Electrochemical impedance spectroscopy (EIS) is a widely recognized method used to evaluate the electrochemical characteristics of batteries. This technique demonstrates a strong relationship between impedance and battery states, such as temperature, state of health (SOH), and state of charge (SOC). EIS promises much more than simply cell-level analysis. Because of its non-destructive characteristics, its use has been explored for electric vehicle and stationary storage system applications where battery cells are interconnected in complex configurations to provide valuable information about battery module performance.
This poster proposes an innovative methodology that shifts the focus from individual cells to battery modules and incorporates the interactions of cells connected in series. In real-world applications where the cells in a battery module exhibit temperature, SOC, and SOH imbalances, it is critical to understand the overall impedance of the battery modules fully. This study aims to investigate the potential of module-level EIS measurements to effectively diagnose internal cell imbalances without disassembling and examining each cell individually.
Module-level EIS measurements were conducted on several battery modules comprising three cells connected in series. The modules consist of both fresh and aged cells with different arrangements to investigate the effects of SOH imbalances on the total impedance of the module. The results of the impedance measurements were analyzed using approaches, including single-point impedance diagnostic and support vector machine (SVM) classification. We aimed to reveal the potential of module-level EIS in detecting cell imbalances within the module using these analytical methods. Our results show that SOH imbalances across different SOC levels can be detected using single-point impedance diagnostic. This result is further enhanced by the SVM classification results, which have a high accuracy rate.