A novel data-model fusion state-of-health estimation approach for lithium-ion batteries

Abstract

In order to ensure the efficient, reliable, and safe operation of the lithium-ion battery system, an accurate battery state-of-health estimation is essential and remaining challenges. Here we propose a novel data-model fusion battery state-of-health estimation approach based on open-circuit-voltage parametric modeling considering the correlation between capacity degradation and the open-circuit-voltage changes. An open-circuit-voltage model is built to capture the aging behavior associated with the reactions progress in the cell. Then the battery state-of-health estimation approach is developed based on the correlation between capacity fade and the changes of the open-circuit-voltage model parameters. In addition, a data-driven based method is applied to identify the parameters of the proposed battery model to obtain the open-circuit-voltage online. The proposed state-of-health estimation approach has been verified by the cells experienced different aging paths. The results show that the average relative errors of the state-of-health estimation for all cells are less than 3% against different aging paths and levels.