Abstract
The state of charge (SOC) estimation plays important role in the battery energy storage system (BESS). Nowadays many semiconductor companies are paying more and more attention and investment to support many researchers to implement the state of charge for the batteries storage. the key to optimize the batteries storage is determine SOC value based on accuracy methods. a number of brief methods for SOC determination have been studied and compared with traditional methods the adaptive methods shown precise result because didn’t consider the dynamic effect of the batteries. In this paper, we use combination methods to estimate the SOC for lead-acid battery storage under two charge techniques namely Maximum Power Point Tracking – Plus Width Module (MPPT- PWM) when considering the effect of voltage drops on the estimation of SOC. The model uses the coulomb counting as an algorithm to determine the SOC and set it as a target in the backpropagation function in artificial neural network in MATLAB program (R2016a 64-bit (win64)). The simulation results show that the model is very precise to estimate the SOC in realistic operation.
Highlights
Solar electric technology is growing very quickly and its worldwide use is increasing rapidly as well prices of other electric energy sources rise
Those models are namely artificial neural network (ANN), extend Kalman filter (EKF), unscented Kalman filter (UKF), Neurofuzzy model, a support vector machine (SVM) [32,33,34,35,36,37,38,39,40], Those models are very accurate for state of charge (SOC) estimation as they depend on the training data collected during the experiment
SOC is calculated based on the coulomb counting in previous models, i.e, equations (5) to (8), and the calculated SOC is used as a target in ANN
Summary
Solar electric technology is growing very quickly and its worldwide use is increasing rapidly as well prices of other electric energy sources rise. Solar energy systems have two forms, namely, off-grid (island) and grid-connected Both types of solar PV system are different in component and design. Both of the systems have backup but in the off-grid (stand-alone), the backup is required to connect with batteries to store the energy and use it during night or cloudy day. Off grid system is more sensitive to factors such as shading, design on shorter day in the year, activities during the day and the future demand These factors can increase the size of the component and directly influence the capital cost of the system as demonstrated [16, 17, 18]. The results are obtained based on the combination methods namely coulomb counting (AH) and artificial neural network backpropagation function (BP)
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