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Probabilistic Residual Life Prediction for Lithium-ion Batteries Based on Bayesian LS-SVR
Received date: 2012-11-28
Revised date: 2013-04-22
Online published: 2013-04-23
Supported by
Aeronautical Science Foundation of China (20100751010, 2010ZD11007)
An online probabilistic prediction approach for the residual life of a lithium-ion battery is proposed by using the Bayesian least squares support vector regression (LS-SVR). First, historical degradation data of the lithium-ion battery are selected through a sliding window. Then the selected data are formed into training samples by the phase space reconstruction theory, with the minimum embedding dimension calculated by Cao's method. Secondly, a predicting model based on least squares support vector regression is trained by a three level Bayesian inference framework. Then in the iterative prediction stage, Monte Carlo method is applied to manage the uncertainty and its propagation in the multi-step prediction, which is achieved by approximating the continuous distribution with a group of discrete particles and predicting the future health status of the battery based on the principle of "non intersecting degradation trajectories" and the Gaussian process assumption. Finally, by counting the number of particles which pass through the predetermined failure threshold, the probability distribution of the battery residual life can therefore be estimated. Comparative experiments are conducted between Bayesian LS-SVR and Gaussian process regression (GPR) using the public battery data sets provided by National Aeronautics and Space Administration Ames Research Center. The results demonstrate that the Bayesian LS-SVR method has higher prediction accuracy and confidence.
CHEN Xiongzi , YU Jinsong , TANG Diyin , WANG Yingxun . Probabilistic Residual Life Prediction for Lithium-ion Batteries Based on Bayesian LS-SVR[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013 , 34(9) : 2219 -2229 . DOI: 10.7527/S1000-6893.2013.0223
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