Comparison of Prognostic Algorithms Based on PF and EKF for Estimating Remaining Useful Life of Li-Ion Batteries

Abstract:

To estimate remaining useful life (RUL) of Li-ion batteries is a key factor for correct and safe battery management, particularly for the development of Battery Management System (BMS). A lumped parameter model which integrates the non-linear open-circuit voltage, current, temperature, cycle number, and remaining capacity and other dynamic characteristics is created based on the battery electrical characteristics is presented. A particle filter (PF) algorithm which syncretizes Li-ion battery electrochemical working process is proposed according to the sequence importance of re-sampling to predict its discharge end time in single cycle time and cycle life. Besides, for comparison, a extended Kalman filter (EKF) algorithm is also proposed to estimate the RUL based on the same statistics. Simulation results show that the PF algorithm according to lumped parameter model has a better precision in estimating RUL compared with the EKF algorithm.

• Periodical: Applied Mechanics and Materials (Volume 529)
• Pages: 616-620
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.529.616
• Online since: June 2014
• Authors: Jin Zhang*, An Tong Gao, Wen Bing Wang, Yu Sheng Han, Rong Gang Chen, Feng Zhang
• Keywords: EKF, End-of-Discharge, Li-Ion Battery, Lumped Parameter Model, PF

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