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Reducing Tyre Wear Emissions of Automated Articulated Vehicles through Trajectory Planning.
Papaioannou, Georgios; Maroof, Vallan; Jerrelind, Jenny; Drugge, Lars.
Afiliação
  • Papaioannou G; Cognitive Robotics, TU Delft, 2628 CD Delft, The Netherlands.
  • Maroof V; The Centre for ECO2 Vehicle Design, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.
  • Jerrelind J; Engineering Mechanics, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.
  • Drugge L; The Centre for ECO2 Vehicle Design, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.
Sensors (Basel) ; 24(10)2024 May 16.
Article em En | MEDLINE | ID: mdl-38794033
ABSTRACT
Effective emission control technologies and eco-friendly propulsion systems have been developed to decrease exhaust particle emissions. However, more work must be conducted on non-exhaust traffic-related sources such as tyre wear. The advent of automated vehicles (AVs) enables researchers and automotive manufacturers to consider ways to further decrease tyre wear, as vehicles will be controlled by the system rather than by the driver. In this direction, this work presents the formulation of an optimal control problem for the trajectory optimisation of automated articulated vehicles for tyre wear minimisation. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one to minimise tyre wear in fixed time cases. Specific boundaries and constraints are applied to the problem to ensure the vehicle's stability and the feasibility of the solution. According to the results, a small increase in the journey time leads to a significant decrease in the mass loss due to tyre wear. The employment of articulated vehicles with low powertrain capabilities leads to greater tyre wear, while excessive increases in powertrain capabilities are not required. The conclusions pave the way for AV researchers and manufacturers to consider tyre wear in their control modules and come closer to the zero-emission goal.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article