Energy Efficient Motion Control for a Light Rail Vehicle Using The Big Bang Big Crunch Algorithm

Abstract: In this manuscript, an emerging evoluitonary optimization technique, Big Bang - Big Crunch algorithm, is used for an energy-efficient train operation in light rail network of Eskisehir is studied. Cruising and coasting, two basic motion phases of train, should be taken into consideration in order to decrease energy consumption. Determining the optimal switching points from one motion phase into another is key in the energy saving. Switching points for optimal train operation are modeled for the Big Bang Big Crunch algorithm platform and globally optimum set of switching points is obtained as its output, which has proved to be suitable for solving a wide class of global optimization problems. Obtained results are verified through simulations of the operation for five consecutive stations between Buyukdere and Stadyum. Test paths are created considering actual grade and track length. Also certain real life constraints are taken into account such as punctuality and maximum speed limit. It is shown that using the optimal switching points in the simulations results in a significant energy saving.

[1]  Chung-Fu Chang,et al.  Optimising train movements through coast control using genetic algorithms , 1997 .

[2]  Dewang Chen,et al.  An intelligent train operation algorithm via gradient descent method and driver's experience , 2013, 2013 IEEE International Conference on Intelligent Rail Transportation Proceedings.

[3]  Phil Howlett,et al.  The Optimal Control of a Train , 2000, Ann. Oper. Res..

[4]  Metin Gokasan,et al.  Optimal trajectory planning by Big Bang-Big Crunch algorithm , 2014, 2014 International Conference on Control, Decision and Information Technologies (CoDIT).

[5]  K. Ichikawa.  Application of Optimization Theory for Bounded State Variable Problems to the Operation of Train , 1968 .

[6]  G. Ghodrati Amiri,et al.  A New Damage Detection Method: Big Bang-Big Crunch (BB-BC) Algorithm , 2013 .

[7]  P. Howlett,et al.  A note on the calculation of optimal strategies for the minimization of fuel consumption in the control of trains , 1993, IEEE Trans. Autom. Control..

[8]  P. Howlett.  An optimal strategy for the control of a train , 1990, The Journal of the Australian Mathematical Society. Series B. Applied Mathematics.

[9]  Mehmet Turan Soylemez,et al.  Coasting point optimisation for mass rail transit lines using artificial neural networks and genetic algorithms , 2008 .

[10]  Eugene Khmelnitsky,et al.  On an optimal control problem of train operation , 2000, IEEE Trans. Autom. Control..

[11]  Phil Howlett,et al.  Application of critical velocities to the minimisation of fuel consumption in the control of trains , 1992, Autom..

[12]  Tad Gonsalves,et al.  Multi objective particle swarm optimization algorithm for the design of efficient ATO speed profiles in metro lines , 2014, Eng. Appl. Artif. Intell..

[13]  Michael P. Polis,et al.  Reducing energy consumption through trajectory optimization for a metro network , 1975 .

[14]  Clive Roberts,et al.  Single-Train Trajectory Optimization , 2013, IEEE Transactions on Intelligent Transportation Systems.

[15]  Rongfang Rachel Liu,et al.  Energy-efficient operation of rail vehicles , 2003 .

[16]  Ibrahim Eksin,et al.  A new optimization method: Big Bang-Big Crunch , 2006, Adv. Eng. Softw..

[17]  Wei-Song Lin,et al.  Energy-Saving Automatic Train Regulation Using Dual Heuristic Programming , 2012, IEEE Transactions on Vehicular Technology.

[18]  Masafumi Miyatake,et al.  Optimization of Train Speed Profile for Minimum Energy Consumption , 2010 .

[19]  M. Meyer,et al.  An algorithm for the optimal control of the driving of trains , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[20]  Tin Kin Ho,et al.  Coast control for mass rapid transit railways with searching methods , 2004 .

[21]  Engin Yesil,et al.  Strategy optimization of a solar car for a long-distance race using Big Bang — Big Crunch optimization , 2013, 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI).