Development of All-In-One Virtual Application Process for Efficient R&H Performance Development

Jinhee Lee (Hyundai Motor)

In this paper, a new virtual application process called R&H All-In-One Application Process is proposed. This process is developed to achieve more efficient R&H performance development. This process consists of 3 sub-process. First one is the CarMaker model generating process based on CAT data that provides reliable vehicle model from SPMD and inertia database rapidly. Second process is the CarMaker design of experiment automation analysis process and model based benchmarking process. This process provides the environment for analyzing the effect of R&H performance on changes in the combination of the vehicle parameter sets and test conditions but also, contribution analysis through systematic separation method. The last one is the Handling/Steering feel test database with integrated analysis platform. Through proposed process, it is expected that engineers related R&H performance development can conduct efficient virtual activity above a certain level.

Simultaneous Identification of Spring Constant and Torsional Friction in the Frequency Region Higher than the Resonance Frequency of a Two-Inertia Resonance System

Tetsuya Yada･Toshimasa Miyazaki･Padron Juan (Nagaoka University of Technology)･Yoshihisa Hojo (Toyo Denki Seizo)

In multi-inertia resonance systems, accurate parameter acquisition is important for proper modeling and vibration control. In this paper, we propose a method to simultaneously identify the spring constant and torsional friction in the frequency range higher than the resonance frequency of a two-inertia resonance system. The effectiveness of the proposed method is confirmed by simulation and experiments.

Development of Drivability Sensory Evaluation with Machine Learning and Quantification of Knowledge

Takafumi Asano･Kosuke Tsuchiya･Kenji Kashiwakura･Takuma Kawaguchi･Haruki Shimura･Ryosuke Atarashi･Takashi Kaneko･Ryoya Kanahori (SUBARU)

The evaluation of the combination of complex driving operations and vehicle behavior by expert drivers is necessary to enhance drivability performance. In this study, an evaluation method using Machine Learning developed to reduce extensive testing efforts and share knowledge of experts. Furthermore, the method was using engine bench system. This utilization involved predicting acceleration from ECU data, conducting evaluations independent of the vehicle.

Conversion Method of Vibration Condition using Vibration Energy

Tomoya Shimomura･Fumikazu Matsuda･Toshikatsu Hatakeyama (Nissan Motor)･Katsuhiko Nakamura･Tatsuki Okunaga (IMV)

The 6-degree-of-freedom (6DOF: 3 translation and 3 rotation) vibration test is suitable for reproducing vibrations in a real environment.
However, there are only few shakers capable of performing a 6 DOF vibration test.
To conduct vibration tests using a single-axis vibration shaker, we developed a method to convert vibration conditions from 6 DOF to single-axis vibration utilizing vibration energy in addition to considering fatigue damage.

AI-Powered Vehicle Concept Development
-Disruptive Method to Reduce Time-to-Market and Select Right Vehicle Architecture & Technology-

Mario Oswald･Joerg Schlager (AVL List)･Kisu Lee･Sungho An (Hyundai Motor)･Stefan Kellner･Nathan De Kerpel (AVL List)

This publication covers a comprehensive methodology based on AI (artificial intelligence) presenting a paradigm shift in the selection of the right technology for all vehicle systems. The overall goal is to not only meet but also balance vehicle-level targets, setting the stage for a more streamlined and efficient development process. The center of this innovative approach are multi-layered and cascaded neural networks, which act as powerful tools enabling precise and simultaneous prediction of vehicle attributes like energy efficiency, range, charging, performance, vehicle dynamics and driveability. Furthermore, these networks even provide insights for the optimization of geometric vehicle architecture and crash.