In this paper we investigate the problem of optimising the speed of a vehicle over a fixed path for minimum time traversal We utilise a change of variables that has been known since the 1980s, although the resulting convexity of the problem was not noted until recently The contributions of this paper are three fold First, we extend the convexification of the problem to a more general framework Second, we identify a wide range of vehicle models and constraints which can be included in this expanded framework Third, we develop and implement an algorithm that allows these problems to be solved in real time, on embedded systems, with a high degree of accuracy

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Minimum-time speed optimisation over a fixed path

Direct yaw moment control actuated through electric drivetrains and friction brakes: Theoretical design and experimental assessment

It is well known that vehicle slip angle is one of the most difficult parameters to measure on a vehicle during testing or racing activities. Moreover, the appropriate sensor is very expensive and it is often difficult to fit to a car, especially on race cars. We propose here a strategy to eliminate the need for this sensor by using a mathematical tool which gives a good estimation of the vehicle slip angle. A single-track car model, coupled with an extended Kalman filter, was used in order to achieve the result. Moreover, a tuning procedure is proposed that takes into consideration both nonlinear and saturation characteristics typical of vehicle lateral dynamics. The effectiveness of the proposed algorithm has been proven by both simulation results and real-world data.

Development and validation of a Kalman filter-based model for vehicle slip angle estimation

The safety benefits of torque-vectoring control of electric vehicles with multiple drivetrains are well known and extensively discussed in the literature. Also, several authors analyze wheel torque control allocation algorithms for reducing the energy consumption while obtaining the wheel torque demand and reference yaw moment specified by the higher layer of a torque-vectoring controller. Based on a set of novel experimental results, this study demonstrates that further significant energy consumption reductions can be achieved through the appropriate tuning of the reference understeer characteristics. The effects of drivetrain power losses and tire slip power losses are discussed for the case of identical drivetrains at the four vehicle corners. Easily implementable yet effective rule-based algorithms are presented for the set-up of the energy-efficient reference yaw rate, feedforward yaw moment and wheel torque distribution of the torque-vectoring controller.

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Energy-Efficient Torque-Vectoring Control of Electric Vehicles With Multiple Drivetrains

This paper presents a novel hybrid observer structure to estimate the lateral tire forces and road grip potential without using any tire–road friction model. The observer consists of an Extended Kalman Filter structure, which incorporates the available prior knowledge about the vehicle dynamics, a feedforward Neural Network structure, which is used to estimate the highly nonlinear tire behavior, and a Recursive Least Squares block, which predicts the road grip potential. The proposed observer was evaluated under a wide range of aggressive maneuvers and different road grip conditions using a validated vehicle model, validated tire model, and sensor models in the simulation environment IPG CarMaker
 ®
 . The results confirm its good and robust performance.

Yvonne Schuhmacher

Papers

Race Car Vehicle Dynamics