## 2 Related Works  
A battery using the Internet of Things (IoT) is a technology that allows the monitoring of battery operation and performance in various applications. This involves the integration of battery monitoring systems with IoT infrastructure to enable real-time data acquisition, communication, and analysis. Several studies have explored the topic of battery monitoring using IoT. Ref. [26] proposed a battery monitoring system based on the IoT, which included a data acquisition system, communication system, and monitoring platform. Ref. [27] presented a battery monitoring system developed for a smart microgrid, utilizing IoT technology for data acquisition, cloud storage, and the human-machine interface (HMI). The average execution time for data acquisition to the cloud server in this system was found to be 19.54 \(\pm\) 18.00 s, with a satisfactory result for data reliability [27]. Energy efficiency is another important aspect of IoT-based battery monitoring. Ref. [28] discussed the concept of the energy-efficient IoT and its potential to improve communication and information exchange in IoT systems. Ref. [29] focused on implementing an absolute battery monitoring system using the IoT to enhance battery life in battery-powered electric vehicles. This paper describes the use of a Cuk converter and voltage-based methods to determine the state of charge of batteries. Battery monitoring using IoT is a rapidly developing field with applications in various sectors, including smart grids, healthcare, and electric vehicles. The integration of the IoT technology enables real-time monitoring, data analysis, and improved battery performance [26; 27; 28; 29].  
Battery voltage prediction using Gaussian process regression (GPR) has been explored in several studies. Ref. [1] proposed two algorithms based on the GPR framework to predict the voltage corresponding to the future current profile demanded by the battery load. These data-driven algorithms aim to handle diverse battery operating conditions and have been tested on real-time industrial datasets, thereby demonstrating their viability for practical use [1]. Additionally, ref. [30] presented a comprehensive study using a long short-term memory-based method to predict the battery voltage. Although this paper does not explicitly mention Gaussian process regression, it provides insights into the effectiveness of different architectures and normalization techniques for voltage prediction. The study concluded that temperature had no noticeable impact on prediction accuracy, and applying data normalization did not consistently improve accuracy across different temperatures.  
A method for predicting the voltage and lifetime of lead-acid batteries using machine-learning techniques was proposed [31]. The prediction models were developed using three modes: M1, M2, and M3. M1 was formed by four fixed voltage values over four weeks, M2 was formed by four previous voltage values from the prediction time, and M3 was formed by combining the four previous data with the last predicted data. The training data consisted of recordings from 10 lead acid batteries over 155 weeks, whereas the examined data for the model were captured over 105 weeks. A Convolutional Neural Network (CNN) was selected as the machine-learning method to train and predict the battery model. The M1 model did not achieve accurate predictions, except for the linear case. The M2 model successfully predicted the battery voltage and lifetime, although the curve did not fit smoothly. The M3 model achieved high prediction accuracy with a smooth curve. It is important to note that prediction accuracy is influenced by the number of input parameters used in the prediction and the need for consecutive input parameters. It is important to note that prediction accuracy is influenced by the number of input parameters used and the need for consecutive input parameters [31].