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

Shengjin Tang, Chuanqiang Yu, Xue Wang, Xiaosong Guo and Xiaosheng Si
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Shengjin Tang: High-Tech Institute of Xi'an, Xi'an, Shaanxi 710025, China
Chuanqiang Yu: High-Tech Institute of Xi'an, Xi'an, Shaanxi 710025, China
Xue Wang: Department of Precision Instrument, Tsinghua University, Beijing 100084, China
Xiaosong Guo: High-Tech Institute of Xi'an, Xi'an, Shaanxi 710025, China
Xiaosheng Si: High-Tech Institute of Xi'an, Xi'an, Shaanxi 710025, China

Energies, 2014, vol. 7, issue 2, 1-28

Abstract: 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.

Keywords: lithium-ion batteries; remaining useful life; the Wiener process; measurement error; prediction; truncated normal distribution; maximum likelihood estimation; Bayesian; expectation maximization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (36)

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