Lithium-ion batteries remaining useful life prediction using Wiener process and unscented particle filter
نویسندگان
چکیده
منابع مشابه
Application of Unscented Particle Filter in Remaining Useful Life Prediction of Lithium-ion Batteries
Accurate prediction of the remaining useful life of a faulty component is important to the health management of the system. It gives operators information about when the component should be replaced. This paper studied the remaining useful life prediction of the lithium-ion batteries. Some work has been done to solve this problem, but it still remains challengeable. Particle filter (PF) is a re...
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Accurate prediction of the remaining useful life of a faulty component is important to the prognosis and health management of a system. It gives operators information about when the component should be replaced. In recent years, a lot of research has been conducted on battery reliability and prognosis, especially the remaining useful life prediction of the lithium-ion batteries. Particle filter...
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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 c...
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Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is ...
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The remaining useful life (RUL) prediction of Lithium-ion batteries is closely related to the capacity degeneration trajectories. Due to the self-charging and the capacity regeneration, the trajectories have the property of multimodality. Traditional prediction models such as the support vector machines (SVM) or the Gaussian Process regression (GPR) cannot accurately characterize this multimoda...
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ژورنال
عنوان ژورنال: Journal of Power Electronics
سال: 2019
ISSN: 1598-2092,2093-4718
DOI: 10.1007/s43236-019-00016-3