In this work, a consistency detection method is proposed, to overcome the inconsistencies in the use of large-scale lead-carbon energy storage batteries (LCESBs) and the difficulties of large-scale detection for LCESBs. Based on the chemical materials and physical mechanisms of LCESBs, the internal and external factors that affect the consistency and their characterization parameters are analyzed. The inconsistent characterization parameters, such as voltage, temperature, and resistance, are used to construct a high-dimensional random matrix and calculate the matrix eigenvalue. Single loop theorem and average spectral radius are then employed to carry out preliminary consistency detection. Next, short-term discharge experiments are conducted on individual batteries with inconsistent initial screening. The voltage and temperature data is collected, and sequential overlapping derivative (SOD) transformation is performed to extract the characteristics of voltage and temperature changes. The consistency of individual cells using the Wasserstein distance is quantitatively characterized. Finally, the reliability of the consistency detection method is evaluated by the confusion matrix. The large amounts of actual measurement data shows a false negative rate of the algorithm of 0 and an accuracy of 99.94%. This study shows that using random matrix theory for preliminary detection is suitable for processing high-dimensional data of large-scale energy storage power plants. Using SOD for precise detection can amplify the voltage, temperature, and resistance differences of inconsistent batteries, making the consistency detection more accurate.