This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Analysis of City Bus Driving Cycle Features for the Purpose of Multidimensional Driving Cycle Synthesis
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Driving cycles are typically used for estimation of vehicle fuel/energy consumption and CO2 emissions. In most of applications only the vehicle velocity vs. time profile is considered as a driving cycle, while a road slope is typically omitted. Since the road slope significantly impacts the fuel consumption, it should be included into realistic driving cycles for hilly roads. As a part of wider research of multidimensional driving cycle synthesis, this paper focuses on analysis of a broad city bus driving cycle dataset recorded in the city of Dubrovnik. The analysis is aimed at revealing the impact of road slope on velocity and acceleration distributions, and clustering the recorded data into several groups reflecting various driving and traffic congestion characteristics. Finally, the Markov chain method is employed to synthesize 3D driving cycles for the selected data clusters, where the Markov chain states include vehicle velocity, vehicle acceleration, and road slope. The synthesized cycles are validated to ensure their representativeness in terms of faithful description of main features of the recorded driving cycles.
CitationTopić, J., Skugor, B., and Deur, J., "Analysis of City Bus Driving Cycle Features for the Purpose of Multidimensional Driving Cycle Synthesis," SAE Technical Paper 2020-01-1288, 2020, https://doi.org/10.4271/2020-01-1288.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
- Liessner, R., Dietermann, A.M., Bäker, B., and Lüpkes, K. , “Derivation of Real-World Driving Cycles Corresponding to Traffic Situation and Driving Style on the Basis of Markov Models and Cluster Analyses,” IET Conference Publications, 2016, doi:10.1049/cp.2016.0961.
- Naranjo, W., Camargo, L.E.M., Pereda, J.E., and Cortes, C. , “Design of Electric Buses of Rapid Transit Using Hybrid Energy Storage and Local Traffic Parameters,” IEEE Transactions on Vehicular Technology 66(7):5551-5563, 2017, doi:10.1109/TVT.2016.2616401.
- Zhang, F., Guo, F., and Huang, H. , “A Study of Driving Cycle for Electric Special-purpose Vehicle in Beijing, 8th International Conference on Applied Energy,” Energy Procedia 105:4884-4889, 2017, doi:10.1016/j.egypro.2017.03.967.
- Geller, B.M. and Bradley, T.H. , “Analyzing Drive Cycles for Hybrid Electric Vehicle Simulation and Optimization,” Journal of Mechanical Design 137(4), 2015, doi:10.1115/1.4029583.
- Shankar, R., Marco, J., and Assadian, F. , “The Novel Application of Optimization and Charge Blended Energy Management Control for Component Downsizing within a Plug-In Hybrid Electric Vehicle,” Energies 5:4892-4923, 2012, doi:10.3390/en5124892.
- Musardo, C., Rizzoni, G., Guezennec, Y., and Staccia, B. , “A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management,” European Journal of Control 11(4-5):509-524, 2005, doi:10.3166/ejc.11.509-524.
- Škugor, B., Hrgetić, M., and Deur, J. , “GPS Measurement-Based Road Slope Reconstruction with Application to Electric Vehicle Simulation and Analysis,” in 8th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES), Dubrovnik, Croatia, 2015.
- Škugor, B. and Deur, J. , “Delivery Vehicle Fleet Data Collection, Analysis and Naturalistic Driving Cycles Synthesis,” International Journal of Innovation and Sustainable Development 10(1):19-39, 2016, doi:10.1504/IJISD.2016.073412.
- Lee, T.K. and Filipi, Z.S. , “Synthesis of Real-World Driving Cycles Using Stochastic Process and Statistical Methodology,” International Journal of Vehicle Design 57(1):17-36, 2011, doi:10.1504/IJVD.2011.043590.
- Lee, T.K. and Filipi, Z.S. , “Synthesis and Validation of Representative Real-World Driving Cycles for Plug-In Hybrid Vehicles,” in IEEE Vehicle Power and Propulsion Conference (VPPC), 2010, doi:10.1109/VPPC.2010.5729040.
- Van Duin, J.H.R., Tavasszy, L.A., and Quak, H.J. , “Towards E(lectric) - Urban Freight: First Promising Steps in the Electric Vehicle Revolution,” European Transport\Trasporti Europei 54(9), 2013, ISSN:1825-3997.
- Silvas, E., Hereijgers, K., Peng, H., Hofman, T. et al. , “Synthesis of Realistic Driving Cycles with High Accuracy and Computational Speed, Including Slope Information,” IEEE Transactions on Vehicular Technology 65(6):4118-4128, 2016, doi:10.1109/TVT.2016.2546338.
- Škugor, B. and Deur, J. , “Synthetic Driving Cycles-Based Modelling of Extended Range Electric Vehicle Fleet Energy Demand,” in EVS30 Symposium, Stuttgart, Germany, Oct. 9-11, 2017.
- Lee, T.K. and Filipi, Z. , “Real-World Driving Pattern Recognition for Adaptive HEV Supervisory Control: Based on Representative Driving Cycles in Midwestern US,” in SAE World Congress & Exhibition, 2012, https://doi.org/10.4271/2012-01-1020.
- Lee, T.K., Adornato, B., and Filipi, Z.S. , “Synthesis of Real-World Driving Cycles and Their Use for Estimating PHEV Energy Consumption and Charging Opportunities: Case Study for Midwest/US,” IEEE Transactions on Vehicular Technology 60(9):4153-4163, 2011, doi:10.1109/TVT.2011.2168251.
- Lei, Z., Cheng, D., Liu, Y., Qin, D. et al. , “A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition,” Energies (1):10, 54, 2017, doi:10.3390/en10010054.
- Zhang, S. and Xiong, R. , “Adaptive Energy Management of a Plug-In Hybrid Electric Vehicle Based on Driving Pattern Recognition and Dynamic Programming,” Applied Energy 155:68-78, 2015, doi:10.1016/j.apenergy.2015.06.003.
- Wei, Z., Xu, Z., and Halim, D. , “Study of HEV Power Management Control Strategy Based on Driving Pattern Recognition,” Energy Procedia 88:847-853, 2016, doi:10.1016/j.egypro.2016.06.062.
- Yu, H., Tseng, F., and McGee, R. , “Driving Pattern Identification for EV Range Estimation,” in IEEE International Electric Vehicle Conference, 2012, doi:10.1109/IEVC.2012.6183207.
- Park, J., Chen, Z., Kiliaris, L., Kuang, M.L. et al. , “Intelligent Vehicle Power Control Based on Machine Learning of Optimal Control Parameters and Prediction of Road Type and Traffic Congestion,” IEEE Transactions on Vehicular Technology 58(9):4741-4756, 2009, doi:10.1109/TVT.2009.2027710.
- Rasmussen, C.E. and Williams, C.K.I. , Gaussian Processes for Machine Learning (The MIT Press, 2006), ISBN:026218253X.
- Li, Y. and Wu, H. , “A Clustering Method Based on K-Means Algorithm,” Physics Procedia 25:1104-1109, 2012, doi:10.1016/j.phpro.2012.03.206.