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

被引：231
作者：
Tang, Shengjin [1 ]
Yu, Chuanqiang [1 ]
Wang, Xue [2 ]
Guo, Xiaosong [1 ]
Si, Xiaosheng [1 ]
机构：
[1] High Tech Inst Xian, Xian 710025, Shaanxi, Peoples R China
[2] Tsinghua Univ, Dept Precis Instrument, Beijing 100084, Peoples R China
来源：
ENERGIES | 2014年 / 7卷 / 02期
关键词：
lithium-ion batteries; remaining useful life; the Wiener process; measurement error; prediction; truncated normal distribution; maximum likelihood estimation; Bayesian; expectation maximization algorithm; RESIDUAL-LIFE; MAINTENANCE MANAGEMENT; DEGRADATION PROCESS; BURN-IN; PROGNOSTICS; MODEL; STATE; FILTER; DISTRIBUTIONS; PARAMETERS;
D O I：
10.3390/en7020520
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
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.
引用
收藏
页码：520 / 547
页数：28
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