Add-On Type Internal Resistance and Current Sensor Fault Estimation Algorithm Based on the Correlation of the Current-Voltage Data

Lithium-ion batteries have established themselves as a critical energy source for electric vehicles and energy storage systems. As their usage continues to grow, the issues of battery currents due to current sensor failures can lead to overcharging, over-discharging, and potentially hazardous incidents such as battery fires. To address this issue, this study proposes a current sensor fault detection algorithm based on the analysis of the data covariance matrix. The proposed algorithm is designed as an add-on type solution, enabling calculations using only voltage data extracted from the Battery Management System (BMS). It detects current sensor faults in real-time, thereby mitigating potential risks. Furthermore, the study demonstrates the algorithm’s applicability by cross-validating its performance using data from both new and aged battery models. Through this approach, the algorithm addresses current sensor fault issues across various battery states, contributing to the prevention of critical incidents such as battery fires.