satellite lithium-ion battery remaining useful life estimation with an iterative updated rvm fused with the kf algorithm

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ID: 136375
2018
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Abstract
Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life (RUL) estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine (RVM) is a data-driven algorithm used to estimate a battery’s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery’s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.
Reference Key
song2018chinesesatellite Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Yuchen SONG;Datong LIU;Yandong HOU;Jinxiang YU;Yu PENG
Journal Cancer epidemiology
Year 2018
DOI
10.1016/j.cja.2017.11.010
URL
http://www.sciencedirect.com/science/article/pii/S1000936117302455
Keywords
kalman filter lithium-ion battery relevance vector machine astronautics aeronautics

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