Energy Efficient Driving Optimization of Electrical Vehicles Considering the Road Characteristics

Yıl 2021, Cilt: 9 Sayı: 2, 161 - 170, 30.04.2021

Hasan Eroğlu , Yasin Oğuz

https://doi.org/10.17694/bajece.902485

Cited By: 1

Öz

Electric vehicles, which are an important part of sustainable energy technologies, occupy an important place in our daily life. More efficient use of electric vehicles will ensure more efficient use of sustainable energy sources. It is not possible for the human brain to determine the most efficient driving characteristics by the drivers. In this study, energy efficient driving optimization of electric vehicles was realized. Along the route, optimum speeds were determined in terms of energy, by using the road and engine characteristics. Geographical information systems and genetic algorithm have been used effectively in the solution of the problem. The effectiveness of the proposed algorithm was revealed with many test studies. With this study, an algorithm that provides an energy-efficient driving for electrical vehicles was developed. The results will contribute to the development of electric vehicle technologies.

Anahtar Kelimeler

Electric Vehicles, Energy optimization, Autonomous vehicle

Kaynakça

• [1] H. Kumaş, C. Gencer, and H. Maraş, “Aǧir araçlar i̇çi̇n yol eǧi̇mi̇ ve vi̇raj yariçapi di̇kkate alinarak en hizli güzergâhin beli̇rlenmesi̇,” J. Fac. Eng. Archit. Gazi Univ., vol. 27, no. 2, pp. 385–395, 2012.
• [2] A. Artmeier and J. Haselmayr, “The optimal routing problem in the context of battery-powered electric vehicles,” in Workshop: CROCS at …, 2010, no. APRIL 2010, pp. 1–13, [Online]. Available: http://www.cis.cornell.edu/ics/compsust-org/crocs-at-cpaior10/papers/crocs-at-cpaior10-Artmeier.pdf.
• [3] M. Sachenbacher, M. Leucker, A. Artmeier, and J. Haselmayr, “Efficient Energy-Optimal Routing for Electric Vehicles,” in Proc. Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011, no. January 2011, pp. 1402–1407.
• [4] A. M. Bozorgi, M. Farasat, and A. Mahmoud, “A Time and Energy Efficient Routing Algorithm for Electric Vehicles Based on Historical Driving Data,” IEEE Trans. Intell. Veh., vol. 2, no. 4, pp. 308–320, 2017, doi: 10.1109/tiv.2017.2771233.
• [5] Y. Wang, J. Jiang, and T. Mu, “Context-aware and energy-driven route optimization for fully electric vehicles via crowdsourcing,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 3, pp. 1331–1345, 2013, doi: 10.1109/TITS.2013.2261064.
• [6] M. F. and O. Basir, “Optimal energy/time routing in battery-powered vehicles,” 2016.
• [7] O. Rawashdeh and R. Abousleiman, “Electric vehicle modelling and energy-efficient routing using particle swarm optimisation,” IET Intell. Transp. Syst., vol. 10, no. 2, pp. 65–72, Mar. 2016, doi: 10.1049/iet-its.2014.0177.
• [8] C. Li, T. Ding, X. Liu, and C. Huang, “An Electric Vehicle Routing Optimization Model With Hybrid Plug-In and Wireless Charging Systems,” IEEE Access, vol. 6, pp. 27569–27578, 2018, doi: 10.1109/ACCESS.2018.2832187.
• [9] M. Vajedi and N. L. Azad, “Ecological adaptive cruise controller for plug-in hybrid electric vehicles using nonlinear model predictive control,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 1, pp. 113–122, 2016, doi: 10.1109/TITS.2015.2462843.
• [10] Z. Lin, “Optimizing and Diversifying Electric Vehicle Driving Range for U.S. Drivers,” Transp. Sci., vol. 48, no. 4, pp. 635–650, 2014, doi: 10.1287/trsc.2013.0516.
• [11] R. Cook, A. Molina-Cristobal, G. Parks, C. Osornio Correa, and P. J. Clarkson, “Multi-objective Optimisation of a Hybrid Electric Vehicle: Drive Train and Driving Strategy,” in Evolutionary Multi-Criterion Optimization, S. Obayashi, K. Deb, C. Poloni, T. Hiroyasu, and T. Murata, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 330–345.
• [12] L. Kang, H. Shen, and A. Sarker, “Velocity Optimization of Pure Electric Vehicles with Traffic Dynamics Consideration,” in 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Jun. 2017, pp. 2206–2211, doi: 10.1109/ICDCS.2017.220.
• [13] J. Huang, Y. Liu, M. Liu, M. Cao, and Q. Yan, “Multi-Objective Optimization Control of Distributed Electric Drive Vehicles Based on Optimal Torque Distribution,” IEEE Access, vol. 7, pp. 16377–16394, 2019, doi: 10.1109/ACCESS.2019.2894259.
• [14] X. Wu, D. Freese, A. Cabrera, and W. A. Kitch, “Electric vehicles’ energy consumption measurement and estimation,” Transp. Res. Part D Transp. Environ., vol. 34, pp. 52–67, 2015, doi: 10.1016/j.trd.2014.10.007.
• [15] H. Mehrjerdi and R. Hemmati, “Stochastic model for electric vehicle charging station integrated with wind energy,” Sustain. Energy Technol. Assessments, vol. 37, no. October 2019, p. 100577, Feb. 2020, doi: 10.1016/j.seta.2019.100577.
• [16] F. Orecchini, A. Santiangeli, and F. Zuccari, “Hybrid-electric system truth test: Energy analysis of Toyota Prius IV in real urban drive conditions,” Sustain. Energy Technol. Assessments, vol. 37, no. December 2018, p. 100573, Feb. 2020, doi: 10.1016/j.seta.2019.100573.
• [17] C. Bian, G. Yin, L. Xu, and N. Zhang, “Active collision algorithm for autonomous electric vehicles at intersections,” IET Intell. Transp. Syst., vol. 13, no. 1, pp. 90–97, 2019, doi: 10.1049/iet-its.2018.5178.
• [18] X. Dong, Q. Lin, M. Xu, and Y. Cai, “Artificial bee colony algorithm with generating neighbourhood solution for large scale coloured traveling salesman problem,” IET Intell. Transp. Syst., vol. 13, no. 10, pp. 1483–1491, Oct. 2019, doi: 10.1049/iet-its.2018.5359.
• [19] A. Fukushima, T. Yano, S. Imahara, H. Aisu, Y. Shimokawa, and Y. Shibata, “Prediction of energy consumption for new electric vehicle models by machine learning,” IET Intell. Transp. Syst., vol. 12, no. 9, pp. 1174–1180, Nov. 2018, doi: 10.1049/iet-its.2018.5169.
• [20] W. B. A. Karaa, A. S. Ashour, D. Ben Sassi, P. Roy, N. Kausar, and N. Dey, “Medline text mining: An enhancement genetic algorithm based approach for document clustering,” Intell. Syst. Ref. Libr., vol. 96, pp. 267–287, 2016, doi: 10.1007/978-3-319-21212-8_12.
• [21] A. Hiassat, A. Diabat, and I. Rahwan, “A genetic algorithm approach for location-inventory-routing problem with perishable products,” J. Manuf. Syst., vol. 42, pp. 93–103, Jan. 2017, doi: 10.1016/j.jmsy.2016.10.004.