Interactive Extraction of 1D-CAE Modeling Requirements and Determination of Model Configuration using Generative AI

Kyohei Naito･Junichi Ichimura･Yuki Wakimoto (NewtonWorks)

We address the modeling process of idealizing real systems according to analysis objectives, which often becomes superficial due to time constraints. We propose a methodology to define the required information and extract it interactively using Generative AI. We discuss the process of eliciting appropriate assumptions and abstraction levels through dialogue, structuring them into engineering-consistent specifications to achieve model definitions that are both efficient and capture the essence.

Automated Implementation of 1D-CAE Models and Support for Checking Consistency with Requirements

Junichi Ichimura (The Open University of Japen)･Kyohei Naito･Yuki Wakimoto (NewtonWorks)

We discuss the automated implementation of 1D-CAE models based on input specifications and the method to support checking their consistency with requirements and physical feasibility. The AI analyzes and reports reasons for discrepancies between results and specifications. We propose a Human-in-the-Loop process where engineers verify specification conformity based on these reports and determine refinement strategies to optimize the model.

Development of MBD method that simultaneously considers power management, thermal performance, vehicle motion performance, etc by using the cross-domain model
-Model development and application to vehicle development-

Shigemitsu Takahashi･Aoto Utsumi･Yudai Matsumoto･Masayuki Tani (Nissan Motor)

This paper describes the outline of the cross domain model CAEM (Collective Automotive Engineering Model) and its application to vehicle development MBD. Focusing on energy flow, a model that can be predicted from completely identical design information in an integrated environment of power management, thermal performance, and vehicle motion performance is developed. And, it is possible to do module selection considering across domains trade-offs.

Virtual Validation Methodology and Credibility Assessment for Automated Driving Systems with Applications to NCAP 2029

Prof. Dr. Reza Rezaei･Dr. Christian Lang･Kento Fukuhara･Gnana Prakash Reddy Madduri･Dr. Simon Olma (IAV)･Dr. Dai Araki (Toshiba Digital Solutions Corporation)

This paper presents a methodology for virtual validation and credibility assessment based on EU 2022/1426 and ISO 3540x. The methodology incorporates co-simulation for overall system modeling and validation of automated driving (AD) Level 3+ systems, integrating evidence-based real-world scenarios. Furthermore, it is designed to align with NCAP 2029 requirements, ensuring applicability for virtual testing and crash avoidance safety rating programs. By combining advanced physics-based validation techniques to ensure simulation credibility with a novel LLM-based scenario creation, the framework supports the virtual development and validation of AD systems, addressing future needs for cost reduction, reliability, and extensive mileage testing.

Integrated Model-Based Development Process for Vehicle Thermal Energy System

CHOO BUMSEOK･AN HYUNMO･JO CHANWOONG (Hyundai Motor)

This study introduces an integrated model for automotive thermal energy systems for model-based development (MBD), aligned with ISO 26262 standards. The model consolidates system data such as configurations, functions, and performance into a unified digital platform to enhance collaboration. A structured development process is proposed using tools like MathWorks System Composer and GT-SUITE, with automation for thermal flow analysis. The approach is validated through a Rankine cycle case study, demonstrating improved consistency and efficiency.

Model-Based and Data-Driven Software Architectures for EV Powertrain and Energy Management Systems

Hadi Moztarzadeh (Advanced Propulsion Centre UK)

The shift toward software-defined vehicles (SDVs) is accelerating demand for advanced software architectures that enable higher-fidelity control and optimisation of EV powertrains. This paper examines emerging SDV trends and readiness level, including centralised compute, service-oriented architectures, continuous software deployment, and data-centric control—and their implications for powertrain functionality. A hybrid model-based and data-driven framework is proposed, integrating physics-based control with machine-learning modules for state estimation, thermal prediction, and energy optimisation. System-level analysis shows that major global EV manufacturer integrating more SDV-enabling architectures to enhance computational capacity, cross-domain coordination, and real-time adaptability, enabling more efficient and predictive EV powertrain strategies.