The influence of motion on handling dynamics analysis in full vehicle simulators

This paper aims to assess the potential of a full vehicle simulator for use in vehicle handling dynamics analysis. Ideally, a sensitive and trained test driver can feel the differences in vehicle parameter set-up using a simulator and variation experiments will ultimately help aid the design process of a new vehicle, or vehicle control system. The potential of the simulator is measured using the motion system and focuses on the feedback this provides. It is shown that the motion is important for handling analysis and its characteristic response should be tailored to suit a specific restricted handling manoeuvre by utilising specific degrees of freedom

Vehicle tyre and handling model identification using an extended Kalman filter

This paper uses an Extended Kalman filter in an unusual way to identify a vehicle handling model and its associated tyre model. The method can be applied as an off-line batch process, or in real-time; here we concentrate on batch analysis of data from a Jaguar XJ test vehicle. The Identifying Extended Kalman Filter (IEKF) uses the full state measurement that is available from combination GPS / inertia instrumentation packs. Previous IEKF studies have shown success in identifying a bicycle model with a tyre force function for each axle. This paper extends to identification of a single, load dependent tyre model which applies to all four wheelstations, identified within a yaw-roll-sideslip model structure. The resulting model provides impressive open-loop state replication, including accurate tyre slip prediction across the fully nonlinear slip range of the tyre

Structured learning of a non-linear dynamic system component for vehicle motion simulation

Here we consider the design of a general subsystem model which is required to operate in series with the known dynamics of a plant or actuator, to achieve a desired overall system response. The example model is used in series with a motion platform to emulate vehicle handling dynamics. The method works in stages, first isolating the required linear response by fitting a frequency response function, then modelling this with a fixed order linear system in modal canonical form. A nonlinear saturation is then optimised for each modal state. The results are demonstrated for simulated and vehicle test data, and these achieve the principal objectives, of low state and parameter order. Some limitations to the method emerge – principally that there remain challenges to extension of the model to multi-input / output operation

The identifying extended Kalman filter: parametric system identification of a vehicle handling model

This article considers a novel method for estimating parameters in a vehicle-handling dynamic model using a recursive filter. The well-known extended Kalman filter - which is widely used for real-time state estimation of vehicle dynamics - is used here in an unorthodox fashion; a model is prescribed for the sensors alone, and the state vector is replaced by a set of unknown model parameters. With the aid of two simple tuning parameters, the system self-regulates its estimates of parameter and sensor errors, and hence smoothly identifies optimal parameter choices. The method makes one contentious assumption that vehicle lateral velocity (or body sideslip angle) is available as a measurement, along with the more conventionally available yaw velocity state. However, the article demonstrates that by using the new generation of combined GPS/inertial body motion measurement systems, a suitable lateral velocity signal is indeed measurable. The system identification is thus demonstrated in simulation, and also proved by successful parametrization of a model, using test vehicle data. The identifying extended Kalman filter has applications in model validation - for example, acting as a reference between vehicle behaviour and higher-order multi-body models - and it could also be operated in a real-time capacity to adapt parameters in model-based vehicle control applications