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.
Highlights
Lithium-ion batteries have been widely used in many fields, e.g., consumer electronics, electric vehicles, marine systems, aircrafts, satellites, etc., due to their high power density, low weight, long lifetime, low self-discharge rate, no memory effect and other advantages [1,2]
The remainder of this paper is organized as follows: Section 2 develops the measurement error (ME) model and derives the remaining useful life (RUL) distribution; in Section 3, we present a two-step maximum likelihood estimation (MLE) method to estimate the fixed parameters; in Section 4, we discuss the parameters updating for the WPME and propose a heuristic algorithm; numerical examples and a case study are provided in Section 5; and Section 6 draws the main conclusions
This paper proposes a novel RUL prediction algorithm for lithium-ion batteries based on the WPME
Summary
Lithium-ion batteries have been widely used in many fields, e.g., consumer electronics, electric vehicles, marine systems, aircrafts, satellites, etc., due to their high power density, low weight, long lifetime, low self-discharge rate, no memory effect and other advantages [1,2]. Peng and Tseng [31] incorporated the random effects into the modeling of the WPME and presented a MLE method for a type of items This method has been applied to the nonlinear Wiener process [33]. A classic work about the updating of the random parameters is proposed by Gebraeel et al [43], whose model established a linkage between the past and current degradation data of the congeneric items by a Bayesian mechanism. The remainder of this paper is organized as follows: Section 2 develops the ME model and derives the RUL distribution; in Section 3, we present a two-step MLE method to estimate the fixed parameters; in Section 4, we discuss the parameters updating for the WPME and propose a heuristic algorithm; numerical examples and a case study are provided in Section 5; and Section 6 draws the main conclusions
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
