Improving the state of charge estimation of reused lithium-ion batteries by abating hysteresis using machine learning technique

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2020-12
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
Journal of Energy Storage, Volume 32
Abstract
The accuracy of the state of charge (SoC) estimation is of great importance to the operational safety of a battery pack, especially for secondary applications with retired batteries. Here, a novel approach combining Sigma-point Kalman filter and machine learning technique based on an equivalent circuit model is proposed to improve the state of charge estimation accuracy of a reused battery pack (LiFePO4) by abating the negative effect of the hysteresis phenomenon. Compared to traditional estimation methods, this approach can reduce the root mean square error by up to 8.3%. The maximum estimation error for three experimental tests is only 0.016 being within acceptable range and demonstrating the effectiveness of the proposed approach.
Description
Keywords
Battery, Equivalent circuit model, Hysteresis phenomenon, Machine learning, Sigma-point Kalman filter, State of charge
Other note
Citation
Xu , Z , Wang , J , Fan , Q , Lund , P D & Hong , J 2020 , ' Improving the state of charge estimation of reused lithium-ion batteries by abating hysteresis using machine learning technique ' , Journal of Energy Storage , vol. 32 , 101678 . https://doi.org/10.1016/j.est.2020.101678