## 2 Literature Survey Materials and Methods
Figure 1: The workflow of data-driven battery SOH estimation models [39]. have played a crucial role in overcoming these limitations [45]. The introduction of Li-ion batteries, known for their high energy density, lightweight, and long cycle life, has been pivotal in enhancing the performance and capabilities of modern drones [46]. Due to several key advantages, Li-ion batteries have become the preferred choice for powering drones. Their high energy density allows drones to fly longer distances and perform extended missions without frequent recharging [47]. It is crucial in delivery services, where drones must cover significant distances to transport goods [48]. Additionally, their relatively lightweight nature helps optimize the drone's payload capacity and overall flight dynamics [49]. This weight efficiency is crucial for drones used in precision agriculture and aerial photography, where carrying additional sensors or cameras is necessary. The performance of Li-ion batteries is a critical factor in determining the efficiency and reliability of drone operations. Advanced battery management systems (BMSs) have been developed to monitor and optimize the performance of Li-ion batteries in real-time, ensuring safe and efficient energy usage. As a result, drones powered by Li-ion batteries can achieve higher reliability and longer operational life, reducing maintenance costs and downtime. Looking forward, innovations such as solid-state batteries, which offer higher energy densities and improved safety features, are expected to drive the next wave of advancements in drone technology. Additionally, research into hybrid power systems combining Li-ion batteries with other energy sources, such as solar panels or hydrogen fuel cells, could lead to longer flight times and greater operational flexibility [50]. The performance of Li-ion batteries in drones is significantly affected by the operational conditions encountered during flights. Research has shown that various factors, such as flight duration, payload weight, and environmental conditions, can influence battery performance and longevity. Extended flight times and heavier payloads often increase energy consumption and faster battery depletion. Moreover, temperature fluctuations during flights can adversely affect the chemical stability and capacity of Li-Ion batteries, potentially leading to diminished performance and reduced lifespan [51; 52]. Studies have demonstrated that exposure to extreme temperatures can cause irreversible damage to battery cells, impacting their efficiency and safety [53]. ML methods have been increasingly used to model and predict battery performance to address these challenges. Advanced ML techniques, including CNNs and RNNs, have been employed to forecast the state-of-charge (SoC) and state-of-health (SOH) of batteries based on real-time flight data. Ensemble learning approaches have also been applied to enhance prediction accuracy by combining multiple models to handle the complexities of battery behavior better [8; 46]. These ML models can analyze flight parameters such as speed, altitude, and temperature to provide more precise forecasts of battery performance, aiding in developing effective battery management systems (BMSs). As research progresses, integrating ML with predictive maintenance systems and exploring new battery technologies are expected to further improve battery performance and operational safety in drones [54].