# Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
## 6 Conclusions  
In order to solve the problem of low accuracy of SOH and RUL prediction caused by the difficulty of establishing the feature engineering of Li-ion batteries, this paper proposes an SOH and RUL prediction model based on manifold learning and LightGBM, and conducts relevant experiments to obtain the following conclusions:  
1. In this paper, various features including average voltage decay, IC curve parameters, generalized multiscale entropy values, and autocorrelation coefficients are extracted from different angles, and the high-dimensional features are mapped to the low-dimensional space based on the L-ISOMAP manifold learning method, which effectively achieves dimensional simplification. The final obtained health factor sequences have high Spearman correlation coefficients between them and the true capacity.
2. In the field of battery SOH estimation, the feature engineering and prediction model established in this paper can effectively achieve its accurate prediction. In dataset A, the battery \(R^{2}\) is higher than 0.98. In dataset B, the feature engineering and model  
Figure 15: Prediction results analysis for the test set of dataset B. (**a**) Prediction results for the test set of dataset B; (**b**) dataset B test set prediction error.  
established in this paper also achieve good prediction results, which proves that the method in this paper has good generalizability.
3. Comparing with the random model and XGboost model commonly used for battery SOH prediction, the LightGBM model used in this paper has better performance and better results in several indicators, such as MSE and RMSE, and the MSE is reduced by 2.700 \(\times\) 10\({}^{-4}\) and 0.9685 \(\times\) 10\({}^{-4}\) compared with the random forest algorithm and XGBoost, respectively, where the AE indicators are all 0, indicating that the model has a lower RUL prediction error.
4. In the comparison experiments of different stream shape learning algorithms, the L-ISOMAP stream shape learning method used in this paper significantly outperforms the rest of the methods in terms of SOH prediction, and its prediction results have the highest overlap with the true capacity curve and perform the best.  
Conceptualization, J.Y.; data curation, M.Z.; methodology, J.Y.; project administration, M.Z.; resources, M.Z. and J.Y.; validation, J.Y.; visualization, T.F.; writing--original draft, J.Y.; writing--review and editing, M.Z. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of the Higher Education Institute of Anhui Province (KJ2020A0309), National Natural Science Foundation of China (51874010).  
The access URL for dataset A in the manuscript is: [[https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/)]([https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/)) (accessed on 10 February 2023).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.