## 1 Introduction  
Battery Energy Storage Systems (BESSs) are promising technologies used for applications in the energy industry, and are considered a core element of achieving climate change mitigation and energy transition. Currently, not only the deployment of many electric but also hybrid vehicles worldwide has led to an increase in the demand for BESSs, specifically due to their long-life cycle and high energy density [1]. In operation, the battery's dynamic performance consists of charging and discharging profiles, which can be characterized experimentally by measuring the voltage under constant charge and discharge current inputs. It is important to specify that the level of rate discharge is divided into three levels: low rate, medium rate, and high rate [2]. Additionally, the voltage and current parameters control the charging profile, which usually consists of periods of constant voltage (CV) or/ and constant current (CC). The State of Charge (SOC) is an indicator that expresses the current available capacity of a BESS as a percentage of nominal capacity [2]. Several methodologies have been proposed by the scientific community to achieve the estimation of the SOC in both experimental and analytical manners, among the most relevant are Impedance Spectroscopy, DC internal resistance, Coulomb Counting, and Open-Circuit Voltage (OCV), which are described in reference [3]. The performance of a BESS during its lifetime is measured according to the gradual degradation of the system due to irreversible chemical or physical changes, which take place in operating processes until the battery is no longer capable of satisfying the user's needs. The State of Health (SOH) is defined as an indicator that measures the life cycle of a battery in comparison with its initial capacity as well as its degradation level; therefore,the remaining capacity is the main point to analyze [3]. Remarkable studies have shown that SOH monitoring methods optimize the performance of a BESS, all by recognizing an ongoing or sudden degradation of battery cells, which can lead to failures in mobility systems. In 2016, [PERSON] et al. conducted a study that categorized different SOH methodologies in Battery Management System (BMS) applications, stating the strengths and weaknesses to test and validate the developed algorithms [4]. Additionally, recent research work has contributed to the development of new techniques to achieve predictive maintenance, such as hybrid methods combining empirical mode decomposition (EMD) and particle filter (PF), posed by [PERSON] et al. in 2023 [5]. The Key Performance Indicators (KPIs) of a BESS play the most significant role in the operation and in the implementation of the algorithm to train, validate, and test the battery modeling. With new advances in the field of Machine Learning and Artificial Intelligence, accurate methods can achieve SOC and SOH estimations; however, domain knowledge of a BESS that improves the physics of the system is a crucial step to understanding the mechanism of performance and incorporate hybrid models to satisfy different user needs. The main goal of the current research work is to provide battery modeling to assess operating mechanisms in the charging and discharging processes, all to optimize Health and Charge estimations based on Remaining Useful Lifetime (RUL) through regression algorithms and binary classifiers. The corresponding steps of the research work are summarized in the following schema, which is illustrated in Figure 1.  
The rest of the paper is organized as follows: In Section 2, the problem statement and the motivation of this research are explained. Section 3 describes and implements the methodologies, focusing on Feature Engineering and Exploratory Data Analysis. Section 4 provides the results based on binary classifiers and regression algorithms within the framework of BESSs. Finally, in Section 5, a conclusion is provided to encourage the continuation of this work based on more advanced methodologies to achieve fault diagnostics and predictive maintenance.