## 3 Proposed Work
### Data Preprocessing  
Figure 5 illustrates a detailed workflow of the proposed methodology, beginning with a structured preprocessing pipeline applied to the raw dataset. The dataset comprised 15,065 samples with 9 input features. Step 1: Data Cleaning: Perform a sanity check, handle missing values, find duplicate values, and detect and remove outliers. The final dataset comprised 14,845 samples with 9 input features. Step 2: Addressing Class Imbalance: Since RUL values skew toward shorter cycles as degradation progresses, techniques like SMOTE (Synthetic Minority Oversampling Technique) were applied to balance the dataset. Figure 4: Features F6 and F7 in Current Charge Cycle. Figure 5: Lithium-Ion Battery RUL Prediction Workflow. Step 3: Data Transformation: Feature Engineering was used to derive new features from raw data, such as the rate of change in voltage during discharge and the time spent at specific voltage thresholds. Normalization was done by scaling continuous features using Min-Max normalization to ensure uniformity and improve model performance. Example: Voltage levels were scaled to a 0-1 range. Step 4: Data Splitting: The dataset was consistently divided into 70% for training and 30% for testing across all the models used in this study. This ensured a fair evaluation of model performance and maintained uniformity in comparison metrics. Step 5: Exploratory Data Analysis (EDA): Correlation analysis examines relationships between features/parameters and the target variable RUL. Heatmaps visually represent correlation amongst the datasets, where each cell represents the correlation coefficient between two variables. Heatmaps also make use of color coding to represent correlation coefficients. This makes it easy to identify strong and weak correlations at a glance. Strong correlations are observed between RUL and Cycle Count, Decrement Time 3.6 V to 3.4 V (s), and Time at 4.15 V as evident in Figure 6 below. The preprocessing phase resulted in a clean, balanced, and feature-rich dataset optimized for developing machine learning models. Preprocessing improved model interpretability and prediction accuracy by addressing inconsistencies in raw data and engineering meaningful features. Additionally, the scalable and replicable workflow ensures the dataset accurately represents the degradation behaviors of Li-ion batteries. The effectiveness of each model was assessed by using multiple metrics to provide a comprehensive evaluation of performance. The main idea behind an accuracy check is to use a set of performance metrics to see if the original aim is not far-fetched from the anticipated one. Below are the evaluation performance metrics:  
1. Mean Squared Error (MSE): MSE quantifies how far predicted values deviate from actual values by averaging the squared differences between them, with lower values indicating better predictive performance. Formula: \(\text{MSE}=\Sigma(\text{y}_{1}-\text{y}_{1})^{2}/\text{n}\), where \(\text{y}_{1}\) is the actual value, \(\text{y}_{1}\) is the predicted value, and n is the number of samples. 2. Mean Absolute Error (MAE): MAE represents the average absolute differences between predicted and actual values. It evaluates how much, on average, the  
Figure 6: Correlation matrix showing the relationships between input features and RUL. predictions deviate from the actual values. A lower MAE indicates a more precise model. Formula: MAE = \(\Sigma\left|y_{\mathrm{i}}-\hat{y}_{\mathrm{i}}\right|/\mathrm{n}\). * Root Mean Squared Error (RMSE): RMSE is calculated as the square root of the mean of the squared differences between predicted and actual values. It quantifies the model's prediction error by assessing the standard deviation between predicted and true values, with lower RMSE indicating higher accuracy. Formula: \(\mathrm{RMSE}=\sqrt{(\Sigma(y_{\mathrm{i}}-\hat{y}_{\mathrm{i}})^{2}/\mathrm{n})}\).