A Fusion Method Based on Unscented Particle Filter and Minimum Sampling Variance Resampling for Lithium-ion Battery Remaining Useful Life Prediction
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Abstract
It is important to predict the capacity of lithium-ion battery for future cycles to assess its health condition and to estimate remaining useful life (RUL). Particle filter approaches are widely applied into the estimation of battery capacity. However, after several iterations, the degeneracy and impoverishment of particles can cause unreliable and inaccurate prediction results in particle filter (PF). In this paper, a fusion method is proposed by integrating unscented Kalman filter (UKF) and minimum sampling variance resampling (MSVR) into the standard PF for RUL prediction of batteries. The UKF is employed to generate the proposal distribution of particles, which is used by PF to calculate the weights of particles. Next, the MSVR algorithm is introduced for performing resampling procedure to improve the performance. Finally, the performance of the proposed method is validated and compared to other predictors with four different battery datasets from NASA. According to the results, the integrated method has high reliability and prediction accuracy.
How to Cite
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battery remaining useful life prediction, unscented particle filter, resampling
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