Statistical modeling of OCV-curves for aged battery cells

Today it is standard to use equivalent circuit models to describe the dynamic behavior of Li-ion batteries. The parameters and the states of the model are often estimated with model-based approaches which require accurate Open Circuit Voltage (OCV) curves to relate OCV to State of Charge (SoC). However, batteries are inevitably subjected to aging with the consequence that the OCV-curve is changing with time. In this paper we propose a method for modeling the changes of the OCV-curve based on statistical information rather than from electrochemistry. The proposed model has only one free parameter to update, namely capacity based SoH. From laboratory experimental data it is demonstrated that the proposed model can significantly reduce the average deviation from an aged OCV-curve compared to keeping the OCV-curve from the beginning of life. Furthermore, the potential of the method in an estimation context is illustrated by using it together with an Extended Kalman Filter (EKF) for estimation of SoC. Both the maximum and root mean square errors are significantly reduced compared to when the OCV-curve at beginning of life is used.