This research developed an application for electrical vehicles charging stations (EVCS) to estimate the optimum day-ahead bidding profiles in frequency containment reserves (FCR) markets and this paper presents the chastic methodology behind this application. To achieve this, first, deterministic models are developed to culate the maximum FCR that could be provided by each charging event (cycle) of an electric vehicle (EV). models are established based on the technical requirements of FCR in the Nordic flexibility market, namely frequency containment reserve for normal operation (FCR-N) and frequency containment reserve for turbances (FCR-D). In the next step, these deterministic models will be combined with historical data of charging records in EVCS to calculate the probability density functions of the FCR profiles. Finally, the proposed cation estimates the optimum FCR profiles, which maximise the expected profit of EVCS from participating day-ahead flexibility market, by performing a stochastic optimisation. The performance of the proposed cation is evaluated by using empirical charging data of public EVCS in Helsinki area.