Utilizando o Modo de Dirigir do Motorista de Veículo Elétrico para o Planejamento e Roteirização de Viagem
Abstract
With the increasing adoption of electric vehicles worldwide, some limitations have emerged in their usage. The main limitations include low autonomy and a scarcity of charging points. In this work, we describe a software architecture for planning a stop at charging stations along a trip, by prediction of battery charge to be spent along the path. We describe the main components of this architecture and evaluate regression methods for the car consumption prediction module. We also use a real dataset built from an electric vehicle usage to validate the architecture concept and its viability analyzing multiple linear regression machine learning models. To further validate the architecture, we make a comparison between a simulated and a real trip.
References
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Alpaydin, E. (2020). Introduction to machine learning. MIT press.
ANATEL (2022). Anatel infraestrutura. [link], Acessado em 07/01/2022.
Arabie, P., Hubert, L., and De Soete, G. (1996). Clustering and classification. World Scientific.
Bailey, C., Jones, B., Clark, M., Buck, R., and Harper, M. (2022). Electric vehicle autonomy: Realtime dynamic route planning and range estimation software. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pages 2696-2701.
Bolovinou, A., Bakas, I., Amditis, A., Mastrandrea, F., and Vinciotti, W. (2014). Online prediction of an electric vehicle remaining range based on regression analysis. In 2014 IEEE International Electric Vehicle Conference (IEVC), pages 1-8. IEEE.
Chen, M.-S., Hwang, C.-P., Ho, T.-Y., Wang, H.-F., Shih, C.-M., Chen, H.-Y., and Liu, W. K. (2019a). Driving behaviors analysis based on feature selection and statistical approach: a preliminary study. The Journal of Supercomputing, 75(4):2007-2026.
Chen, S., Sun, W., Li, Y., and Shi, L. (2019b). On the relationship between energy consumption and driving behavior of electric vehicles based on statistical features. In 2019 Chinese Control Conference (CCC), pages 3782-3787.
Chen, W., Lin, Y., and Chen, W. (2019c). Comparisons of machine learning algorithms for driving behavior recognition using in-vehicle can bus data. In 2019 Joint 8th International Conference on Informatics, Electronics Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision Pattern Recognition (icIVPR), pages 268-273.
Delnevo, G., Di Lena, P., Mirri, S., Prandi, C., and Salomoni, P. (2019). On combining big data and machine learning to support eco-driving behaviours. Journal of Big Data, 6(1):64.
Eberly, L. E. (2007). Multiple linear regression. Topics in Biostatistics, pages 165-187.
Fugiglando, U., Massaro, E., Santi, P., Milardo, S., Abida, K., Stahlmann, R., Netter, F., and Ratti, C. (2019). Driving behavior analysis through can bus data in an uncontrolled environment. IEEE Transactions on Intelligent Transportation Systems, 20(2):737-748.
Gönül, Ö., Duman, A. C., and Güler, Ö. (2021). Electric vehicles and charging infrastructure in turkey: An overview. Renewable and Sustainable Energy Reviews, 143:110913.
Gorner, M. (2020). Electric vehicles: Analysis tracking report: June 2020. Disponível em: https://www.iea.org/reports/electric-vehicles. Acesso em: 19/07/2020.
Hucho, W. (1998). Commercial vehicles. Aerodynamics of Road Vehicles, fourth ed., SAE International, Warrendale, Pa., United States, pages 131-237.
Jia, S., Hui, F., Li, S., Zhao, X., and Khattak, A. J. (2020). Long short-term memory and convolutional neural network for abnormal driving behaviour recognition. IET Intelligent Transport Systems, 14(5):306-312.
Krishnamurthy, V., Nezafati, K., Bae, J., Gursoy, E., Zhong, M., and Singh, V. (2019). Classification of driving behavior events utilizing kinematic classification and machine learning for down sampled time series data. In 2019 IEEE International Conference on Big Data (Big Data), pages 3789-3796.
Lebrouhi, B., Khattari, Y., Lamrani, B., Maaroufi, M., Zeraouli, Y., and Kousksou, T. (2021). Key challenges for a large-scale development of battery electric vehicles: A comprehensive review. Journal of Energy Storage, 44:103273.
McKinsey (2023). O futuro da mobilidade no brasil: uma rota para eletrificação. [link], Acessado em 27/01/2023.
Noussan, M. (2020). Public EV charging in Italy: policies, business models and future scenarios. MDPI.
Ping, P., Qin, W., Xu, Y., Miyajima, C., and Takeda, K. (2019). Impact of driver behavior on fuel consumption: Classification, evaluation and prediction using machine learning. IEEE Access, 7:78515-78532.
Siami, M., Naderpour, M., and Lu, J. (2020). A mobile telematics pattern recognition framework for driving behavior extraction. IEEE Transactions on Intelligent Transportation Systems, pages 1-14.
Stanton, N. A. and Allison, C. K. (2020). Driving towards a greener future: an application of cognitive work analysis to promote fuel-efficient driving. Cognition, Technology & Work, 22(1):125-142.
Velandia Vargas, J. E., Seabra, J. E. A., Cavaliero, C. K. N., Walter, A. C. S., Souza, S. P., and Falco, D. G. (2020). The new neighbor across the street: An outlook for battery electric vehicles adoption in brazil. World Electric Vehicle Journal, 11(3).
Wang, G., Zhang, Y., Fang, Z., Wang, S., Zhang, F., and Zhang, D. (2020). Faircharge: A data-driven fairness-aware charging recommendation system for large-scale electric taxi fleets. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 4(1).
Published
2023-05-22
How to Cite
DOS-REIS, Marcelo; TEIXEIRA, Fabiano Costa; MARQUES-NETO, Humberto T..
Utilizando o Modo de Dirigir do Motorista de Veículo Elétrico para o Planejamento e Roteirização de Viagem. In: URBAN COMPUTING WORKSHOP (COURB), 7. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 15-28.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2023.740.
