Abstract: A load prediction method for a component of a vehicle includes a model creation process. A load model is created using an identification method based on a training-drive data group, a training vehicle data group and a training load group. The training-drive data group contains training-drive data sets, each containing route data and/or accompanying drive data for a training drive of a training vehicle on a training route. The training vehicle data group comprises vehicle data from the training vehicle used on the training drive. The training load group includes training load data including a load of the component that corresponds to a training-drive data set. The load model approximates the occurring load on a predetermined training route or with predetermined accompanying drive data or according to predetermined vehicle data. In a model evaluation process, the load prediction is determined using the load model.

Abstract: A method for determining feature signal filters for preparing signal measurement data sequences of a plurality of measurement variables for experimentally determining a mathematical model which maps model measurement data for at least one target signal sensor (7) on the basis of detected measurement data of a plurality of feature signal sensors (5) is disclosed. Further disclosed is a method for determining a mathematical model (16) which maps model measurement data for at least one target signal sensor (7) on the basis of detected measurement data of a plurality of feature signal sensors (5), wherein training input measurement data sequences (19) ascertained by the feature signal sensors (5) are mapped onto at least one training output measurement data sequence (18) ascertained by at least one target signal sensor (7).

Abstract: A load prediction method for a component of a vehicle includes a model creation process. A load model is created using an identification method based on a training-drive data group, a training vehicle data group and a training load group. The training-drive data group contains training-drive data sets, each containing route data and/or accompanying drive data for a training drive of a training vehicle on a training route. The training vehicle data group comprises vehicle data from the training vehicle used on the training drive. The training load group includes training load data including a load of the component that corresponds to a training-drive data set. The load model approximates the occurring load on a predetermined training route or with predetermined accompanying drive data or according to predetermined vehicle data. In a model evaluation process, the load prediction is determined using the load model.