Do the Right Things - Tyre Performance Parameter Evaluation by using Cross-Linked Cause Effect Chain Models Coupled with Behaviour Models

摘要

Handling response, stability and ride comfort are important aspects of the overall vehicle dynamics behaviour of a car. They have a significant impact on the customer's driving experience, which is a key purchasing criterion. The tyres play a vital role in this context. The selection of the best performing tyres for a special vehicle variant is a major challenge for the OEMs and requires a lot of validation efforts. Conventionally, the ride and handling performance are evaluated by both objective and subjective evaluation on the test track, which is a time consuming and capital intensive process. For saving expensive road tests and particularly to reduce the number of system prototypes needed for testing, the examination of correlations between system parameters, vehicle handling characteristics and driver evaluation holds great potential. A deep understanding of the coherences allows to define a measurable target behaviour which is directly linked to a desired driver feeling. The characteristics of the system can be validated with regard to this target behaviour during the whole development process - beginning from the very early phase where no prototypes are available. This paper will present how to identify and quantify the correlation between the characteristics of a vehicle system tested on a test bench and the subjective driver evaluation in order to finally make a qualitative decision about the best performing system by using the Semantic Validation Platform (SVP). The coherences will be represented in behaviour models generated by AVL CAMEO. The SVP guides engineers through a systematic validation process: The first step is to model assumed causal relations between vehicle system quantities, the environment and the driver rating in a so called Cause & Effect Chain. Afterwards the necessary tests are planned and designed based on the principles of Design of Experiments (DoE) using the intelligent test automation solution AVL CAMEO. This software significantly reduces the test effort and interfaces with different physical and virtual test beds to execute the tests automatically. Eventually, the generated knowledge and the outcome of the validation process is fed back into the SVP in order to make it accessible and comprehensible throughout the whole organisation and usable for other projects. The methodology of the SVP will be demonstrated using the example of tyre performance parameter evaluation. This paper will show the application of the SVP and AVL CAMEO on the example of the correlation between different tyre variants and their influence on the vehicle handling performance and the driver evaluation. The behaviour models provide a solid base for the pre-selection of the optimal tyres for a specific vehicle variant, reducing the necessary time and costs in compare to the conventional process.