Abstract
Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on Dempster-Shafer Theory and the Support Vector Regression-Particle Filter
Highlights
Lithium-ion batteries (LIBs) have been widely used in various electronic devices to provide the necessary electrical energy for the normal operation of equipment
This study proposes a model of LIB remaining useful life (RUL) prediction based on Dempster-Shafer theory (DST) and support vector regression-particle filter (SVR-particle filter (PF)) (combination of Equations (60), (61), (62), and (63))
In the process of predicting the RUL of LIBs based on DST
Summary
Lithium-ion batteries (LIBs) have been widely used in various electronic devices to provide the necessary electrical energy for the normal operation of equipment. Because recursive filtering algorithms can be used to analyze, estimate, and predict battery failure using measured battery data based on the state-space model of the battery, they have often been applied in the prediction of the RUL of LIBs [15]. The present study proposes a novel method based on DST and the SVR-PF for predicting the RUL of LIBs by combining the RUL prediction results derived from various independent data sources to ensure an accurate prediction when the available data are relatively sparse. The predicted impedance values are used as the measurement output to establish the model for predicting the RUL of LIBs based on the SVR-PF, which is established as follows: λ∗. DST combines views of the same issue derived from different sources, and simultaneously eliminates all conflicting views to obtain a reliable posterior BPA function after fusion
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