Photovoltaic-powered zeta converter with optimization algorithm for improved electric vehicle battery charging efficiency

In general, the Electric vehicle (EV) charging systems are encountered with fluctuating energy inputs, inefficient power conversion processes, unstable operational adaptability, and reduced overall efficiency. These factors limit the reliability of performance under various dynamic conditions. This research work proposes a photovoltaic (PV)-powered Zeta Converter-based charging system for enhancing the stability and operational optimization of the charging process. The designed system dynamically adjusts the charging parameters under changing solar and load situations, thereby improving operational stability within the simulation framework and maintaining a proper balance of performance. Additionally, this study developed two control algorithms namely Dynamic Charging Optimization Algorithm (DCOA) and Multivariable Function-Based Optimized Charging Algorithm (MFOA) for optimizing the adaptability of the converter function more effectively. The effectiveness of the converter, both with and without the application of these specifically designed algorithms, is analyzed under several different test metrics, including changing irradiation difference, input and output voltage level, conversion efficiency, and ripple quantity measurement. Simulation results indicate that the converter-stage efficiency reaches a peak value of 96.2% under standard irradiation (1000 W/m 2 ), while the overall PV-to-battery system efficiency attains 79.4% when cumulative conversion and switching losses are considered. Further, optimal charging performance is ensured by the required output power generation. The reduction of voltage ripples to 0.2 V as against 0.8 V in conventional charging systems indicates improved power quality (PQ) within the simulation. Furthermore, the optimal charging performance of the developed converter system is achieved under the simulated operating conditions, which indicates improved performance and operational reliability.