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Research Article

Data-driven probabilistic energy consumption estimation for battery electric vehicles with model uncertainty

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Pages 1986-2003 | Received 28 Apr 2023, Accepted 21 Oct 2023, Published online: 10 Nov 2023

References

  • Abadi, M., A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, and S. Ghemawat, et al. 2015. TensorFlow: Large-scale machine learning on heterogeneous Systems. Software available from tensorflow.org. https://www.tensorflow.org/
  • Ahmad, F., A. Iqbal, I. Ashraf, M. Marzband, and I. Khan. 2022. Optimal location of electric vehicle charging station and its impact on distribution network: A review. Energy Reports 8:2314–33. doi:10.1016/j.egyr.2022.01.180.
  • Alateef, S., and N. Thomas. 2023. “Energy consumption estimation for electric vehicles using routing API data.” In Computer Performance Engineering: 18th European Workshop, EPEW 2022, Santa Pola, Spain, September 21–23, 2022, Proceedings, 37–53. Berlin, Heidelberg. Springer-Verlag. 10.1007/978-3-031-25049-1_3.
  • Alejandro, M., C. Guéret, J. E. Mendoza, and J. G. Villegas. 2017. The electric vehicle routing problem with nonlinear charging function. Transportation Research Part B: Methodological 103:87–110. Green Urban Transportation https://www.sciencedirect.com/science/article/pii/S0191261516304556.
  • Alvarez, D., F. S. G. Alberto, J. Eugenio Naranjo, J. Javier Anaya, and F. Jimenez. 2014. Modeling the driving behavior of electric vehicles using smartphones and neural networks. IEEE Intelligent Transportation Systems Magazine. 6(3):44–53.http://ieeexplore.ieee.org/document/6861542/.
  • Al-Wreikat, Y., C. Serrano, and J. Ricardo Sodré. 2021. Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving. Applied Energy 297:117096. https://linkinghub.elsevier.com/retrieve/pii/S0306261921005444.
  • Basso, R., B. Kulcsár, and I. Sanchez-Diaz. 2021. Electric vehicle routing problem with machine learning for energy prediction. Transportation Research Part B: Methodological 145:24–55. https://linkinghub.elsevier.com/retrieve/pii/S0191261520304549.
  • Basso, R., B. Kulcsár, I. Sanchez-Diaz, and Q. Xiaobo. 2022. Dynamic stochastic electric vehicle routing with safe reinforcement learning. Transportation Research Part E: Logistics & Transportation Review 157:102496. doi:10.1016/j.tre.2021.102496.
  • Blundell, C., J. Cornebise, K. Kavukcuoglu, and D. Wierstra. 2015. “Weight uncertainty in neural network.” In International conference on machine learning, Lille, France, 1613–22. PMLR.
  • Breiman, L. 2001. Random Forests. Machine Learning. 45(1):5–32.http://link.springer.com/10.1023/A:1010933404324.
  • Cauwer, D., J. V. M. Cedric, and T. Coosemans. 2015. Energy consumption prediction for electric vehicles based on real-world data. Energies. 8(8):8573–93.http://www.mdpi.com/1996-1073/8/8/8573.
  • Cauwer, D., W. V. Cedric, 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.
  • Chen, R., X. Liu, L. Miao, and P. Yang. 2020. Electric vehicle Tour planning considering range anxiety. Sustainability 12 (9):3685. doi:10.3390/su12093685.
  • Dillon, J. V., I. Langmore, D. Tran, E. Brevdo, S. Vasudevan, D. Moore, B. Patton, A. Alemi, M. Hoffman, and R. A. Saurous. 2017. ”Tensorflow distributions.” arXiv preprint arXiv:1711.10604.
  • Fazeli, S. S., S. Venkatachalam, R. Babu Chinnam, and A. Murat. 2020. Two-stage stochastic choice modeling approach for electric vehicle charging station network design in urban communities. IEEE Transactions on Intelligent Transportation Systems 22 (5):3038–53. doi:10.1109/TITS.2020.2979363.
  • 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.
  • Graves, A. 2011. Practical variational inference for neural networks. Advances in Neural Information Processing Systems 24.
  • Hepeng, L., Z. Wan, and H. Haibo. 2019. Constrained EV charging scheduling based on safe deep reinforcement learning. IEEE Transactions on Smart Grid 11 (3):2427–39. doi:10.1109/TSG.2019.2955437.
  • Hinton, G. E., and D. Van Camp. 1993. “Keeping the neural networks simple by minimizing the description length of the weights.” In Proceedings of the sixth annual conference on Computational learning theory, 5–13.
  • Huang, Y.-W., C. Prehofer, W. Lindskog, R. Puts, P. Mosca, and G. Kauermann. 2022. “Predictive energy management for battery electric vehicles with hybrid models.” In International Conference on Intelligent Transport Systems, Lisbon, Portugal, 182–196. Springer.
  • Jiang, J., Y. Yuanbin, H. Min, Q. Cao, W. Sun, Z. Zhang, and C. Luo. 2023. Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression. Energy 263:125866. doi:10.1016/j.energy.2022.125866.
  • Jun, B., Y. Wang, S. Shao, and Y. Cheng. 2018. Residual range estimation for battery electric vehicle based on radial basis function neural network. Measurement 128:197–203. doi:10.1016/j.measurement.2018.06.054.
  • Kiureghian, A. D., and O. Ditlevsen. 2009. Aleatory or epistemic? Does it matter? Structural Safety. 31(2):105–12. Risk Acceptance and Risk Communication. https://www.sciencedirect.com/science/article/pii/S0167473008000556.
  • Korkas, C. D., S. Baldi, S. Yuan, and E. B. Kosmatopoulos. 2017. An adaptive learning-based approach for nearly optimal dynamic charging of electric vehicle fleets. IEEE Transactions on Intelligent Transportation Systems 19 (7):2066–75. doi:10.1109/TITS.2017.2737477.
  • Korkas, C. D., M. Terzopoulos, C. Tsaknakis, and E. B. Kosmatopoulos. 2022. Nearly optimal demand side management for energy, thermal, EV and storage loads: An approximate dynamic programming approach for smarter buildings. Energy and Buildings 255:111676. doi:10.1016/j.enbuild.2021.111676.
  • Krogh, B., O. Andersen, and K. Torp. 2015. “Analyzing electric vehicle energy consumption using very large data sets.” In Database Systems for Advanced Applications: 20th International Conference, DASFAA 2015, 471–87. Hanoi, Vietnam, April 20-23, 2015, Proceedings, Part II 20. Springer.
  • Lab, C. R. E. A. T. E. n.d. ChargeCar - recharging your daily commute. http://www.chargecar.org/data.
  • Liu, J., G. Lin, C. Rehtanz, S. Huang, Y. Zhou, and L. Yong. 2022. Data-driven intelligent EV charging operating with limited chargers considering the charging demand forecasting. International Journal of Electrical Power & Energy Systems 141:108218. doi:10.1016/j.ijepes.2022.108218.
  • 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.
  • Miri, I., A. Fotouhi, and N. Ewin. 2021. Electric vehicle energy consumption modelling and estimation—A case study. International Journal of Energy Research 45 (1):501–20. doi:10.1002/er.5700.
  • Pengshun, L., Y. Zhang, K. Zhang, Y. Zhang, and K. Zhang. 2021. Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data. Applied Energy 298:117204. doi:10.1016/j.apenergy.2021.117204.
  • Petkevicius, L., S. Saltenis, A. Civilis, and K. Torp. 2021. “Probabilistic deep learning for electric-vehicle energy-use prediction.” In 17th International Symposium on Spatial and Temporal Databases, 85–95. New York, NY, USA: Association for Computing Machinery. Accessed 2021-11-24. 10.1145/3469830.3470915.
  • Prins, R., R. Hurlbrink, and L. Winslow. 2013. Electric vehicle energy usage modelling and measurement. International Journal of Modern Engineering 13 (1):5–12.
  • Sarrafan, K., D. Sutanto, K. Muttaqi, and G. Town. 2017. Accurate range estimation for an electric vehicle including changing environmental conditions and traction system efficiency. Faculty of Engineering and Information Sciences - Papers: Part B 7 (2):117–24. https://ro.uow.edu.au/eispapers1/210.
  • Steinstraeter, M., J. Buberger, and D. Trifonov. 2020. Battery and heating data in real driving cycles. doi:10.21227/6jr9-5235.
  • 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. https://www.hindawi.com/journals/jat/2019/4109148/.
  • Tannahill, V. R., K. M. Muttaqi, and D. Sutanto. 2016. Driver alerting system using range estimation of electric vehicles in real time under dynamically varying environmental conditions. IET Electrical Systems in Transportation 6 (2):107–16. doi:10.1049/iet-est.2014.0067.
  • Ullah, I., K. Liu, T. Yamamoto, M. Zahid, and A. Jamal. 2021. Electric vehicle energy consumption prediction using stacked generalization: An ensemble learning approach. International Journal of Green Energy 18 (9):896–909. doi:10.1080/15435075.2021.1881902.
  • Vatanparvar, K., S. Faezi, I. Burago, M. Levorato, and M. Abdullah Al Faruque. 2019. Extended range electric vehicle with driving behavior estimation in energy management. IEEE Transactions on Smart Grid. 10(3):2959–68.https://ieeexplore.ieee.org/document/8315470/.
  • Wan, Z., L. Hepeng, H. Haibo, and D. Prokhorov. 2018. Model-free real-time EV charging scheduling based on deep reinforcement learning. IEEE Transactions on Smart Grid 10 (5):5246–57. doi:10.1109/TSG.2018.2879572.
  • Xinkai, W., D. Freese, A. Cabrera, and W. A. Kitch. 2015. Electric vehicles’ energy consumption measurement and estimation. Transportation Research, Part D: Transport & Environment 34:52–67. doi:10.1016/j.trd.2014.10.007.
  • Xuewei, Q., W. Guoyuan, K. Boriboonsomsin, and M. J. Barth. 2018. Data-driven decomposition analysis and estimation of link-level electric vehicle energy consumption under real-world traffic conditions. Transportation Research, Part D: Transport & Environment 64:36–52. https://linkinghub.elsevier.com/retrieve/pii/S1361920916307714.
  • Yang, Y., Y. Zhang, and X. Meng. 2020. A data-driven approach for optimizing the EV charging stations network. Institute of Electrical and Electronics Engineers Access 8:118572–92. doi:10.1109/ACCESS.2020.3004715.
  • Younes, Z., L. Boudet, F. Suard, M. Gérard, and R. Rioux. 2013. “Analysis of the main factors influencing the energy consumption of electric vehicles.” In 2013 International Electric Machines Drives Conference, Chicago, IL, USA, 247–53.
  • Zhang, J., Z. Wang, P. Liu, and Z. Zhang. 2020. Energy consumption analysis and prediction of electric vehicles based on real-world driving data. Applied Energy 275:115408. doi:10.1016/j.apenergy.2020.115408.
  • Zheng, B., H. Peter, L. Zhao, and L. Hongwei 2016. “A Hybrid Machine Learning Model for Range Estimation of Electric Vehicles.” In 2016 IEEE Global Communications Conference (GLOBECOM), 1–6. Washington, DC, USA, Dec. IEEE. http://ieeexplore.ieee.org/document/7841506/.
  • Zhou, D., Z. Guo, Y. Xie, H. Yuheng, D. Jiang, Y. Feng, and D. Liu. 2022. Using Bayesian deep learning for electric vehicle charging station load forecasting. Energies 15 (17):6195. doi:10.3390/en15176195.
  • Zixuan, Z., S. Yang, C. Sauer, A. Teraji, Y. Yamauchi, T. Hirata, A. J. Bisci, and Y. Lu Murphey. 2022. “Structured deep learning models for accurate prediction of real-world driving speed for short and long-term horizons.” In 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 1–8. IEEE.

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