Particle Flow Filter: An Ingenious Method to Predict the Remaining Useful Life of Lithium-Ion Batteries

The use of particle filter to predict the remaining useful life of lithium-ion batteries is challenged by particle degradation, which can reduce prediction accuracy and result in computational resource waste. To address this issue, we propose a novel approach that leverages particle flow filter to estimate the state of health of lithium-ion batteries. Our method first establishes a dual exponential empirical model based on the performance degradation mechanism of lithium-ion batteries. We then use particle flow filter to predict the remaining capacity of the batteries. Experimental results demonstrate the effectiveness of our approach, which achieves high prediction accuracy without suffering from particle degradation.