Energy storage has become a fundamental component in renewable energy systems, especially those including batteries. However, in charging and discharging processes, some of the parameters are not controlled by the battery’s user. That uncontrolled working leads to aging of the batteries and a reduction of their life cycle. Therefore, it causes an early replacement. Development of control methods seeks battery protection and a longer life expectancy, thus the constant-current–constant-voltage method is mostly used. However, several studies show that charging time can be reduced by using fuzzy logic control or model predictive control. Another benefit is temperature control. This paper reviews the existing control methods used to control charging and discharging processes, focusing on their impacts on battery life. Classical and modern methods are studied together in order to find the best approach to real systems.

Accurate and efficient battery modeling is essential to maximize the performance of isolated energy systems and to extend battery lifetime. This paper proposes a battery model that represents the charging and discharging process of a lead-acid battery bank. This model is validated over real measures taken from a battery bank installed in a research center placed at “El Chocó”, Colombia. In order to fit the model, three optimization algorithms (particle swarm optimization, cuckoo search, and particle swarm optimization + perturbation) are implemented and compared, the last one being a new proposal. This research shows that the identified model is able to estimate real battery features, such as state of charge (SOC) and charging/discharging voltage. The comparison between simulations and real measures shows that the model is able to absorb reading problems, signal delays, and scaling errors. The approach we present can be implemented in other types of batteries, especially those used in stand-alone systems.

Based on measurements of global solar radiation carried out in the National University of Colombia (located in Bogotá, at 74° 4' West, 4° 35' North, 2580 m altitude) for the period 2003-2009, a statistical modeling based on time series was made to forecast the accumulated mean daily global solar radiation in Bogotá. This analysis was done using the programs EXCEL 2007 and SPSS 16; the lost data were determined with the technique of lineal trend. Using the test of Dickey - Fuller it was established that the time series are not stationary, but seasonal. Using the methodology of Box and Jenkins to convert the original data to a stationary series, it was found that the series which better fitted to the one built with experimentally measured data, can be obtained using the ARIMA (1, 0, 0) model, which can be mathematically represented by the equation X _t = 3562.78 + 0.518 X _t-1 + ε _t , where the error ε _t is distributed like a normal (0, 1) white noise.