New trends in vehicle usage as e. g. Carsharing, automated driving, teleoperated driving as well as electric vehicles lead to a changing relationship between the driver and his vehicle as well as to changed technical circumstances inside the vehicle. Thereby, a drive can barely detect deviations of the vehicle dynamic behavior, as the driver doesn't necessarily know the normal behavior of the vehicle. Therefore, a diagnosis system for chassis system and vehicle dynamics behavior is necessary.
A control system shall be developed which delivers further information on the current state of the chassis system and vehicle parameters to be able to characterize the functionality of a vehicle in terms of its chassis system and vehicle dynamic behavior.
Based on the problem of the driver not being able to detect part defects or deviations from its normal functionality, an automated state monitoring system for the chassis system and vehicle dynamics behavior is needed. At first, potential failure sources and their impacts, available estimation algorithms as well as usable sensors need to get identified. Based on that, a definition of suitable mathematical models, its level of detail as well as model parameters for the representation of the part functionality is required. For combining the identified models, suitable parameter identification algorithms need to be identified. Therefore, not only classical parameter estimation algorithms but also modern algorithms as neural networks might be suitable. The performance of the developed diagnosis system shall get evaluated by measurements at vehicle.