Abstract
This paper develops a methodology for constructing a representative electric vehicle (EV) urban driving cycle as a basis for studying the differences in estimated energy consumption, taking Xi’an as an example. The test route is designed in accordance with the overall topological structure of the urban roads in the study region and the results of a traffic flow survey. Wavelet decomposition and reconstruction are utilized to preprocess the original data. Principal component analysis (PCA) is used to reduce the number of the kinetic parameters. The fuzzy C-means (FCM) clustering algorithm is used to cluster the driving segments. A representative EV urban driving cycle is constructed in accordance with the time proportions of three classes of driving segments and the correlation coefficients of the characteristic parameters. Finally, the differences in energy consumption estimates obtained using the constructed Xi’an EV urban driving cycle (XA-EV-UDC) and the international driving cycles are studied. The comparison shows that when international driving cycles are used to estimate the energy consumption and driving range of EVs, large relative errors will result, with energy consumption errors of 9.65 to 21.17% and driving range errors of 20.10 to 38.14%. Therefore, to accurately estimate energy consumption and driving range of EVs under real-world driving conditions, representative EV driving cycles for each typical city and region should be constructed.
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References
Arun NH, Mahesh S, Ramadurai G, Nagendra SMS (2017) Development of driving cycles for passenger cars and motorcycles in Chennai, India. Sustain Cities Soc 32:508–512. https://doi.org/10.1016/j.scs.2017.05.001
Berzi L, Delogu M, Pierini M (2016) Development of driving cycles for electric vehicles in the context of the city of Florence. Transp Res Part D Transp Environ 47:299–322. https://doi.org/10.1016/j.trd.2016.05.010
Bishop JDK, Axon CJ, Mcculloch MD (2012) A robust, data-driven methodology for real-world driving cycle development. Transp Res Part D Transp Environ 17(5):389–397. https://doi.org/10.1016/j.trd.2012.03.003
Brady J, O’Mahony M (2016) Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas. Appl Energy 177:165–178. https://doi.org/10.1016/j.apenergy.2016.05.094
Cheng Z, Xia B, You C, Mi CC (2015) A novel energy management method for series plug-in hybrid electric vehicles. Appl Energy 145:172–179. https://doi.org/10.1016/j.apenergy.2015.02.004
Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. https://doi.org/10.1080/01969727308546046
Esteves-Booth A, Muneer T, Kirby H, Kubie J, Hunter J (2001) The measurement of vehicular driving cycle within the city of Edinburgh. Transp Res D 6(3):209–220. https://doi.org/10.1016/s1361-9209(00)00024-9
Fellah M, Singh G, Rousseau A, Pagerit S, Nam E, Hoffman G (2009) Impact of real-world drive cycles on PHEV battery requirements. SAE. https://doi.org/10.4271/2009-01-1383
Fotouhi A, Montazeri-Gh M (2013) Tehran driving cycle development using the k-means clustering method. Sci Iran 20(2):286–293. https://doi.org/10.1016/j.scient.2013.04.001
Galgamuwa U, Perera L, Bandara S (2015) Developing a general methodology for driving cycle construction: comparison of various established driving cycles in the world to propose a general approach. J Transp Technol 5(04):191–203. https://doi.org/10.4236/jtts.2015.54018
Galgamuwa U, Perera L, Bandara S (2016a) Development of a driving cycle for Colombo, Sri Lanka: an economical approach for developing countries. J Adv Transp 50(7):1520–1530. https://doi.org/10.1002/atr.1414
Galgamuwa U, Perera L, Bandara S (2016b) A representative driving cycle for the southern expressway compared to existing driving cycles. Transp Dev Econn 2(2):22. https://doi.org/10.1007/s40890-016-0027-4
Gao Q, Wang L, Wang Y, Wang C (2016) Crushing analysis and multiobjective crashworthiness optimization of foam-filled ellipse tubes under oblique impact loading. Thin-Walled Struct 100:105–112. https://doi.org/10.1016/j.tws.2015.11.020
Gao Q, Zhao X, Wang C, Wang L, Ma Z (2018) Multi-objective crashworthiness optimization for an auxetic cylindrical structure under axial impact loading. Mater Des 143:120–130. https://doi.org/10.1016/j.matdes.2018.01.063
Günther R, Wenzel T, Wegner M, Rettig R (2017) Big data driven dynamic driving cycle development for busses in urban public transportation. Transp Res Part D Transp Environ 51(March 2017):276–289. https://doi.org/10.1016/j.trd.2017.01.009
He H, Guo J, Zhou N, Sun C, Peng J (2017) Freeway driving cycle construction based on real-time traffic information and global optimal energy management for plug-in hybrid electric vehicles. Energies 10(11):1796. https://doi.org/10.3390/en10111796
Ho SH, Wong YD, Chang WC (2014) Developing Singapore driving cycle for passenger cars to estimate fuel consumption and vehicular emissions. Atmos Environ 97:353–362. https://doi.org/10.1016/j.atmosenv.2014.08.042
Huertas JI, Díaz J, Cordero D, Cedillo K (2018) A new methodology to determine typical driving cycles for the design of vehicles power trains. Int J Interact Des Manuf 12(1):319–326. https://doi.org/10.1007/s12008-017-0379-y
Hung WT, Tong HY, Cheung CS (2005) A modal approach to vehicular emissions and fuel consumption model development. Air Waste Manage 55:1431–1440. https://doi.org/10.1080/10473289.2005.10464747
Hung WT, Tong HY, Lee CP, Ha K, Pao LY (2007) Development of a practical driving cycle construction methodology: a case study in Hong Kong. Transp Res Part D Transp Environ 12(2):115–128. https://doi.org/10.1016/j.trd.2007.01.002
Johnstone IM, Silverman BW (1997) Wavelet threshold estimators for data with correlated noise. J R Stat Soc Seri B (Stat Methodol) 59(2):319–351. https://doi.org/10.1111/1467-9868.00071
Kamble SH, Mathew TV, Sharma GK (2009) Development of real-world driving cycle: case study of Pune, India. Transp Res Part D Transp Environ 14(2):132–140. https://doi.org/10.1016/j.trd.2008.11.008
Knez M, Muneer T, Jereb B, Cullinane K (2014) The estimation of a driving cycle for Celje and a comparison to other european cities. Sustainable Cities & Society 11(2–3):56–60. https://doi.org/10.1016/j.scs.2013.11.010
Kurnia JC, Sasmito AP, Shamim, T (2017) Performance evaluation of a pem fuel cell stack with variable inlet flows under simulated driving cycle conditions. Appl Energy 206:751–764. https://doi.org/10.1016/j.apenergy.2017.08.224
Lourido WN, Munoz LE, Pereda, JE (2015) A Methodology to Obtain a Synthetic Driving Cycle through GPS Data for Energy Analysis. Vehicle Power and Propulsion Conference (pp.1-5). IEEE. https://doi.org/10.1109/vppc.2015.7352876
Naranjo Lourido W, Munoz LE, Pereda JE (2015) A methodology to obtain a synthetic driving cycle through GPS data for energy analysis. Vehicle power and propulsion conference (pp. 1-5). IEEE. https://doi.org/10.1109/vppc.2015.7352876
Pandian S, Gokhale S, Ghoshal AK (2009) Evaluating effects of traffic and vehicle characteristics on vehicular emissions near traffic intersections. Transp Res Part D: Transp Environ 14(3):180–196. https://doi.org/10.1016/j.trd.2008.12.001
Peng J, He H, Xiong R (2016) Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming. Appl Energy 185:1633–1643. https://doi.org/10.1016/j.apenergy.2015.12.031
Peng Y, Zhuang Y, Yang H (2018) Development of a representative driving cycle for urban buses based on the k-means cluster method. Clust Comput 2:1–10. https://doi.org/10.1007/s10586-017-1673-y
Ruspini EH (1970) Numerical methods for fuzzy clustering. Inf Sci 2(3):319–350. https://doi.org/10.1016/S0020-0255(70)80056-1
Seers P, Nachin G, Glaus M (2015) Development of two driving cycles for utility vehicles. Transp Res Part D Transp Environ 41:377–385. https://doi.org/10.1016/j.trd.2015.10.013
Smith R, Shahidinejad S, Blair D, Bibeau EL (2011) Characterization of urban commuter driving profiles to optimize battery size in light-duty plug-in electric vehicles. Transp Res Part D Transp Environ 16(3):218–224. https://doi.org/10.1016/j.trd.2010.09.001
Tamsanya N, Chungpaibulpatana S (2009) Influence of driving cycles on exhaust emissions and fuel consumption of gasoline passenger Car in Bangkok. J Environ Sci 21:604–611. https://doi.org/10.1016/S1001-0742(08)62314-1
Tate E, Harpster MO, Savagian PJ (2009) The electrification of the automobile: from conventional hybrid, to plug-in hybrids, to extended-range electric vehicles. SAE Int J Passenger Cars Electron Electr Syst 1(1):156–166. https://doi.org/10.4271/2008-01-0458
Tong HY, Hung WT (2010) A framework for developing driving cycles with on road driving data. Transp Rev 30(5):589–615. https://doi.org/10.1080/01441640903286134
Tzirakis E, Pitsas K (2006) Vehicle emissions and driving cycles: comparison of the Athens driving cycle (adc) with ece-15 and european driving cycle (edc). Global Nest 8(3):282–290
Xie S, He H, Peng J (2017) An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses. Appl Energy 196. https://doi.org/10.1016/j.apenergy.2016.12.112
Zhang S, Xiong R (2015) Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming. Appl Energy 155:68–78. https://doi.org/10.1016/j.apenergy.2015.06.003
Zhao X, Yu Q, Yu M, Tang Z (2018a) Research on an equal power allocation electronic differential system for electric vehicle with dual-wheeled-motor front drive based on a wavelet controller. Adv Mech Eng 10(2):24. https://doi.org/10.1177/1687814018760039
Zhao X, Xu S, Ye Y, Yu M, Wang G (2018b) Composite braking AMT shift strategy for extended-range heavy commercial electric vehicle based on LHMM/ANFIS braking intention identification. Clust Comput 1–16. https://doi.org/10.1007/s10586-018-1888-6
Funding
This research is funded by the National Key R&D Program of China (2017YFC0803904), National Natural Science Foundation of China (51507013), China Postdoctoral Science Foundation (2018T111006, 2017M613034), Postdoctoral Science Foundation of Shaanxi Province (2017BSHEDZZ36), Shaanxi Province Industrial Innovation Chain Project (2018ZDCXL-GY-05-03-01), Shaanxi Provincial Key Research and Development Plan Project (2018ZDXM-GY-082), and Shaanxi Innovative Talents Promotion Plan Project (2018KJXX-005).
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Zhao, X., Ma, J., Wang, S. et al. Developing an electric vehicle urban driving cycle to study differences in energy consumption. Environ Sci Pollut Res 26, 13839–13853 (2019). https://doi.org/10.1007/s11356-018-3541-6
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DOI: https://doi.org/10.1007/s11356-018-3541-6