Edoardo Di Nunzio
On the preview-based nonlinear model predictive control for the tyre slip management.
Rel. Alessandro Vigliani, Angelo Domenico Vella. Politecnico di Torino, Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo), 2023
|
|
PDF (Tesi_di_laurea)
- Tesi
Accesso limitato a: Solo utenti staff fino al 1 Dicembre 2026 (data di embargo). Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (17MB) |
Abstract
The automotive industry is witnessing the rise of V2X connectivity and powertrain electrification, ushering in novel control solutions. Many current electric vehicles adopt centralized powertrain architectures comprising a sole onboard motor, a one-speed transmission, an open differential, half-shafts, and constant velocity joints. The interplay between torsional drivetrain dynamics and wheel behaviour is significantly influenced by the open differential, particularly in split- μ scenarios, instances where varying tire-road friction coefficients exist between the two wheels of an axle. These effects are mitigated using anti-jerk controllers. Although extensive literature delves into traction control methods for managing individual wheel slip, there is a knowledge gap in two key areas: model-based traction control techniques tailored to centralized powertrains; and traction control mechanisms that utilize advance knowledge of anticipated tire-road friction and elevation conditions, such as data from V2X communication, to enhance wheel slip tracking performance.
This study introduces nonlinear model predictive control strategies designed for Traction control system (TCS) within electric powertrains equipped with Onboard Motor (OBM), single speed gearbox and open differential, and In-wheel Motor (IWM)
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
Ente in cotutela
Aziende collaboratrici
URI
![]() |
Modifica (riservato agli operatori) |
