An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine

Abstract

Lithium-ion batteries (LiBs) are the most important part of electric vehicle (EV) systems. Because there are two different degradation rates during LiB degradation, there are many two-phase models for LiBs. However, most of these methods do not consider the randomness of the changing point in the two-phase model and cannot update the change time in real time. Therefore, this paper proposes a method based on the combination of the two-phase Wiener model and an extreme learning machine (ELM). The two-phase Wiener model is used to derive the mathematical expression of the remaining useful life (RUL), and the ELM is implemented to adaptively detect the changing point. Based on the Poisson distribution, the distribution of the changing time is derived as a gamma distribution. To evaluate the theoretical results and practicality of the proposed method, we perform both numerical and practical simulations. The results of the simulations show that due to the precise and adaptive detection of changing points, the proposed method produces a more accurate RUL prediction than existing methods. The error of our method for detecting the changing point is about 4% and the mean prediction error of RUL in the second phase is improved from 4.39 cycles to 1.61 cycles.

Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory

“…In the next two articles, the authors address the prognosis issues for lithium-ion batteries and turbofan engines. Chen X. et al [11] investigated the possibility of the detection of change points and the prediction of the remaining useful life (RUL) of the two-phase Wiener process model (TPWPM) for lithium-ion batteries (LiBs). The authors successfully used the biphasic Wiener model to obtain a mathematical expression of the remaining useful life and the extreme learning machine (ELM) method for adaptive change point detection.…”

A reliability estimation method based on two‐phase Wiener process with evidential variable using two types of testing data

“…Some similar researches can also be found in Refs. [31][32][33][34][35] However, although a number of studies have been performed to model the two-phase degradation processes, how to effectively utilize the degradation dataset and precisely estimate the model parameters are still considered as a challenge in engineering practice, especially under sample conditions.…”

Remaining useful cycle life prediction of lithium-ion battery based on TS fuzzy model

“…In Lee et al (2021), a robust and reliable estimation method of the remaining service life of lithium-ion batteries in electric vehicles based on a deep neural network is proposed to predict the remaining service life by monitoring the batteries' internal resistance. A robust and reliable method based on deep neural networks is proposed in Chen et al (2021) to estimate the RUL of lithium-ion batteries in electric vehicles. Results show that this method performs accurate adaptive detection of change points and has higher robust prediction accuracy than existing methods.…”