Today, rail transport systems are widely used in the world. Because of the high consumption of energy in these systems, finding ways to optimize their energy consumption is important. One of the best ways to save more energy and prevent the losses of rail transportation is using the optimal speed profile. In this article, intelligent algorithms, involving ant colony optimization for continuous domain and genetic algorithm, are applied to the energy efficiency problem of electrical trains for various track gradients and curvatures. With proper determination of switching points in the moving strategies such as acceleration, cruising, and coasting, the optimal speed profile for the safe journey of trains will be obtained. For the simulation, real data from rail tracks of Tehran metro have been incorporated.
Wei L, Qunzhan L and Bing T. Energy saving train control for urban railway train with multi-population genetic algorithm. In: International forum on information technology and applications, vol. 2, IFITA’09, 2009, pp. 58–62. IEEE.
2.
Khanbaghi M and Malhame RP. Reducing travel energy costs for a subway train via fuzzy logic controls. In: Proceedings of the 1994 IEEE international symposium on intelligent control, 1994, pp.99–104.
3.
HowlettPGMilroyIPPudneyPJ. Energy-efficient train control. Control Eng Pract1994; 2: 193–200.
4.
Milroy IP. Aspects of automatic train control. PhD Thesis, Loughborough University of Technology, 1980.
5.
AsnisIADmitrukAVOsmolovskiiNP. Solution of the problem of the energetically optimal control of the motion of a train by the maximum principle. USSR Comput Math Math Phys1985; 25: 37–44.
6.
HowlettP. An optimal strategy for the control of a train. J Aust Math Soc Series B: Appl Math1990; 31: 454–471.
7.
HowlettP. Optimal strategies for the control of a train. Automatica1996; 32: 519–532.
8.
HowlettP. The optimal control of a train. Ann Oper Res2000; 98: 65–87.
9.
KhmelnitskyE. On an optimal control problem of train operation. IEEE Trans Automat Control2000; 45: 1257–1266.
10.
MiyatakeMMatsudaK. Energy saving speed and charge/discharge control of a railway vehicle with on board energy storage by means of an optimization model. IEEJ Trans Electr Electr Eng2009; 4: 771–778.
11.
MiyatakeMKoH. Optimization of train speed profile for minimum energy consumption. IEEJ Trans Electr Electr Eng2010; 5: 263–269.
12.
KeB-RLinC-LLaiC-W. Optimization of train-speed trajectory and control for mass rapid transit systems. Control Eng Pract2011; 19: 675–687.
13.
ZhuanXXiaX. Optimal scheduling and control of heavy haul trains equipped with electronically controlled pneumatic braking systems. IEEE Trans Control Syst Technol2007; 15: 1159–1166.
14.
KimKChienSIJ. Optimal train operation for minimum energy consumption considering track alignment, speed limit, and schedule adherence. J Transport Eng2010; 137: 665–674.
15.
ZhangLZhuanX. Optimal operation of heavy-haul trains equipped with electronically controlled pneumatic brake systems using model predictive control methodology. IEEE Trans Control Syst Technol2014; 22: 13–22.
16.
Zhang L, Zhuan X and Xia X. Optimal operation of heavy haul trains using model predictive control methodology. In: IEEE international conference on service operations, logistics, and informatics (SOLI), 2011, pp.402–407.
17.
BesinovicNQuagliettaEGoverdeRMP. A simulation-based optimization approach for the calibration of dynamic train speed profiles. J Rail Transport Plan Manag2013; 3: 126–136.
18.
SuSLiXTangT. A subway train timetable optimization approach based on energy-efficient operation strategy. IEEE Trans Intell Transport Syst2013; 14: 883–893.
19.
KimKChienSIJ. Simulation-based analysis of train controls under various track alignments. J Transport Eng2010; 136: 937–948.
20.
SuSTangTChenL. Energy-efficient train control in urban rail transit systems. Proc IMechE, Part F: J Rail and Rapid Transit2015; 229: 446–454.
21.
DomínguezMFernándezACucalaAP. Optimal design of metro automatic train operation speed profiles for reducing energy consumption. Proc IMechE, Part F: J Rail and Rapid Transit2011; 225: 463–474.
22.
KimDNSchonfeldPM. Benefits of dipped vertical alignments for rail transit routes. J Transport Eng1997; 123: 20–27.
23.
ChangCSChuaCSQuekHB. Development of train movement simulator for analysis and optimisation of railway signalling systems. International conference on Developments on Mass Transit systems, London1998, pp. 243–248.
24.
JongJCChangS. Algorithms for generating train speed profiles. J East ASIA Soc Transport Stud2005; 6: 356–371.
25.
JongJCChangS. Models for estimating energy consumption of electric trains. J East Asia Soc Transport Stud2005; 6: 278–291.
26.
ColeyDA. An introduction to genetic algorithms for scientists and engineers, Singapore: World Scientific, 1999.
27.
GoldbergDEHollandJH. Genetic algorithms and machine learning. Mach Learn1988; 3: 95–99.
28.
LuSHillmansenSHoTK. Single-train trajectory optimization. IEEE Trans Intell Transport Syst2013; 14(2): 743–750.
29.
MahlenbeinHSchomischMBornJ. The parallel genetic algorithm as function optimizer. Parallel Comput1991; 17: 619–632.
30.
SchmittLM. Theory of genetic algorithms. Theor Comput Sci2001; 259: 1–61.
31.
SochaKDorigoM. Ant colony optimization for continuous domains. Eur J Oper Res2008; 185: 1155–1173.
32.
SochaK. Ant colony optimisation for continuous and mixed-variable domains, Germany: VDM Publishing, 2009.
