## 4 Experimental Results and Discussion
### Discussion  
During the process of lithium-ion battery health state estimation, it is subject to various interferences. The temperature parameter significantly impacts the battery's performance. When the temperature exceeds the normal range (20-40 \({}^{\circ}\)C), it can lead to electrode material detachment and SEI layer damage, affecting battery performance. Low temperatures can exacerbate aging. [PERSON] et al. proposed an integrated artificial neural network technique  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Battery** & **Methods** & _RMSE_ & _MAPE_ & _RA_ & _R_\({}^{2}\) \\ \hline \multirow{5}{*}{CS2\_38} & A1 & 0.0313 & 0.0498 & 0.9498 & 0.9820 \\  & A2 & 0.0196 & 0.0312 & 0.9689 & 0.9891 \\  & A3 & 0.0235 & 0.0364 & 0.9635 & 0.9910 \\  & A4 & 0.0122 & 0.0151 & 0.9848 & 0.9953 \\  & A5 & 0.0068 & 0.0093 & 0.9898 & 0.9930 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Battery** & **Methods** & _RMSE_ & _MAPE_ \\ \hline \multirow{5}{*}{B0005} & EMD-TCN & 0.0080 & - \\  & EMD-GRU-ARIMA & 0.0060 & 0.0062 \\  & Proposed method & 0.0058 & 0.0067 \\ \hline \multirow{5}{*}{B0006} & EMD-TCN & 0.014 & - \\  & EMD-GRU-ARIMA & 0.0075 & 0.0098 \\  & Proposed method & 0.0071 & 0.0085 \\ \hline \multirow{5}{*}{B0007} & - & - & - \\  & EMD-GRU-ARIMA & 0.0056 & 0.0048 \\  & Proposed method & 0.0054 & 0.0062 \\ \hline \multirow{5}{*}{B0018} & EMD-TCN & 0.0190 & - \\  & - & - & - \\ \cline{1-1}  & Proposed method & 0.0086 & 0.0093 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of Our SOH Estimation Method with Other Methods on the NASA Dataset.  
for accurately assessing the electrical and thermal performance of lithium-ion batteries [36]. By introducing two heating mechanisms into a thermal model and utilizing artificial neural networks to capture the relationship between temperature and state of charge for effective prediction, this algorithm has low time complexity and strong adaptability. [PERSON] et al. proposed an effective series resistance model to study the relationship with battery life [37], resulting in small estimation errors. It can serve as a novel indicator for evaluating the health status of lithium-ion batteries. [PERSON] et al. proposed a model based on the equivalent circuit to establish the thermal and electrical characteristics of batteries [38]. The input signals are current and ambient temperature, and the outputs are voltage and state of charge. It evaluates the state of charge to reflect battery health. Such models can deduce battery performance degradation mechanisms based on the battery's internal physical structure, making parameter determination easy and models simple, but requiring a complex calibration process. Reference [39] considers the influence of road gradients on the health state of lithium-ion batteries during electric vehicle operation. It proposes an approach that combines Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) to mitigate battery health degradation during uphill driving using learned predictive control strategies, addressing real-world problems. Due to the complexity of environmental factors, this method places high demands on deep learning models. In future research on State of Health prediction models for lithium-ion batteries, a deeper investigation into the aging mechanisms of lithium-ion batteries should be conducted. Multiple influencing factors should be fully considered to achieve accurate modeling and health state estimation based on the characteristics of data and models.