The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy.

Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.

Accurate state-of-health (SOH) prediction of lithium-ion batteries (LIBs) plays an important role in improving the performance and assuring the safe operation of the battery energy storage system (BESS). Deep extreme learning machine (DELM) optimized by the improved sparrow search algorithm (ISSA) is developed to predict the SOH of LIBs under random load conditions in the paper. Firstly, two indirect health indicators are extracted from the random partial discharging voltage and current data, which are chosen as the inputs of DELM by the Pearson correlation analysis. Then, ISSA is presented by combining the elite opposition-based learning (EOBL) and the Cauchy-Gaussian mutation strategy to increase the diversity of sparrow populations and prevent them from falling into the local optimization. Finally, the ISSA-DELM model is utilized to estimate the battery SOH. Experimental results illustrate the high accuracy and strong robustness of the proposed approach compared with other methods.

The prediction of Remaining useful life (RUL) and the estimation of State of health (SOH) are extremely important issues for operating performance of Lithium‐ion (Li‐ion) batteries in the Battery management system (BMS). A multi‐scale prediction approach of RUL and SOH is presented, which combines Wavelet neural network (WNN) with Unscented particle filter (UPF) model. The capacity degradation data of Li‐ion batteries are decomposed into the low‐frequency degradation trend and high‐frequency fluctuation components by Discrete wavelet transform (DWT). Based on the WNN‐UPF model, the long‐term RUL of Li‐ion batteries is predicted with the low‐frequency degradation trend data. The high‐frequency fluctuation data and RUL prediction results are integrated effectively to estimate the short‐term SOH of Li‐ion batteries. The experimental results show that the proposed method achieves high accuracy and strong robustness, even if the prediction starting point is set to the early stage of Li‐ion batteries' lifespan.