A Study on the Development of Architecture Virtual Driving Performance using Concept Model

2024-01-2723

04/09/2024

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
An architecture virtual driving performance development process and strategy were established using the concept model. Driving performance concept models for each level and performance, that can be utilized in the architecture stage, were developed. Advanced concept models such as smart driver and comfort models were developed for reliable emergency handling and comfort performance prediction. System characteristic DB(DataBase) structure was designed and formed to utilize the concept model for major vehicle platforms and models. System characteristics can be configured by automatically extracting system characteristics from ADAMS model or SPMD(Suspension Parameters Measuring Device) DB. In addition, when the concept model is completed by updating the weight, specifications and tire characteristic of the new vehicle platform, handling and ride comfort performance can be analyzed. We can predict the coverage performance of the vehicle platform and review the development direction by referring to the development target. This architecture virtual driving performance development process was applied to new skateboard platform development. In first architecture development stage, the driving performance was predicted and the satisfaction level, that can be compared to the vehicle target, was expressed as a percentage. In second architecture development stage, using the target cascading method, system characteristics, that satisfy vehicle targets for independent mode, were proposed. Vehicle development efficiency can be increased through this virtual performance development in the early stage such as architecture.
Meta TagsDetails
DOI
https://doi.org/10.4271/2024-01-2723
Pages
7
Citation
Kim, Y., Na, S., Park, P., Lim, J. et al., "A Study on the Development of Architecture Virtual Driving Performance using Concept Model," SAE Technical Paper 2024-01-2723, 2024, https://doi.org/10.4271/2024-01-2723.
Additional Details
Publisher
Published
Apr 09
Product Code
2024-01-2723
Content Type
Technical Paper
Language
English