A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon

Abstract

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.

Remaining Useful Life Prediction and State of Health Diagnosis of Lithium-Ion Battery Based on Second-Order Central Difference Particle Filter

“…P andP are constantly corrected during the filtering process. The first-order and second-order mean difference matrix for every particle can be defined as in (6) and 7based on the interpolation approximation formula (3) and the four square root decomposition operators obtained by (5). (1) xv (k)…”

“…ICA and DVA use the normalized incremental capacity peak (IC, dQ/dV − V ) and the peak of the differential voltage curve (DV, dV /dQ − Q), respectively, to estimate the remaining capacity. Artificial neural network (ANN) [5]- [8], support vector machine (SVM) [9], the Box-Cox transformation [10] and the Wiener process [11] are datadriven methods that describe the inherent degradation relationship and trend of the battery by machine learning. Hybrid methods that are combinations of ANN, SVM and other datadriven methods can overcome the limitations of an individual method by better exploiting all available information [12], [13].…”

An Improved Unscented Particle Filter Method for Remaining Useful Life Prognostic of Lithium-ion Batteries With Li(NiMnCo)O2 Cathode With Capacity Diving

“…In addition to common preprocessing strategies, the capacity self-recharge brought by performance tests during aging tests should be taken into account. After quantities of work done to data preprocessing for a long time, recently in 2019, Pang et al [25] made the problem resolved effectively using a combination of wavelet decomposition and artificial intelligence methods, and made RUL predictions to compare the performance of three proposed models showing the WDT-NARNN method among them to be reasonable and suitable. Besides, Zhao et al [26] verified the degradation process by applying RVM and GM methods to the proposed capacity regeneration and normal degradation model, developing a hybrid method for RUL estimation, and Xu et al [30] adopted Wienner process methods to make a successful RUL prediction, with the consideration of capacity relaxation effects.…”

A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries

“…It can simulate the implicit relationship between the observations and the objective quantities by extracting valid information from the available data. Data-driven methods contain Wiener Process (WP) [16], neural network (NN) [17][18][19][20], support vector machine (SVM) [21], relevance vector machine (RVM) [22], machine learning (ML) [23], deep learning (DL) [24], autoregressive sliding model (AR) [25], and the Gaussian Process regression (GPR) [26]. For example, in [16], first, the authors introduce the Reproductive Useful Time (RUT).…”

“…Experimental results show that this method can effectively improve the accuracy of RUL prediction. In [20], the authors employ a wavelet decomposition technology to separate the capacity regeneration process from the normal degradation process, and then used the nonlinear autoregressive neural network to predict battery capacity. The experimental results show that this method has, not only high RUL prediction accuracy, but is also less affected by different prediction starting points.…”