This thesis studies the modeling of complex systems. In this framework, the approach is based on Second Order Form Model (SOFM). In order to use this kind of models, properties such as the reachability, the observability, the gramians and the Markov parameters, well known for state-space representation, are extended to the SOFM. During the co-simulation of a system, its physical parts are interconnected to models which simulate the system environement and the simulation is performed in real time. However, the simulation of high order models consumes to much time to be performed in real time. Therefore, model order reduction methods are studied. Particularly, new methods preserving SOFM structure with a good approximation error are presented. These developments are applied to the vehicle dynamic. Hence, a vehicle SOFM model is developed. The created model is a blockwise model where each blocks describes a part of the vehicle. This model is complex and non-linear. In order to apply the model order reduction methods, model linearisation is necessary. The block modeling allows to linearise the full model or allows to linearise some part of the model. Then, the identification of the model parameters is done by vehicle sub-system. In addition, an interconnection method is proposed to build a monobloc model in order to reduce it. Finally, co-simulations of the model vehicle rear part interconnected to the physical front part of the vehicle show the capacity to make co-simulation with the reduced models.
