The remaining useful life (RUL) of LIBs is important in the prognostics and health management of battery systems. However, an accurate RUL prediction is difficult to achieve. Using experimental historical data, this study builds a battery degradation model for estimating battery working state and maintaining and replacing equipment in a timely manner to ensure a stable operation. A method for predicting the RUL of LIBs that employs artificial bee colony (ABC) and support vector regression (SVR) is proposed to improve prediction accuracy. SVR can deal with problems such as small samples, nonlinearity, and time-series analysis. However, SVR is problematic when applied to kernel parameter selection. The ABC algorithm is accordingly employed to optimize the SVR kernel parameters. A simulation with experimental data is conducted by utilizing the NASA Ames Prognostics Center of Excellence battery datasets to validate the proposed method. Results show that parameter optimization with the ABC algorithm is better than that with the PSO algorithm. Furthermore, the ABC-SVR method is more accurate than PSO-SVR and other existing methods are. Therefore, the proposed method achieves high prediction accuracy and prediction stability when used to predict the RUL of LIBs.

Lithium-ion batteries offer excellent advantages of high efficiency, small size, and low cost, but their instability and inconformity remain challenging. Sudden failure of batteries may cause serious accidents, endangering the safety of people’s lives and properties. Advanced remaining useful life prediction methods for batteries can effectively avoid those accidents. In this article, we proposed a novel hybrid method with the purpose of enhancing battery remaining useful life prediction precision and robustness. Based on improved complete ensemble empirical mode decomposition with adaptive noise algorithm, utilizing a special-designed interpolation reconstruction mechanism, the battery capacity degradation series was decomposed into a trend subseries and several fluctuation subseries. Weighted least square support vector machine and long short-term memory network are then established to perform prediction for the trend subseries and the fluctuation ones, respectively. A complementary series of experiments is designed to verify the effectiveness of the proposed method. The simulation results represent that the proposed method achieves higher prediction accuracy and robustness over other comparison models. The proposed approach provides a promising and effective alternate for lithium-ion batteries remaining useful life prediction without relying on the cell dynamic process, which is meaningful for cases with limited measurement parameters or limited computational power.

The accurate prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is a key, challenging research direction. In this study, a battery degradation model is built based on the LIB dataset of NASA. A data decomposition prediction method, which extracts the linear trend from the capacity degradation data and then predicts the residuals of time series with nonlinear relations, is proposed. Then, an autoregressive integrated moving average-long short-term memory (ARIMA-LSTM) combined model for predicting the RUL of LIBs is established. The linear trend of capacity degradation is predicted by the ARIMA model and the LSTM model is established to predict the nonlinear residuals separated from the capacity degradation. The summation of both obtains the final battery RUL prediction result. Compared with the LSTM model and prediction models of similar types in existing literature in recent two years, the experimental results show that the RUL prediction error of the proposed model in this article is no more than one cycle, which is smaller than that of all the models involved in the comparison. Thus, this method has higher prediction accuracy and model generalization ability in the prediction of the remaining useful life of LIBs.

Remaining useful life (RUL) prediction of lithium‐ion batteries (LIBs) plays a very important role in the prognostics and health management (PHM). Accurately predicting RUL of batteries can maintain and replace the batteries in advance to guarantee the safety and stability of the energy storage system (ESS). A method based on improved ant lion optimization and support vector regression (IALO‐SVR) is proposed to accurately predict RUL of LIBs. The ALO algorithm easily falls into the local optimal solution, the levy flight algorithm is utilized to improve the shortcoming of the ALO algorithm. With the mathematical comparison of particle swarm optimization (PSO), differential evolution (DE), and ALO algorithms, the results indicate that the IALO algorithm has higher convergence accuracy. Experimental data simulations were performed using the battery datasets of NASA Prognostics Center of Excellence (PCoE) and the Center for Advanced Life Cycle Engineering (CALCE) to verify the proposed method. Through comparison with SVR, PSO‐LSSVM, and ALO‐SVR methods, the results indicate that the RUL prediction is more accurate based upon the IALO‐SVR method. Therefore, the proposed method can provide high prediction accuracy in battery health prognosis.