Multi Objective optimization of a car vehicle coupling modeFRONTIER and MDI-ADAMS | www.esteco.com

Multi Objective optimization of a car vehicle coupling modeFRONTIER and MDI-ADAMS

Author(s): 
Perna, Poian, Poloni (University of Trieste)

When designing the suspension system for a car, a compromise has to be found between several contrasting objectives. On the one hand, one wants to set the suspensions so that they allow an efficient and safe handling of the vehicle; on the other hand, comfort cannot be sacrificed too much.

Of course the right trade-off will depend on the car segment and its target market, even though it is generally useful to consider different trade-offs for different set-ups of the same car model. The challenge is then to find a suspensions configuration that represents a good compromise between handling and comfort over different terrains and driving conditions. This is comfort over different terrains and driving conditions. This is not always easy, as, for example, hard suspensions will lead to good cornering but to bad handling, and discomfort, over step obstacles.

 

 

 

 

 

 

 

We have used modeFRONTIER to show how it could be used to quickly and efficiently find a set of suspension designs that represent the best possible trade-offs between handling and comfort. We have used modeFRONTIER to show how it could be used to quickly and efficiently find a set of suspension designs that represent the best possible trade-offs between handling and comfort. The problem was defined by parameterizing the front and rear suspensions dampers and springs, and by testing the vehicle (a Ferrari 512TR) behaviour over a constant radius left turn, a lane changed, and a step obstacle. The car behaviour was then simulated using MSC-Adams, and the handling and comfort were estimated from the chassis pitch, roll, and yaw accelerations, and from the forces and lateral slip of the four tires. Finally, the steering bar acceleration was also considered. modeFRONTIER was then used to optimize the car behaviour. This was done by first running the MOGA algorithm, and by subsequently identifying the designs on the Pareto Frontier. Later on, the Decision Making tool was used to extract the design that best represented our desired trade-off for a sports car.  

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