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Remaining Useful Life Prediction of Lithium Batteries Based on Extended Kalman Particle Filter
Author(s) -
Zhang Ning,
Xu Aidong,
Wang Kai,
Han Xiaojia,
Hong Wenhuan,
Hong Seung Ho
Publication year - 2021
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.23287
Subject(s) - particle filter , battery (electricity) , kalman filter , computer science , battery capacity , exponential function , extended kalman filter , degradation (telecommunications) , algorithm , control theory (sociology) , mathematics , artificial intelligence , power (physics) , telecommunications , physics , control (management) , quantum mechanics , mathematical analysis
The prognosis of time‐to‐failure for a battery can avoid the failure caused by battery performance loss. In this paper, a novel and effective algorithm is proposed to predict the remaining useful life of lithium‐ion batteries. The extended Kalman particle filter is used to improve particle degradation problem existing in standard particle filter algorithm. In order to fit battery capacity degradation, a transformed model is proposed based on double exponential empirical degradation model. It can reduce the number of parameters and the training difficulty of parameters; it also matches the form of state transfer equation. In order to improve prediction accuracy, the auto regression model is introduced to correct observation values produced by observation equation. Experimental results show that the proposed algorithm can effectively improve the accuracy of prediction compared with other algorithms. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.