Fuzzy based BSA optimization for maximum power point tracking controller performance evaluation

The world transportation sector is transitioning from traditional fuel vehicles to electric vehicles (EV) in order to minimize carbon emissions from vehicles. Furthermore, for a cleaner environment, it is better to charge the EV using clean power such as solar photovoltaic (PV) systems. However, PV generating an insignificant sum of power which is not sufficient to apply in a specific application. To solve this issue, it is essential to improve the energy conversion efficiency by the Maximum Power Point Tracking (MPPT). In the literature, various algorithms have been used to enhance the MPPT efficiency which still have different limitations. Therefore, this study is developed to improve the MPPT for solar car system charge using Fuzzy Logic Controller (FLC) as the input of the voltage and current of PV systems is variable. Moreover, to overcome the FLC limitations and enhance the performance of the controller, the FLC-based Backtracking Search Algorithm (BSA) optimization method is developed for harvesting maximum power from the solar panel. The power and voltage output of MPPT with and without the optimization is compared to show the optimized controller efficiency. In comparison to the controller with the optimization method, the efficiency is increased significantly (10.36% at 10s for power) than the non-optimized method. In sum, the results have shown the ability of FLC based-BSA optimization method to enhance the efficiency of MPPT for solar-powered cars linking to the swift alteration of insolation due to the dynamic motion of the vehicle.