## 6 Conclusions and Future Trends  
This study presented a comprehensive review to evaluate the performances of different machine learning and deep learning approaches for SoH prediction. Various criteria were employed in the analysis to determine the impact and success of different models on SoH prediction. The advantages and limitations of each model were carefully examined, and the results were evaluated. It has been observed that regression-based approaches, especially models such as GPR, have achieved significant success in SoH prediction. It has also shown acceptable results in DT and SVM. It has been determined that Bi-LSTM had higher accuracy rates compared to other models. These results have demonstrated the potential of different machine learning and deep learning techniques in battery health prediction. Referring to the previous literature, hybrid machine learning methods have not been applied to LIBs' SoH prediction. In accordance with this, our novelty clearly has shown that these hybrid methods supply us with better, more efficient, and successful results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & & **Input Data** & **Estimation Error** & **Voltage Range (V)** & **Temperature (°C)** \\ \hline \multirow{4}{*}{ELM-based} & \multirow{4}{*}{ELM [29]} & Temperature, terminal & \multirow{4}{*}{RMSE: 1.0\%} & \multirow{4}{*}{2.75 to 4.2} & \multirow{4}{*}{25} \\  & & voltage, polarization & & & \\  & & resistance, ohmic resistance, & & MAE: 1.72\% & & \\  & & and current & & & \\ \cline{2-6}  & \multirow{4}{*}{MELM [30]} & Mean polarized internal & \multirow{4}{*}{MAE: less than 0.6\%} & \multirow{4}{*}{4.2 to 2.75} & \multirow{4}{*}{10-25-45} \\  & & capacitance, mean ohmic & & & \\  & internal resistance, and & & & & \\  & discharge capacity & & & & \\ \hline \multirow{4}{*}{SVR/SVM-based} & PSO-LSSVR [31] & SoC, ohmic resistance, and capacity & \multirow{4}{*}{RMSE: 3\%} & N/A & \multirow{4}{*}{25} \\  & & & & & \\ \cline{2-6}  & SVR/GA [32] & Current/voltage & MSE: 3.6569 \(\times\) 10\({}^{-4}\) & 3.6 to 2.0 & \multirow{4}{*}{25} \\ \cline{2-6}  & \multirow{4}{*}{LSSVM [33]} & Average temperature, time & \multirow{4}{*}{RMSE: 3.42\%} & \multirow{4}{*}{4.2 to 2.7} & \multirow{4}{*}{4-24-44} \\  & & interval of an equal charging & & & \\  & current/voltage difference, & & & & \\  & temperature changing rate, & & & & \\  & and charging capacity in & & & & \\  & constant current/voltage & & & & \\ \cline{2-6}  & Fixed-size & \multirow{4}{*}{Charging voltage profile} & \multirow{4}{*}{Max: 2\%} & \multirow{4}{*}{4.2 to 2.75} & \multirow{4}{*}{25} \\  & LSSVM [34] & & & & \\ \cline{2-6}  & Ensemble & \multirow{4}{*}{Voltages} & MSE: less than 0.3\% & \multirow{4}{*}{3.6 to 2.0} & \multirow{4}{*}{25} \\  & learning/SVR [35] & & & & \\ \hline \multirow{4}{*}{ANN-based} & ANN [36] & Five features from different & \multirow{4}{*}{RMSE: 5.41\%} & \multirow{4}{*}{4.2 to 2.7, 2.5 and 2.2} & \multirow{4}{*}{24} \\  & voltage regions & & MAE: 3.52\% & & & \\ \cline{2-6}  & \multirow{4}{*}{FFNN [37]} & Charging capacity & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & GRU-RNN [38] & Temperature, voltage, and & & & & \\ \cline{2-6}  & current & & & & \\ \hline \multirow{4}{*}{Transfer learning-based} & Semi-supervised & Characteristics of IC curves, & \multirow{4}{*}{RMSE: less than 2.5\%} & \multirow{4}{*}{4.2 to 2.7, 2.5 and 2.2} & \multirow{4}{*}{Room temperature} \\  & transfer learning [39] & & entropy of discharge voltage, & & MAE: 2\% \\ \cline{1-1}  & and ratio of the CC mode & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of previous studies in the literature. These results are very suitable to be integrated into the BMS software algorithm, especially for EVs. Future studies may focus on various trends for further advancement and development of this field. First, improving the data collection processes and using larger, more diverse datasets might be an important step toward improving the model's performance.