[3E1-GS-10] AI application:

[3E1-GS-10-02] Building of Multibody Dynamics and LSTM Hybrid Model and Application to Automobile Component Characteristics Optimization

〇Ryu Chikasue¹, Roberto Gonzalez Flores¹, Seijun Morita¹, Kazuhiro Tanaka¹, Kenji Kudo¹ (1. Mazda Motor Corporation)

Keywords:Industry, Automobile, Multibody Dynamics, Machine Learning, Optimization

To deliver comfortable driving experience free from unpleasant noises and vibrations, we are building an efficient and globally optimized development process by optimizing and visualizing feasible region through CAE. We developed surrogate models using simulation results of powertrain system as training data to reduce CAE simulation time. However, in early stage of development where system structures and specifications frequently change, effective use of surrogate models is limited because the repeated re-collecting of training data costs a significant amount of time. In this study, we suggested a hybrid approach using LSTM to represent non-linear components that cause simulation difficulties in a powertrain multibody dynamics model. As a result, we accurately reproduced CAE simulation results across 16 conditions including different powertrain systems (FF, FR, HEV) and drive scenes. A maximum 90% reduction of simulation time was confirmed, and we applied this approach to optimization of drivetrain vibration performance.