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Tytuł artykułu

Application of the Bayesian inference method to synthesize urban driving cycle speed schedules using measured data

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Standardized driving cycles are widely used to simulate real-world vehicle operating conditions. They are used for estimating vehicle ranges, evaluating emissions, and designing powertrain characteristics. Recently, due to the intensive electrification of urban public transport fleets, estimating electric energy consumption has gained significant importance. This process requires up-to-date, validated driving cycles developed from realworld operating condition measurements. This paper presents an original method for synthesizing the driving cycle based on a stochastic approach using Bayesian inference. It first determines probability distributions of vehicle speed for the entire line. Next, all trips for the line are divided into segments of uninterrupted movement. A probability distribution of speed is also constructed for each of these segments. Then, quality fit indicators are calculated for each segment. Segments that support the hypothesis of the total distribution with maximum likelihood are then selected using Bayesian inference. A driving cycle for the selected line is synthesized by combining several segments with the highest degree of fit. The condition for applying the method is having a large dataset of measurements obtained during actual trips. The method can be effectively implemented computationally, ensuring high accuracy in reflecting the driving conditions in a selected area. The method has low computational requirements and a high capacity to adapt to changing operational conditions. This paper presents the results of applying this method to three bus lines in Warsaw and uses publicly available traffic data. The proposed method can be used for various applications, including estimating energy consumption for route travel, assessing the impact of traffic management changes on driving smoothness, and determining the load on electric bus engines and batteries.
Czasopismo
Rocznik
Strony
31--44
Opis fizyczny
Bibliogr. 28 poz.
Twórcy
  • Warsaw University of Technology, Faculty of Transport; Koszykowa 75, 00-662 Warsaw, Poland
  • Warsaw University of Technology, Faculty of Transport; Koszykowa 75, 00-662 Warsaw, Poland
Bibliografia
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  • 2. Jeong, N.T. & Yang, S.M. & Kim, K.S. & Wang, M.S. & Kim, H.S. & Suh, M.W. Urban driving cycle for performance evaluation of electric vehicles. Int J Automot Technol. 2016. Vol. 17(1). P. 145-151.
  • 3. Ho, S.H. & Wong, Y.D. & Chang, V.W.C. Developing Singapore Driving Cycle for passenger cars to estimate fuel consumption and vehicular emissions. Atmos Environ. 2014. Vol. 97. P. 353-362.
  • 4. Nyberg, P. & Frisk, E. & Nielsen, L. Driving Cycle Equivalence and Transformation. IEEE Trans Veh Technol. 2017. Vol. 66(3). P. 1963-1974.
  • 5. Giakoumis, E. & Zachiotis, A. Investigation of a diesel-engined vehicle’s performance and emissions during the WLTC driving cycle – comparison with the NEDC. Energies. 2017. Vol. 10(2). No. 240.
  • 6. Masłowski, D. & Kulińska, E. & Krzewicki, Ł. Alternative methods of replacing electric batteries in public transport vehicles. Energies. 2023. Vol. 16(15). No. 5828.
  • 7. Kozłowski, M. & Czerepicki, A. Quick electrical drive selection method for bus retrofitting. Sustainability. 2023. Vol. 15(13). No. 10484.
  • 8. Jagiełło, A. & Wołek, M. & Bizon, W. Comparison of tender criteria for electric and diesel buses in poland—has the ongoing revolution in urban transport been overlooked? Energies. 2023. Vol. 16(11). No. 4280.
  • 9. Electromobility and Alternative Fuels Act. Available at: https://sip.lex.pl/akty-prawne/dzu-dziennikustaw/elektromobilnosc-i-paliwa-alternatywne-18683445.
  • 10. Czerepicki, A. & Choromański, W. & Kozłowski, M. & Kazinski, A. Analysis of the problem of electric buses charging in urban transport. Sci Tech. 2020. Vol. 19(4). P. 349-355.
  • 11. Torabi, S. & Bellone, M. & Wahde, M. Energy minimization for an electric bus using a genetic algorithm. Eur Transp Res Rev. 2020. Vol. 12(1). P. 2.
  • 12. Brzeziński, J. Gdansk bought electric buses for PLN 62 million. These, however, are not suitable for operation... Available at: https://moto.pl/MotoPL/7,88389,30398966,gdansk-kupil-za-62-mln-zlelektryczne-autobusy-te-jednak-nie.html.
  • 13. Jing, Z. & Wang, T. & Zhang, S. & Wang, G. Development method for the driving cycle of electric vehicles. Energies. 2022. Vol. 15(22). No. 8715.
  • 14. Esser, A. & Zeller, M. & Foulard, S. & Rinderknecht, S. Stochastic synthesis of representative and multidimensional driving cycles. SAE Int J Altern Powertrains. 2018. Vol. 7(3). P. 2018-01–0095.
  • 15. Zhang, M. & Shi, S. & Cheng, W. & Shen, Y. Self-adaptive hyper-heuristic Markov chain evolution for generating vehicle multi-parameter driving cycles. IEEE Trans Veh Technol. 2020. Vol. 69(6). P. 6041-52.
  • 16. Li, L. &, Sun, H. & Tao, F. & Fu, Z. Driving cycle prediction based on Markov chain combined with driving information mining. Proc Inst Mech Eng Part D J Automob Eng. 2023. P. 09544070231171741.
  • 17. Shi, S. & Lin, N. & Zhang, Y. & Cheng, J. & Huang, C. & Liu, L. & et al. Research on Markov property analysis of driving cycles and its application. Transp Res Part D Transp Environ. 2016. Vol. 47. P 171-181.
  • 18. Huang, D. & Xie, H. & Ma, H. & Sun, Q. Driving cycle prediction model based on bus route features. Transp Res Part D Transp Environ. 2017. Vol. 54. P. 99-113.
  • 19. Zhao, X. & Ye, Y. & Ma, J. & Shi, P. & Chen, H. Construction of electric vehicle driving cycle for studying electric vehicle energy consumption and equivalent emissions. Environ Sci Pollut Res. 2020. Vol. 27(30). P. 37395-37409.
  • 20. Chen, X. & Cheng, Z. & Jin, J.G. & Trépanier, M. & Sun, L. Probabilistic forecasting of bus travel time with a Bayesian gaussian mixture model. Transp Sci. 2023 Vol. 57(6).
  • 21. Tang. J & Heinimann, H. & Han, K. & Luo, H. & Zhong, B. Evaluating resilience in urban transportation systems for sustainability: A systems-based Bayesian network model. Transp Res Part C Emerg Technol. 2020. Vol. 121. No. 102840.
  • 22. Warsaw City Administration. Open data. Available at: https://api.um.warszawa.pl/.
  • 23. OpenStreet Map. Available at: https://www.openstreetmap.org/.
  • 24. Subramaniam, A & Ibrahim, N.A. & Jabar, S.N. & Rahman, S.A. Driving cycle tracking device big data storing and management. Int J Electr Comput Eng. 2022. Vol. 12(2). No. 1402.
  • 25. Valera, J.J. & Heriz, B. & Lux, G. & Caus, J. & Bader, B. Driving cycle and road grade on-board predictions for the optimal energy management in EV-PHEVs. In: 2013 World Electric Vehicle Symposium and Exhibition (EVS27). IEEE. 2013. P. 1-10.
  • 26. Jaskowski, P. & Tomczuk, P. & Chrzanowicz, M. Construction of a measurement system with GPS RTK for operational control of street lighting. Energies. 2022. Vol. 15(23). No. 9106.
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  • 28. Den, Z. & Xu, L & Liu, H. & Hu, X. & Duan, Z. & Xu, Y. Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles. Appl Energy. 2023. Vol. 339. No. 120954.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7f5ee3f6-8197-4c55-b9cc-e12cda100651
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