![](bg8.png)
REFERENCES
Albuquerque, D. (2022). Electric vehicle x driving
range prediction github repository. https://github.com/
davidalb97/TFM18-2122i. Accessed: 2022-12-17.
Alpaydin, E. (2020). Introduction to machine learning. The
MIT Press, fourth edition.
Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Ka-
mar, E., Nagappan, N., Nushi, B., and Zimmermann,
T. (2019). Software engineering for machine learning:
A case study. In 2019 IEEE/ACM 41st International
Conference on Software Engineering: Software Engi-
neering in Practice (ICSE-SEIP), pages 291–300.
Bose, I. and Mahapatra, R. (2001). Business data mining -
a machine learning perspective. Information & Man-
agement, 39(3):211–225.
Breiman, L. (2001). Random forests. Machine Learning,
45(1):5–32.
Brighente, A., Conti, M., Donadel, D., and Turrin, F.
(2021). Evscout2.0: Electric vehicle profiling through
charging profile. CoRR, abs/2106.16016.
Coutinho, D. (2021a). Classic eMini project: Electrification
of a classic mini, technical report. Draft version.
Coutinho, D. (2021b). Classic EV X project driving range
prediction. Technical report. Draft version.
De Cauwer, C., Verbeke, W., Coosemans, T., Faid, S., and
Van Mierlo, J. (2017). A data-driven method for
energy consumption prediction and energy-efficient
routing of electric vehicles in real-world conditions.
Energies, 10(5).
Egbue, O. and Long, S. (2012). Barriers to widespread
adoption of electric vehicles: An analysis of consumer
attitudes and perceptions. Energy Policy, 48:717–729.
Special Section: Frontiers of Sustainability.
Gaete-Morales, C., Kramer, H., Schill, W.-P., and Zerrahn,
A. (2021). An open tool for creating battery-electric
vehicle time series from empirical data, emobpy. Sci-
entific Data, 8(1):152.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The
elements of statistical learning. Springer, 2nd edition.
Kohonen, T. (2001). Self-organizing maps. Springer se-
ries in information sciences, 30. Springer, Berlin, 3rd
edition.
Lee, C.-H. and Wu, C.-H. (2015). A novel big data model-
ing method for improving driving range estimation of
EVs. IEEE Access, 3:1980–1993.
Liu, K., Wang, J., Yamamoto, T., and Morikawa, T. (2018).
Exploring the interactive effects of ambient temper-
ature and vehicle auxiliary loads on electric vehicle
energy consumption. Applied Energy, 227:324–331.
Transformative Innovations for a Sustainable Future
Part III.
Liu, K., Yamamoto, T., and Morikawa, T. (2017). Impact of
road gradient on energy consumption of electric vehi-
cles. Transportation Research Part D: Transport and
Environment, 54:74–81.
Oh, G., Leblanc, D., and Peng, H. (2019). Vehicle energy
dataset (VED), a large-scale dataset for vehicle energy
consumption research.
Smuts, M., Scholtz, B., and Wesson, J. (2017). A criti-
cal review of factors influencing the remaining driving
range of electric vehicles. In 1st International Con-
ference on Next Generation Computing Applications
(NextComp), pages 196–201.
Song, Y. and Hu, X. (2021). Learning electric vehicle driver
range anxiety with an initial state of charge-oriented
gradient boosting approach. Journal of Intelligent
Transportation Systems, 0(0):1–19.
Sun, X.-H., Yamamoto, T., and Morikawa, T. (2015).
Stochastic frontier analysis of excess access to mid-
trip battery electric vehicle fast charging. Transporta-
tion Research Part D: Transport and Environment,
34:83–94.
Sun, X.-H., Yamamoto, T., and Morikawa, T. (2016). Fast-
charging station choice behavior among battery elec-
tric vehicle users. Transportation Research Part D:
Transport and Environment, 46:26–39.
Ullah, I., Liu, K., Yamamoto, T., Zahid, M., and Jamal, A.
(2021). Electric vehicle energy consumption predic-
tion using stacked generalization: an ensemble learn-
ing approach. International Journal of Green Energy,
18(9):896–909.
Varga, B., Sagoian, A., and Mariasiu, F. (2019). Prediction
of electric vehicle range: A comprehensive review of
current issues and challenges. Energies, 12(5).
Wolpert, D. (1992). Stacked generalization. Neural Net-
works, 5(2):241–259.
Wright, S. (1921). Correlation and causation. J. Agricul-
tural Research, 20:557–585.
Yilmaz, M. and Krein, P. (2013). Review of battery charger
topologies, charging power levels, and infrastructure
for plug-in electric and hybrid vehicles. IEEE Trans-
actions on Power Electronics, 28(5):2151–2169.
Yoong, M., Gan, Y., Gan, G., Leong, C., Phuan, Z., Cheah,
B., and Chew, K. (2010). Studies of regenerative brak-
ing in electric vehicle. In 2010 IEEE Conference on
Sustainable Utilization and Development in Engineer-
ing and Technology, pages 40–45.
Zhang, Y., Wang, W., Kobayashi, Y., and Shirai, K. (2012).
Remaining driving range estimation of electric vehi-
cle. In IEEE International Electric Vehicle Confer-
ence, pages 1–7.
Zhao, L., Yao, W., Wang, Y., and Hu, J. (2020). Machine
learning-based method for remaining range prediction
of electric vehicles. IEEE Access, 8:212423–212441.
Zheng, B., He, P., Zhao, L., and Li, H. (2016). A hybrid ma-
chine learning model for range estimation of electric
vehicles. In IEEE Global Communications Confer-
ence (GLOBECOM), pages 1–6.
Zhou, L., Pan, S., Wang, J., and Vasilakos, A. (2017). Ma-
chine learning on big data: Opportunities and chal-
lenges. Neurocomputing, 237:350–361.
Estimating Electric Vehicle Driving Range with Machine Learning
343