Anti-jerk controllers actively suppress the torsional oscillations of automotive drivetrains, caused by abrupt variations of the traction torque. The main benefits are: i) enhanced passengers' comfort; and ii) increased component life. Extensive literature deals with the design of anti-jerk controllers for electric powertrains with on-board motors, i.e., in which the electric motor is part of the sprung mass of the vehicle, and transmits torque to the wheels through a transmission, half-shafts and constant velocity joints. Nevertheless, a complete and structured comparison of the performance of the different control options is still missing. This study addresses the gap through the assessment of six anti-jerk controllersfive exemplary formulations from the literature, and one novel formulation based on explicit nonlinear model predictive control (eNMPC). All proposed control structures have the potential to be implemented on production vehicles. A set of objective performance indicators is defined to assess the controllers, which are tuned through an optimizationbased routine. Results show that the wheel speed input is critical to enhance controller performance, but may lead to reduced robustness.

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V2X connectivity and powertrain electrification are emerging trends in the automotive sector, which enable the implementation of new control solutions. Most of the production electric vehicles have centralized powertrain architectures consisting of a single central on-board motor, a single-speed transmission, an open differential, half-shafts, and constant velocity joints. The torsional drivetrain dynamics and wheel dynamics are influenced by the open differential, especially in split-𝝁 scenarios, i.e., with different tire-road friction coefficients on the two wheels of the same axle, and are attenuated by the socalled anti-jerk controllers. Although a rather extensive literature discusses traction control formulations for individual wheel slip control, there is a knowledge gap on: a) model based traction controllers for centralized powertrains; and b) traction controllers using the preview of the expected tire-road friction condition ahead, e.g., obtained through V2X, for enhancing the wheel slip tracking performance. This study presents nonlinear model predictive control formulations for traction control and anti-jerk control in electric powertrains with central motor and open differential, and benefitting from the preview of the tire-road friction level. The simulation results in straight line and cornering conditions, obtained with an experimentally validated vehicle model, as well as the proof-of-concept experiments on an electric quadricycle prototype, highlight the benefits of the novel controllers.

Anti-jerk controllers compensate for the torsional oscillations of automotive drivetrains, caused by swift variations of the traction torque. In the literature model predictive control (MPC) technology has been applied to anti-jerk control problems, by using a variety of prediction models. However, an analysis of the influence of the prediction model complexity on anti-jerk control performance is still missing. To cover the gap, this study proposes six anti-jerk MPC formulations, which are based on different prediction models and are fine-tuned through a unified optimization routine. Their performance is assessed over multiple tip-in and tip-out maneuvers by means of an objective indicator. Results show that: i) low number of prediction steps and short discretization time provide the best performance in the considered nominal tip-in test; ii) the consideration of the drivetrain backlash in the prediction model is beneficial in all test cases; iii) the inclusion of tire slip formulations makes the system more robust with respect to vehicle speed variations and enhances the vehicle behavior in tip-out tests; however, it deteriorates performance in the other scenarios; and iv) the inclusion of a simplified tire relaxation formulation does not bring any particular benefit.

This paper presents a traction controller for combined driving and cornering conditions, based on explicit nonlinear model predictive control. The prediction model includes a nonlinear tire force model using a simplified version of the Pacejka Magic Formula, incorporating the effect of combined longitudinal and lateral slips. Simulations of a front-wheel-drive electric vehicle with multiple motors highlight the benefits of the proposed formulation with respect to a controller with a tire model for pure longitudinal slip. Objective performance indicators provide a performance assessment in traction control scenarios.