Prediction of Remaining Useful Life of the Lithium-Ion Battery Based on Improved Particle Filtering - Wu, Tiezhou; Zhao, Tong; Xu, Siyun (Frontiers in Energy Research, 2022)
Abstract: Remaining useful life (RUL) prediction of Lithium-ion batteries plays an important role in battery failure prediction and health management (PHM). Accurately predicting the battery RUL can maintain and replace batteries with potential safety hazards in advance to ensure the safety and reliability of the energy storage system. In the prediction of the remaining service life of lithium-ion batteries, it is difficult to ensure the accuracy of battery life prediction due to the problem of particle degradation and the influence of singular values in the particle filter algorithm. In view of these problems, this paper introduces the unscented Kalman algorithm to improve the particle filter algorithm from the perspective of re-weighting the particles, so as to improve the accuracy of the prediction results of the remaining service life of lithium-ion batteries. The improved particle filter is simulated and verified using the battery sample data in the Arbin experimental test platform. Comparing the simulation results with the traditional particle filter method, it is proved that the improved particle filter method proposed in this paper can provide more accurate battery RUL prediction results, and can effectively improve the accuracy and robustness of the remaining service life prediction of lithium-ion batteries.