Utilizando o Modo de Dirigir do Motorista de Veículo Elétrico para o Planejamento e Roteirização de Viagem
Resumo
Com o aumento da adoção de veículos elétricos no mundo, começam a aparecer as limitações em sua utilização, tais como a baixa autonomia e a escassez de pontos de recarga. Este artigo apresenta uma arquitetura de software de uma aplicação capaz de prever a autonomia de carros elétricos e planejar a parada em postos de recarga ao longo de um trajeto de viagem. Além da descrição dos principais componentes da arquitetura, este trabalho também apresenta uma avaliação de métodos de regressão para o módulo de previsão de consumo do carro. Dados reais de um veículo elétrico foram coletados e utilizados para validar o conceito e a viabilidade da arquitetura, através da análise de modelos de aprendizado de máquina baseados em regressão linear múltipla. Ainda para validar a arquitetura, foi feita uma comparação entre uma viagem simulada e outra real.
Referências
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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.
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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).
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).
Publicado
22/05/2023
Como Citar
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: WORKSHOP DE COMPUTAÇÃO URBANA (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.
