Robust Parameter Identification Strategy for Lead Acid Battery Model
Hegazy Rezk,
Seydali Ferahtia,
Rania M. Ghoniem,
Ahmed Fathy,
Mohamed M. Ghoniem,
Reem Alkanhel
Affiliations
Hegazy Rezk
Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia
Seydali Ferahtia
Laboratoire de Génie Électrique, Department of Electrical Engineering, University of M’sila, M’sila 28000, Algeria
Rania M. Ghoniem
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Ahmed Fathy
Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Mohamed M. Ghoniem
Department of Computer, Mansoura University, Mansoura 35516, Egypt
Reem Alkanhel
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
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.
