Electric vehicle energy consumption taking into account the route topology
- Details
- Category: Content №2 2024
- Last Updated on 01 May 2024
- Published on 30 November -0001
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Authors:
O.S.Beshta, orcid.org/0000-0002-4648-0260, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
O.O.Beshta, orcid.org/0000-0001-6397-3262, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
S.S.Khudolii, orcid.org/0000-0003-2342-1556, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
T.O.Khalaimov*, orcid.org/0000-0002-0171-8503, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
V.S.Fedoreiko, orcid.org/0000-0001-5822-3002, Ternopil Volodymyr Hnatiuk National Pedagogical University, Ternopil, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (2): 104 - 112
https://doi.org/10.33271/nvngu/2024-2/104
Abstract:
Purpose. Determining the impact of the route topology factor on the costs of mechanical work of an electric vehicle is the main task of this work. The impact is determined by calculating the costs of mechanical work during the movement of an electric vehicle, taking into account energy recovery. The task also includes assessment of the forces acting on an electric vehicle using the example of the 2014 Nissan Leaf AZEO.
Methodology. The paper uses a mathematical model that estimates the amount of mechanical work required to overcome one of the chosen routes, taking into account energy recovery. Evaluation is performed using the most common standardized cycle WLTC class 3b.
Findings. The result of the research is a developed mathematical model that will allow one to effectively estimate the amount of mechanical work to overcome the given route and the possible recovery energy. The proposed method makes it possible to determine the most economical route from the starting point to the destination, taking into account the cost of mechanical energy.
Originality. A description of the main components affecting the consumption of electricity is given, taking into account the full picture of the forces acting on the electric vehicle during movement.
Practical value. The obtained results are of practical importance for choosing the most optimal route of the electric vehicle, which contributes to the efficient use of energy. The proposed technique can be used in practice to plan routes from the point of view of maximum energy recovery.
Keywords: electric vehicle, route topology, energy recovery, optimal route, energy efficiency, WLTC
References.
1. Jiao, Z., Ma, J., Zhao, X., Zhang, K., Meng, D., & Li, X. (2022). Development of a Rapid Inspection Driving Cycle for Battery Electric Vehicles Based on Operational Safety. Sustainability, 14, 5079. https://doi.org/10.3390/su14095079.
2. Egede, P., Dettmer, T., Herrmann, Ch., & Kara, S. (2015). Life Cycle Assessment of Electric Vehicles – A Framework to Consider Influencing Factors. Procedia CIRP, 29, 233-238. https://doi.org/10.1016/j.procir.2015.02.185.
3. Zaini, Z. (2020). Braking control strategies of modern hybrid and electric vehicles. Journal of Physics: Conference Series, 1469, 012173. https://doi.org/10.1088/1742-6596/1469/1/012173.
4. Jimenez Bermejo, D., Hernandez, S., Fraile-Ardanuy, J., Serrano Romero, J., Pozo, R., & Alvarez, F. (2018). Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones. Energies, 11, 412. https://doi.org/10.3390/en11020412.
5. Qi, X., Wu, G., Boriboonsomsin, K., & Barth, M. (2017). Data-driven decomposition analysis and estimation of link-level electric vehicle energy consumption under real-world traffic conditions. Transportation Research Part D: Transport and Environment, 64. https://doi.org/10.1016/j.trd.2017.08.008.
6. DieselNet (2019). Worldwide Harmonized Light Vehicles Test Cycle (WLTC). Retrieved from https://dieselnet.com/standards/cycles/wltp.php.
7. Tutuianu, M., Bonnel, P., Ciuffo, B., Haniu, T., Ichikawa, N., Marotta, A., Pavlovic, J., & Steven, H. (2015). Development of the World-wide harmonized Light duty Test Cycle (WLTC) and a possible pathway for its introduction in the European legislation. Transportation Research Part D Transport and Environment, 40, 61-75. https://doi.org/10.1016/j.trd.2015.07.011.
8. UNECE (2005, April 1). Global Technical Regulations (GTRs). Retrieved from https://unece.org/transport/standards/transport/vehicle-regulations-wp29/global-technical-regulations-gtrs.
9. Babangida, A., & Szemes, P. T. (2021). Electric Vehicle Modelling and Simulation of a Light Commercial Vehicle Using PMSM Propulsion. Hungarian Journal of Industry and Chemistry, 49(1), 37-46. https://doi.org/10.33927/HJIC-2021-06.
10. Choi, G., & Jahns, T. M. (2013). Design of electric machines for electric vehicles based on driving schedules. 2013 International Electric Machines & Drives Conference, 54-61. https://doi.org/10.1109/IEMDC.2013.6556192.
11. Hayes, J., & Davis, K. (2014). Simplified electric vehicle powertrain model for range and energy consumption based on EPA coast-down parameters and test validation by Argonne National Lab data on the Nissan Leaf, 1-6. https://doi.org/10.1109/ITEC.2014.6861831.
12. Emadi, A. (Ed.) (2014). Advanced Electric Drive Vehicles (1st ed.). CRC Press. https://doi.org/10.1201/9781315215570.
13. Mediouni, H., Ezzouhri, A., Charouh, Z., El Harouri, K., El Hani, S., & Ghogho, M. (2022). Energy Consumption Prediction and Analysis for Electric Vehicles: A Hybrid Approach. Energies, 15, 6490. https://doi.org/10.3390/en15176490.
14. Kropiwnicki, J., & Furmanek, M. (2019). Analysis of the regenerative braking process for the urban traffic conditions. Combustion Engines, 178(3), 203-207. https://doi.org/10.19206/CE-2019-335.
15. Arat, M., & Bolarinwa, E. (2015). Rolling Resistance Effect of Tire Road Contact in Electric Vehicle Systems. SAE Technical Papers, 2015. https://doi.org/10.4271/2015-01-0624.
16. Vodovozov, V., Raud, Z., Lehtla, T., Rassõlkin, A., & Lillo, N. (2014). Comparative analysis of electric drives met for vehicle propulsion. 2014 9 th International Conference on Ecological Vehicles and Renewable Energies, EVER 2014, 1-8. https://doi.org/10.1109/EVER.2014.6844125.
17. Ben-Marzouk, M., Guy, C., Pelissier, S., Sari, A., & Venet, P. (2018). Determination of the electric vehicles driving modes in real life conditions by classification methods. 2018 IEEE International Conference on Industrial Technology (ICIT), 2060-2065. https://doi.org/10.1109/ICIT.2018.8352506.
18. Kravets, V., Ziborov, K., Bas, K., & Fedoriachenko, S. (2019). Combined method for determining the optimal flow distribution plan for mining, urban electric vehicles and for charging stations. E3S Web of Conferences, 123, 01029. https://doi.org/10.1051/e3sconf/201912301029.
19. Deng, Y., Lu, K., Liu, T., Wang, X., Shen, H., & Gong, J. (2023). Numerical Simulation of Aerodynamic Characteristics of Electric Vehicles with Battery Packs Mounted on Chassis. World Electric Vehicle Journal, 26. https://doi.org/10.3390/wevj14080216.
20. Google Maps (2024). Dnipro, Dnipropetrovsk oblast, Ukraine. Retrieved from https://www.google.com/maps/place/Dnipro,+Dnipropetrovsk+Oblast,+Ukraine.
21. Google Earth (2024). Dnipro, Dnipropetrovsk oblast, Ukraine. Retrieved from https://earth.google.com/web/@48.4501043,34.9830107,122.47063345a,36000d,35y,0h,0t,0r.
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