@inproceedings{29934,
  abstract     = {{Tire and road wear are a major source of emissions of nonexhaust particulate matter (PM) and make up the largest share of microplastics in the environment. To reduce tire wear through numerical optimization of a vehicle's suspension system, fast simulations of the representative usage of a vehicle are needed. Therefore, this contribution evaluates if instead of a full simulation of a representative test drive, only specific driving maneuvers resulting from a clustering of the driving data can be used to predict tire wear. As a measure for tire wear, the friction work between tire and road is calculated. It is shown that enough clusters result in negligible deviations between the total friction work of the full simulation and the cluster simulations as well as between the distributions of the friction work over the tire width. The calculation time can be reduced to about 1% of the full simulation.}},
  author       = {{Muth, Lars and Noll, Christian and Sextro, Walter}},
  booktitle    = {{Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021}},
  editor       = {{Orlova, Anna and Cole, David}},
  isbn         = {{978-3-031-07304-5}},
  keywords     = {{Tire Wear, Vehicle Dynamics, Clustering, Virtual Test}},
  location     = {{Saint Petersburg, Russia}},
  publisher    = {{Springer}},
  title        = {{{Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data}}},
  doi          = {{10.1007/978-3-031-07305-2_92}},
  year         = {{2022}},
}