Lifetime Prediction of Lead-Acid Batteries in Base-Transceiver Station

Wibawa, Unggul; Pratama, B. A.; Hasanah, Rini Nur

doi:10.18517/ijaseit.7.4.3133

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Rechargeable batteries are commonly used in various applications, such as data centers, telecommunication base stations, radio microwave systems, government buildings, storing energy in solar panels, and supplying energy to electric vehicles, medical devices, and consumer electronics [1,2]. They are frequently utilized as backup sources in the event of a failure in primary supply systems.…”

Section: Introductionmentioning

confidence: 99%

Online Voltage and Degradation Value Prediction of Lead Acid Battery Using Gaussian Process Regression

Winata,

Surantha

2023

Applied Sciences

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Monitoring battery voltage is important to ensure a steady supply of energy. A crucial aspect to avoid failure is estimating the voltage required by the battery load. Lead acid batteries play a vital role as engine starters when the generators are activated. The generator engine requires an adequate voltage to initiate the power generation process. This article discusses three prediction models for estimating the voltage and degradation values based on data-driven methods. The machine-learning models used were Gaussian process regression (GPR), Support Vector Regression (SVR), and Random Forest. The prediction results were compared using evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2). The implementation of the Internet of Things (IoT) was demonstrated to collect real-time battery data using a voltage sensor and a temperature sensor as inputs for the prediction model. According to the experiment, the Random Forest algorithm provided highly accurate predictions, with an RMSE of 0.0401, MAE of 0.0241, and R-squared of 0.9651.

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Section: Introductionmentioning

confidence: 99%

Online Voltage and Degradation Value Prediction of Lead Acid Battery Using Gaussian Process Regression

Winata,

Surantha

2023

Applied Sciences

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“…Batteries are used in many daily life activities, such as in portable telecommunication equipments, multimedia devices, hybrid electric vehicles (HEV) [1], electric vehicles (EV) [2,3], energy harvesting, base-transceiver station (BTS) in the telecommunication system [4], wearable computing devices [5], wireless sensor network (WSN) [6,7] and energy storage in smartgrid networks [8,9]. A battery in a system must be used properly in order to reach its lifetime as long as possible.…”

Section: Introductionmentioning

confidence: 99%

VRLA battery state of health estimation based on charging time

Zainuri¹,

Wibawa²,

Rusli³

et al. 2019

TELKOMNIKA

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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.

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“…The invention of lead–acid batteries is about 100 years earlier than lithium-ion batteries, but many studies and designs have been published for research on lithium-ion batteries, and there are many opportunities for research on lead–acid batteries (Lyu et al., 2017; Wibawa et al., 2017). However, the interesting thing is that most of the vehicle applications are still dominated by lead–acid batteries.…”

Section: Introductionmentioning

confidence: 99%

A prediction method for voltage and lifetime of lead–acid battery by using machine learning

Wang

Hendrick

Horng

et al. 2019

Energy Exploration & Exploitation

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Lead–acid battery is the common energy source to support the electric vehicles. During the use of the battery, we need to know when the battery needs to be replaced with the new one. In this research, we proposed a prediction method for voltage and lifetime of lead–acid battery. The prediction models were formed by three kinds mode of four-points consecutive voltage and time index.The first mode was formed by four fixed voltages value during four weeks, namely M1. The second mode was formed by four previous voltage values from prediction time, namely M2. Third mode was formed by the combinations of four previous data with the last predicted data, namely M3. The training data were recorded from 10 lead–acid batteries. We separated between training data and testing data. Data collection for training were recorded in 155 weeks. The examined data for the model was captured in 105 weeks. Three of batteries were selected for prediction. Machine learning methods were used to create the batteries model of voltage and lifetime prediction. Convolutional Neural Network was selected to train and predict the battery model. To compare our model performance, we also performed Multilayer Perceptron with the same data procedure. Based on experiment, M1 model did not achieve the correct prediction besides the linear case. M2 model successfully predicted the battery voltage and lifetime. The M2 curve was almost the same with real-time measurement, but the curve was not fitting smoothly. M3 model achieved the high prediction with smooth curve. According to our research on lead–acid battery voltage prediction, we give the following conclusions and suggestions to be considered. The accuracy of prediction is affected by the number of input parameters is used in prediction. The input parameters need to have time consecutive.

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Lifetime Prediction of Lead-Acid Batteries in Base-Transceiver Station

Cited by 4 publications

References 12 publications

Online Voltage and Degradation Value Prediction of Lead Acid Battery Using Gaussian Process Regression

Online Voltage and Degradation Value Prediction of Lead Acid Battery Using Gaussian Process Regression

VRLA battery state of health estimation based on charging time

A prediction method for voltage and lifetime of lead–acid battery by using machine learning

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