Particle-filtering-based failure prognosis via sigma-points: Application to Lithium-Ion battery State-of-Charge monitoring
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- 作者：David E. Acuñ ; a ; davacuna@ing.uchile.cl" class="auth_mail" title="E-mail the corresponding author ; Marcos E. Orchard
- 关键词：Prognostics and health management ; Uncertainty characterization ; Particle filters ; Battery State-of-Charge
- 刊名：Mechanical Systems and Signal Processing
- 出版年：2017
- 出版时间：15 February 2017
- 年：2017
- 卷：85
- 期：Complete
- 页码：827-848
- 全文大小：948 K
文摘
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Novel definition for Time-of-Failure (ToF) probability applied to online prognosis.
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Novel prognosis algorithm based on Gaussian Mixture Models and sigma-points.
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Improves the characterization of the tails of the ToF probability mass function.
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Algorithm tested and validated for battery State-of-Charge prognosis.