On the preview-based nonlinear model predictive control for the tyre slip management

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). Leveraging foreknowledge of tyre-road friction levels, the research outcomes encompass computer simulations involving straight-line driving scenarios, employing a rigorously validated vehicle model. In this research endeavour, an Nonlinear Model Predictive Control (NMPC) framework takes centre stage, orchestrating preview-driven Traction control system within the domain of V2X-enabled connected vehicles. By utilizing advanced knowledge of upcoming tyre-road friction coefficients and road irregularities, this NMPC formulation takes control to enhance the effectiveness of wheel slip management. The fusion of anticipatory information and control precision ushers in a paradigm shift in vehicular performance . Central attention is focused on analyzing control formulation and evaluating potential benefits, while implementation complexities like connectivity are not within the scope of this analysis. Underpinning this advanced control strategy are meticulous proof-of-concept experiments conducted on an electric vehicle prototype. These empirical trials not only underscore the controller’s real-time adaptability but also spotlight the tangible enhancements in wheel slip control prowess stemming from the foresight into tyre-road friction coefficients. The empirical validation journey intersects seamlessly with simulated scenarios, wherein a rigorously vetted model stands as a surrogate for the electric powertrain’s dynamic traits. These simulated conclusions serve as the backdrop for sensitivity analyses, wherein the performance gains of the preview-centric controller are scrutinized across a spectrum of dynamic attributes – ranging from time constants to pure time delays – that characterize diverse electric powertrains. This evaluation demonstrates the controller’s ability to enhance vehicle performance in a variety of situations. To sum up, this study delves into the in-depth exploration of the realization of an evolved Nonlinear Model Predictive Control (NMPC) Traction control system (TCS) that takes advantage of the pre-emptive information about the road ahead in terms of elevation profile and friction coefficient. The primary objective is to assess the feasibility of such a system.