The continuous and precise modulation of the driving and braking torques of each wheel is considered the ultimate goal for controlling the performance of a vehicle in steady-state and transient conditions. To do so, dedicated torque-vectoring (TV) controllers that allow optimal wheel torque distribution under all possible driving conditions have to be developed. Commonly, vehicle TV controllers are based on a hierarchical approach, consisting of a high-level supervisory controller that evaluates a corrective yaw moment and a low-level controller that defines the individual wheel torque reference values. The problem of the optimal individual wheel torque distribution for a particular driving condition can be solved through an optimization-based control-allocation (CA) algorithm, which must rely on the appropriate selection of the objective function. With a newly developed offline optimization procedure, this paper assesses the performance of alternative objective functions for the optimal wheel torque distribution of a four-wheel-drive (4WD) fully electric vehicle. Results show that objective functions based on the minimum tire slip criterion provide better control performance than functions based on energy efficiency.

Electric vehicles (EVs) with four individually controlled drivetrains are over-actuated systems, and therefore, the total wheel torque and yaw moment demands can be realized through an infinite number of feasible wheel torque combinations. Hence, an energy-efficient torque distribution among the four drivetrains is crucial for reducing the drivetrain power losses and extending driving range. In this paper, the optimal torque distribution is formulated as the solution of a parametric optimization problem, depending on the vehicle speed. An analytical solution is provided for the case of equal drivetrains, under the experimentally confirmed hypothesis that the drivetrain power losses are strictly monotonically increasing with the torque demand. The easily implementable and computationally fast wheel torque distribution algorithm is validated by simulations and experiments on an EV demonstrator, along driving cycles and cornering maneuvers. The results show considerable energy savings compared to alternative torque distribution strategies.

Latest advances in road profile sensors make the implementation of pre-emptive suspension control a viable option for production vehicles. From the control side, model predictive control (MPC) in combination with preview is a powerful solution for this application. However, the significant computational load associated with conventional implicit model predictive controllers (i-MPCs) is one of the limiting factors to the widespread industrial adoption of MPC. As an alternative, this paper proposes an explicit model predictive controller (e MPC) for an active suspension system with preview. The MPC optimization is run offline, and the online controller is reduced to a function evaluation. To overcome the increased memory requirements, the controller uses the recently developed regionless e MPC approach. The controller was assessed through simulations and experiments on a sport utility vehicle demonstrator with controllable hydraulic suspension actuators. For frequencies <4 Hz, the experimental results with the regionless e MPC without preview show a ~10% reduction of the root mean square value of the vertical acceleration of the sprung mass with respect to the same vehicle with a skyhook controller. In the same frequency range, the addition of preview improves the heave and pitch acceleration performance by a further 8% to 21%.

Fully electric vehicles (FEVs) with individually controlled powertrains can significantly enhance vehicle response to steering-wheel inputs in both steady-state and transient conditions, thereby improving vehicle handling and, thus, active safety and the fun-to-drive element. This paper presents a comparison between different torque-vectoring control structures for the yaw moment control of FEVs. Two second-order sliding-mode controllers are evaluated against a feedforward controller combined with either a conventional or an adaptive proportional-integral-derivative (PID) controller. Furthermore, the potential performance and robustness benefits arising from the integration of a body sideslip controller with the yaw rate feedback control system are assessed. The results show that all the evaluated controllers are able to significantly change the understeer behavior with respect to the baseline vehicle. The PID-based controllers achieve very good vehicle performance in steady-state and transient conditions, whereas the controllers based on the sliding-mode approach demonstrate a high level of robustness against variations in the vehicle parameters. The integrated sideslip controller effectively maintains the sideslip angle within acceptable limits in the case of an erroneous estimation of the tire-road friction coefficient.

This paper presents a traction control (TC) system for electric vehicles with in-wheel motors, based on explicit nonlinear model predictive control. The feedback law, available beforehand, is described in detail, together with its variation for different plant conditions. The explicit controller is implemented on a rapid control prototyping unit, which proves the real-time capability of the strategy, with computing times on the order of microseconds. These are significantly lower than the required time step for a TC application. Hence, the explicit model predictive controller can run at the same frequency as a simple TC system based on proportional integral (PI) technology. High-fidelity model simulations provide: 1) a performance comparison of the proposed explicit nonlinear model predictive controller (NMPC) with a benchmark PI-based traction controller with gain scheduling and anti-windup features, and 2) a performance comparison among two explicit and one implicit NMPCs based on different internal models, with and without consideration of transient tire behavior and load transfers. Experimental test results on an electric vehicle demonstrator are shown for one of the explicit NMPC formulations.

A significant challenge in electric vehicles with multiple motors is how to control the individual drivetrains in order to achieve measurable benefits in terms of vehicle cornering response, compared to conventional stability control systems actuating the friction brakes. This paper presents a direct yaw moment controller based on the combination of feedforward and feedback contributions for continuous yaw rate control. When the estimated sideslip exceeds a pre-defined threshold, a sideslip-based yaw moment contribution is activated. All yaw moment contributions are entirely tunable through model-based approaches, for reduced vehicle testing time. The purpose of the controller is to continuously modify the vehicle understeer characteristic in quasi-static conditions and increase yaw and sideslip damping during transients. Skid-pad, step-steer and sweep steer tests are carried out with a front-wheel-drive fully electric vehicle demonstrator with two independent drivetrains. The experimental test results of the electric motor-based actuation of the direct yaw moment controller are compared with those deriving from the friction brake-based actuation of the same algorithm, which is a major contribution of this paper. The novel results show that continuous direct yaw moment control allows significant "on-demand" changes of the vehicle response in cornering conditions and to enhance active vehicle safety during extreme driving maneuvers