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One goal of modern power train control systems in heavy trucks is to damp driveline oscillations using appropriate controllers. Modern control algorithms like state-space controllers are based on a state-space model, which should accurately characterize the real process behavior. Otherwise, optimal control can not be guaranteed. These state-space models include a huge number of parameters, which have to be identified by an identification process. However, existing driveline models contain two serious problems: an increasing offset over time between measured and simulated data and an inadequate detection of the longitudinal dynamics of the truck. Therefore, this article deals with two goals: to optimize the offline identification process for the special use in driveline systems and to establish an online adaptation of the model parameters to guarantee an optimal model fit.