Microelectronics Reliability Vol. 70, pp. 59-69, 2017

Interacting multiple model particle filter for prognostics of lithium-ion batteries


Xiaohong Sua, Shuai Wanga, Michael Pechtb, Lingling Zhaoa, Zhe Yea
aSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
b CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA

Abstract:

We propose a new data-driven prognostic method based on the interacting multiple model particle filter (IMMPF) for determining the remaining useful life (RUL) of lithium-ion (Li-ion) batteries and the probability distribution function (PDF) of the associated uncertainty. The method applies the IMMPF to different state equations. Modeling the battery capacity degradation is very important for predicting the RUL of Li-ion batteries. In this study, improvements are made on various Li-ion battery capacity models (i.e., polynomial, exponential, and Verhulst models). Further, three different one-step state transition equations are developed, and the IMMPF method is applied to estimate the RUL of Li-ion batteries with the use of the three improved models. The PDF of the predicted RUL is obtained by combining the PDFs obtained with each individual model. We conduct four case studies to validate the proposed method. The results are as follows:
(1) the three improved models require fewer parameters than the original models,
(2) the proposed prognostic method shows stable and high prediction accuracy, and
(3) the proposed method narrows the uncertainty PDF of the predicted RUL of Li-ion batteries.

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