## 4 Experimental Results and Analysis
### The Comparison of SOH Prediction Results Using Different Prediction Models  
In the field of battery SOH estimation, deep learning (DL) and transfer learning (TL), as data-driven approaches, have demonstrated significant application potential. Deep learning models, particularly those based on recurrent neural networks (RNN) such as LSTM and GRU, are capable of effectively capturing the nonlinear characteristics of battery performance over time. However, their high computational complexity, substantial data requirements, and poor interpretability limit their widespread use in real-time applications [41]. The transfer learning-based multistage LIBs' SOH estimation model proposed in reference [42] demonstrates strong transferability under small sample data, but its modeling and training processes are relatively complex. The model reveals the hidden dynamic characteristics of batteries through phase space reconstruction, relying on precise parameter selection and high-dimensional data decomposition. Cycling discrepancy learning (CDL) extracts domain-invariant features using Kullback-Leibler divergence (KLD), resulting in high computational complexity. The multistage estimation framework combines LSTM and temporal capsule network (TemCap), with the latter having a complex structure and consuming significant training resources. Although the online transfer strategy enhances adaptability, it relies on error compensation from shallow neural networks, further increasing complexity. Overall, while the model performs well with small samples, its complex modeling and training processes limit its practical application. Reference [43] proposes a transfer learning-based method for estimating the SOH of LIBs, which can achieve high-precision prediction even with small data samples. The method automatically extracts features using a CNN and reduces the distribution difference between training and testing battery data through an improved maximum mean discrepancy (MMD). Although the method demonstrates strong transferability with small samples, its modeling and training processes are complex. The computational costs of CNN and MMD are high, especially when processing high-dimensional data, significantly increasing training time. Additionally, model hyperparameters need to be adjusted through trial and error, further increasing modeling complexity and limiting its widespread use in real-time applications. Reference [44] proposes a battery SOH estimation method based on degradation pattern recognition and transfer learning, suitable for small sample data. The method identifies battery degradation patterns using K-means clustering and improves estimation accuracy by combining LSTM networks with transfer learning. Although the method exhibits good transferability with small samples, its modeling and training processes are complex. LSTM training is time-consuming, and the fine-tuning process in transfer learning increases computational complexity. Moreover, model hyperparameters need to be adjusted through \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Battery** & **Training Data** & **MAE (\%)** & **MAPE (\%)** & **RMSE (\%)** \\ \hline \multirow{3}{*}{CS2\_35} & 50\% & 0.3321 & 0.4371 & 0.4417 \\  & 60\% & 0.3431 & 0.4597 & 0.4603 \\  & 70\% & 0.3375 & 0.4643 & 0.4412 \\ \hline \multirow{3}{*}{CS2\_36} & 50\% & 0.4753 & 0.6183 & 0.5929 \\  & 60\% & 0.4674 & 0.6153 & 0.5725 \\  & 70\% & 0.5205 & 0.7031 & 0.6697 \\ \hline \multirow{3}{*}{CS2\_37} & 50\% & 0.3844 & 0.4955 & 0.4878 \\  & 60\% & 0.4066 & 0.5331 & 0.5151 \\  & 70\% & 0.4191 & 0.5545 & 0.4953 \\ \hline \multirow{3}{*}{CS2\_38} & 50\% & 0.3446 & 0.4286 & 0.4618 \\  & 60\% & 0.3584 & 0.4512 & 0.4741 \\ \cline{1-1}  & 70\% & 0.4128 & 0.5264 & 0.5307 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SOH estimation errors for different batteries in different proportions of the training dataset. trial and error, further increasing modeling complexity and limiting its widespread use in real-time applications.