NN FMU: Deep Learning Models for Next-Gen xiL Vehicle Simulation Renault Nissan Technology and Business Centre India

Format:

Book

Conference/Event

Author/Creator:

Srinivasan, Rangarajan, author.

Contributor:

Ashok Bharde, Pooja

Chehire, Marc

Mhetras, Mayur

Conference Name:

Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Functional Mock-up Units (FMUs) have become a standard for enabling co-simulation and model exchange in vehicle development. However, traditional FMUs derived from physics-based models can be computationally intensive, especially in scenarios requiring real-time performance. This paper presents a Python-based approach for developing a Neural Network (NN) based FMU using deep learning techniques, aimed at accelerating vehicle simulation while ensuring high fidelity. The neural network was trained on vehicle simulation data and trained using Python frameworks such as TensorFlow. The trained model was then exported into FMU, enabling seamless integration with FMI-compliant platforms. The NN FMU replicates the thermal behavior of a vehicle with high accuracy while offering a significant reduction in computational load. Benchmark comparisons with a physical thermal model demonstrate that the proposed solution provides both efficiency and reliability across various driving conditions. The paper discusses the workflow for model training and integration strategies for deep learning models within simulation tools like AMESIM and Simulink. With this approach significant time reduction is observed without affecting the accuracy when compared with the physical model. NN FMU also reduces efforts up to 40 % compared with traditional FMU conversion. CPU improvement from physical to NN FMU model achieved greater than 30 % reduction with the same accuracy. NN FMU maintains FMI compatibility and can be directly used in a wide range of XiL applications such as Model-in-the-Loop (MiL), Software-in-Loop (SiL), and Hardware-in-Loop (HiL) testing scenarios. This NN FMUs opens pathways for hybrid modelling approaches that combine data-driven and physics-based paradigms for automotive simulations

Notes:

Vendor supplied data

Publisher Number:

2026-26-0452

Access Restriction:

Restricted for use by site license