Reduced Order Modeling Technology with AI for Model-Based-Development Toyota Technical Development Corporation

Format:

Book

Conference/Event

Author/Creator:

Inagaki, Takahiro, author.

Contributor:

Iwata, Motofumi

Nakane, Naoto

Nasu, Tadaaki

Takeshige, Minoru

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

This paper introduces reduced-order modeling techniques with Artificial Intelligence (AI) for Model-Based Development (MBD). In vehicle development, detailed physical models are replaced by reduced-order models (ROM) to expedite simulations. With recent advancements in AI-based reduced-order modeling, it is expected that modeling work will become more efficient, leading to reduced simulation times. However, the range of simulations (Model-in-the-Loop Simulation - MILS, Hardware-in-the-Loop Simulation - HILS, bench-system) compatible with ROM is limited. To overcome this limitation, this study leverages the ONNX format (Open Neural Network Exchange), a universally supported format among machine learning frameworks, and the Functional Mock-up Interface (FMI), a standard interface format for simulation tools, to enable general-purpose embedded technology with ROM.This study employs a vehicle model in engine surge simulations to validate AI-based reduced-order modeling for MBD. In MILS simulations, the ONNX-format model, trained using Long Short-Term Memory (LSTM), is integrated into an FMI-format model compatible with the simulation environment. This FMI-format model is then incorporated into MILS/HILS/bench systems, confirming its capability for accurate simulations. Thus, we have successfully established AI-based reduced-order modeling technology for comprehensive MBD

Notes:

Vendor supplied data

Publisher Number:

2024-01-2850

Access Restriction:

Restricted for use by site license