###### Abstract
They help identify patterns and anomalies in battery data, estimating remaining useful life (RUL) and state of health (SOH) [4]. Statistical and mathematical modeling techniques like Kalman Filtering, Coulomb Counting, and Equivalent Circuit Models (ECMs) are essential for estimating the state of charge (SOC) and simulating battery behavior under different conditions [5]. Data-driven approaches, including Principal Component Analysis (PCA) [6] and Time Series Analysis with methods like ARIMA [7], simplify and model battery data to forecast future performance trends. Hybrid models that combine physics-based and data-driven approaches enhance prediction accuracy and reliability. Explainable AI (XAI) techniques such as SHAP (SHapley Additive exPlanations) [8] and LIME (Local Interpretable Model-agnostic Explanations) [9] make complex models more interpretable by quantifying feature contributions and providing local explanations for individual predictions. Advanced neural networks like Long Short-Term Memory (LSTM) [10] and auto encoders are used for time-series data analysis and anomaly detection. Ensemble learning techniques, including boosting and bagging, combine multiple weak learners to create predictive solid models, enhancing accuracy and reducing overfitting [11]. By employing these techniques, researchers and engineers can develop robust models to predict Li-ion battery performance, optimize usage, and extend battery life, leading to safer and more efficient battery management in various applications. The advantage of using artificial neural networks to predict the performance of Li-ion batteries in drone flights is that these models can learn complex and non-linear data patterns with high accuracy. Traditional predictive control models have limited ability to adapt to battery variability and unexpected conditions because they are based on certain physical assumptions. In contrast, artificial neural networks, especially deep learning models such as CNN and RNN, are more effective at learning sequential patterns and complex relationships in battery data. This is due to their ability to capture temporal and spatial features. This allows for more flexible and reliable predictions when forecasting trends in battery life, state of charge, and power consumption. In addition, the performance of neural networks improves as the amount of data increases, allowing more successful modeling of the uncertainties that can occur during flight and in different environmental conditions. With these features, artificial neural networks offer a more dynamic and adaptive solution than traditional methods, increasing both the accuracy and operational safety of battery performance prediction. The study's primary aim is to leverage a CNN-RNN-based ensemble learning model, enhanced with the XAI model, to predict the performance of Li-ion batteries during drone flights accurately. The study seeks to develop an ensemble learning framework combining CNNs and RNNs to capture spatial and temporal features from battery performance data. The model will predict key performance metrics of Li-ion batteries in drones, such as remaining useful life and charge/discharge efficiency. Additionally, the study aims to integrate explainable AI techniques to interpret the model's predictions, ensuring transparency and interpretability by identifying which features or data points are most significant in the model's decision-making process. The research will validate the predictive accuracy of the ensemble learning model through extensive testing and comparison with existing methods, demonstrating its practical applicability in real-world drone flight scenarios for real-time battery performance monitoring. Ultimately, the insights gained from the predictions will be used to optimize drone operation, enhancing flight efficiency, safety, and battery management.