Open AccessComparative study of lithium‐ion battery open‐circuit‐voltage online estimation methods
Author(s) -
Meng Jianwen,
Boukhnifer Moussa,
Diallo Demba
Publication year - 2020
Publication title -
iet electrical systems in transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.588
H-Index - 26
ISSN - 2042-9746
DOI - 10.1049/iet-est.2019.0026
Subject(s) - open circuit voltage , lithium ion battery , battery (electricity) , voltage , lithium (medication) , ion , materials science , electrical engineering , automotive engineering , cell voltage , computer science , engineering , chemistry , power (physics) , physics , medicine , thermodynamics , electrode , organic chemistry , anode , endocrinology
As an important property and distinct characteristic of different lithium‐ion batteries, open‐circuit‐voltage (OCV) online estimation can provide useful information for battery monitoring and fault diagnosis. However, studies dedicated to battery OCV estimation are not as much as the research efforts on state‐of‐charge determination and parameter identification such as capacity and resistance. Hence, a general discussion for selecting the battery OCV estimation algorithm is proposed in this study. To this end, modelling process of extended state‐space model and autoregressive exogenous model is presented in detail. Four estimation algorithms, namely, Luenberger observer, Kalman filter, recursive least‐square with forgetting factor and recursive least‐square with variable forgetting factor are selected and compared in terms of estimation accuracy, computational cost, parameter tuning and robustness to parameter variations. Based on real battery cell parameters and environmental conditions, simulation results have shown that even if they are less robust to model uncertainty, observer‐based methods exhibit better estimation performances than regression‐based ones.