The aim of this study was to characterize W' recovery kinetics in response to a partial W' depletion. We hypothesized that W' recovery following partial depletion would be better described by a biexponential than by a monoexponential model. Nine healthy men performed a ramp incremental exercise test, three to five constant load trials to determine critical power and W', and ten experimental trials to quantify W' depletion. Each experimental trial consisted of two constant load work bouts (WB1 + WB2) interspersed by a recovery interval. WB1 was designed to evoke a 25% or 75% W' depletion (DEP25% + DEP75%). Subsequently, participants recovered for 30, 60, 120, 300 or 600 s, and then performed WB2 to exhaustion in order to calculate the observed W' recovery (W'OBS). W'OBS data were fitted using monoexponential and biexponential models, both with a variable and a fixed model amplitude. Root mean square error (RMSE) and Akaike information criterion (AICc) were calculated to evaluate the models' goodness-of-fit. The biexponential model fits were associated with overall lower RMSE values (0.4-5.0%) compared to the monoexponential models (2.9-8.0%). However, ΔAICc resulted in negative values (-15.5 and -23.3) for the model fits where the amplitude was free, thereby favoring the use of a monoexponential model for both depletion conditions. For the model fits where the amplitude was fixed at 100%, ΔAICc was negative for DEP25% (-15.0), but positive for DEP75% (11.2). W'OBS values were strongly correlated between both depletion conditions (r = 0.92), and positively associated with V̇O2peak, CP and GET (r = 0.67-0.77). The present study results did not provide evidence in favor of a biexponential modeling technique to characterize W' recovery following partial depletion. Moreover, we demonstrated that fixed t values were insufficient to model W' recovery across different depletion levels, and that W' recovery was positively associated with aerobic fitness. These findings underline the importance of employing variable and individualized t values in future predictive W' models.