Possibilities for determining the energy consumption of electric locomotives during acceleration and constant-speed traction
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Abstract
Electric locomotives are integral to sustainable railway transportation, where optimizing energy consumption is crucial for efficiency and environmental impact reduction. This study investigates energy usage during acceleration and constant-speed traction in Siemens Taurus 0470-series locomotives operating on the Sopron–Győr railway line (Line 8 in Hungary). Using empirical data from onboard computer displays, video recordings, and Optical Character Recognition (OCR), the research applies statistical correlation methods to analyze energy consumption trends. The study identifies key influencing factors, including acceleration energy correction coefficients (α1 = 1.2981, α2 = 1.3151) and specific energy consumption values at constant speeds, averaging 0.00204 kWh/kN/km at 120 km/h with a 21.12% relative standard deviation value. Heatmaps illustrate energy consumption patterns, highlighting peak usage near stations and track turnouts. The findings support refining energy models and driving strategies while emphasizing the potential benefits of regenerative braking, timetable optimization, and advanced driver assistance systems. By integrating these insights, railway operations can achieve enhanced energy efficiency and long-term sustainability.
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References
Baur, K. G. (2003). Taurus-Lokomotiven für Europa. EK-Verlag, Freiburg im Breisgau.
Bina, M., Biassoni, F. (2023). Travel experience and reasons for the use and nonuse of local public transport: A case study within the community interregional project SaMBA (Sustainable Mobility Behaviors in the Alpine Region). Sustainability. 15(24), 16612. DOI: 10.3390/su152416612
Chen, W., Peng, F., Liu, Z., Li, Q., Dai, C. (2013). System integration of China’s first PEMFC locomotive. Journal of Modern Transportation. 21(3), 163–168. DOI: 10.1007/s40534-013-0020-0
Cheremisin, V., Istomin, S., Perestenko, A. (2020). Artificial intelligence methods to control the energy efficiency of electric rolling stock online. E3S Web of Conferences. 175, 05046. DOI: 10.1051/e3sconf/202017505046
Cole, C., Sun, Y. Q., Wu, Q., Spiryagin, M. (2023). Exploring hydrogen fuel cell and battery freight locomotive options using train dynamics simulation. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. 238(3), 310–321. DOI: 10.1177/09544097231166477
Currie, G., Reynolds, J., Logan, D., Young, K. (2023). “Tram wrong way” international experience and mitigation of track switch errors. Transportation Research Record. 2677(6), 631–643. DOI: 10.1177/03611981221149432
Dižo, J., Blatnický, M., Harušinec, J., Suchánek, A. (2022). Assessment of dynamics of a rail vehicle in terms of running properties while moving on a real track model. Symmetry. 14(3), 536. DOI: 10.3390/sym14030536
Domanov, K., Shatohin, A., Nezevak, V., Cheremisin, V. (2019). Improving the technology of operating electric locomotives using electric power storage device. E3S Web of Conferences. 110, 01033. DOI: 10.1051/e3sconf/201911001033
Ézsiás, L., Tompa, R., Fischer, S. (2024). Investigation of the possible correlations between specific characteristics of crushed stone aggregates. Spectrum of Mechanical Engineering and Operational Research. 1(1), 10–26. DOI: 10.31181/smeor1120242
Fischer, S. (2015). Traction energy consumption of electric locomotives and electric multiple units at speed restrictions. Acta Technica Jaurinensis. 8(3), 240–256. DOI: 10.14513/actatechjaur.v8.n3.384
Fischer, S. (2025). Investigation of the settlement behavior of ballasted railway tracks due to dynamic loading. Spectrum of Mechanical Engineering and Operational Research. 2(1), 24–46. DOI: 10.31181/smeor21202528
Fischer, S., Kocsis Szürke, S. (2023). Detection process of energy loss in electric railway vehicles. Facta Universitatis, Series: Mechanical Engineering. 21(1), 81–99. DOI: 10.22190/FUME221104046F
Fischer, S., Harangozó, D., Németh, D., Kocsis, B., Sysyn, M., Kurhan, D., Brautigam, A. (2024). Investigation of heat-affected zones of thermite rail weldings. Facta Universitatis, Series: Mechanical Engineering. 22(4), 689–710. DOI: 10.22190/FUME221217008F
Fischer, S., Hermán, B., Sysyn, M., Kurhan, D., Kocsis Szürke, S. (2025). Quantitative analysis and optimization of energy efficiency in electric multiple units. Facta Universitatis, Series: Mechanical Engineering, accepted manuscript in Online first. DOI: 10.22190/FUME241103001F
Ihme, J. (2022). Running Resistances of Rail Vehicles. In: Rail vehicle technology. Springer, Wiesbaden. 31–50. DOI: https://doi.org/10.1007/978-3-658-36969-9
Istomin, S. (2018). The use of correlation and regression analysis for assessment of the energy effectiveness of the DC electric locomotives auxiliary equipment. MATEC Web of Conferences. 239, 01038. DOI: 10.1051/matecconf/201823901038
Istomin, S., Perestenko, A., Dang, C. T. (2018). Development of the system of visual control of electric power consumption by electric rolling stock. MATEC Web of Conferences. 239, 01035. DOI: 10.1051/matecconf/201823901035
Kaleybar, H. J., Brenna, M., Li, H., Zaninelli, D. (2022). Fuel cell hybrid locomotive with modified fuzzy logic based energy management system. Sustainability. 14(14), 8336. DOI: 10.3390/su14148336
Kuchak, A. T. J., Marinkovic, D., Zehn, M. (2020). Finite Element Model Updating: Case Study of a Rail Damper. Structural Engineering and Mechanics. 73, 27–35. DOI: 10.12989/sem.2020.73.1.027
Kuchak, A. T. J., Marinkovic, D., Zehn, M. (2021). Parametric investigation of a rail damper design based on a lab-scaled model. Journal of Vibration Engineering & Technologies. 9, 51–60. DOI: 10.1007/s42417-020-00209-2
Liang, H., Zhang, Y., Yang, P., Wang, L., Gao, C. (2023). Comparison and analysis of prediction models for locomotive traction energy consumption based on the machine learning. IEEE Access. 11, 38502–38513. DOI: 10.1109/access.2023.3268531
Lu, A. Allen, J. G. (2024). Intermittent electrification with battery locomotives and the post-diesel future of North American freight railroads. Transportation Research Record: Journal of the Transportation Research Board. 2678(11), 1691–1718. DOI: 10.1177/03611981241245996
Lu, Q., He, B., Gao, Z., Che, C., Wei, X., Ma, J., Zhang, Z., Luo, J. (2019). An optimized regulation scheme of improving the effective utilization of the regenerative braking energy of the whole railway line. Energies, 12(21), 4166. DOI: 10.3390/en12214166
Lukaszewicz, P. (2007). Running resistance: Results and analysis of full-scale tests with passenger and freight trains in Sweden. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. 221(2), 183–193. DOI: 10.1243/0954409JRRT89
Ma, C., Huang, B., Basher, M. K., Rob, M. A., Jiang, Y. (2024). Fuzzy PID control design of mining electric locomotive based on permanent magnet synchronous motor. Electronics, 13(10), 1855. DOI: 10.3390/electronics13101855
Macassa, G. (2023). Public perceptions of sustainable physical activity and active transportation: a pilot qualitative study in Gävle and Maputo. Sustainability. 15(21), 15354. DOI: 10.3390/su152115354
Madleňák, R., Masek, J., Madleňáková, L., Chinoracký, R. (2023). Eye-tracking investigation of the train driver’s: A case study. Applied Sciences. 13(4), 2437. DOI: 10.3390/app13042437
Mandić, M., Uglešić, I., Milardić, V. (2009). Electric railway power consumption. Journal of Energy – Energija. 58(4), 384–407. DOI: 10.37798/2009584306
Mikhailov, E., Semenov, S., Shvornikova, H., Gerlici, J., Kovtanets, M., Dižo, J., Blatnický, M., Harušinec, J. (2021). A study of improving running safety of a railway wagon with an independently rotating wheel’s flange. Symmetry. 13(10), 1955. DOI: 10.3390/sym13101955
Nikitenko, A., Kostin, M., Mishchenko, T., Hoholyuk, O. (2022). Electrodynamics of power losses in the devices of inter-substation zones of AC electric traction systems. Energies. 15(13), 4552. DOI: 10.3390/en15134552
Oraegbune, O., Ugwu, O. (2020). Delivering sustainable transport infrastructure projects (railway) in Nigeria: Frameworks, indicators, method and tools. Nigerian Journal of Technology. 39(3). DOI: 10.4314/njt.v39i3.4
Rajibayev, D., Miryakubov, A., Mavlanov, A. (2023). Study of the traction converter control system of the uzelr series of electric locomotives. E3S Web of Conferences. 458, 03016. DOI: 10.1051/e3sconf/202345803016
Ren, J., Zhang, Q., Liu, F. (2020). Analysis of factors affecting traction energy consumption of electric multiple unit trains based on data mining. Journal of Cleaner Production. 262, 121374. DOI: 10.1016/j.jclepro.2020.121374
Rochard, B. P., Schmid, F. (2000). A review of methods to measure and calculate train resistances. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. 214(4), 185–199. DOI: 10.1243/0954409001531306
Rodriguez-Cabal, M. Á., Herrera‐Jaramillo, D. A., Bastidas‐Rodríguez, J. D., Ceballos, J. P. V., Montes-Villa, K. S. (2022). Methodology for the estimation of electrical power consumed by locomotives on undocumented railroad tracks. Energies. 15(12), 4256. DOI: https://doi.org/10.3390/en15124256
Saukenova, I., Oliskevych, M., Taran, I., Toktamyssova, A., Aliakbarkyzy, D., Pelo, R. (2022). Optimization of schedules for early garbage collection and disposal in the megapolis. Eastern-European Journal of Enterprise Technologies, 1(3), 13–23. DOI: 10.15587/1729-4061.2022.251082
Siemens (2024). VECTRON 230 km/h, Travel high speed flexibly across Europe. URL: https://assets.new.siemens.com/siemens/assets/api/uuid:c0d82542-6247-4cad-925a-c4a6d260530d/siemens-mobility-vectron-datasheet-230kmh.pdf (Accessed: 10 March 2025)
Tollner, D., Zöldy, M. (2022). Long term utilisation effect on vehicle battery performance. In 2022 IEEE 1st International Conference on Cognitive Mobility (CogMob). IEEE. 79–84. DOI: 10.1109/CogMob55547.2022.10118087
de la Torre, R., Corlu, C. G., Faulín, J., Onggo, B. S., Juan, A. A. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications. Sustainability. 13(3), 1551. DOI: 10.3390/su13031551
Volkov, V., Taran, I., Volkova, T., Pavlenko, O., Berezhnaja, N. (2020). Determining the efficient management system for a specialized transport enterprise. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2020(4), 185–191. DOI: 10.33271/nvngu/2020-4/185
Waleghwa, B., Ioannides, D. (2024). “Everyone wants to drive there”: Challenges to transport sustainability in rural tourism destinations. International Journal of Tourism Research. 26(6), e2810. DOI: 10.1002/jtr.2810
Yan, J., Wang, H., Zhong, S., Lan, Y., & Huang, K. (2018). Control strategy for the energy optimization of hybrid regenerative braking energy utilization system used in electric locomotive. Mathematical Problems in Engineering. 2018, 1–13, 510487. DOI: 10.1155/2018/2510487
Zarifyan, A., Mustafin, A., Valentseva, E., Shapshal, A. (2021). Capacity utilization level of freight electric locomotives and evaluation of expense reduction on consumed energy due to modernization. Transport Problems, 16(4), 5–14. URL: https://www.researchgate.net/publication/357333160_CAPACITY_UTILIZATION_LEVEL_OF_FREIGHT_ELECTRIC_LOCOMOTIVES_AND_EVALUATION_OF_EXPENSE_REDUCTION_ON_CONSUMED_ENERGY_DUE_TO_MODERNIZATION (Accessed: 10 March 2025)
Zöldy, M. (2024). Changes at mobility space use in the cognitive mobility era. Acta Technica Jaurinensis, 17(4), 163–168. DOI: 10.14513/actatechjaur.00750
Zöldy, M., Baranyi, P. (2023). The Cognitive Mobility Concept. Infocommunications Journal. 2023(Special Issue), 35–40. DOI: 10.36244/ICJ.2023.SI-IODCR.6
Zöldy, M., Dömötör, F. (2022). Noise and vibration test of electric drive cars. In 2022 IEEE 1st International Conference on Cognitive Mobility (CogMob). IEEE. 121–124. DOI: 10.1109/CogMob55547.2022.10118037
Zöldy, M., Pathy-Nagy, Z. (2022). Evaluating of E-Vehicle Gear Noise. In 2022 IEEE 1st International Conference on Cognitive Mobility (CogMob). IEEE. 93–96. DOI: 10.1109/CogMob55547.2022.10117985
Zöldy, M., Baranyi, P., Török, Á. (2024). Trends in Cognitive Mobility in 2022. Acta Polytechnica Hungarica. 21(7), 189–202. DOI: 10.12700/APH.21.7.2024.7.11