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,...
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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 batteryocvestimation 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 estimationalgorithms, 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.
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