Co-Simulation Methods for Holistic Vehicle Design: A Comparison 2020-01-1017
Vehicle development involves the design and integration of subsystems of different domains to meet performance, efficiency, and emissions targets set during the initial developmental stages. Before a physical prototype of a vehicle or vehicle powertrain is tested, engineers build and test virtual prototypes of the design(s) on multiple stages throughout the development cycle. In addition, controllers and physical prototypes of subsystems are tested under simulated signals before a physical prototype of the vehicle is available. Different departments within an automotive company tend to use different modelling and simulation tools specific to the needs of their specific engineering discipline. While this makes sense considering the development of the said system, subsystem, or component, modern holistic vehicle engineering requires the constituent parts to operate in synergy with one-another in order to ensure vehicle-level optimal performance. Due to the above, integrated simulation of the models developed in different environments is necessary. While a large volume of existing co-simulation related publications aimed towards engineering software developers, user-oriented publications on the characteristics of integration methods are very limited. This paper reviews the current trends in model integration methods applied within the automotive industry. The reviewed model integration methods are evaluated and compared with respect to an array of criteria such as required workflow, software requirements, numerical results, and simulation speed by means of setting up and carrying out simulations on a set of different model integration case studies. The results of this evaluation constitute a comparative analysis of the suitability of each integration method for different automotive design applications. This comparison is aimed towards the end-users of simulation tools, who in the process of setting up a holistic high-level vehicle model, may have to select the most suitable among an array of available model integration techniques, given the application and the set of selection criteria.