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Article
Lead Acid Battery Modeling for PV Applications
Journal of Electrical Engineering
  • Rami Haddad, Georgia Southern University
  • Adel El Shahat, Georgia Southern University
  • Youakim Kalaani, Georgia Southern University
Document Type
Article
Publication Date
6-1-2015
Abstract

Lead-Acid batteries continue to be the preferred choice for backup energy storage systems. However, the inherent variability in the manufacturing and component design processes affect the performance of the manufactured battery. Therefore, the developed Lead-Acid battery models are not very flexible to model this type of variability. In this paper, a new and flexible modeling of a Lead-Acid battery is presented. Using curve fitting techniques, the model parameters were derived as a function of the battery’s state of charge based on a modified Thevenin equivalent model. In addition, the charge and discharge characteristics of the derived model were investigated and validated using a real NP4-12 YUASA battery manufacturer's data sheet to match performance at different capacity rates. Furthermore, an artificial neural network based learning system with back-propagation technique was used for estimating the model parameters using MATLAB software. The proposed neural model had the ability to predict values and interpolate between the learning curves data at various characteristics without the need of training. Finally, a closed-form analytical model that connects between inputs and outputs for neural networks was presented. It was validated by comparing the target and outp

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Article under a Creative Commons license. Article obtained from the Journal of Electrical Engineering.

Citation Information
Rami Haddad, Adel El Shahat and Youakim Kalaani. "Lead Acid Battery Modeling for PV Applications" 2Journal of Electrical Engineering Vol. 15 (2015) p. 17 - 24
Available at: http://works.bepress.com/adel-el-shahat/47/