## 4 Results
### Binary Classifiers  
Naive Bayes, Decision Tree, and Logistic Regression are the selected binary classifiers to perform the predictions for the charge and discharge of a battery. It is important to point out that the main purpose of this section is to implement an algorithm that optimizes the operation of the battery by identifying the profile status of the independent variables so that estimations of the SOC and SOH can be automatically performed. Decision Tree is a Machine Learning algorithm commonly used for classification problems, usually based on developing predictions for a target variable. This algorithm is defined as non-parametric and consists of nodes and branches as principal components, while splitting, stopping, and pruning are model-building steps. Using Decision Tree is considered a potential methodology with several advantages, such as feasible interpretation and understanding, outlier robustness, a non-parametric approach that can deal without considering distributional assumptions, and a simplicity of complex relationships between input and output variables [7]. Logistic Regression is a supervised Machine Learning technique implemented for binary classification problems when the predicted variable is considered categorical. Mathematically, it is based on a logistic function with the purpose of modeling a binary output variable whose range is bounded between 0 and 1, the latter being the main difference compared to Linear Regression. It is necessary to mention that Logistic Regression uses a loss function defined as Maximum Likelihood Estimation (MLE), which is defined as a conditional probability. The advantages of Logistic Regression are the ease of implementa  
Figure 6: Remaining capacity vs. number of cycles. The horizontal line denotes the EOL criteria specified at 70% of the total rated capacity. tion and satisfactory performance achieved with linearly separated classes. Mathematical foundations can be found in references [8; 9]. Naive Bayes is a probabilistic methodology that predicts the classification of a specific target variable described by a set of feature vectors. The mathematical basis corresponds to the Bayes theorem, in which prior distribution is updated into posterior based on empirical information. Naive Bayes assumption is computed by a likelihood calculation that considers conditional independence between the features and a given class label. Like Logistic Regression, this algorithm implements the MLE to estimate the probabilistic parameters and predict the classification output. It has been demonstrated that Naive Bayes achieves meaningful results in practical applications, such as systems performance management, text classification, medical diagnosis, etc. [10]. To start the implementation of the binary classifiers, the dataset was labeled according to the profile status, either charge or discharge. After that, two approaches were considered to assess the most accurate and optimal methodology during battery operation. In this target output, the profile status is considered 1 when the battery is charged and 0 if it is discharged; however, it is fundamental to avoid confusion with the charge status performed in the Feature Engineering process, in which the charge current values are updated based on the charge change for each iteration. The following steps summarize the computational algorithm:  
* The dataset is processed, and the vector of classes is generated based on the profile status, either charge or discharge, which is defined as the target variable. * Two approaches are considered to start the implementation of the binary classifiers; therefore, separated arrays are generated. The first approach consists of taking the initial features (i.e., current and voltage) as the independent variables, while the second approach considers the KPIs generated in the Feature Engineering process. * In each separated array, all features and target variables are randomized to provide the algorithm with a more complex dataset that the binary classifiers will process automatically.