In a passive radio-frequency identification (RFID) system the reader communicates with the tags using the EPC Global UHF Class 1 Generation 2 (EPC Gen-2) protocol with dynamic framed slotted ALOHA. Due to the unique challenges presented by a low-power, random link, the channel efficiency of even the most modern passive RFID system is less than 40%. Hence, a variety of methods have been proposed to estimate the number of tags in the environment and set the optimal frame size. Some of the algorithms in the literature even claim system efficiency beyond 90%. However, these algorithms require fundamental changes to the underlying protocol framework which makes them ineligible to be used with the current hardware running on the EPC Gen-2 platform and this infrastructure change of the existing industry will cost billions of dollars. Though numerous types of tag estimation algorithms have been proposed in the literature, none had their performance analyzed thoroughly when incorporated with the industry standard EPC Gen-2. In this study, we focus on some of the algorithms which can be utilized on today’s current hardware with minimal modifications. EPC Gen-2 already provides a dynamic platform in adjusting frame sizes based on subsequent knowledge of collision slots in a given frame. We choose some of the popular probabilistic tag estimation algorithms in the literature such as Dynamic Frame Slotted ALOHA (DFSA) – I, and DFSA – II, and rule based algorithms such as two conditional tag estimation (2CTE) method and incorporate them with EPC Gen-2 using different strategies to see if they can significantly improve channel efficiency and dynamicity. The results from each algorithm are also evaluated and compared with the performance of pure EPC Gen-2. It is important to note that while integrating these algorithms with EPC Gen-2 to modify the frame size, the protocol is not altered in any substantial way. We also kept the maximum system efficiency for any MAC layer protocol using DFSA as the upper bound to have an impartial comparison between the algorithms. Finally, we present a novel and comprehensive analysis of the probabilistic tag estimation algorithms (DFSA-I & DFSA-II) in terms of their statistically significant correlations between channel efficiency, algorithm estimation accuracy and algorithm utilization rate as the existing literature only look at channel efficiency with no auxiliary analysis. In this study, we use a scalable and flexible simulation framework and created a light-weight, verifiable Gen-2 simulation tool to measure these performance parameters as it is very difficult, if not impossible, to calculate system performance analytically. This framework can easily be used to test and compare more algorithms in the literature with Gen-2 and other DFSA based approaches.