A multivariate nonlinear regression model for the resistance power of a light rail vehicle

Year 2021, Volume: 8 Issue: 1, 33 - 38, 31.03.2021

Mine Sertsöz , Mehmet Fidan

https://doi.org/10.31593/ijeat.798799

Abstract

In Turkey, 20% of energy use caused by transportation. Light rail transportation has developing nowadays. However, it is very important to issue having information about the energy consumption of the light rail vehicle according to different both vehicle and railway situations.
In Turkey, approximately 20% of the energy expended is spent on transportation. Light rail transportation technology is still in an evolving process. In this development process, it is crucial to have information about the energy consumption of the light rail vehicle according to the different situations of both the vehicle and the railway.
It is necessary to predict the power losses that will occur under different driving conditions sensitively to ensure energy efficiency in light rail systems. The most important of these power losses is the resistance loss caused by contact with the route. Resistance loss is dependent multiple environmental conditions. The most important of these conditions can be listed as the weight of the light rail vehicle, the instantaneous speed of the vehicle, the curve of the route, the ramp slope of the route and the friction force arising from these conditions. Resistance loss is proportional and linearly dependent to some of these variables while others show reverse or nonlinear dependence. Due to these different types of dependencies, it is seen that a single multivariate nonlinear model is needed to explain the loss of resistance in all different conditions. In this study, a new and accurate model for resistance losses has been developed by fitting numerical values obtained from different scenarios to multivariate nonlinear regression model.

Keywords

energy efficiency, nonlinear regression, optimization, railway

Supporting Institution

None

Project Number

-

Thanks

-

References

• Krug, S. 2008. Public transport: Energy efficiency of light train systems. SAVE.
• Wang, J. and Rakha, H.A. 2017. Electric train energy consumption modeling. Applied Energy, 193, 346-355.
• Mittal, R.K. 1977. “Energy intensity of intercity passenger rail. Final report,"; Department of Transportation, Washington, DC (USA). Office of Univ. Research, DOT/RSPD/DPB-50-78/7 United States 10.2172/6300399 Dept. of Transportation, Washington, DC. https://www.osti.gov/servlets/purl/6300399 .(30 March 2021.)
• Kim, K. and Chien, S. I.-J. 2011. Optimal train operation for minimum energy consumption considering track alignment, speed limit, and schedule adherence. Journal of Transportation Engineering, 137(9), 665-674.
• Sicre, C., Cucala, A.P., Fernández, A. and Lukaszewicz, P. 2012. Modeling and optimizing energy‐efficient manual driving on high‐speed lines. IEEJ Transactions on Electrical and Electronic Engineering, 7(6), 633-640.
• Su, S., Li, X., Tang, T. and Gao, Z. 2013. A Subway Train Timetable Optimization Approach Based on Energy-Efficient Operation Strategy. IEEE Transactions on Intelligent Transportation Systems, 14(2), 883-893.
• González-Gil, A., Palacin, R., Batty, P. and Powell, J. 2014. A systems approach to reduce urban rail energy consumption. Energy Conversion and Management, 80, 509-524.
• Li, X. and Lo, H.K. 2014. An energy-efficient scheduling and speed control approach for metro rail operations. Transportation Research Part B: Methodological, 64, pp. 73-89.
• Golovitcher, I.M. 2001. Energy efficient control of rail vehicles. IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat. No. 01CH37236), 1:IEEE, 658-663.
• Jenks, C.W., Goldstein, L.D., Avery, A.P., Delaney, E.P. and Lamberton, S. 2015. National cooperative rail research program (ncrrp) report 3: Comparison of passenger rail energy consumption with competing modes. Washington (DC): Transportation Research Board.
• A. Moawad, N. Kim, N. Shidore, and A. Rousseau, "Assessment of vehicle sizing, energy consumption and cost through large scale simulation of advanced vehicle technologies," Argonne National Lab.(ANL), Argonne, IL (United States), 2016.
• Lewis, A.M., Kelly, J.C. and Keoleian, G.A. 2012. Evaluating the life cycle greenhouse gas emissions from a lightweight plug-in hybrid electric vehicle in a regional context. IEEE International Symposium on Sustainable Systems and Technology (ISSST), 2012: IEEE, 1-6.
• Lewis, A.M., Kelly, J.C. and Keoleian, G.A. 2014. Vehicle lightweighting vs. electrification: life cycle energy and GHG emissions results for diverse powertrain vehicles. Applied Energy, 126, 13-20.
• De Gennaro, M., Paffumi, E., Scholz, H. and Martini, G. 2014. GIS-driven analysis of e-mobility in urban areas: An evaluation of the impact on the electric energy grid. Applied Energy, 124, 94-116.
• Rambaldi, L., Bocci, E. and Orecchini, F. 2011. Preliminary experimental evaluation of a four wheel motors, batteries plus ultracapacitors and series hybrid powertrain. Applied Energy, 88(2), 442-448.
• Abousleiman, R. and Rawashdeh, O. 2015. Energy consumption model of an electric vehicle. IEEE Transportation Electrification Conference and Expo (ITEC), 2015: IEEE, 1-5.
• Rajashekara, K. and Rathore, A.K. 2015. Power conversion and control for fuel cell systems in transportation and stationary power generation. Electric Power Components and Systems, 43(12), 1376-1387.
• Wang, J. and Rakha, H.A. 2016. Modeling fuel consumption of hybrid electric buses: Model development and comparison with conventional buses. Transportation Research Record, 2539(1), 94-102.
• Aradi, S., Bécsi, T. and Gáspár, P. 2015. Estimation of running resistance of electric trains based on on-board telematics system. International Journal of Heavy Vehicle Systems, 22(3), 277-291.
• Spinalbese, A. 2018. An Experimental Method For The Estimation Of The Adhesion Coefficient And The Running Resistance Of A Train. Master of Science in Mechanical Engineering, School Of Industrial And Information Engineering, Politecnico Di Milano.
• Michálek, T. 2017. Modification of train resistance formulae for container trains based on operational run-down tests. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 232(6), 1588-1597, 2017.
• Kwon, H. 2017. A study on the resistance force and the aerodynamic drag of Korean high-speed trains. Vehicle System Dynamics, 56(8), 1250-1268.
• Hobson, S. 2015. Bombardier Flexity Outlook tram (EsTram - Eskişehir Tramvay).https://www.flickr.com/photos/30389998@N03/16742194366/ .(30 March 2021.)
• Bates, D.B. and Watts, D.G. Nonlinear regression analysis and its applications, Wiley, New York, 1988.
• Dumouchel, W. and O’Brien, F. 1989. Integrating a robust option into a multiple regression computing environment. Computer science and statistics: Proceedings of the 21st symposium on the interface, American Statistical Association Alexandria, VA, 297-302.