Robust Parameter Identification Strategy for Lead Acid Battery Model

Abstract

The most popular approach for smoothing renewable power generation fluctuations is to use a battery energy storage system. The lead-acid battery is one of the most used types, due to several advantages, such as its low cost. However, the precision of the model parameters is crucial to a reliable and accurate model. Therefore, determining actual battery storage model parameters is required. This paper proposes an optimal identification strategy for extracting the parameters of a lead-acid battery. The proposed identification strategy-based metaheuristic optimization algorithm is applied to a Shepherd model. The bald eagle search algorithm (BES) based identification strategy provided excellent performance in extracting the battery’s unknown parameters. As a result, the proposed identification strategy’s total voltage error has been reduced to 2.182 × 10−3, where the root mean square error (RMSE) between the model and the data is 6.26 × 10−5. In addition, the optimization efficiency achieved 85.32% using the BES algorithm, which approved its efficiency.