Parameters Estimations for Storage Unit based on Performance Characteristics | ||
| The International Conference on Electrical Engineering | ||
| Article 61, Volume 9, 9th International Conference on Electrical Engineering ICEENG 2014, May 2014, Pages 1-21 PDF (1.23 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/iceeng.2014.30482 | ||
| Author | ||
| Adel El Shahat | ||
| Engineering Science Department, Faculty of Petroleum & Mining Engineering, Suez University, Egypt. | ||
| Abstract | ||
| Storage systems are needed to provide electricity during the night or on cloudy days to meet the load requirements. Lead-acid batteries are so important even in grid connected applications storage to improve system reliability. So, this is paper aims to address a new method of Electrical Parameters Estimations for this type of storage devices based on Performance Characteristics with the aid of Artificial Neural Network (ANN). To help designers, researchers and users in pointing the direction for indigenous research in electricity storage technologies. First, the parameters of the battery model are identified depending on curve fitting with the aid of improved Thevenin battery model, and the model is validated with a 12 V, 4Ah lead-acid battery Yuasa NP4-12 Battery. The model parameters and characteristics are well depicted in the form of 3D figures. Second, the ANN technique is used to estimate Thevenin Electric Model' parameters in the form of ANN models with their algebraic equations. The proposed outputs for the models are: Discharging Resistance, Shunt Resistance, Back e.m.f. and Charging Resistance; each one is deduced with Battery Characteristics as inputs for every previous outputs: Charging/Discharging Rate, State of Charge, Time, Voltage, and Current. ANN models are created with suitable numbers of layers and neurons, which trained, simulated, checked and their algebraic equations are concluded accurately with excellent regression constant for all almost 1. | ||
| Keywords | ||
| Parameters; Lead-acid battery; model; Neural network; and estimation | ||
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