Battery state of health (SoH) is an important parameter of the battery's ability to store and deliver electrical energy. Various methods have been so far developed to calculate the battery SoH, such as through the calculation of battery impedance or battery capacity using Kalman Filter, Fuzzy theory, Probabilistic Neural Network, adaptive hybrid battery model, and Double Unscented Kalman Filtering (D-UKF) algorithm. This paper proposes an approach to estimate the value of battery SoH based on the charging time measurement. The results of observation and measurements showed that a new and used batteries would indicate different charging times. Unhealthy battery tends to have faster charging and discharging time. The undertaken analysis has been focused on finding out the relationship between the battery SoH and the charging time range. To validate the results of this proposed approach, the use of battery capacity method has been considered as comparison. It can be concluded that there is a strong correlation between the two discussed SoH estimation methods, confirming that the proposed method is feasible as an alternative SoH estimation method to the widely known battery capacity method. The correlation between the charging-disharging times of healthy and unhealthy batteries is very prospective to develop a battery charger in the future with a prime advantage of not requiring any sensor for the data acquisition.
The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This paper describes a method of predicting the availability of solar radiation using the Extreme Learning Machine (ELM) method. It is based on the artificial intelligence methods and known to have a good prediction accuracy. To measure the performance of the ELM method, a conventional forecasting method using the Multiple Linear Regression (MLR) method has been used as a comparison. The implementation of both the ELM and MLR methods has been tested using the solar radiation data of the Basel City, Switzerland, which are available to public. Five years of data have been divided into training data and testing data for 6 case-studies considered. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) have been used as the parameters to measure the prediction results based on the actual data analysis. The results show that the obtained average values of RMSE and MAE by using the ELM method respectively are 122.45 W/m<sup>2</sup> and 84.04 W/m<sup>2</sup>, while using the MLR method they are 141.18 W/m<sup>2</sup> and 104.87 W/m<sup>2</sup> respectively. It means that the ELM method proved to perform better than the MLR method, giving 15.29% better value of RMSE parameter and 24.79% better value of MAE parameter.
The battery is an essential component in providing continuous electricity supply using renewable energy sources. It can be found in many daily applications, such as in the telecommunication system, radio microwave system, emergency lighting, the backup system of power plants, even in a photovoltaic system. It is often used as the backup source in case of a failure in the main supply system. The duration of how long the battery can still supply energy to loads without being charged is defined as the battery autonomy day. If during its daily utilization the battery often exceeds its autonomy day, it can result in the deterioration of the battery lifetime. It produces the deviation of the battery lifetime specification which has been previously determined by the manufacturer. This paper presents the results of battery lifetime prediction at a base-transceiver station (BTS) of Telkomsel Company in Indonesia. It has two main purposes which are to evaluate the policy of autonomy day and to predict the remaining lifetime of the battery before reaching its time limit. The obtained results show that there have been some alterations from the batteries' former policy of autonomy day, from 72 hours to 43.03 hours and 43.26 hours for both existing batteries respectively with considered depth-of-discharge (DOD) of 20%. By using a linear data curve fitting, the results of calculation and analysis indicate that the remaining useful lifetime of both batteries were 5.72 years and 5.77 years. Another approach using an exponential data curve fitting resulted in the remaining lifetime of 7.12 years and 7.16 years for both batteries respectively.
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