Matthijs Klomp

Longitudinal Velocity and Road Slope Estimation in Hybrid Electric Vehicles Employing Early Detection of Excessive Wheel Slip

Vehicle speed is one of the important quantities in vehicle dynamics control. Estimation of the slope angle is in turn a necessity for correct dead reckoning from vehicle acceleration. In the present work, estimation of vehicle speed is applied to a hybrid vehicle with an electric motor on the rear axle and a combustion engine on the front axle. The wheel torque information, provided by electric motor, is used to early detect excessive wheel slip and improve the accuracy of the estimate. A best-wheel selection approach is applied as the observation variable of a Kalman filter which reduces the influence of slipping wheels as well as reducing the computational effort. The performance of the proposed algorithm is illustrated on a test data recorded at a winter test ground with excellent results, even for extreme conditions such as when all four wheels are spinning.

Longitudinal force distribution using quadratically constrained linear programming

In this paper, a new method is presented for the optimisation of force distribution for combined traction/braking and cornering. In order to provide a general, simple and flexible problem formulation, the optimisation is addressed as a quadratically constrained linear programming (QCLP) problem. Apart from fast numerical solutions, different driveline configurations can be included in the QCLP problem in a very straightforward fashion. The optimisation of the distribution of the individual wheel forces using the quasi-steady-state assumption is known to be useful for the study of the influence of particular driveline configurations on the combined lateral and longitudinal grip envelope of a particular vehicle–driveline configuration. The addition of the QCLP problem formulation makes another powerful tool available to the vehicle dynamics analyst to perform such studies.

On optimal recovery from terminal understeer

This paper addresses the problem of terminal understeer and its mitigation via integrated brake control. The scenario considered is when a vehicle enters a curve at a speed that is too high for the tyre–road friction limits and an optimal combination of braking and cornering forces is required to slow the vehicle down and to negotiate the curve. Here, the driver commands a step steering input, from which a circular arc reference path is inferred. An optimal control problem is formulated with an objective to minimize the maximum off-tracking from the reference path, and two optimal control solutions are obtained. The first is an explicit analytical solution for a friction-limited particle; the second is a numerically derived open-loop brake control sequence for a nonlinear vehicle model. The particle solution is found to be a classical parabolic trajectory associated with a constant acceleration vector of the global mass center. The independent numerical optimization for the vehicle model is found to approximate closely the kinematics of the parabolic path reference strategy obtained for the particle. Using the parabolic path reference strategy, a closed-loop controller is formulated and verified against the solution from numerical optimization. The results are further compared with understeer mitigation by yaw control, and the parabolic path reference controller is found to give significant improvement over yaw control for this scenario.

Influence of front/rear drive force distribution on the lateral grip and understeer of all-wheel drive vehicles

The increased control potential in all-wheel drive vehicles enables the vehicle to maintain understeer characteristics from the linear tire range up to the grip limit. This provides consistent feedback to the driver over a wider range of operating conditions than for two-wheel drive vehicles. In order to aid the development of current and emerging driveline systems, the authors see a need for improved theory and methods (numerical and graphical) describing the influence of drive force distribution on these said factors. In this work the methods for computing the lateral grip margin for a general drive force distribution are developed for four special cases; front-wheel drive, rear-wheel drive, rigid all-wheel drive; and finally, an optimal front/rear drive force distribution.

Integrated evasive manoeuvre assist for collision mitigation with oncoming vehicles

ABSTRACT Development and deployment of steering based collision avoidance systems are made difficult due to the complexity of dealing with oncoming vehicles during the evasive manoeuvre. A method to mitigate the collision risk with oncoming vehicles during such manoeuvres is presented in this work. A point mass analysis of such a scenario is first done to determine the importance of speed for mitigating the collision risk with the oncoming vehicle. A characteristic parameter was identified, which correlates well with the need to increase or decrease speed, in order to reduce the collision risk. This finding was then verified in experiments using a Volvo XC90 test vehicle. A closed-loop longitudinal acceleration controller for collision mitigation with oncoming vehicles is then presented. The longitudinal control is combined with yaw stability control using control allocation to form an integrated controller. Simulations in CarMaker using a validated XC90 vehicle model and the proposed controller showed consistent reductions in the collision risk with the oncoming vehicle.

Real-time simulation of elasto-kinematic multi-body vehicle models

This paper presents the development of a real-time capable high fidelity vehicle dynamics model based on differential algebraic equations (DAE) that is compiled into ODE form using index reduction, symbolic manipulation and equation sorting. This model is shown to closely match an offline high fidelity model based on multibody dynamics and high index solvers. The real-time model directly references the model parameters used by the offline model and can be distributed as compiled models using standard interfaces. The compiled real-time models reads data files at initialization so it is possible to change parameters in the model without recompiling the model. The real-time performance is achieved by using inline integration with an implicit Euler solver to get stable execution with fixed time steps and by parallelizing the model to be able to separate the calculations onto multiple processor cores. The presented approach gives a more accurate and configurable realtime model compared to the current, lookup table based solution.

Optimal motion control for collision avoidance at Left Turn Across Path/Opposite Direction intersection scenarios using electric propulsion

ABSTRACT Collision avoidance at intersections involving a host vehicle turning left across the path of an oncoming vehicle (Left Turn Across Path/Opposite Direction) have been studied in the past, but mostly using simplified interventions and rarely considering the possibility of crossing the intersection ahead of a bullet vehicle. Such a scenario where the driver preference is to avoid a collision by crossing the intersection ahead of a bullet vehicle is considered in this work. The optimal vehicle motion for collision avoidance in this scenario is determined analytically using a particle model within an optimal control framework. The optimal manoeuvres are then verified through numerical optimisations using a two-track vehicle model, where it was seen that the wheel forces followed the analytical global force angle result independently of the other wheels. A Modified Hamiltonian Algorithm controller for collision avoidance that uses the analytical optimal control solution is then implemented and tested in CarMaker simulations using a validated Volvo XC90 vehicle model. Simulation results showed that collision risk can be significantly reduced in this scenario using the proposed controller, and that more benefit can be expected in scenarios that require larger speed changes.

Virtual verification of automotive steering systems

The vehicle industry is in a transformation where software and electronics are revolutionizing the way we engineer the cars of the future. This is particularly true for steering systems, which have developed from passive mechanical systems to now enabling advanced driver support systems and the evolution toward fully autonomous driving. With this ever increasing complexity, relying only on physical testing is no longer practical due to slow feedback loops from testing back to development and the lack of repeatability. The question addressed in this paper is how computational methods can help to increase test coverage, shorten development cycles and enable continuous integration of software for steering systems. In particular the development, validation and application of methods to virtually release steering systems for passenger vehicles is presented.