An algorithm to optimize speed profiles of the metro vehicles for minimizing energy consumption

The calculation of optimal driving speed profiles for metro vehicles that improve energy saving and, thus, reduce costs, are investigated. The optimization problem is solved in two different steps: in the first one, a Dynamic Programming Optimization (DPO) algorithm (including slopes and curves effects) is used to find a set of pseudo-optimal speed cycles; the objective is to minimize the electrical energy used for traction subject to constraints such as travel time, trip distance, acceleration limits, etc. In the second optimization step, the speed profiles set are evaluated in a simulation tool implemented to estimate the power flow among vehicles through the metro network. A test campaign monitoring the saved energy and main electric variables have been implemented and the presented results showing the effectiveness of the performance of the proposed optimization algorithm.

[1]  D. Czarkowski,et al.  Energy minimization for catenary-free mass transit systems using Particle Swarm Optimization , 2012, 2012 Electrical Systems for Aircraft, Railway and Ship Propulsion.

[2]  Moon-Ho Kang A GA-based Algorithm for Creating an Energy-Optimum Train Speed Trajectory , 2011 .

[3]  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).

[4]  Donald E. Kirk,et al.  Optimal control theory : an introduction , 1970 .

[5]  Conversion and delivery of electrical energy in the 21st century , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[6]  Bart De Schutter,et al.  Optimal trajectory planning for trains using mixed integer linear programming , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

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

[8]  Kazunori Kojima,et al.  Transport Energy Efficiency: Implementation of IEA Recommendations since 2009 and Next Steps , 2010 .

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

[10]  Rochdi Trigui,et al.  Vehicle trajectory optimization for application in ECO-driving , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[11]  J. Van Mierlo,et al.  Quasi-static simulation method for evaluation of energy consumption in hybrid light rail vehicles , 2008, 2008 IEEE Vehicle Power and Propulsion Conference.

[12]  Xuan Vu,et al.  Optimal train control: Analysis of a new local optimization principle , 2011, Proceedings of the 2011 American Control Conference.

[13]  R Barrero,et al.  Stationary or onboard energy storage systems for energy consumption reduction in a metro network , 2010 .

[14]  R. Barrero,et al.  Energy savings in public transport , 2008, IEEE Vehicular Technology Magazine.

[15]  Chung-Yuen Won,et al.  A calculation of predicting the expected life of super-capacitor following current pattern of railway vehicles , 2007, 2007 7th Internatonal Conference on Power Electronics.

[16]  Felix Schmid,et al.  A review of methods to measure and calculate train resistances , 2000 .

[17]  Hossein Iman-Eini,et al.  Energy recovery in a metro network using stationary supercapacitors , 2011, 2011 2nd Power Electronics, Drive Systems and Technologies Conference.

[18]  Masafumi Miyatake,et al.  Optimal speed control of a train with On-board energy storage for minimum energy consumption in catenary free operation , 2009, 2009 13th European Conference on Power Electronics and Applications.

[19]  Antonio Piccolo,et al.  Siting and sizing of stationary SuperCapacitors in a Metro Network , 2013, AEIT Annual Conference 2013.

[20]  Stefano Di Cairano,et al.  Cloud-computing based velocity profile generation for minimum fuel consumption: A dynamic programming based solution , 2012, 2012 American Control Conference (ACC).