###### Abstract  
Li-ion batteries are important in modern technology, especially for drones, due to their high energy density, long cycle life, and lightweight properties. Predicting their performance is crucial for enhancing drone flight safety, optimizing operations, and reducing costs. This involves using advanced techniques like machine learning (e.g., Convolutional Neural Network-CNNs, Recurrent Neural Network-RNNs), statistical modeling (e.g., Kalman Filtering), and explainable AI (e.g., SHAP, LIME, PDP) to forecast battery behavior, extend battery life, and improve drone efficiency. The study aims to develop a CNN-RNN-based ensemble model, enhanced with explainable AI, to predict key battery metrics during drone flights. The model's predictions will aid in enhancing battery performance via continuous, data-driven monitoring, improve drone safety, optimize operations, and reduce greenhouse gas emissions through advanced recycling methods. In the present study, comparisons are made for the behaviors of two different drone Li-ion batteries, numbered 92 and 129. The ensemble model in Drone 92 showed the best performance with MAE (0.00032), RMSE (0.00067), and R\({}^{2}\) (0.98665) scores. Similarly, the ensemble model in Drone 129 showed the best performance with MAE (0.00030), RMSE (0.00044), and R\({}^{2}\) (0.98094) performance metrics. Similar performance results are obtained in the two predictions. However, drone 129 has a minimally lower error rate. When the Partial Dependence Plots results, which are one of the explainable AI (XAI) techniques, are interpreted with the decision tree algorithm, the effect of the Current (A) value on the model estimations in both drone flights is quite evident. When the current value is around \(-4\), the model is more sensitive and shows more changes. This study will establish benchmarks for future research and foster advancements in drone and battery technologies through extensive testing. Li-ion; CNN; RNN; ensemble; XAI; PDP 2024 14 10816 10.3390/app1423108161610.3390/app1423108161610. risks and avoid potential accidents and losses. Secondly, it optimizes flight duration and range, allowing drones to cover larger areas and perform tasks more efficiently. Accurate battery performance predictions enable better mission planning and scheduling, which is essential for time-sensitive operations like delivery services and emergency response. Thirdly, it ensures cost-effectiveness by informing maintenance and replacement schedules, extending battery life, and reducing unnecessary expenses. Additionally, performance data analysis supports proactive maintenance strategies, preventing unexpected downtimes and enhancing operational reliability. From an environmental perspective, optimizing battery use reduces waste and promotes sustainability. Efficient energy management minimizes environmental impact and supports the development of more sustainable battery technologies. Lastly, predicting battery performance drives technological advancement, encouraging innovation in drones and battery systems. It enables the development of safer, more efficient, and more powerful drone technologies. Therefore, predicting Li-ion battery performance in drone flights is vital for safety, efficiency, cost savings, environmental sustainability, and technological progress [2]. Performance prediction of Li-ion batteries involves various advanced techniques that leverage data analysis, machine learning, and statistical modeling. Machine learning models such as neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are utilized to capture complex patterns in battery performance data [3]. CNNs handle spatial data effectively, while RNNs are suited for sequential data, making them ideal for predicting battery life cycles and performance trends. Support Vector Machines (SVMs) and Random Forests are also used for regression and classification tasks.