In this study, we demonstrate that the utilization of modeling CV offers a promising new quantitative approach for elucidating charge storage mechanisms in two categories: diffusion-controlled and capacitive-controlled processes. Furthermore, we compared the results with Dunn's and Trasatti's methods, highlighting discrepancies and limitations in these approaches. Our research underscores the importance of constructing models that accurately represent the entire CV system, enabling a deeper understanding of charge storage mechanisms. Consequently, our findings pave the way for the advancement of more efficient and effective energy storage technologies. Display omitted •CV data were simulated and compared with conventional relationships.•Highlighting the advantages and limitations of using conventional approaches.•Studying the impact of resistance and CPE exponent on charge storage mechanisms.•Emphasizing the significance of further developing CV models. As electrochemical energy storage continues to gain importance, researchers have been exploring novel materials and electrode designs to enhance performance. While these innovations have significantly improved the performance of energy storage devices, the specific mechanisms responsible for their success remain unclear. One powerful tool for gaining insights into how modifications to the electrode can enhance cell performance is cyclic voltammetry (CV). However, interpreting CV data can be challenging, and simple analytical relations are often inadequate for accurate assessment. Moreover, different analytical methods can yield conflicting results, leading to confusion within the research community and hindering progress in the field. To address these challenges, our study aims to investigate the contributions of surface and diffusion-controlled processes to charge storage in supercapacitor applications. We will employ conventional methods to examine how these processes can lead to the misinterpretation of CV data and identify the advantages and limitations of different analytical approaches. Our research underscores the importance of developing models that faithfully replicate the system of interest to gain insights into charge storage mechanisms. By identifying these key factors, our findings could pave the way for the development of more efficient and effective energy storage technologies.