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.
Comparative study of lithium‐ion battery open‐circuit‐voltage online estimation methods
IET Electrical Systems in Transportation ; 10 , 2 ; 162-169
01.06.2020
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
fault diagnosis , battery cell parameters , secondary cells , lithium compounds , estimation algorithms , state‐space methods , Kalman filter , observers , autoregressive exogenous model , battery management systems , extended state‐space model , lithium‐ion battery open‐circuit‐voltage online estimation methods , parameter identification , recursive estimation , Kalman filters , variable forgetting factor , autoregressive processes , battery OCV estimation algorithm , least squares approximations , modelling process , state‐of‐charge determination , recursive least‐square , Luenberger observer , estimation accuracy , environmental conditions , computational cost , battery monitoring
Comparative study of lithium-ion battery open-circuit-voltage online estimation methods
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