Ming Yin∗,∗∗
Li-ion battery, improved AR-PF model, remaining useful life
Li-ion batteries have been widely used in national defence fields such as aerospace and weapons, and their remaining useful life (RUL) prediction has become a current research hotspot. Aiming at the problem of poor tracking data of accelerated degradation in auto regressive (AR) model, an influencing factor modified AR-particle filter (AR-PF) model was introduced. Three sets of test data from Center of Advance Life Cycle Engineering at the University of Maryland were used to calculate the RUL and probability density function to verify the effectiveness of the algorithm. The method of modified AR-PF realizes that the RUL prediction error of Li-ion battery is more accurate, which has a certain application value.
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