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Research Article

Electric vehicle energy consumption prediction using stacked generalization: an ensemble learning approach

ORCID Icon, ORCID Icon, , &
Pages 896-909 | Received 03 Nov 2020, Accepted 20 Jan 2021, Published online: 26 Feb 2021

References

  • Alobaidi, M. H., F. Chebana, and M. A. Meguid. 2018. Robust ensemble learning framework for day-ahead forecasting of household based energy consumption. Applied Energy 212:997–1012. doi:10.1016/j.apenergy.2017.12.054.
  • Altman, N. S. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46 (3):175–85.
  • Amirhosseini, B., and S. H. Hosseini. 2018. Scheduling charging of hybrid-electric vehicles according to supply and demand based on particle swarm optimization, imperialist competitive and teaching-learning algorithms. Sustainable Cities and Society 43:339–49. doi:10.1016/j.scs.2018.09.002.
  • Bolovinou, A., I. Bakas, A. Amditis, F. Mastrandrea, and W. Vinciotti. 2014. Online prediction of an electric vehicle remaining range based on regression analysis. In 2014 IEEE International Electric Vehicle Conference (IEVC), 1–8. IEEE, Palazzo dei Congressi Florence, Italy - December 17–19, 2014.
  • Brady, J., and M. O’Mahony. 2016. Development of a driving cycle to evaluate the energy economy of electric vehicles in Urban Areas. Applied Energy 177:165–78. doi:10.1016/j.apenergy.2016.05.094.
  • Breiman, L. 2001. Random Forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
  • Chen, X. M., M. Zahiri, and S. Zhang. 2017. Understanding ridesplitting behavior of on-demand ride services: an ensemble learning approach. Transportation Research Part C: Emerging Technologies 76:51–70. doi:10.1016/j.trc.2016.12.018.
  • Chen, Y., G. Wu, R. Sun, A. Dubey, A. Laszka, and P. Pugliese. 2020. A review and outlook of energy consumption estimation models for electric vehicles. ArXiv Preprint. ArXiv:2003.12873. https://arxiv.org/abs/2003.12873
  • Chlopek, Z., J. Lasocki, P. Wójcik, and A. J. Badyda. 2018. Experimental investigation and comparison of energy consumption of electric and conventional vehicles due to the driving pattern. International Journal of Green Energy 15 (13):773–79. doi:10.1080/15435075.2018.1529571.
  • D’Adamo, I., and P. Rosa. 2019. A structured literature review on obsolete electric vehicles management practices. Sustainability 11 (23):6876. doi:10.3390/su11236876.
  • De Cauwer, C., J. Van Mierlo, and T. Coosemans. 2015. Energy consumption prediction for electric vehicles based on real-world data. Energies 8 (8):8573–93. doi:10.3390/en8088573.
  • De Cauwer, C., W. Verbeke, T. Coosemans, S. Faid, and J. Van Mierlo. 2017. A data-driven method for energy consumption prediction and energy-efficient routing of electric vehicles in real-world conditions. Energies 10 (5):608. doi:10.3390/en10050608.
  • Divina, F., A. Gilson, F. Goméz-Vela, M. García Torres, and J. F. Torres. 2018. Stacking ensemble learning for short-term electricity consumption forecasting. Energies 11 (4):949. doi:10.3390/en11040949.
  • Egbue, O., and S. Long. 2012. Barriers to widespread adoption of electric vehicles: an analysis of consumer attitudes and perceptions. Energy Policy 48:717–29. doi:10.1016/j.enpol.2012.06.009.
  • Frendo, O., J. Graf, N. Gaertner, and H. Stuckenschmidt. 2020. Data-Driven Smart Charging for Heterogeneous Electric Vehicle Fleets. Energy and AI 1:100007.
  • Fukushima, A., T. Yano, S. Imahara, H. Aisu, Y. Shimokawa, and Y. Shibata. 2018. Prediction of energy consumption for new electric vehicle models by machine learning. IET Intelligent Transport Systems 12 (9):1174–80. doi:10.1049/iet-its.2018.5169.
  • Genikomsakis, K. N., and G. Mitrentsis. 2017. A computationally efficient simulation model for estimating energy consumption of electric vehicles in the context of route planning applications. Transportation Research Part D: Transport and Environment 50:98–118. doi:10.1016/j.trd.2016.10.014.
  • Hastie, T., S. Rosset, J. Zhu, and H. Zou. 2009. Multi-Class Adaboost. Statistics and Its Interface 2 (3):349–60. doi:10.4310/SII.2009.v2.n3.a8.
  • Howey, D. A., R. F. Martinez-Botas, B. Cussons, and L. Lytton. 2011. Comparative measurements of the energy consumption of 51 electric, hybrid and internal combustion engine vehicles. Transportation Research Part D: Transport and Environment 16 (6):459–64. doi:10.1016/j.trd.2011.04.001.
  • Hughes, S., S. Moreno, W. F. Yushimito, and G. Huerta-Cánepa. 2019. Evaluation of Machine Learning Methodologies to Predict Stop Delivery Times from GPS Data. Transportation Research Part C: Emerging Technologies 109:289–304. doi:10.1016/j.trc.2019.10.018.
  • Jonsson, L., M. Borg, D. Broman, K. Sandahl, S. Eldh, and P. Runeson. 2016. Automated Bug Assignment: Ensemble-Based Machine Learning in Large Scale Industrial Contexts. Empirical Software Engineering 21 (4):1533–78. doi:10.1007/s10664-015-9401-9.
  • Jonsson, L., D. Broman, K. Sandahl, and S. Eldh. 2012. Towards Automated Anomaly Report Assignment in Large Complex Systems Using Stacked Generalization. In 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation, 437–46. IEEE, Montreal, April 17–21, 2012.
  • Kambly, K. R., and T. H. Bradley. 2014. Estimating the HVAC Energy Consumption of Plug-in Electric Vehicles. Journal of Power Sources 259:117–24. doi:10.1016/j.jpowsour.2014.02.033.
  • Khairalla, M. A., X. Ning, N. T. Al-Jallad, and M. O. El-Faroug. 2018. Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model. Energies 11 (6):1605. doi:10.3390/en11061605.
  • Kim, E., J. Lee, and K. G. Shin. 2013. Real-Time Prediction of Battery Power Requirements for Electric Vehicles. In 2013 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), 11–20. IEEE, Philadelphia, PA, USA.
  • Lee, C.-H., and C.-H. Wu. 2015. A novel big data modeling method for improving driving range estimation of EVs. IEEE Access 3:1980–93. doi:10.1109/ACCESS.2015.2492923.
  • Levin, M. W., M. Duell, and S. T. Waller. 2014. Effect of road grade on networkwide vehicle energy consumption and ecorouting. Transportation Research Record 2427 (1):26–33. doi:10.3141/2427-03.
  • Li, W., P. Stanula, P. Egede, S. Kara, and C. Herrmann. 2016. Determining the main factors influencing the energy consumption of electric vehicles in the usage phase. Procedia Cirp 48:352–57. doi:10.1016/j.procir.2016.03.014.
  • Liaw, B. Y., and M. Dubarry. 2007. From driving cycle analysis to understanding battery performance in real-life electric hybrid vehicle operation. Journal of Power Sources 174 (1):76–88. doi:10.1016/j.jpowsour.2007.06.010.
  • Lin, X., G. Zhang, S. Wei, and Y. Yin. 2020. Energy consumption estimation model for dual-motor electric vehicles based on multiple linear regression. International Journal of Green Energy 17 (8):488–500.
  • Liu, D., H. Wang, Y. Peng, W. Xie, and H. Liao. 2013. Satellite lithium-ion battery remaining cycle life prediction with novel indirect health indicator extraction. Energies 6 (8):3654–68. doi:10.3390/en6083654.
  • Liu, G., M. Ouyang, L. Lu, J. Li, and J. Hua. 2015. A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications. Applied Energy 149:297–314. doi:10.1016/j.apenergy.2015.03.110.
  • Liu, H., Q. Li, B. Yan, L. Zhang, and Y. Gu. 2019a. Bionic electronic nose based on mos sensors array and machine learning algorithms used for wine properties detection. Sensors 19 (1):45. doi:10.3390/s19010045.
  • Liu, H., Q. Li, D. Yu, and Y. Gu. 2019b. Air quality index and air pollutant concentration prediction based on machine learning algorithms. Applied Sciences 9 (19):4069. doi:10.3390/app9194069.
  • Liu, K., J. Wang, T. Yamamoto, and T. Morikawa. 2016. Modelling the Multilevel Structure and Mixed Effects of the Factors Influencing the Energy Consumption of Electric Vehicles. Applied Energy 183:1351–60. doi:10.1016/j.apenergy.2016.09.082.
  • Liu, K., J. Wang, T. Yamamoto, and T. Morikawa. 2018. Exploring the Interactive Effects of Ambient Temperature and Vehicle Auxiliary Loads on Electric Vehicle Energy Consumption. Applied Energy 227:324–31. doi:10.1016/j.apenergy.2017.08.074.
  • Liu, K., T. Yamamoto, and T. Morikawa. 2017. Impact of Road Gradient on Energy Consumption of Electric Vehicles. Transportation Research Part D: Transport and Environment 54:74–81. doi:10.1016/j.trd.2017.05.005.
  • Loh, W.-Y. 2011. Classification and Regression Trees. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery 1 (1):14–23.
  • Rezvani, Z., J. Jansson, and J. Bodin. 2015. Advances in consumer electric vehicle adoption research: a review and research agenda. Transportation Research Part D: Transport and Environment 34:122–36. doi:10.1016/j.trd.2014.10.010.
  • Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing 67:93–104. doi:10.1016/j.isprsjprs.2011.11.002.
  • Shankar, R., and J. Marco. 2013. Method for estimating the energy consumption of electric vehicles and plug-in hybrid electric vehicles under real-world driving conditions. IET Intelligent Transport Systems 7 (1):138–50. doi:10.1049/iet-its.2012.0114.
  • She, Z.-Y., Q. Sun, -J.-J. Ma, and B.-C. Xie. 2017. What are the barriers to widespread adoption of battery electric vehicles? a survey of public perception in Tianjin, China. Transport Policy 56:29–40. doi:10.1016/j.tranpol.2017.03.001.
  • Sun, S., J. Zhang, J. Bi, and Y. Wang. 2019. a machine learning method for predicting driving range of battery electric vehicles. Journal of Advanced Transportation 2019:1–14.
  • Tewari, S., and U. D. Dwivedi. 2020. A comparative study of heterogeneous ensemble methods for the identification of geological lithofacies. Journal of Petroleum Exploration and Production Technology 1–20. doi:10.1007/s13202-020-00963-9.
  • Travesset-Baro, O., M. Rosas-Casals, and E. Jover. 2015. Transport energy consumption in mountainous roads. a comparative case study for internal combustion engines and electric vehicles in Andorra. Transportation Research Part D: Transport and Environment 34:16–26. doi:10.1016/j.trd.2014.09.006.
  • Vaz, W., A. K. Nandi, R. G. Landers, and U. O. Koylu. 2015. Electric vehicle range prediction for constant speed trip using multi-objective optimization. Journal of Power Sources 275:435–46. doi:10.1016/j.jpowsour.2014.11.043.
  • Ves, A. V., N. Ghitescu, C. Pop, M. Antal, T. Cioara, I. Anghel, and I. Salomie. 2019. A stacking multi-learning ensemble model for predicting near real time energy consumption demand of residential buildings. In 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP), 183–89. IEEE. Cluj-Napoca, Romania september 5–7, 2019
  • Wager, G., J. Whale, and T. Braunl. 2016. Driving electric vehicles at highway speeds: the effect of higher driving speeds on energy consumption and driving range for electric vehicles in Australia. Renewable and Sustainable Energy Reviews 63:158–65. doi:10.1016/j.rser.2016.05.060.
  • Wang, J., K. Liu, and T. Yamamoto. 2017. Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations. Energies 10 (1):129. doi:10.3390/en10010129.
  • Wang, Y., B. Seo, B. Wang, N. Zamel, K. Jiao, and X. C. Adroher. 2020. Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology. Energy and AI :100014.
  • Wang, Z., Y. Wang, and R. S. Srinivasan. 2018. A Novel Ensemble Learning Approach to Support Building Energy Use Prediction. Energy and Buildings 159:109–22. doi:10.1016/j.enbuild.2017.10.085.
  • Wolpert, D. H. 1992. Stacked Generalization. Neural Networks 5 (2):241–59. doi:10.1016/S0893-6080(05)80023-1.
  • Wolpert, D. H., and W. G. Macready. 1997. No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1 (1):67–82. doi:10.1109/4235.585893.
  • Wu, X., M. Aviquzzaman, and Z. Lin. 2015. Analysis of plug-in hybrid electric vehicles’ utility factors using GPS-based longitudinal travel data. Transportation Research Part C: Emerging Technologies 57:1–12. doi:10.1016/j.trc.2015.05.008.
  • Wu, X. D., F. A. Cabrera, and W. A. Kitch. 2015. Electric vehicles’ energy consumption measurement and estimation. Transportation Research Part D: Transport and Environment 34:52–67. doi:10.1016/j.trd.2014.10.007.
  • Xing, Y., E. W. Ma, K. L. Tsui, and M. Pecht. 2011. Battery Management Systems in Electric and Hybrid Vehicles. Energies 4 (11):1840–57. doi:10.3390/en4111840.
  • Xu, G. W., L. K. Xu, and Z. Song. 2011. An Intelligent Regenerative Braking Strategy for Electric Vehicles. Energies 4 (9):1461–77. doi:10.3390/en4091461.
  • Xu, G. S., W. J. Li, and D. Zhao. 2020. Moving towards sustainable purchase behavior: examining the determinants of consumers’ intentions to adopt electric vehicles. Environmental Science Pollution Research 27:22535–22546..
  • Yuan, X., L. Li, H. Gou, and T. Dong. 2015. Energy and environmental impact of battery electric vehicle range in China. Applied Energy 157:75–84. doi:10.1016/j.apenergy.2015.08.001.
  • Zahid, M., Y. Chen, A. Jamal, and M. Q. Memon. 2020a. Short term traffic state prediction via hyperparameter optimization based classifiers. Sensors 20 (3):685. doi:10.3390/s20030685.
  • Zahid, M., Y. Chen, S. Khan, A. Jamal, M. Ijaz, and T. Ahmed. 2020b. Predicting risky and aggressive driving behavior among taxi drivers: do spatio-temporal attributes matter?. International Journal of Environmental Research and Public Health 17 (11):3937. doi:10.3390/ijerph17113937.
  • Zhang, D. 2017. A Coefficient of Determination for Generalized Linear Models. The American Statistician 71 (4):310–16. doi:10.1080/00031305.2016.1256839.
  • Zhang, R., and E. Yao. 2015a. Electric vehicles’ energy consumption estimation with real driving condition data. Transportation Research Part D: Transport and Environment 41:177–87. doi:10.1016/j.trd.2015.10.010.
  • Zhang, R., and E. Yao. 2015b. Eco-Driving at Signalised Intersections for Electric Vehicles. IET Intelligent Transport Systems 9 (5):488–97. doi:10.1049/iet-its.2014.0145.
  • Zhang, Y., W. Wang, Y. Kobayashi, and K. Shirai. 2012. Remaining Driving Range Estimation of Electric Vehicle. In 2012 IEEE International Electric Vehicle Conference, 1–7. IEEE, Greenville, SC.
  • Zhao, X., J. Ma, S. Wang, Y. Ye, Y. Wu, and M. Yu. 2019. Developing an electric vehicle urban driving cycle to study differences in energy consumption. Environmental Science and Pollution Research 26 (14):13839–53. doi:10.1007/s11356-018-3541-6.
  • Zhao, X., Y. Ye, J. Ma, P. Shi, and H. Chen. 2020. Construction of electric vehicle driving cycle for studying electric vehicle energy consumption and equivalent emissions. Environmental Science and Pollution Research 27 (30):37395–37409.

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