In racing applications, the constant search for improvement has been changing new projects’ development approach. The time available for designing has become smaller, while requirements on performance improvement continuously persist in the daily routine of competition engineering. Since suspension systems play a big role in vehicle dynamics, affecting directly the tire performance of the vehicle under design, hence, the overall performance of the car on the track, computational methods are proven to be a crucial tool for developing new prototypes and making decisions. Under this point of view, optimization techniques have exploited new computational resources, which are constantly getting cheaper, to search for new engineering solutions. This work presents the methodology used and the results obtained from the kinematics optimization of a Formula Student suspension system through the application of heuristics method techniques. A kinematic solver of a double wishbone suspension is implemented in object-oriented programming and is used to analysis and, furthermore, optimize the output channels to match the desired behaviour for each one of the vehicle’s axles. The adopted search approach was genetic algorithm, given the multi-objective optimization nature of the problem that is the synthesis of a double-wishbone suspension system. This approach leads to reduced design time and returns a suboptimal solution, based on the weight functions, given the parameters of the search algorithm, discussed in the text.
Multi-objective optimization of the kinematic behaviour in double wishbone suspension systems using genetic algorithm
Sae Technical Papers
2020 SAE Brasil Congress & Exhibition ; 2020
2021-03-26
Conference paper
English
European Patent Office | 2020
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