To enhance a photovoltaic system's performance, it is important to understand the electrical characteristics of its main components: photovoltaic modules and batteries. Models can simulate these components, but often require knowing specific parameters. Batteries are most complex as they alone deviate from ideality. The performance of batteries is primarily influenced by various factors such as electrolyte concentration, temperature, internal resistance, charging/discharging rate, and state of charge. Therefore, it is crucial to accurately understand battery behavior throughout operation. While electrolyte concentration is the most effective indicator of state of charge, it is difficult to measure directly in photovoltaic systems. Thus, voltage can be considered a good indicator of state of charge and battery behavior. Understanding battery behavior is vital for accurate modeling.

Models' electric characteristics were assessed and derived as functions of state of charge using curve fitting to represent real performance without simplicity loss. This paper compares the improved Thévenin model to the Partnership for a New Generation of Vehicle (PNGV) model for 200 Ah lead-acid batteries. It also aims to link battery capacity/state of charge accurately to voltage by validating PNGV's 3% root-mean-square error (RMSE). While modifying Thévenin expressed dynamics, it did not fit the data below 50% state of charge appropriately. PNGV experimentally validated for real-time use discharging to 20% state of charge, accurately expressing behavior across the range.