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Neural Network Storage Unit Parameters Modeling
International Journal of Industrial Electronics and Drives (2014)
  • Adel El Shahat, Suez University
This paper addresses a new method of electrical parameters modelling for lead-acid battery as a storage device based on performance characteristics using artificial neural network with suitable numbers of layers and neurons, with excellent regression constants. First, battery parameters are identified by curve fitting with improved Thevenin model, and validated with a 12 V, 4 Ah lead-acid battery. Second, ANN technique is used to model Thevenin electric model parameters. Models outputs are: discharging resistance, shunt resistance, back e.m.f. and charging resistance; each one is deduced with battery characteristics as inputs: charging/discharging rate, state of charge, time, voltage, and current. Finally, discharging and charging characteristics of the battery model are implemented for more visibility. These models easily identify parameters and characteristics for this battery type with capacity ranges 0.05, 0.1, 0.2, 0.4, 0.6, 1, 2 and 3 CA. Error and comparisons figures are adopted for validation purposes.
  • Lead-acid battery,
  • Artificial neural network,
  • ANN
Publication Date
Citation Information
Adel El Shahat. "Neural Network Storage Unit Parameters Modeling" International Journal of Industrial Electronics and Drives Vol. 1 Iss. 4 (2014) p. 249 - 274
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