Optimization of truck tyres selection
Doctoral thesis, 2018

This thesis, which consists of an introduction and five appended papers, concerns the optimal selection of tyres for a variety of vehicle configurations as well as operating environments. The selection problem stems from a project cooperation between Chalmers University of Technology and Volvo Group Trucks Technology. We analyze the selection problem from a mathematical optimization point of view. The overall purpose is to reduce the tractive energy required to run the vehicle. We develop a computationally efficient vehicle dynamics model of the vehicle, the tyres, and the operating environment. The tyres are represented by a surrogate model of the rolling resistance coefficient, which measures the energy losses caused by the tyres. The properties of the surrogate model called for a methodology for connecting expert knowledge about a general simulation-based function with its radial basis function interpolation. An algorithm for the solution of a large set of instances of a simulation-based optimization problem with continuous variables has been developed and tested on a set of problem instances. This algorithm enables an efficient computation of approximately optimal tyre designs (represented by continuous variables) for each vehicle configuration and operating environment specification. A splitting algorithm for simulation-based optimization problems with categorical variables has been developed and evaluated on a set of test problems. This algorithm outperforms all algorithms applicable to this class of optimization problems, and finds an approximately optimal tyres configuration. Since each execution of this algorithm requires many computationally expensive evaluations of the simulation-based objective function, it cannot be used to solve the full tyres selection problem. The two latter algorithms are then combined to enable the efficient solution of many instances of a simulation-based optimization problem with categorical variables. The resulting algorithm is applied to a couple of instances of the tyres selection problem. Our experiments show that the optimization methodology developed enables a computationally efficient solution of the truck tyres selection problem, in the combinatorial domain of possible vehicle configurations and operating environment specifications. Putting our methodology into practice will involve many challenges besides the problems studied in this thesis; however we have shown that our methodology can be utilized in the sales tool at Volvo.

simulation-based optimization

radial basis function

efficient solution

categorical variables

rolling resistance coefficient

truck tyres

approximately optimal solution

vehicle dynamics

surrogate model

Pascal, Hörsalsvägen 1, Chalmers.
Opponent: Kaisa Miettinen, University of Jyväskylä, Finland.

Author

Zuzana Nedelkova

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Integration of expert knowledge into radial basis function surrogate models

Optimization and Engineering,; Vol. 17(2016)p. 577-603

Journal article

Modelling of optimal tyres selection for a certain truck and transport application

International Journal of Vehicle Systems Modelling and Testing,; Vol. 12(2017)p. 284-303

Journal article

NedÄ›lková, Z., Cromvik, C., Lindroth, P., Patriksson, M., and Strömberg, A.-B., A splitting algorithm for simulation-based optimization problems with categorical variables

NedÄ›lková, Z., Efficient solution of many instances of a simulation-based optimization problem with categorical variables utilizing a partition of the decision space

TyreOpt - Fuel consumption reduction by tyre drag optimization

Chalmers, 2012-01-01 -- 2018-05-04.

Swedish Energy Agency, 2012-01-01 -- 2015-12-31.

Driving Forces

Sustainable development

Areas of Advance

Transport

Energy

Subject Categories

Computational Mathematics

Vehicle Engineering

Computer Science

Roots

Basic sciences

ISBN

978-91-7597-614-3

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4295

Publisher

Chalmers University of Technology

Pascal, Hörsalsvägen 1, Chalmers.

Opponent: Kaisa Miettinen, University of Jyväskylä, Finland.

More information

Latest update

3/8/2019 1