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Automotive magnetorheological dampers: modelling and parameter identification using contrast-based fruit fly optimisation

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Abstract

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.

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Acknowledgements

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.

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Authors and Affiliations

  1. Faculty of Engineering, Environment and Computing, Coventry University, Priory Street, Coventry, CV1 5FB, UK

    Stratis Kanarachos & Michael E. Fitzpatrick

  2. Automotive Engineering Group, Department of Mechanical Engineering, Technische Universität Ilmenau, Ehrenbergstr. 15, 98693, Ilmenau, Germany

    Dzmitry Savitski

  3. School of Civil Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., Zografou Campus, 157 80, Athens, Greece

    Nikos Lagaros

  4. Engineering and Computing Building - EC 4-07, Faculty of Engineering, Environment and Computing, Coventry University, 3, Gulson Road, Coventry, CV1 2JH, UK

    Stratis Kanarachos

Authors
  1. Stratis Kanarachos
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  4. Michael E. Fitzpatrick
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Correspondence to Stratis Kanarachos.

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Communicated by V. Loia.

Appendix A

Appendix A

See Tables 10, 11, 12, 13 and 14.

Table 10 Statistical analysis of the optimisation results obtained by applying Genetic Algorithm, 30 independent times
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Table 11 Statistical analysis of the optimisation results obtained by applying Differential Evolution, 30 independent times
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Table 12 Statistical analysis of the optimisation results obtained by applying, 30 independent times, Particle Swarm Optimisation
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Table 13 Statistical analysis of the optimisation results obtained by applying, 30 independent times, Artificial Bee Colony
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Table 14 Statistical analysis of the optimisation results obtained by applying, 30 independent times, c-FOA
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Kanarachos, S., Savitski, D., Lagaros, N. et al. Automotive magnetorheological dampers: modelling and parameter identification using contrast-based fruit fly optimisation. Soft Comput 22, 8131–8149 (2018). https://doi.org/10.1007/s00500-017-2757-6

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