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As racing vehicles become more complex, optimising the interaction between subsystems becomes critical for racing performance. In this work, we incorporate two such subsystems into a vehicle model. We investigate the performance benefits of a four-wheel-drive vehicle with independent control over its in-hub motors and active control on the rear wing flap rotation. The performance is evaluated by solving minimum lap time optimal control problems (OCP) for various vehicle configurations. The OCP is transformed into a nonlinear programming problem through direct collocation and is solved by an interior point method. The four-wheel-drive configuration performs better than rear-wheel drive in terms of lap time, finishing the Barcelona circuit 4 % faster. The benefits come mainly from higher longitudinal accelerations. Active aerodynamic control improves performance regardless of the propulsion configuration, leading to another 1 % of lap time improvement on the Barcelona circuit. The optimal aerodynamic control strategy is different between propulsion configurations, particularly on corner exits. This model enables the exploration of optimal torque vectoring controls and their interaction with a vehicle's active aerodynamics. This is needed to guide the design of the increasingly complex racing vehicle controllers of the future. [ABSTRACT FROM AUTHOR]

Step increase of electric motor torque results in wheel slip during the acceleration of battery electric vehicle (BEV), making vehicle body instability paramount. This is particularly challenging for regulating the torque of electric motor to keep stability in the process of acceleration, for which regulating the wheel slip to an ideal set-point is difficult for the central driving powertrain that adopts one electric motor to drive both left and right wheels by a differential. While much of the research on acceleration slip regulation has focused on solving the wheel anti-slip of internal combustion powertrain by electronic stability programme, comparatively little is known about the wheel slip control by electric motor torque of the central driving powertrain that is the most common style of driveline in the market of BEV currently. Here we discuss a series of studies on the acceleration slip regulation by electric motor torque that, collectively, design an approach of how the nonlinear model predictive controller based on a novel driving dynamics model regulates the wheel slip to an ideal set-point from the ideal slip ratio curve. Testing and validating this approach embedded into vehicle control unit are carried out in a BEV to fully realise the wheel slip control by electric motor torque during the acceleration. [ABSTRACT FROM AUTHOR]

Future vehicle localisation technologies enable major enhancements of vehicle dynamics control. This study proposes a novel vehicle stability control paradigm, based on pre-emptive control that considers the curvature profile of the expected path ahead in the computation of the reference direct yaw moment and braking control action. The additional information allows pre-emptive trail braking control, which slows down the vehicle if the predicted speed profile based on the current torque demand is deemed incompatible with the reference trajectory ahead. Nonlinear model predictive control is used to implement the approach, in which also the steering angle and reference yaw rate provided to the internal model are varied along the prediction horizon, to account for the expected vehicle path. Two pre-emptive stability control configurations with different levels of complexity are proposed and compared with the passive vehicle, and two state-of-the-art nonlinear model predictive stability controllers, one with and one without non-pre-emptive trail braking control. The performance is assessed along obstacle avoidance tests, simulated with a high-fidelity model of an electric vehicle with in-wheel motors. Results show that the pre-emptive controllers achieve higher maximum entry speeds – up to ∼34% and ∼60% in high and low tyre-road friction conditions – than the formulations without preview. [ABSTRACT FROM AUTHOR]

In order to make full use of the direct yaw control (DYC) in electric vehicles with four in-wheel motors (IWMs), this paper presents an adaptive SMC control scheme to improve the handling and stability based on a novel stability index proposed to quantitatively represent stability level. Firstly, a vehicle stability evaluation method is designed based on the front and rear tire slip angles phase plane and the quantitative stability index is derived according to the area where the states stay and the changing trend of the states. Secondly, a sliding mode controller (SMC) is designed to track the reference obtained by a two degrees of freedom (DoF) vehicle model as the desired vehicle motion. To enhance both vehicle handling and stability, the weight of the sliding surface is adaptively scheduled according to this stability index. The torque of each wheel is then distributed by solving the optimal adhesion rate consumption problem. Finally, the effectiveness of the proposed method is verified by CarSim–Simulink co-simulation. The simulation results illustrate that the proposed method can effectively improve vehicle handling and stability and has good robustness for the uncertainties of vehicle sideslip angle and road friction coefficient. [ABSTRACT FROM AUTHOR]

Active safety systems contribute significantly to the safe driving of autonomous vehicles in hazardous circumstances. However, conventional active safety systems that mainly depend on braking intervention may not yield the desired vehicle behaviour in critical situations where the tyre forces tend to saturate. Over-actuation through individually cambering the wheels provides a possibility of overcoming this difficulty, as extra lateral tyre forces can potentially be produced. This paper presents active camber for improving the path following and yaw stability performance of over-actuated autonomous electric vehicles (AEVs). With a modified Dugoff tyre model, the camber effect on the lateral tyre force is modelled as an effective linear component. The modified tyre model, together with a double-track vehicle model, is utilised for active camber of the AEVs. The camber controller is developed in the framework of model predictive control (MPC), including both actuator- and safety-related constraints. The camber controller is investigated at different camber rates and road friction levels, in terms of path following, yaw stability and vehicle velocity gain. [ABSTRACT FROM AUTHOR]

For performance improvement of modern vehicle dynamic control systems, exact knowledge of the vehicle motion state and the road conditions is essential. Addressing this problem, the development and validation of a state observer for a four-wheel drive electric vehicle is examined in this article. In particular, a reliable and accurate estimation of the tire slip in longitudinal and lateral direction at each wheel is desired. For this, an Unscented Filter is used to estimate the vehicle chassis and wheel dynamics. At the same time, the estimator is able to adapt the tire model according to the current road conditions by using wheel-individual tire-road friction coefficients. In the beginning, details on the used vehicle model as well as the estimator tuning and implementation procedure are given. The validation takes place first in a vehicle simulation environment to point out the performance of the observer especially in severe driving situations, like a sudden change of road friction conditions during a slalom manoeuver. Afterwards, driving tests with an electric four-wheel drive vehicle are carried out to proof the presented observer concept under real-world conditions. All performed driving manoeuvers are described and the test results are presented and evaluated. [ABSTRACT FROM AUTHOR]

Accurate identification of vehicle inertial parameters is essential to the design of vehicle dynamics control systems. In this paper, a novel vehicle inertial parameter identification method based on the dual H infinity filter (DHIF) for electric vehicles (EVs) is proposed. The filter algorithm employs a nonlinear longitudinal vehicle model with three vehicle states. A hierarchical framework is engaged by the DHIF to estimate the vehicle states and inertial parameters concurrently. In order to minimise the disturbance of unknown noise, the vehicle states are estimated by using the linear H infinity filter (LHIF), while the nonlinear H infinity filter (NHIF) utilises the observed states to identify the vehicle inertial parameters. Finally, the proposed estimation method is verified and compared through the dSPACE based hardware-in-the-loop (HIL) simulation experiments. The results indicate that the DHIF-based estimation method is effective to identify the vehicle inertial parameters with high precision, remarkable robustness, and quick convergence. [ABSTRACT FROM AUTHOR]