In vehicle production, commissioning and testing processes of electric and electronic components are essential for value creation and quality assurance. The emergence of software-defined vehicles, however, leads to an increased scope and complexity of these processes as software functions depend on electric and electronic components for perception, execution, and processing tasks. In this context, this paper tackles a common challenge: Software that is deployed in vehicle production to implement commissioning and testing processes is developed upon specifications that define prerequisites, procedures, and target results in natural language. Therefore, extensive human interpretation and manual translation into executable code are needed being susceptible to errors as well as time-consuming. The large number of vehicle configurations and rapid changes in vehicle software further complicate the development of commissioning and testing software, particularly as verbose textual dependency

Köhler, Katja, El Asad, Aiman, Hahn, Michael, Reuss, Hans-Christian

An Application-Oriented Process Model for Selecting Uncertainty Quantification Methods in Machine Learning

2026-01-0773

7/1/2026

Uncertainty quantification (UQ) is increasingly recognized as essential when machine learning (ML) is employed in domains that are safety-relevant, cost-intensive, or legally binding, such as the product engineering of battery electric vehicle (BEV) energy systems. UQ methods aim to estimate the aleatoric, epistemic or both uncertainties associated with the predictions of a machine learning model. However, the landscape of UQ methods is diverse and rapidly evolving, with no single approach proving optimal across all tasks. Consequently, the selection of methods in practice is often driven by experience, constrained by limited comprehensive knowledge, time, and implementation capacity. This paper introduces an application-oriented process model supporting data scientists in selecting UQ methods in ML by adapting the SPALTEN [1] problem-solving methodology and the Algorithm Selection Process Model (ASPM) into an Algorithm Selection Process Model for Uncertainty Quantification (UQ-ASPM

Holderied, Niklas, Hörtling, Stefan, Bause, Katharina, Düser, Tobias

Kolmogorov-Arnold Networks in Industrial Application of Truck Development

2026-01-0760

7/1/2026

Kolmogorov-Arnold Networks (KANs) are a novel mathematical method to generate data-driven AI surrogate models. Compared to neural networks based on the MLP standard (Multi-Layer Perceptron), these offer further mathematical interpretability and thus allow improved validation of AI for industrial applications. In this paper, we use KANs to generate an AI vehicle model of a truck as a mathematically precise AI surrogate model. To do this, we combine the KAN approach with the approach of Neural Ordinary Differential Equations (Neural ODEs) to generate predictions for the time-series of the truck’s velocity. Furthermore, we compare the results of the AI based on KANs with the traditional approach using MLP in terms of model size, accuracy, and computational time in order to evaluate advantages and disadvantages of the KAN approach. The best AI-KAN vehicle model identified in this way is then embedded in a co-simulation via the Functional Mockup Interface standard, thus opening up a wide

Vaudrevange, Patrick K.S., Halverson, James, Ruehle, Fabian, Fabcic, Tomaz, Dingler, Christian, Piskala Dilipkumar, Santhoshkumar, Ibrahim, Muhammed, Herrnberger, Michael, Kasper, Johanna, Türk, Lars, Keckeisen, Michael

Harmonizing Generative AI and ISO 26262 for Safe and Intelligent ADAS Systems

2026-01-0785

7/1/2026

This paper investigates the integration of Artificial Intelligence (AI) within radar-based perception for Advanced Driver Assistance Systems (ADAS) under safety considerations aligned with ISO 26262 [1] for functional safety and ISO 21448 (SOTIF) [2] for performance-related safety of the intended functionality. The study evaluates a hybrid architecture in which AI-based perception modules are combined with deterministic supervisory mechanisms to maintain safety compliance. A simulation-based case study using CARLA with radar sensor modeling is presented to compare a deterministic radar perception pipeline with an AI-enhanced approach under nominal and degraded environmental conditions. Performance is evaluated using precision, recall, and F1 score metrics. Results indicate improved recall and F1 score under adverse scenarios for the AI-based perception module, accompanied by a moderate increase in false positives. The paper discusses architectural constraints required to limit non

Jain, Yesha

Multi-Agent LLM Architecture for Systems Engineering Automation

2026-01-0777

7/1/2026

The increasing complexity of modern software-intensive systems, particularly in the automotive domain, demands new approaches to bridge the gap between high-level engineering specifications and executable, safety-compliant code. This need is amplified by the rapid transition toward software-defined vehicles, where highly dynamic, updateable software functions significantly enlarge the scope and frequency of engineering activities and require scalable, transparent, and adaptive development processes. While recent advances in Large Language Models have demonstrated strong capabilities in automating tasks such as requirements analysis, code generation, and documentation, their deployment in safety-critical engineering workflows remains challenging due to the need for transparency, traceability, and controlled decision-making. This paper presents a modular multi-agent Large Language Model (LLM) pipeline that automates key steps of the systems engineering lifecycle - from requirement

Padubrin, Marcel, Kulzer, André Casal, Guerocak, Erol

Vision-Language Model Training for Code Generation from Requirements and Model-Based Artifacts

2026-01-0770

7/1/2026

Recent advancements in Vision-Language Models have opened new possibilities for bridging the gap between Systems Engineering artifacts and automated code generation. Traditional Large Language Models are primarily trained on textual data and generic code repositories, which limits their ability to interpret graphical engineering artifacts such as Simulink block diagrams or system architecture models. In safety-critical domains like the automotive industry, these graphical models are central to development workflows and must remain closely aligned with textual requirements and implementation code to ensure traceability, compliance, and functional correctness. This paper proposes a Vision-Language Model-centered multimodal training framework for code generation that integrates textual requirements, graphical model-based artifacts, and annotated source code into a unified learning process. By leveraging models which combine vision encoders with language backbones, the approach enables the

Padubrin, Marcel, Kulzer, Andre Casal, Guerocak, Erol

Development of a Digital Twin of an Electrical Powertrain in a Testbench Environment for Efficiency Estimations

2026-01-0744

7/1/2026

The aim of this work is to develop a modular, real-time-capable digital twin of an electric powertrain based on machine learning (ML)-based model structures and a systematic, component-oriented architecture with a focus on efficiency estimation in test bench environments. The further goal here is to enable virtual testing, which can be used for frontloading and thus both prevent errors and increase the speed of product development. Based on a comprehensive set of measured and derived test bench data, a multi-stage procedure is implemented that integrates data acquisition, physically informed feature selection, modeling at the component and subsystem level, and hybrid coupling strategies. The digital twin captures inverter, electric machine, and mechanical transmission stages and generates consistent predictions of key variables such as torque, speed, power factors, and subsystem as well as overall drivetrain efficiency. The methodology enables a systematic comparison of black box, dark

Kopp, Lennart, Proksch, Daniel, Ockert, Niels, Karthaus, Carsten, Kley, Markus

Multi-Agent LLM Workflow for Regulatory-Driven Requirement Generation in Automotive Software Development

2026-01-0787

7/1/2026

The increasing regulatory complexity in automotive development places significant pressure on engineering teams to derive complete and correct requirements. This paper presents a multi-agent-based large language model (LLM) workflow designed to support requirement extraction from technical specifications and regulatory documents in compliance with automotive requirement guidelines. The approach structures the requirement derivation process across collaborating agents that interpret specification and regulatory text, generate candidate requirements for the early engineering activities, and cross-validate their outputs to improve consistency and traceability. To evaluate the applicability of the workflow in an industrial context, we applied it to the draft Euro 7 emissions regulation. The agents produced requirements for relevant functional domains, which were subsequently reviewed by domain experts at FEV. The evaluation focused on correctness, completeness, and coverage. Results

Abdalla, Abdelrahman, Schäfers, Lukas, Schmidt, Fabian, Schaub, Joschka, Lee, Sung-Yong, Andert, Jakob

Production-Ready Automated ECU Calibration Using Residual Reinforcement Learning

2026-01-0780

7/1/2026

Electronic Control Units (ECUs) have played a pivotal role in transforming motorcars of yore into the modern vehicles we see on our roads today. They actively regulate the actuation of individual components and thus determine the characteristics of the whole system. In this, the behavior of the control functions heavily depends on their calibration parameters which engineers traditionally design by hand. This is taking place in an environment of rising customer expectations and steadily shorter product development cycles. At the same time, legislative requirements are increasing while emission standards are getting stricter. Considering the number of vehicle variants on top of all that, the conventional method is losing its practical and financial viability. Prior work has already demonstrated that optimal control functions can be automatically developed with reinforcement learning (RL); since the resulting functions are represented by artificial neural networks, they lack

Kampmeier, Andreas, Badalian, Kevin, Koch, Lucas, Lee, Sung-Yong, Andert, Jakob

Application of Deterministic and AI-Based Methods to Accelerate Component-Level Development in Electric Vehicles: A High-Voltage Wiring Harness Case Study

2026-01-0766

7/1/2026

The global automotive landscape is undergoing a significant paradigm shift driven by the rapid development cycles of emerging competitors, leaving traditional European OEMs with a critical time-to-market gap. To bridge this gap, automotive engineering must pivot from traditional hardware-based processes toward agile, digital data-driven methodologies. This paper presents a feasibility study on the implementation of data-centric approaches in component development, evaluated using the high-voltage wiring harness (HVWH) as a representative example. The HVWH serves as a practical validation case for the presented methodologies, covering both Artificial Intelligence (AI) based and deterministic methods. The study provides a detailed assessment of various AI-based and deterministic methodologies at specific stages of the product development process, targeting both product design and the product development process itself. The objective is to reduce time-to-market at the component-level by

Bode, Jana Pascal, Kröll, Sarah, Vohwinkel, Nikolaus, Paetzold-Byhain, Kristin

Interpretable Tire Force Modeling for Formula Student Vehicle Dynamics and Lap Time Applications

2026-01-0762

7/1/2026

Accurate tire models are a key enabler for vehicle dynamics simulation, control design, and lap time optimization, particularly in the context of Formula Student race cars, where vehicle setups and tire characteristics differ significantly from production vehicles. State-of-the-art tire models, such as Pacejka’s Magic Formula, generally provide high prediction accuracy. However, their predefined functional structure and large number of coupled parameters are designed for broad applicability across many tire types rather than for specific racing tires. This often results in limited interpretability, nontrivial parameter identification, and unnecessary model complexity for specialized applications such as Formula Student. This paper presents a data-driven approach for deriving compact and physically interpretable tire force models using symbolic regression. The proposed method employs an intelligent tree search to systematically explore the space of mathematical expressions and identify

Anselment, Marcel, Borowski, Julian, Rudolph, Stephan

Who’s Riding? Machine Learning-Powered Rider Identification from E-Bike CAN Data

2026-01-0778

7/1/2026

This study investigates the feasibility of identifying individual e-bike riders based on CAN data using machine learning techniques. Datasets from 12 test riders performing various predefined cycling tasks on a dynamometer test bench are collected and used to ensure controlled and reproducible conditions. The recorded CAN data includes various sensor signals, such as power output, cadence, torque, and the used support mode. After pre-processing, two different methods of feature extraction are tested and compared, one based on snapshots of the data and one based on driving events such as braking and accelerating, measured by calculating statistics of the riding data over sliding windows. A range of machine learning models is employed to classify riders based on their distinct riding patterns using the extracted features. The evaluated models comprise KNN, Random Forest and Naïve Bayes. The findings demonstrate the efficacy of machine learning in differentiating riders, with Random

Simmann, Gabriel, Rauch, Yannick, Beißert, Florian, Kriesten, Reiner

BO-NRPPO: A Bayesian Optimization–Tuned Proximal Policy Optimization Framework for Semi-Active Suspension Control

10-10-03-0025

6/25/2026

Semi-active suspension systems enhance ride comfort and handling performance by adaptively modulating damping characteristics. However, conventional model-based controllers often fail to maintain optimal performance under uncertain and time-varying vehicle conditions. This article proposes Bayesian Optimization–Tuned Proximal Policy Optimization with Non-Parametric Rewards (BO-NRPPO), a novel reinforcement learning (RL) framework that integrates Bayesian Optimization (BO) with Proximal Policy Optimization (PPO) and a non-parametric reward function (NRF). The proposed approach enables adaptive self-tuning, data-driven reward shaping, and uncertainty-aware policy learning. Moreover, a Trapezoidal Simple Moving Average (TSMA)–based reward normalization scheme is introduced to accelerate convergence and stabilize training. Simulation results across diverse driving scenarios demonstrate that BO-NRPPO outperforms the passive suspension, the classical Linear Quadratic Regulator (LQR), and PPO

Chen, Guoying, Wang, Xinyu, Wang, Jiaqi, Zhan, Xinwang, Bi, Chenxiao, Cong, Shiqi, Hua, Min, Sun, Tianjun, Gao, Zhenhai

Best-in-Class NVH Optimisation for Electric Powertrains, Holistic Front-Loading Approaches

2026-01-0689

6/20/2026

Achieving best-in-class Noise, Vibration, and Harshness (NVH) in electric powertrains demands a paradigm shift in development methodology. This paper presents a practice-oriented overview of simulation methods in NVH development methodology for electric drive units. This includes target cascading and multi-objective optimisation, and by attacking NVH at the source using KPIs early in the design cycle, significant reductions in development time and reliance on traditional testbed loops are realised. Machine learning (Neural Network) algorithms are utilized to find the best-in-class design, using multi-objective optimisation as well as refining simulation accuracy by adding tolerance effects while target cascading ensures alignment of system-level performance objectives down to subsystem contributions. Combined, these strategies enable rapid and robust NVH optimisation, using simulation for next-generation electric powertrain development. Several applications and real-life examples

Mehrgou, Mehdi, Garcia de Madinabeitia, Inigo, Graf, Bernhard, Gojo, Josef

AI-Based Package Tray Design Method for Controlling Acoustic Modes in a Sedan

2026-01-0716

6/20/2026

In this study, we propose a methodology for predicting the acoustic modes and natural frequencies of a sedan using artificial intelligence and demonstrate the feasibility of controlling its acoustic characteristics by modifying the hole distribution of the package tray. In typical sedan structures, the cabin cavity and trunk cavity are acoustically coupled through holes in the package tray. The distribution of these holes significantly affects the natural acoustic modes and frequencies of the vehicle. However, once the exterior shape of the vehicle is finalized during the design stage, options for structural modifications to mitigate noise issues caused by these modes become extremely limited. To address this challenge efficiently, we develop a deep learning-based neural network model trained on data derived from a simplified acoustic analysis model of a sedan that includes a package tray. Finite element analysis is performed to generate acoustic modes and natural frequencies, which

Lee, Jin Woo, Cho, Jaeho, Nam, Younsic, Han, Yongha

NoiseSphere: An AI-Based Approach for Dynamic and Large-Scale Noise Mapping

2026-01-0719

6/20/2026

Noise pollution is a major environmental and health challenge, yet its strong spatial and temporal variability makes comprehensive mapping highly complex. Current approaches under the European Noise Directive (END) provide only partial coverage and often lack temporal dynamics. The NoiseSphere project, funded by the Austrian Research Promotion Agency FFG, develops an AI-based methodology for dynamic, large-scale noise prediction and mapping. A machine learning model is trained on heterogeneous data sources, including semantically enriched open Sentinel-2 satellite imagery, OpenStreetMap road data and existing noise maps. The model is refined through integration of noise emission data and validated using targeted in-situ measurements. A case study in an urban environment (Graz, Austria) demonstrates the model’s applicability. By combining remote sensing, traffic dynamics, and machine learning, NoiseSphere enables predictive noise mapping even in regions not covered by current

Girstmair, Josef

Active Learning Supported Metadata Extraction from NVH Simulations

2026-01-0720

6/20/2026

Simulations can only be searched, reused and leveraged as training data for machine learning methods if suitable metadata are related. Manually obtaining these metadata is time-consuming and requires expert knowledge. Consequently, there often is a lack of metadata and this prohibits the reutilization of simulation data. Therefore, automated frameworks for metadata extraction are essential to obtain metadata information quickly, effortlessly and cost-efficiently. At present, there are no toolboxes for Finite-Element-Simulation data. Nevertheless, machine learning methods are a promising solution for this task. Training classical supervised machine learning methods for metadata generation often faces the lack of labeled data since manual labelling can be very costly. Therefore, rule-based extraction algorithms are used as an alternative for fundamental metadata extraction. For more enhanced tasks they are often not feasible. Active Learning is a suitable technique to overcome this

Luegmair, Marinus, Gröttrup, Sören

A Self-Learning Framework for NVH CAE Analysis

2026-01-0718

6/20/2026

The vibro-acoustic performance of a vehicle is a critical factor in customer perception of quality and comfort, yet optimizing for Noise, Vibration, and Harshness (NVH)—specifically road noise—presents a persistent challenge in the modern automotive development cycle. While advanced Finite Element Method (FEM) analysis is essential, the increasing complexity and volume of CAE simulation data often overwhelm manual interpretation, potentially leading to prolonged development times or compromises in final comfort quality. To address these challenges, this paper introduces the application of CDH/ACE (Autonomous Computational Experiments), a framework that integrates conventional CAE simulation workflows with advanced machine learning in an iterative, cyclic process. This creates an exceptionally user-friendly and self-correcting system that autonomously defines, performs, and learns from computational experiments. By leveraging machine learning algorithms to build robust predictive models

Visser, Rene

Unsupervised Feature Frequency Map Extraction for Simple C-EPS Defect Detection

2026-01-0717

6/20/2026

In recent years, the automotive industry has actively explored the application of various AI-based models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Autoencoders, and Transformers to improve defect detection rates at the End-of-Line (EOL) stage. However, implementing these approaches in the Noise, Vibration, and Harshness (NVH) area face several practical challenges: ① extended evaluation times compared to other data types, which limit the quantity of training data and lead to overfitting; ② label imbalance caused by the relatively small amount of defect data; ③ reduced labeling accuracy due to human error; ④ decreased robustness under domain shifts such as changes in jig fixtures, test environments, and signal-to-noise ratio (SNR); ⑤ diminished model reliability when new defect arise during development; and ⑥ constraints imposed by compatibility requirements with existing test equipment. This study proposes a Convolutional Autoencoder (CAE

Park, Jun-Seo, Jo, Hyeon-Choel, Cho, In-Je, Seo, Jae-Yong, Yoo, Seong-Sik

SAE TOMORROW TODAY - Coming Soon: The Humanoid Revolution

135726/16/2026

Are humanoid robots moving from science fiction to reality? The answer is yes, and it may be faster than anyone predicted. Listen in as we sit down with David Kehr, President of Humanoid Robotics at Schaeffler, to discuss how the company is leveraging decades of expertise to power the next generation of physical AI. From deploying humanoids in manufacturing plants to advancing actuator technology and AI-powered simulation through NVIDIA Omniverse, this conversation dives into the real-world challenges and opportunities shaping the robotic revolution. Whether it's scaling humanoids safely or the future of dexterous robotic hands, you'll learn why automotive manufacturing expertise matters, and how humanoids could eventually move beyond factories into logistics, healthcare, and even the home. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we

Patterson, Lori

Challenges in Predicting Fatigue Damage during Agricultural Operations: Insights from Machine Learning Models

02-19-03-0017

6/10/2026

Agricultural vehicles operating in rough environments experience increased fatigue damage accumulation, which may decrease machine safety and reliability. Autonomous agricultural machines offer an opportunity to incorporate fatigue damage considerations into path planning. This work investigates whether machine learning can predict fatigue damage to a tractor chassis using light detection and ranging (LiDAR)-based terrain features, vehicle speed, and rotational vehicle state data (e.g., triaxial angle, angular velocity, and angular acceleration). Fatigue damage was estimated using the Rupp filter and the Durability Transfer Concept. Following poor predictive performance of the machine learning models, an exploratory analysis of damage histograms, dominant frequency, and acceleration magnitude was performed. Results indicated that most estimated fatigue damage occurred in the 0–2 Hz band, which coincides with the frequency range of terrain-induced acceleration. On-road driving led to

Govers, Megan Emily, Hamilton-Wright, Andrew, Hassan, Marwan, Oliver, Michele L.

A Review of Welding Techniques in Metal Additive Manufacturing: Advancements, Challenges, and Future Directions

05-19-04-0029

6/10/2026

The present review evaluates recent advances in the development of Welding-Based Additive Manufacturing (WBAM) technologies using arc, high-energy density, solid-state, and hybrid welding systems by providing an interdisciplinary assessment of technological aspects, sensing, process optimization, and multi-process strategies. It is concluded that, in spite of considerable progress in process optimization and control, there exist numerous paradoxes associated with relationships among process conditions, structure, and properties, especially those related to heat input effects on material microstructure and performance. An important finding is the fragmentation of predictive modeling approaches, where physics-based and data-driven methods remain inadequately integrated, limiting generalizability and accuracy. Another important conclusion is related to the dominance of the effect of thermal history and multi-physical phenomena on the mechanical performance of the material produced by WBAM

Santhana Babu, A.V., John Rajan, A., Mishra, Aishwary, Chakravarthy, P., Jayabalakrishnan, D.

Can Large Language Models Enable Sustainable Driving Behavior? A Study on Large Language Model-Based Eco-Driving

13-07-02-0006

6/9/2026

Large language models (LLMs) have shown remarkable capabilities for perceiving driving environments and making interpretable, logical decisions for autonomous driving. However, their potential for more comprehensive driving strategies, especially concerning energy efficiency, remains underexplored. Most existing studies primarily focus on driving safety, which may inadvertently increase energy consumption. To address this issue, this study explores the use of LLMs as high-level controllers to jointly optimize driving safety and energy efficiency. A textual prompt is designed for the LLM, incorporating few-shot examples that describe scenarios, states, and actions. The LLM processes the scenario and state prompts describing the surrounding traffic environment. It generates a high-level control signal, which is then translated into low-level vehicle motion commands in a high-fidelity traffic simulator with realistic physics, vehicle dynamics, road slopes, and network topology

Wang, Haoyu, Li, Zhenning, Wang, Siying, Zhou, Zijing, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi (Shawn), Qi, Hao

Rapid Fuel Distribution Prediction in RCCI Marine Engines Using CFD-Informed, Active Learning Framework

2026-37-0017

6/9/2026

Accurate prediction of in-cylinder fuel distribution (FD) is fundamental to reduced-order combustion modeling and emissions prediction yet remains computationally prohibitive with high-fidelity CFD alone. This work develops a CFD-informed machine-learning surrogate for spatial FD in a large-bore diesel engine, based on a Wärtsilä W20 injector and representative engine conditions. A fully coupled injector–spray–engine CFD framework under engine-like RCCI inert conditions determines the needle-lift profile and resolves the combined effects of injector geometry, needle dynamics, and operating conditions on in-cylinder flow, capturing physical phenomena not reproducible by isolated free-spray simulations. A high-fidelity database is generated using Latin Hypercube Sampling, from which FD is extracted at 15 CAD before top dead center within an annular multi-zone (MZ) representation consistent with reduced-order combustion models. A multi-output Random Forest (RF) surrogate, augmented with

Moradi, Jamshid, Salahi, Mahdi, Heidarabadi, Shadab, Andwari, Amin, Konno, Juho, Wik, Christer, Mikulski, Maciej

Automated Design Sustainability Evaluation Framework: A CAD-Integrated Tool for Real-Time Costing and LCA in Automotive Engineering

2026-37-0044

6/9/2026

As the automotive industry faces increasingly rigorous environmental regulations and an approaching obligation for Digital Product Passports (DPPs), incorporating sustainability metrics into the early design phase has become a necessity. Traditionally, Life Cycle Assessment (LCA) and manufacturing cost estimation are performed during or after the design phase using specific methods and tools, resulting in costly iterations and delayed decision-making. This paper introduces a preliminary computational tool that combines 3D CAD and spreadsheet software via VBA integration. The framework automates the generation of an “Extended Bill of Materials” by extracting geometric and manufacturing data directly from CAD models. This tool’s classification logic is a key innovation that intelligently processes CAD features to identify component categories, such as sheet metal, machined parts, or plastic injections. This automated recognition allows the framework to implement specific algorithmic

Guadagno, Maurizio, Cecconi, Leonardo, Berzi, Lorenzo, Delogu, Massimo

Measurement Framework for Automated Vehicle Systems Interaction

12-09-04-0027

6/9/2026

1Systems level and integration testing are an integral part of the design and development of Automated Vehicles (AVs). Measurement science plays a pivotal role in testing to ensure the safe and efficient operation of AVs. This science establishes a common understanding of the units of measurement, crucial in linking human activities. This article describes the significance of measurement in studying interactions between key system technologies in AVs, including AI for perception, sensing, communications, and cybersecurity. To address the complexities of these interactions, a novel, adaptable, and interactive framework called the System Technology Interaction Model (STIM) is introduced. STIM considers both designed and emergent interactions between these system technologies, allowing AV developers to explore tailored experiments with the flexibility of filtering for focused testing. The framework currently models system interactions statically, not in real-time, to define potential

Griffor, Edward R., Arora, Mahima, Kootbally, Zeid, Nguyen, Vinh

SAE TOMORROW TODAY - The Battery Isn't the Problem--Execution Is

135716/8/2026

Batteries don't fail because of chemistry. They fail because of execution. Qnovo provides advanced software to optimize lithium-ion battery health and safety, proving that competitive advantage isn't just about the battery itself, but how intelligently you manage it. By leveraging sophisticated battery models enhanced with AI and machine learning, the company delivers real-time insights into battery performance and operation. Listen in as we sit down with Nadim Maluf, Co-founder and CEO, to discuss how software has become the soul of modern batteries. From extending smartphone battery warranties to enabling faster, safer EV charging, you'll discover why the future of electrification isn't about new materials ... it's about intelligence, scale, and trust. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends

Patterson, Lori

Investigation into Increasing Authority Involvement in the Automotive Development Process by Applying Best Practices from Aviation

2026-01-5021

6/8/2026

This study examines the involvement of authorities in the development processes of aviation and automotive industries by comparing the depth, frequency, and stages of their engagement. The background of this work is an ongoing research initiative focused on transferring methods from aviation to automotive. The method used in this study is an investigation of best practices across both industries. Based on this investigation, two proposals were developed for managing complex technologies, such as autonomous systems. Both proposals advocate for increased authority involvement, particularly during the early stages of projects. One proposal recommends making this enhanced involvement mandatory, while the other suggests it as a guideline rather than a requirement. To assess the benefits of these proposals, a human-input–based feasibility quantification method was applied. This method assesses feasibility on a scale from 0 to 10, where 0 represents the lowest score, 5 is neutral, and 10 is

Akkus, Yusuf, Annighöfer, Björn

Automotive Engineering: June 2026

26AUTP066/4/2026

New York 2026: diversity on full display New powertrain choices keep popping up on new vehicles from OEMs that debuted at NYIAS this year. Sealing integrity in a Formula 1 limited-slip differential High-temperature hydraulic control in a Formula 1 drivetrain requires dimensional stability, controlled sealing force, and resistance to wear under sustained pressure cycling. Inside the limited-slip differential, the sealing architecture plays a defined mechanical role in maintaining consistent torque management under race conditions. From ADAS to autonomy How engineering thermoplastics can advance sensor-based technologies. Synthetic data and the future of ADAS validation Why ADAS validation can't be solved with more miles alone. Intelligent power distribution will change the way vehicles are designed Electronic fuse (eFuse) technology can create electronic power distribution modules (ePDMs) for architectural flexibility, higher reliability, greater safety, and proactive maintenance

Weight Estimation of Novel Aircraft Configurations in Early Design Phase

2026-26-0773

6/1/2026

Initial weight estimation from Top Level Aircraft Requirements (TLAR) is a critical first step in aircraft design, yet existing empirical methods are inadequate for novel configurations such as those using Liquid Hydrogen (LH2) or Sustainable Aviation Fuels (SAF). This paper presents a hybrid methodology for top-level weight estimation of such unconventional aircraft. The approach is based on modifying a conventional baseline aircraft, integrating a new statistical model with component-specific weight estimations. A multivariate regression model to estimate the empty weight fraction (We/W0) was developed from a dataset of 44 conventional aircraft, yielding an R-squared value of 0.833. This statistical model was integrated with physics-based models for novel components, including cryogenic fuel tanks and fuel systems. The methodology accounts for iterative changes to fuselage structure and parasitic drag. Four configurations were analyzed: fuel types being Jet A1, SAF, LH2 with aft

Goyal, Tushar

Integrating Human factors and Predictive Modeling for Enhanced Aviation Safety: A Data - Driven Approach to Risk Mitigation and System Design

2026-26-0795

6/1/2026

This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence

Valiyaparambil, Praveen

Electric Health-Monitoring Wiring (E-Wiring) for Aircraft

2026-26-0785

6/1/2026

Modern aircraft depend on extensive electrical wiring networks for power distribution, avionics, and control systems; however, these wiring systems are vulnerable to wear, insulation degradation, and arcing over time, leading to safety risks and costly unscheduled maintenance. This paper introduces an advanced Electric Health-Monitoring Wiring (E-Wiring) system that integrates temperature, current, insulation, vibration, and environmental sensors directly into aircraft wiring harnesses to enable continuous monitoring and intelligent fault detection. Data from these embedded sensors are processed through a distributed edge AI network, forming an Electrical Health Monitoring System (EHMS) capable of real-time diagnostics, predictive maintenance, and fault localization. The architecture comprises smart cable segments with sensor nodes, local harness gateways for edge processing, aircraft-level EHMS integration via AFDX/Ethernet, and cockpit or maintenance displays linked to ground-based

Tammana, Bala Sai Sri Rohit, Murthy, Harsha, Mendu, HarikaSivaniSunandha

Drilling Studies on Sustainable Lightweight Hybrid Polymer Matrix High-Performance Nanocomposites

2026-26-0732

6/1/2026

Worldwide, engineers are exploring the possibility of using polymer composites in their quest for lightweight materials. In this study, injection moulding was used to develop a biodegradable polymer PLA composite containing 20 wt.% vetiver fibers (VFs) and 2 wt.% nano-silica (nSiO2) obtained from pearl millet, which is sustainable. Materials need machining as secondary operation that required joining. Desirability analysis was used to examine and optimize machining (drilling) studies that were designed with Taguchi's design (L9 orthogonal array). Surface roughness (SR) and delamination factor (Fd) were taken as outputs, while spindle speed (SS), feed rate (FR), and drill diameter (DD) were the inputs. Drilling studies were performed on a single vertical machining center (VMC). ANOVA identifies that the FR had the most decisive influence on SR (F=559.24, p=0.001785), followed by DD and SS. FR is the dominant contributor to Fd (F=379, p=0.00263), followed by SS and DD. At low SS and high

Senthilkumar, N.

Enabling Digital Transformation in Aerospace: Automated Model Based Engineering

2026-26-0722

6/1/2026

Model-based development (MBD) and Model-based Testing are critical for airborne software compliance with DO-178C and its supplement DO-331, which specifically addresses model-based approaches for software levels A through D. Traditional manual methods increase the documentation and validation burden, leading to inconsistent implementations across the project, and raise the risk of missed defects or gaps in compliance. This paper presents an automation framework designed to align with DO-331 objectives by leveraging fine-tuned large language models (LLM) to automate the generation of high-level textual requirements and low-level model-based requirements. From these, comprehensive test cases are automatically derived, covering normal, edge, mutation based, and dynamic scenarios to ensure a thorough validation of model behavior. Utilizing AI agent, the framework extracts requirements and key parameters from documentation, enabling automated specification analysis and test script

Lalchandani, Tushar, Purushothaman, Kalaivani, Jeppu, Yogananda, Vijaya Kumar, Shree Harsha, Natarajan, Akilandeswari

AI-Led Sustainability Strategy: Driving Product Value from Birth to Disposal

2026-26-0781

6/1/2026

Aerospace products operate within highly complex, safety-critical environments and endure extended lifecycles, often spanning decades. Sustaining their operational value requires rigorous management of Safety, Reliability, and Availability (SRA), while global Environmental, Social, and Governance (ESG) mandates demand parallel progress toward sustainability goals. This paper introduces an AI-driven strategy that integrates these dual imperatives—Sustenance Management and Sustainability Management—within a unified Product Lifecycle (PLC) framework. The proposed approach leverages Artificial Intelligence across five PLC phases: Generative Design, Detailed Design & Verification, Manufacturing & Industrialization, Operations & Maintenance, and End-of-Life Circularity. Anchored by a certified Digital Thread, this framework ensures seamless, auditable data flow from concept to disposal. Using Life-Limiting Parts (LLPs)—such as high-stress turbine discs—as a case study, the paper demonstrates

Srinivasan, Karthik, G.V.V., Ravi Kumar, Vaderahobli, Devaraja Holla, Bhate, Ujwal, Veluri, Sastry

Hybrid Digital Twin for Aero-Engine Bearing Prognostics: A Physics-Informed Machine Learning Approach to RUL Prediction

2026-26-0718

6/1/2026

Unscheduled maintenance due to the failure of critical components, such as aero-engine rolling element bearings, is a leading cause of costly Aircraft-on-Ground (AOG) events; consequently, current time-based maintenance practices are inefficient and prone to risk. This paper develops a resource-efficient Hybrid Digital Twin (HDT) model for an engine bearing, focusing on the dynamic prediction of spall growth due to Rolling Contact Fatigue (RCF), thereby enabling a condition-based maintenance paradigm. The HDT architecture integrates two core models: (1) a physics-informed model that uses established life and fatigue theory to define initial degradation thresholds, and (2) a data-driven Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM) network, for dynamic degradation rate modeling. The methodology utilizes a Monte Carlo simulation coupled with RCF progression equations to generate a large, high-fidelity synthetic run-to-failure dataset under varying

Mohamed, Abbas

AI Based Energy Sustained Series Hybrid Aircraft

2026-26-0776

6/1/2026

This paper investigates the energy consumption characteristics of series hybrid aircraft with a focus on comparing conventional energy management approaches against an AI-powered optimization framework. The study comprehensively models the energy demands of a series hybrid aircraft across all major flight phases, including Idle & Ground Operations, Taxi, Takeoff, Climb, Cruise, Descent, Approach, Landing, and Rollout & Taxi. For each phase, detailed mathematical formulations are developed to capture power requirements and energy flow, incorporating real-time operational parameters to enhance the accuracy of the energy consumption estimations measured in kilowatt-hours (kWh). The AI-based optimization leverages advanced control strategies, specifically Model Predictive Control (MPC) and Reinforcement Learning (RL) algorithms, to dynamically manage the aircraft’s energy systems. MPC is employed to predict and optimize future energy usage by solving constrained optimization problems over

Kanchagar, Amogha

Predictive Model and Analysis of the Resultant Force in the Dry Machining of Mg-4Zn/Si ₃ N ₄ Nanocomposites

2026-26-0762

6/1/2026

The development of lightweight materials for use in aerospace and automotive applications is extremely significant. Magnesium (Mg)-based alloys and composites are good candidate materials from the perspective of low density, good specific strength, and abundance. The Mg-4Zn alloy is one such alloy, which is a lightweight, biocompatible, and eco-friendly Mg-based alloy. In spite of these advantages, there is a strong need and scope to improve its wear resistance and mechanical properties. Mg-4Zn nanocomposites with Si3N4 reinforcements (a biocompatible bioceramic) are hypothesized to possess superior properties. Microstructural analysis of the vacuum stir-cast nanocomposites confirms grain refinement and a consequent increase in microhardness with an increase in Si3N4 reinforcement wt.%. The addition of Si3N4 reinforcement to improve the properties of the Mg-4Zn alloy could introduce challenges in machining. To make products from the nanocomposites, machining them with minimal

N, Anand, Shaju, Tony M, G, Nagamalleswara Rao, D, Bijulal, K, Jayaprakash Reddy, K, Vijayan, Chaman, Joji J

Agentic Digital Thread for Managing the Non-Conformities in Manufacturing of Aerospace Products

2026-26-0763

6/1/2026

Aerospace manufacturing operates within an intricate ecosystem where quality, compliance and traceability are critical to success. Conventional digital thread frameworks provide connectivity but remain largely passive, lacking the intelligence to autonomously manage complex non-conformities across the product lifecycle. This paper introduces an Agentic Digital Thread powered by Agentic AI, designed to transform non-conformity management into an adaptive, self-orchestrating system that actively drives decision-making and corrective actions [1, 4]. The proposed architecture employs a Master Agent to coordinate workflows and maintain end-to-end data continuity, while specialized Agents autonomously manage domain-specific tasks. In the pre-manufacturing phase, these agents proactively validate requirements, material conformity and process planning through integration with PLM, MES, ERP, QMS and supplier systems. In the post-manufacturing phase, the framework extends to concession

Veluri, Sastry, Gopala Krishnan, Kannan

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