Search

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]

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]