Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

By Shengjing Tang, Chuanqiang Yu, Xue Wang, Xiao-song Guo, Xiaosheng Si. 2014

246 citations recorded by the source corpus.

Remaining useful life (RUL) prediction is central to the prognostics and health management (PHM) of lithium-ion batteries. This paper proposes a novel RUL prediction method for lithium-ion batteries based on the Wiener process with measurement error (WPME). First, we use the truncated normal distribution (TND) based modeling approach for the estimated degradation state and obtain an exact and closed-form RUL distribution by simultaneously considering the measurement uncertainty and the distribution of the estimated drift parameter. Then, the traditional maximum likelihood estimation (MLE) method for population based parameters estimation is remedied to improve the estimation efficiency. Additionally, we analyze the relationship between the classic MLE method and the combination of the Bayesian updating algorithm and the expectation maximization algorithm for the real time RUL prediction. Interestingly, it is found that the result of the combination algorithm is equal to the classic MLE method. Inspired by this observation, a heuristic algorithm for the real time parameters updating is presented. Finally, numerical examples and a case study of lithium-ion batteries are provided to substantiate the superiority of the proposed RUL prediction method.

Subfields: Automotive Engineering, Safety, Risk, Reliability and Quality, Software

View Paper: https://www.semanticscholar.org/paper/9f8fcb33263fe39db78ac7ac7009dc4d854ce056

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Jingwen Wei
2. Zonghai Chen
3. Qingqing Zhai
4. Z. Ye
5. Guangzhong Dong
6. Guangcai Zhao
7. Yandan Tan
8. Kailong Liu

In Safety, Risk, Reliability and Quality

1. Qingqing Zhai
2. Zonghai Chen
3. Alireza Doostan
4. Mohammad Hadigol
5. Z. Ye

In Software

1. Qingqing Zhai
2. Z. Ye

Citations in the corpus

• Least squares polynomial chaos expansion: A review of sampling strategies (2017, background): “RUL of an LIB is defined as the amount of time before the battery health falls below a defined threshold, e.g., reaching the cut-off potential at the end of discharge [102], and is studied under the context of prognostics and health management [118].”
• Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression (2018, background): “As one of the most important aspects of battery health management, RUL is defined as the length of time from present time to the EoL, the issue of predicting battery’s RUL could thus be transformed to approximate the probability distribution function (PDF) of the time when its capacity or internal r”
• RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model (2017, methodology): “The simplicity and good applicability of the Wiener process model make it one of the most popular models for degradation modeling [2], [24], [26]–[31].”