Capacity fade in lithium-ion batteries (LIBs) poses challenges for various industries. Predicting and preventing this fade is crucial, and hybrid methods for estimating remaining useful life (RUL) have become prevalent and achieved significant advancements. In this paper, we introduce a hybrid voting ensemble that combines Gradient Boosting, Random Forest, and K-Nearest Neighbors to forecast the fading capacity trend and knee point. We conducted extensive experiments using the CALCE CS2 datasets. The results indicate that our proposed approach outperforms single deep learning methods for RUL prediction and accurately identifies the knee point. Beyond prediction, this innovative method can potentially be integrated into real-world applications for broader use.

In the realm of lithium-ion batteries (LIBs), issues like material aging and capacity decline contribute to performance degradation or potential safety hazards. Predicting remaining useful life (RUL) serves as a crucial method of assessing the health of batteries, thereby enhancing reliability and safety. To reduce the complexity and improve the accuracy and applicability of early RUL predictions for LIBs, we proposed a Mamba-based state space model for early RUL prediction. Due to the impacts of abnormal data, we first use the interquartile range (IQR) method with a sliding window for data cleansing. Subsequently, the top three highest correlated features are selected, and only the first 300 cycling data are used for training. The model has the ability to make forecasts using these few historical data. Extensive experiments are conducted using CALCE CS2 datasets. The MAE, RMSE, and RE are less than 0.015, 0.019, and 0.0261; meanwhile, $R^2$ is higher than 0.99. Compared to the baseline approaches (CNN, BiLSTM, and CNN-BiLSTM), the average MAE, RMSE, and RE of the proposed approach are reduced by at least 29%, 21%, and 36%, respectively. According to the experimental results, the proposed approach performs better in terms of accuracy, robustness, and efficiency.

In the realm of Lithium-ion batteries, issues like material aging and capacity decline contribute to performance degradation or potential safety hazards. Predicting Remaining Useful Life (RUL) serves as a crucial method to assess the health of batteries, thereby enhancing reliability and safety. Currently hybrid approaches for batteries RUL estimation are prevalence and gained fruitful development. In this paper, a hybrid voting ensembles combining Gradient Boosting, Random Forest and K-Nearest Neighbors is proposed to predict the capacity fade trend and knee point. Finally, extensive experiments are conducted using CALCE CS2 datasets. According to the experimental results, the proposed approach supersedes the single deep learning approach for RUL prediction and the knee point is predicted accurately. Besides prediction purpose, this innovated approach can be also proposed to be integrated into the real-world application for wider usage.