Optimal Driving and Charging Efficiency of a Developed Solar Powered Electric Vehicle

Authors

  • Adedotun Adetunla, Ang Kiang Long, Esther Akinlabi

Keywords:

Charging efficiency, Driver heterogeneity, Electric Vehicle, Linear regression, Naïve bayes

Abstract

Renewable energy is essential in these modern times for performing certain tasks and operations because the exhaustible energy sources on which we rely, and use will be depleted in the not-too-distant future. The solar powered vehicle is an important initiative in conserving these natural fossil fuel-based energy sources. This study is based on the performance evaluation of a fabricated low-cost Solar Powered Electric Vehicle (SPEV) for developing nations to overcome the effect of air pollution caused by Internal Combustion Engines (ICE). Additionally, the high cost of fuel (both petrol and diesel) consumed by the ICE will be replaced by the solar powered vehicle which in turn will have a long-term economic effect on the users and the nation in general. The solar car's primary operating idea is to utilise the potential power in a deep cycle battery both during and after charging from the solar panel. Energy supplied from the energized battery is transferred to the engine, which in this case is the electric motor. The electric motor is responsible for forward and reverse movement directions, it is an effective replacement to the internal combustion engine present in most vehicles because it has zero emissions, and no fossil fuel is burnt during its operation. The major challenge in fabricating this vehicle is in designing a suitable chassis that will ensure adequate stability and sustainability of the vehicle. The construction of this vehicle is presented in this study and the incorporation of the solar system is simplified. To carry out performance evaluation of the solar powered vehicle, a linear regression algorithm is employed to determine the charging efficiency of this vehicle under different charging scenarios, while the naïve bayes algorithm is employed to determine the driving distance and heterogeneity. The result shows the developed models can successfully predict the maximum and minimum distance the vehicle can attain at different attributes such as the total weight on the vehicle, the terrain, the speed, and the sun-availability. Finally, 66.6% of the variability in the battery state of charge can be predicted by the linear relationship with solar insolation. This means that the developed model accounts for about two-thirds of the observed variation.

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References

X. Wang, H. Chen, and C. Lin, “Comparison of Different Battery Types for Electric Vehicles Comparison of Different Battery Types for Electric Vehicles,” 2017.

K. Zhong, P. Wang, J. Pei, J. Xu, and Z. Han, “Multiobjective Optimization regarding Vehicles and Power Grids,” vol. 2021, 2021.

M. Shahid, S. Zhuang, H. Mudassir, and M. Haris, “An in-depth analysis of electric vehicle charging station infrastructure , policy implications , and future trends,” Energy Reports, vol. 8, pp. 11504–11529, 2022.

A. Raj et al., “A taxonomy for autonomy in industrial autonomous mobile robots including autonomous merchant ships A taxonomy for autonomy in industrial autonomous mobile robots including autonomous merchant ships,” 2011.

J. Du, M. Pei, B. Jia, and P. Wu, “Optimal Deployment of Dynamic Wireless Charging Lanes for Electric Vehicles considering the Battery Charging Rate,” vol. 2022, 2022.

Z. Lu, Q. Zhang, Y. Yuan, and W. Tong, “Optimal Driving Range for Battery Electric Vehicles Based on Modeling Users ’ Driving and Charging Behavior,” vol. 2020, no. 1, 2020.

J. Jayakumar, B. Nagaraj, S. Chacko, and P. Ajay, “Conceptual Implementation of Artificial Intelligent based E-Mobility Controller in smart city Environment,” vol. 2021, 2021.

V. Iulia and S. Loránd, “A Brief History of Electric Vehicles,” no. September, 2022.

Q. Liu, W. Wang, and X. Hua, “Path Planning Method for Electric Vehicles Based on Freeway Network,” vol. 2021, 2021.

P. Daponte et al., “A review on the use of drones for precision agriculture,” IOP Conf. Ser. Earth Environ. Sci., vol. 275, no. 1, 2019.

T. Pachal, “Electric Solar Vehicle - RayRacer Dr . Isham Panigrahi,” no. April, 2016.

S. Ling, X. Zhu, Q. Wang, and P. Schonfeld, “Prediction of Charging Requirements for Electric Vehicles Based on Multiagent Intelligence,” vol. 2022, 2022.

C. R. Forsythe, K. T. Gillingham, J. J. Michalek, and K. S. Whitefoot, “Technology advancement is driving electric vehicle adoption,” vol. 120, no. 23, pp. 1–7, 2023.

C. K. H. Lee, “Engineering Applications of Artificial Intelligence A review of applications of genetic algorithms in operations management,” Eng. Appl. Artif. Intell., vol. 76, no. May, pp. 1–12, 2018.

N. V Martyushev, B. V Malozyomov, S. N. Sorokova, and E. A. Efremenkov, “Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles,” 2023.

Y. Jin, B. Yu, M. Seo, and S. Han, “Optimal Aggregation Design for Massive V2G Participation in Energy Market,” vol. 8, 2020.

E. Hopkins, D. Potoglou, S. Orford, and L. Cipcigan, “Can the equitable roll out of electric vehicle charging infrastructure be achieved ? Can the equitable roll out of electric vehicle charging infrastructure be achieved ?,” Renew. Sustain. Energy Rev., vol. 182, no. May, p. 113398, 2023.

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Published

26.03.2024

How to Cite

Ang Kiang Long, Esther Akinlabi, . A. A. . (2024). Optimal Driving and Charging Efficiency of a Developed Solar Powered Electric Vehicle . International Journal of Intelligent Systems and Applications in Engineering, 12(3), 1678–1688. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5578

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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