Optimal Driving and Charging Efficiency of a Developed Solar Powered Electric Vehicle - Ang Kiang Long, Esther Akinlabi, Adedotun Adetunla (International Journal of Intelligent Systems and Applications in Engineering, 2024)
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.