## 5 Conclusions  
Aiming at the development trend for green transportation, we conduct SOH prediction method research to focus on LIB for new-energy electric vehicles. The study primarily accomplishes the following three objectives: First, multiple LIB experimental data are collected through charge-discharge cycle experiments on 18,650 LiFePO4 batteries, providing foundational data for LIB SOH prediction. Second, the analyses of SOH prediction for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ranking** & **\#1** & **\#6** & **\#26** \\ \hline
2 & RF improved model 1 & RF improved model 1 & RF improved model 1 \\
3 & Class RF model & Class RF model & Class RF model \\
4 & LSTM model & LSTM model & CNN model \\
5 & CNN model & DT model & LSTM model \\
6 & DT model & CNN model & DT model \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The comprehensive ranking of prediction results for each model. multiple LIBs are conducted using four traditional machine learning models. The results indicate that the Class RF model yields relatively superior prediction results, confirming the feasibility of employing the RF model for LIB SOH prediction in this study. Third, further optimization of the RF model is carried out using the PSO algorithm, followed by the analysis of LIB SOH prediction. The results reveal that the proposed adaptive optimization of the RF improved model with five parameters provides the best SOH prediction, validating the effectiveness of the RF improved model introduced in this paper for LIB SOH prediction. Conducting research on SOH prediction for LIB is beneficial for timely assessment of the lifespan of new-energy electric vehicle LIBs. Simultaneously, it provides valuable insights into the lifespan analysis of the battery in various types of electric vehicles. This research contributes to addressing the issues of reliability and sustainability of power in new-energy electric vehicle batteries, ensuring operational efficiency and the promotional value of new-energy electric vehicles. Consequently, it promotes the rapid development of the new-energy electric vehicle and battery industries, serving a practical purpose in advancing green transportation based on new-energy electric vehicles and playing a significant role in reducing environmental pollution. Due to the lack of data support for different types of LIB, there remains some uncertainty in applying the methods proposed in this paper to SOH prediction for other types of LIB. However, the modeling process for SOH prediction in different types of electric vehicles batteries follows a similar framework, with variations mainly in data collection and preprocessing. Future research will consider the acquisition and processing of data from various types of LIB. Furthermore, since the performance of LIB SOH prediction may vary under different operating conditions and environments, we will continue to explore ways to enhance the model's generalization capability, making it more versatile and reliable. We also plan to expand its applicability to other scenarios, such as battery management systems for large-scale electric vehicles and the control and management of energy storage systems. Conceptualization, Z.L. and R.W. ; methodology, Z.L. ; software, X.Z. ; validation, Z.L., R.W. and X.Z. ; formal analysis, R.W. and Y.L. ; investigation, Z.L. ; resources, Z.L. ; data curation, Y.L. and X.Z. ; writing--original draft preparation, R.W. ; writing--review and editing, Z.L. ; visualization, Y.X. ; supervision, Z.L. ; project administration, Y.X. ; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the University Natural Sciences Research Project of Anhui Province (grant no. 2023 AH040306), the General Project of Anhui Natural Science Foundation (grant no. 2208085 ME147), Anhui Province quality project (grant no. 2022 xjzlts035) and Hefei University Postgraduate Cooperative Education Base Project (grant no. 2021 Yjyxm07).