Automotive magnetorheological dampers: modelling and parameter identification using contrast-based fruit fly optimisation
The present study discusses the mechanical behaviour and modelling of a prototype automotive magnetorheological (MR) damper, which presents different viscous damping coefficients in jounce and rebound. The force generated by the MR damper is measured at different velocities and electrical currents, and a modified damper model is proposed to improve fitting of the experimental data. The model is calibrated by means of parameter identification, and for this purpose a new swarm intelligence algorithm is proposed, that we call the contrast-based Fruit Fly Optimisation Algorithm (c-FOA). The performance of c-FOA is compared with that of Genetic Algorithms, Particle Swarm Optimisation, Differential Evolution and Artificial Bee Colony. The comparison is made on the basis of no a-priori knowledge of the damper model parameters range. The results confirm the good performance of c-FOA under parametric range uncertainty. A sensitivity analysis discusses c-FOA’s performance with respect to its tuning parameters. Finally, a ride comfort simulation study quantifies the discrepancies in the results, for different identified damper model sets. The discrepancies underline the importance of accurately describing MR damper nonlinear behaviour, considering that virtual sign-off processes are increasingly gaining momentum in the automotive industry.
KeywordsModel identification Swarm intelligence Contrast-based fruit fly optimisation Automotive magnetorheological dampers Ride comfort
MEF is grateful for funding from the Lloyd’s Register Foundation, a charitable foundation helping to protect life and property by supporting engineering-related education, public engagement and the application of research. We would like to thank Mr Georgios Chrysakis for developing the MR damper current controller and contributing to the experiments.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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