2013
DOI: 10.1109/tie.2012.2224079
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Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries

Abstract: This paper presents a class of risk measures to be used as damage indicators within particle filtering (PF)-based real-time prognosis algorithms, with application to the case of state-of-charge prediction in lithium-ion batteries. The proposed risk measure not only incorporates the risk of battery failure but also is a measure for the confidence on the prognosis algorithm itself. In addition, a novel simplified PF-based prognostic method is proposed to estimate the battery discharge time, while providing a com… Show more

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Cited by 125 publications
(44 citation statements)
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“…Other methods are also used in the parameter identification of the model. Tseng et al [34] constructed three kinds of regression models based on the statistical data (N order polynomial regression model [35], bivariate polynomial regression model, and the index regression model), and introduced the particle swarm optimization (PSO) algorithm to optimize the model parameters. Simulations indicate that the regression models using discharged voltage and internal resistance as aging parameters can more accurately build a state of health profile than those using cycle numbers.…”
Section: Rul Prognostics Methodologies Based On Artificial Intelligencementioning
confidence: 99%
“…Other methods are also used in the parameter identification of the model. Tseng et al [34] constructed three kinds of regression models based on the statistical data (N order polynomial regression model [35], bivariate polynomial regression model, and the index regression model), and introduced the particle swarm optimization (PSO) algorithm to optimize the model parameters. Simulations indicate that the regression models using discharged voltage and internal resistance as aging parameters can more accurately build a state of health profile than those using cycle numbers.…”
Section: Rul Prognostics Methodologies Based On Artificial Intelligencementioning
confidence: 99%
“…In this study, we develop an algorithm to assess the structural integrity by using particle filtering [8][9][10]. Particle filtering is very suitable for nonlinear systems with the presence of non-Gaussian process and observation noises.…”
Section: Particle Filtering For Structural Assessment With Aementioning
confidence: 99%
“…Building upon our previous works [7][8][9][10][11], we will develop a rigorous structural integrity assessment algorithm based on particle filtering. In the proposed algorithm, we combine the concepts from physics-based models, measurements, and Monte Carlo simulation to realize a long-term prediction of health degradation.…”
Section: -2mentioning
confidence: 99%
“…Battery health is an important topic and its life can be significantly extended by taking a simple measure. Though the main focus on this work is not centered on the battery degradation, it is suggested in various work [41][42][43] to keep the level of Lithium-ion batteries above a certain threshold to extend its life. The more frequent the battery is charged the longer the life is extended, i.e., it is better to discharge 10 % of the battery and recharge to full capacity 10 times than to discharge 50 % of the battery and recharge to full capacity 2 times.…”
Section: Charge Aware Multiplexing Access Implementationmentioning
confidence: 99%