Abstract: Thermocline heat storage tanks, known for their high net energy and exergy efficiency, are becoming a mainstream thermal energy storage solution. However, flow direction switching caused by real-time variations in charging/discharging mass flow creates discontinuities in conventional temperature stratification models. To address this, this study employs continuous smooth functions to approximate the 0-1 variables characterizing working fluid flow direction, resulting in a one-dimensional continuous dynamic stratified temperature model along the tank height. Building on this model, we propose an optimal sensor placement strategy based on sensitivity matrices. The system observability index is redefined using information derived from sensitivity matrices, with the minimum sensor count and optimal positions determined by each location's contribution to system observability. Simulation results demonstrate that the proposed model accurately captures vertical temperature stratification across various operating scenarios when an appropriate smoothing parameter μ is selected. Among all observable sensor combinations with equal sensor numbers, this strategy achieves the smallest mean and variance of RMSE in moving horizon estimation (MHE) observation errors, confirming its superior system observability performance.