energy consumption analysis of FC-EDLC hybrid railway vehicle by dynamic programming
,
2007,
2007 European Conference on Power Electronics and Applications.
[6]
F. R. Salmasi,et al.
Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends
,
2007,
IEEE Transactions on Vehicular Technology.
[7]
Robert E. Tarjan,et al.
Fibonacci heaps and their uses in improved network optimization algorithms
,
1984,
JACM.
[8]
Stephen Warshall,et al.
A Theorem on Boolean Matrices
,
1962,
JACM.