Improved State-of-Charge Estimation for Batteries Using Internal Resistance Variations

Internal resistance of a battery reflects its distinct characteristics, including factors such as state of health (SOH), state of charge (SOC), reversibility, thermal runaway, etc. This paper develops a simple non-invasive battery analysis approach based on modified intermittent current interruption (ICI) methods for characterizing the internal resistance $(\mathbf{R}_{\text{int}})$ and variations of internal resistance $(\Delta \mathbf{R}_{\text{int}})$ at different SOC and different testing currents. The decreasing trend of $\mathbf{R}_{\text{int}}$ while increasing SOC, alongside the notable increase in $\mathbf{R}_{\text{int}}$ coupled with capacity loss, highlights the potential of $\mathbf{R}_{\text{int}}$ as a reliable indicator for assessing the SOC of batteries. More importantly, the paper presents a study on $\Delta \mathbf{R}_{\text{int}}$ between discharged states and charged states and identifies an increasing trend between $\Delta \mathbf{R}_{\text{int}}$ with the usable capacity of batteries under different testing currents. This positive correlation suggests that $\Delta \mathbf{R}_{\text{int}}$ could be an enhanced parameter in non-invasive battery analysis for predicting the SOC of batteries, especially suitable for Battery Management System (BMS) applications. The $\Delta \mathbf{R}_{\text{int}}$ model, which highlights the internal resistance variations caused by charge transfer during lithiation and delithation process, provides a robust solution for predicting the SOC of batteries while overcoming cell-to-cell variations in battery packs.