## 3 Proposed Work
### Data Collection and Preprocessing  
The success of any predictive analytics study hinges on the dataset's quality and the preprocessing steps' robustness. This section elaborates on how the dataset was acquired,prepared, and transformed to enable accurate forecasting of the RUL of Li-ion batteries. The dataset used in this research was sourced from experimental studies done by the Hawaii Natural Energy Institute (HNEI) from the publicly available Battery Archive repository (Battery Archive). It comprises comprehensive cycling data from NMC-LCO 18650 batteries, specifically designed to capture degradation trends under controlled conditions. From that source dataset, features showcase the voltage and current behavior over each cycle. This will be used to predict the RUL of the batteries. The key characteristics of the dataset are as follows:  
Battery Chemistry: Nickel Manganese Cobalt (NMC) and Lithium Cobalt Oxide (LCO) Chemistries, widely used in electric vehicles and portable electronics. Experimental Setup: Batteries were cycled 1000 times to simulate long-term usage. Tests were conducted under controlled temperature conditions (25 \({}^{\circ}\)C) for consistency. The charging method was constant current-constant voltage (CC-CV), with: AC/2 rate for the CC phase. A discharge rate of 1.5 C, accelerating aging for observable degradation. C-rate is the rate at which a battery is charged or discharged relative to its rated capacity (e.g., 1 C = full charge/discharge in 1 h). Features and Variables: The features created as seen in Figures 2-4 reflect various voltages, currents, and time-related behaviors that change as the battery ages. Cycle Index/Count: This measures the number of charge-discharge cycles a battery has experienced. You can detect degradation patterns by observing the relationship between the cycle index and other features like discharge time, voltage, and charging time. F1: Discharge Time (s): As the battery degrades, the time it takes to discharge decreases because the capacity reduces over time. This feature is directly linked to the battery's ability to hold a charge. F2: Decrement 3.6-3.4 V (s): This feature captures how long the battery can discharge from 3.6 V to 3.4 V. This range is commonly associated with the mid-point of the discharge curve for many lithium-ion chemistries, where the voltage begins to decline more rapidly. Monitoring how quickly a battery passes through this zone can reveal aging patterns, as degraded cells tend to lose voltage stability. F3: Max. Voltage Discharge (V) & F4 Min. Voltage Charge (V): Tracking these voltages over cycles reveals how battery efficiency is affected by aging. For example, a drop in the maximum voltage during discharge might indicate internal resistance or capacity loss. F5: Time at 4.15 V (s): The time spent at this voltage may change as the battery ages. Healthy batteries might spend more time at a higher voltage before discharging quickly as they degrade. Tracking this feature could help identify when the battery's ability to maintain a stable voltage declines. F6: Time Constant Current (s): The CC phase duration can change based on how much charge the battery can absorb. A shorter CC phase might indicate a reduction in capacity as the battery cannot accept more current for as long as before. F7: Charging Time (s): Charging time often increases as the battery degrades because it takes longer to charge a battery with reduced capacity fully. This is especially important for applications where fast charging is critical. RUL (Remaining Useful Life): RUL is the most important feature as it is the target variable for prediction. RUL typically represents the number of remaining cycles before the battery fails to meet performance standards, such as holding a sufficient charge or delivering required voltage levels. By monitoring degradation features (F1-F7) over several cycle counts, you can train a machine learning model like to predict RUL.