Thermal energy storage (TES) is one of the key technologies for enabling a higher deployment of renewable energy. In this context, the present study analyzes the modeling strategies of one of the most common TES systems: stratified thermal storage tanks. These systems are essential to many solar thermal installations and heat pumps, among other clean energy technologies. Three different one-dimensional tank models are compared by their computing speed and resilience to long time steps. Two of the models analyzed are numerical, one being explicit and the other one implicit, and the other is analytical. The models are validated against data from experiments carried out considering small-scale stratified tanks, showing that their performance can be improved by using the Advanced Flowrate Distribution (AFD) method. The results show that the analytical model maintains its accuracy with longer time steps and is robust against divergence. Conversely, the numerical models show equivalent performance for short time steps, while the computation time is reduced. Although the AFD method shows promising results by achieving an improvement of 43% in terms of Dynamic Time Warping, its parameter optimization must be generalized for different tank designs, flow rates, and temperatures.