Optimal parameters identification strategy of a lead acid battery model for photovoltaic applications

Extracting the parameters of a lead-acid battery under real-world operating conditions is a significant part of solar photovoltaic (PV) engineering. Usually, the battery management system handles the battery system based on its model. However, its model's parameters can change due to its electrochemical nature. Hence, enhancing the model parameters' accuracy is required to achieve a reliable and accurate model. This research employs an improved methodology for extracting lead-acid battery data outdoors. The suggested method combines numerical and analytical formulations of parametric battery models for solar PV energy storage. The Shepherd model, which considers the battery's non-linear properties, is selected in this paper. Based on a modern meta-heuristic marine predator algorithm, the parameters of two solar lead-acid batteries are discovered using an optimal parameter identification technique (MPA). The MPA exhibits its capability in terms of fast convergence and accuracy. The acquired test results are compared to those produced by the salp swarm algorithm, artificial eco-system optimizer, hunger games search, a new optimization meta-heuristic method that inspires the behavior of the swarm of birds called COOT, and honey badger algorithm in terms of efficiency, convergence speed, and identification accuracy. The findings demonstrated that the MPA outperformed the other optimizers in identifying ability. This optimizer achieved 99.99% identification efficiency for both Bergan and Banner battery types, making it an excellent battery identification option.