## 2 Literature Survey Materials and Methods
ML models are specialized data analysis tools that learn from data, identify patterns, and make decisions with minimal human intervention [30; 31]. Currently, ML-based models are increasingly favored for data-driven SOH estimation and RUL prediction, including neural network (NN)-based models, Gaussian process regression (GPR)-based models [32; 33; 34; 35; 36], and random forest (RF)-based models [37; 38]. These ML models aim to extract features related to battery degradation from directly measured data such as terminal voltage (V), current (I), temperature (T), and time (t). ML models are employed to estimate the battery's SOH by learning battery degradation behaviors during training. The recently developed models are summarized in Figure 1.  
A data-driven state of health (SOH) estimation model operates using large volumes of data without relying directly on the internal degradation mechanisms of the battery. However, incorporating expert knowledge about these mechanisms can enhance the model's performance. The model can use both directly measured and indirectly inferred features as inputs, establishing a robust correlation between these features and the battery's SOH to achieve accurate SOH estimations. Table 1 presents a compilation of cutting-edge data-driven SOH estimation models. This table offers an easy reference for identifying the most advanced benchmarks and the current state-of-art research in battery SOH estimation. Eachmethod listed showcases unique techniques for tasks like State of Health (SOH) estimation and Remaining Useful Life (RUL) prediction. For instance, CNN-AST-LSTM combines 1D-CNN and AST-LSTM for feature extraction with Bayesian optimization for hyperparameter tuning, while RF employs novel SOH indicators for degradation assessment. By positioning our method within this table, we aim to underscore its distinctive contributions, particularly in relation to feature extraction, prediction accuracy, and model adaptability. We will enhance the discussion in the main text to more clearly emphasize how our approach compares to and builds upon these advanced methods. The development of drone technology has significantly advanced over the past decade, revolutionizing various sectors, including agriculture, logistics, surveillance, and entertainment. One of the critical components driving this technological progress is the Li-ion battery, which provides the necessary power for drones to operate efficiently and effectively. Drone technology has evolved from simple remote-controlled devices to highly sophisticated autonomous systems capable of performing complex tasks. Early drones were limited by short flight times and low payload capacities, but advancements in battery technology \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model** & **Task** & **Novelty** & **Ref.** \\ \hline CNN-AST-LSTM NN & SOH estimation, RUL prediction & 1D-CNN and AST-LSTM for feature extraction; Bayesian optimization for hyperparameter adaptation. & [40] \\ \hline Auto-CNN-LSTM & RUL prediction & Autoencoder for features augments; CNN for mining deep Information in features; LSTM for time information extraction in features; DDN for RUL prediction; First order filter for results smoothing. & [41] \\ \hline GPR & SOH estimation & An SOH estimator models the internal resistance as a Gaussian process with a Wiener velocity kernel and a squared exponential kernel. & [42] \\ \hline RF & SOH estimation & Two new SOH indicators are designed for degradation assessment: RF for SOH estimation and a degradation factor ranking approach for exploring battery degradation comprehension based on the permutation-based variable importance computation. & [43] \\ \hline LS-SVM & SOH estimation & ELM for the future voltage profile prediction based on the short-term charging data; A fixed size least squares-based SVM with a mixed kernel function for SOH estimation; Simulated annealing method for parameters optimization. & [44] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some SOH estimation methods.