This article presents a design of experiments (DOE) approach to analyze automobile engine coolant leakage from hose joints. The data includes force measurement at hose joints through physical validation and computer-aided engineering (CAE) simulation results. The proposed approach involves utilizing digital validation data, which simulates the entire experiment using CAE. The novelty of this approach lies in its reliance on digital validation data rather than conventional physical measurements, thus providing cost and time savings for the organization. In this study, the authors investigated the force at the coolant hose joint, which results in oil leakage as the response variable. Nine independent factors were evaluated in this experiment. The study concluded through the identification of critical parameters and opposed regression model to predict force at hose joints.

Koulage, Dasharath Baliram, Mondal, Kanchan, Manerikar, Dattatray Shriniwas

SAE TOMORROW TODAY - Smarter Automotive Testing with AI and Simulation

1352207/03/2025

The new test track isn't asphalt--it's AI and simulation. From hardware-in-the-Loop (HIL) to real-time performance analytics, new innovations are reshaping how vehicles are designed, validated, and brought to market. At the forefront of this transformation is A&D Technology, a leader in integrated test automation and lab management solutions for the transportation and energy markets. By integrating AI-driven analytics with high-fidelity simulation environments, the company is empowering automotive engineers to streamline testing workflows, reduce physical prototypes, and accelerate innovation in everything from EV powertrains to autonomous systems. To learn more, we sat down with Mike Uhl, President & CEO, to discuss how A&D Technology's work in simulation and test automation is helping engineers bring the next generation of mobility technologies to life. 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

Hineman, Marcie

Applying Real World Traffic Data on Machine Learning Methods for Graph Based Cooperation Strategies of Automated Vehicles

2025-01-0286

07/02/2025

With the increasing distribution of smart mobility systems, automated & connected vehicles are more and more interacting with each other and with smart infrastructure using V2X-communication. Hereby, the vehicles’ position, driving dynamics data, or driving intention are exchanged. Previous research has explored graph-based cooperation strategies for automated vehicles in mixed traffic environments based on current V2X-communication standards. Thereby, the focus is set on cooperation optimization and maneuver negotiation. These strategies can be implemented through both centralized and decentralized computational approaches and are conflict-free by design. To enhance these previously established cooperation models, real-world traffic data is used to derive vehicle trajectories, providing a more accurate representation of actual traffic scenarios in order to enhance the practical application of the described methodology. Additionally, machine learning algorithms are employed to train

Flormann, Maximilian, Meyer, Felix, Henze, Roman

An LLM-Based Multi-Turn Task-Oriented Dialogue System for Machine Learning Algorithm Selection in Data Mining

2025-01-0289

07/02/2025

For the systematic application of machine learning during data mining in product development processes, selecting a suitable algorithm is crucial for success. During an empirical study in the automotive industry, a team applying data mining to develop battery systems for battery electric vehicles was accompanied. Here, it could be observed that data mining tasks are often unique during product development processes and can differ in boundary conditions. Depending on these tasks, suitable machine learning algorithms must be selected. Because of the variety of machine learning paradigms, problems, and algorithms, it is often hard to select a suitable algorithm, especially for inexperienced data miners. This paper presents a large language model (LLM)-based, multi-turn, task-oriented dialogue system to support data miners in selecting machine learning algorithms that are suitable for their specific data mining tasks. This approach, called “Algorithm Selection Assistant” (ASA), enables

Hörtling, Stefan, Bause, Katharina, Albers, Albert

Leveraging AI for Automated Code Generation from Systems Engineering Specifications

2025-01-0295

07/02/2025

The increasing complexity of modern vehicles and the automotive industry's shift towards Software Defined Vehicles (SDVs) require innovative solutions to streamline development processes. Traditional methods of software development often struggle to meet the demands for agility, scalability, and precision in this context. In response, this paper presents a novel approach utilizing Artificial Intelligence (AI), specifically Large Language Models (LLMs), to automate the generation of executable code directly from Systems Engineering (SE) specifications. This novel approach aims to transform how SE requirements are converted into implementation-ready code, reducing the inefficiencies and potential errors associated with manual translation. LLMs trained on domain-specific data are capable of interpreting complex requirements, managing dependencies, and generating consistent and accurate code. By integrating LLMs into the automotive software pipeline, companies can improve productivity

Padubrin, Marcel, Reuss, Hans-Christian, Brosi, Frank, Menz, Leonhard, Guerocak, Erol

Prediction of Hazardous Gaseous Emissions from a Gasoline Engine during Cold Starts Using Machine Learning Methods

2025-01-0321

07/02/2025

Internal combustion engines generate higher exhaust emissions of hazardous gases during the initial minutes after engine start. Experimental data from a state-of-the-art turbo-charged 3-cylinder, 999 cc gasoline engine are used to predict cold start emissions using two Machine Learning (ML) models: a Multilayer Perceptron (MLP) which is a fully connected neural network and an Encoder-Decoder Recurrent Neural Network (ED-RNN). Engine parameters and various temperatures are used as input for the models and NOx (Nitrogen Oxides), CO (Carbon monoxide) and unburned hydrocarbon (UHC) emissions are predicted. The dataset includes time series recordings from the Worldwide harmonized Light-duty vehicles Test Cycle (WLTC) and four Real Diving Emissions (RDE) cycles at ambient and initial engine temperatures ranging from -20 °C to +23 °C. In total, 21 cases are considered, consisting of eight different ambient temperatures and five distinct driving cycles. Each case consists of a sequence of 2500

Mangipudi, Manoj, Denev, Jordan A., Bockhorn, Henning, Trimis, Dimosthenis, Koch, Thomas, Debus, Charlotte, Götz, Markus, Zirwes, Thorsten, Hagen, Fabian P., Tofighian, Hesam, Wagner, Uwe, Braun, Samuel, Lanzer, Theodor, Knapp, Sebastian M.

Conception and Experience of Non-Driving Related Activities in Semi-Autonomous Vehicles

2025-01-0260

07/02/2025

While semi-autonomous driving (SAE level 3 & 4) is already partially a reality, the driver still needs to take over driving upon notice. Hence, the cockpit cannot be designed freely to accommodate spaces for non-driving related activities. In the following use case, a mobile workplace is created by integrating a translucent acrylic glass pane into the cockpit and introducing joystick steering of the car. By using the technology Virtual Desktop 1, which is a software layer, any desktop application can be represented freely transformable on arbitrary physical and virtual surfaces. Thus, a complete Windows environment can be distributed across all curved and flat surfaces of an interior. The concept is further enhanced by a voice-driven generative AI which helps to summarize documents. A physical and a virtual demonstrator are created to experience and assess the mobile workspace, the well-being of the driver, external influences, and psychological aspects. The physical demonstrator is a

Beutenmüller, Frank, Reining, Nine, Rosenstiel, Reto, Schmidt, Maximilian, Layer, Selina, Bues, Matthias, Mendonca, Daisy

Reducing Traffic Congestion with Reinforcement Learning-Driven Signal Control Systems

2025-01-0283

07/02/2025

The road network is a critical component of modern urban mobility systems, with signalized traffic intersections playing a pivotal role. Traditionally, traffic light phase timings and durations at intersections are designed by transportation engineers using historical traffic data. Some modern intersections employ trigger-based mechanisms to improve traffic flow; however, these systems often lack global awareness of traffic conditions across multiple intersections within a network. With the increasing availability of traffic data and advancements in machine learning, traffic light systems can be enhanced by modeling them as agents operating in an environment. This paper proposes a Reinforcement Learning (RL) based approach for multi-agent traffic light systems within a simulation environment. The simulation is calibrated using real-world traffic data, enabling RL agents to learn effective control strategies based on realistic scenarios. A key advantage of using a calibrated simulation

Kalra, Vikhyat, Tulpule, Punit, Giuliani, Pio

Development and Evaluation of a Combined Driveline Oscillation and Traction Controller Using Model Predictive Control and Reinforcement Learning: A Comparative Case Study

2025-01-0291

07/02/2025

In electric vehicles, the control of driveline oscillations and tire traction is critical for guaranteeing driver comfort and safety. Yet, achieving sufficient driveline control performance remains challenging in the presence of rapidly varying road conditions. Two promising avenues for further improving driveline control are adaptive model predictive control (MPC) and model-based reinforcement learning (RL). We derive such controllers from the same non-linear vehicle model and validate them through pre-defined test scenarios. The MPC approach employs input and output trajectory tracking with soft constraints to ensure feasible control actions even in the presence of constraint violations and is further supported by a Kalman filter for robust state estimation and prediction. In contrast, the RL controller leverages the model-based DreamerV3 algorithm to learn control policies autonomously, adapting to different road conditions without relying on external information. The results

Uhl, Ramón Tamino, Schüle, Isabel, Ludmann, Laurin, Geist, A. René

Agentic Generation of OpenX Files from Natural Language Descriptions

2025-01-0299

07/02/2025

Usually, scenarios for testing of advanced driver assistance systems (ADAS) are generated utilizing certain scenario and road specification languages such as ASAM OpenSCENARIO and OpenDRIVE. Directly adopting these low-level languages limits the rate in which new scenarios are generated for virtual testing. Natural language (NL) would allow a much broader group of people and artificial intelligences to generate scenarios, increasing test coverage and safety. Instead of trying a direct translation from NL into OpenX, the existing intermediate domain specific language (DSL) stiEF is used. This not only facilitates testing and debugging but also generation, as its grammar can be used as a constraint for a large language model (LLM), which is then able to translate NL into stiEF. A parser is applied in an agentic way that interacts with the LLM until a syntactically correct file is generated, an optional second agent is then used to do basic semantic verification. Finally, the translation

Vargas Rivero, Jose Roberto, Bock, Florian, Menken, Stefan

Leveraging LLM-Based AI Agents for Boosting Vehicle Testing Process

2025-01-0300

07/02/2025

The validation process in research and development involves several complex stages, including test requests, planning, execution, and the analysis and evaluation of results. In the automotive domain, compliance with regulatory standards, such as those required for Euro 7 homologation, adds an additional layer of complexity. Implementing these regulations into operational validation workflows and ensuring their seamless integration with supporting tools remains a significant challenge. Recent advancements in Large Language Models (LLMs) have introduced innovative use cases across various domains. In particular, AI agents powered by LLMs demonstrate immense potential by autonomously performing complex tasks while utilizing user-defined tools. This capability extends far beyond traditional applications like knowledge management or text generation typically associated with LLMs. In this paper, we explore how a modern AI agent can be developed and integrated into existing IT tools for test

Unterschütz, Stefan, Hansen, Björn

Power Hop Detection in High-Torque Vehicles: A Machine Learning Approach with Focus on Data Coverage and Model Generalization

2025-01-0271

07/02/2025

Power hop is a vibration phenomenon that occurs during high accelerations from low speed. In severe cases it can lead to component damage or deformation. Therefore, the affected vehicles must be safeguarded against these vibrations by a safe design of the components and by additional software-based functions. Conventional software-based solutions, such as Traction Control Systems (TCS), often perform delayed interventions and apply harsh torque adjustments that reduce driving comfort. Motivated by these challenges, this paper proposes a novel approach for power hop detection in a high-torque vehicle based on Long Short-Term-Memory Network (LSTM) and real-time measurements. Unlike conventional methods, our LSTM precisely detects the start of power hop, enabling proactive torque adjustments. Due to its impact on vehicle stability, the model must achieve a high level of reliability and robustness. Given the importance of data quality in Machine Learning (ML), we consider data-related

Chehoudi, Moatez, Moisidis, Ioannis, Sailer, Marc, Peters, Steven

Methodology for Real-World Automated Function Development: From Virtual to On-Vehicle Implementation

2025-01-0288

07/02/2025

The automotive industry is increasingly facing challenges stemming from growing system complexities, shortened development cycles, and the demand for rapid time-to-market transitions. Reinforcement learning (RL) has emerged as a promising approach to developing advanced control functions due to its adaptive and autonomous nature. The technique has already demonstrated its viability in virtualised X-in-the-Loop (XiL) environments. However, its application to real-world vehicle systems is inhibited by safety concerns, real-time constraints, and the integration into established software toolchains. This paper introduces a comprehensive methodology for developing practical control functions with RL: starting in a virtual environment, training then transitions to a Hardware-in-the-Loop (HiL) setup, and ultimately proceeds to a real vehicle. Utilising the open-source framework LExCI, the proposed approach facilitates seamless training across multiple development stages and showcases RL’s

Badalian, Kevin, Picerno, Mario, Lee, Sung-Yong, Schaub, Joschka, Andert, Jakob

Customized Signalized Intersection Control for Pedestrian Safety Enhancement through Computer Vision Technologies

09-13-02-0008

06/26/2025

The escalating complexity at intersections challenges the safety of the interaction between vehicles and pedestrians, especially for those with mobility impairments. Traditional traffic control systems detect pedestrians through costly technologies such as LiDAR and radar, limiting their adoption due to high costs and static programming. Therefore, the article proposes a customized signalized intersection control (CSIC) algorithm for pedestrian safety enhancement. This algorithm integrates advanced computer vision (CV) algorithms to detect, track, and predict pedestrian movements in real time, enhancing safety at a signalized intersection while remaining economically viable and easily integrated into existing infrastructure. Implemented at a key intersection in Bellevue, the CSIC system achieves a 100% pedestrian passing rate while simultaneously minimizing the average remaining walk time after crossings. The algorithm used in this study demonstrates the potential of combining CV with

Xia, Rongjing, Fang, Hongchao, Zhang, Chenyang

Deep Learning–Based Prediction of Suspension Dynamics Performance in Multi-Axle Vehicles

15-18-03-0014

06/19/2025

This article is mainly to present a deep learning–based framework for predicting the dynamic performance of suspension systems for multi-axle vehicles, which emphasizes the integration of machine learning with traditional vehicle dynamics modeling. A multitask deep belief network deep neural network (MTL-DBN-DNN) was developed to capture the relationships between key vehicle parameters and suspension performance. Numerical simulation–generated data were utilized to train the model. This model also showed better prediction accuracy and computational speed compared to traditional deep neural network (DNN) models. Full sensitivity analysis has been performed in order to understand how different vehicle and suspension parameters may affect suspension dynamic performance. Furthermore, we introduce the suspension dynamic performance index (SDPI) in order to measure and quantify overall suspension performance and the effectiveness of multiple parameters. The findings highlight the

Lin, Bo-Yi, Lin, Kai-Chun

A Machine Learning–Based Predictive Modeling for Optimizing the Critical Useful Life of Aeroengines: A Field Maintenance Perspective

01-18-02-0009

06/19/2025

Establishing critical useful life plays a central role to determine aeroengine health status including aeroengine parameter changes from adverse material conditions or metal fatigue. The useful life assessment serves to support maintenance teams by enabling predictive maintenance followed by part replacement or conditions improvement. The proposed research works to improve the ability of turbofan aeroengine useful life estimation while targeting practical deployment during maintenance operations at field locations. A field maintenance–oriented ensemble bagged regression model for aeroengines represents the proposed method within this research. The present study reaches an error index of 7.06 with 98.95% model fitness when applying it to critical useful life training data. The projected model received its validation through experiments on test and field datasets. Field tests revealed that among 25 machine learning models the proposed model delivered optimal results since its error index

Singh, Shaktiyavesh Nandan Pratap, Shringi, Rohitashwa, Chaturvedi, Manish, Kumar, Ajay

Research on Combustion Characteristics of Flash-Boiling Spray in Cylinders Based on Machine Learning Methods

2025-01-0205

06/16/2025

With the increasing number of vehicles in operation, exhaust emissions from engines have exerted negative impacts on ecological environments, prompting researchers to actively pursue cleaner and more efficient in-cylinder combustion strategies. Flash-boiling spray technology, capable of generating superior fuel atomization under relatively low injection pressures, has emerged as a promising approach for achieving performance breakthroughs in gasoline direct injection (GDI) engines. While current research primarily focuses on morphological characterization and mechanistic analysis of flash-boiling spray, there remains insufficient understanding of flame development characteristics under flash boiling spray conditions within engine cylinders. This study systematically investigates the combustion characteristics of TPRF and PRF fuels under both subcooled and flash-boiling spray conditions through the integration of image processing and machine learning methodologies. Experimental

Zhang, Weixuan, Shahbaz, Muhammad, Cui, Mingli, Li, Xuesong, Xu, Min

Automated Fiber Placement: Status, Challenges, and Evolution

R-51106/11/2025

Discover the cutting-edge technology driving the next generation of aerospace, automotive, and space exploration. Automated Fiber Placement: Status, Challenges, and Evolution offers a comprehensive dive into AFP—an innovative method that’s transforming how complex, lightweight structures are built. Over the last 50 years, fiber-reinforced composites have revolutionized industries, particularly aerospace, with carbon fiber reinforced polymers at the forefront. AFP has enabled lighter, more durable aircraft with reduced fuel consumption and improved longevity. As the aerospace industry advances with new platforms like the Boeing 787 and Airbus A350, AFP continues to evolve, integrating machine learning and automation to deliver higher efficiency and lower costs. This book serves as an essential guide, presenting a decade of research in clear, accessible language. Whether you're a student, engineer, or industry professional, you'll gain a thorough understanding of AFP and its

Harik, Ramy, Brasington, Alex

Head Accelerations and Concussion Risks in Low-to-Moderate–Speed Rear-End Collisions

2025-01-5041

06/10/2025

Recent studies have investigated head injury metrics, including mild traumatic brain injury (mTBI), or concussion risks, in low- to moderate-speed rear-end collisions, with linear and angular head accelerations contributing to the risk of developing a concussion. The present study analyzes head acceleration values in rear-end collisions at an impact severity of 5–30 km/h delta-V. Biomechanical data was obtained from HIII 50th percentile male anthropomorphic test devices (ATDs) seated in the target subject vehicles and utilizing safety restraints and head rests. Concussion risks were calculated from resultant linear and angular head accelerations recorded in the ATDs, and a linear regression model was used to determine what, if any, relationship existed between these head injury metrics and impact severity. The results indicate that there is a significant and positive relationship between head acceleration metrics and impact severity, particularly in the sagittal plane, with F-values

Garcia, Beatriz, Emanet, Hatice Seyda, Hoffman, Austin

Automotive Engineering: June 2025

25AUTP0606/05/2025

Balancing the scales in Dakar Here's how M-Sport engineers removed some weight - and added some elsewhere - to produce a podium-winning vehicle. Sealing the future of in-wheel motors: low friction and maximum protection V-seals and cassette seals offer critical protection from environmental influences and are helping in-wheel motors set new benchmarks. Taiwan suppliers look around the world for solutions Tariffs are forcing Taiwanese companies to rethink their plans, but it could be worse. U.S. states focus on Taiwan as trade war continues Taiwan's role in the global automotive supply chain continues to increase, which is one reason so many U.S. states are on site. Tariff challenges, EV advances at 360 Mobility We check in on Foxconn, Master Bus, PEWC and NUK at Taiwan's big trade show for the transportation industry. Editorial Off we go Supplier Eye Investment certainty is critical GM picks LMR as its future affordable battery technology Slate unveils bare-bones EV mini truck for

Letter from the Guest Editors

05-18-04-0026

06/02/2025

Letter from the Guest Editors

Zhu, Shun-Peng, Zhan, Zhenfei, Huang, Shiyao

IQSat: Aitech's AI-Enabled Picosatellite for Earth Observation and Imaging Applications

25AERP06_01

06/01/2025

Aitech introduced its new artificial intelligence (AI)-enabled picosatellite constellation platform, IQSat, at the 40th annual Space Symposium in April. The platform is designed to bring ready to use commercial off the shelf (COTS) embedded computing to data heavy earth imaging and pattern recognition applications enabled by AI and machine learning (ML) processing and algorithms performed onboard a constellation of IQSats. Available as an individual platform or in constellations that could include thousands of picosatellites, IQSat will become available to customers in the fourth quarter of 2025.

LightNetDehaze: Lightweight and Unsupervised Single-Image Dense Haze Removal

12-09-01-0005

05/26/2025

Image dehazing techniques can play a vital role in object detection, surveillance, and accident prevention, especially in scenarios where visibility is compromised because of light scattering by atmospheric particles. To obtain a high-quality image or as an initial step in processing, it’s crucial to restore the scene’s information from a single image, given that this is an ill-posed inverse problem. The present approach utilized an unsupervised learning approach to predict the transmission map from a hazy image and used YOLOv8n to detect the car from a clear recovered image. The dehazing model utilized a lightweight parallel channel architecture to extract features from the input image and estimate the transmission map. The clear image is recovered using an atmospheric scattering model and given to the YOLOv8n for car detection. By incorporating dark channel prior loss during training, the model eliminates the need for a paired dataset. The proposed dehazing model with fewer

Dave, Chintan, Patel, Hetal, Kumar, Ahlad

Product Assurance in the Age of Artificial Intelligence

EPR2025011

05/21/2025

Driven by the vast consumer marketplace, the electronics megatrend has reshaped nearly every sector of society. The advancements in semiconductors and software, originally built to serve consumer demand, are now delivering significant value to non-consumer industries. Today, electronics are making inroads into traditionally conservative, safety-critical sectors such as automotive and aerospace. In doing so, electronics—now further propelled by artificial intelligence—are disrupting the functional safety architectures of these cyber-physical systems. Electronics have created the world of cyber-physical systems, raising broader concerns about the broader category of product assurance. Product Assurance in the Age of Artificial Intelligence continues the work of previous SAE Edge Research Reports in examining open research challenges arising from this shift, particularly in automotive systems, as core electronic technologies (e.g., the combination of software and communications) have even

Razdan, Rahul

Leveraging Advanced Data Technologies for Smart Traffic Management

EPR2025008

05/20/2025

The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and

Ercisli, Safak

Edge Artificial Intelligence in Connected, Cooperative and Automated Mobility

EPR2025009

05/20/2025

With many stakeholders involved, and major investments supporting it, the advancements in automated driving (AD) are undoubtedly there. Generally speaking, the motivation for advancing AD is driver convenience and road safety. Regarding the development of AD, original equipment manufacturers, technology start-ups, and AD systems developers have taken different approaches for automated vehicles (AVs). Some manufacturers are on the path toward stand-alone vehicles, mostly relying on onboard sensors and intelligence. On the other hand, the connected, cooperative, and automated mobility (CCAM) approach relies on additional communication and information exchange to ensure safe and secure operation. CCAM holds great potential to improve traffic management, road safety, equity, and convenience. In both approaches, there are increasingly large amounts of data generated and used for AD functions in perception, situational awareness, path prediction, and decision-making. The use of artificial

Van Schijndel-de Nooij, Margriet, Beiker, Sven

SAE TOMORROW TODAY - How Autonomy is Breathing New Life into Farming

1351505/19/2025

Labor shortages are pushing many family farms to the brink. The solution? Autonomous technology which is emerging as a game-changer--boosting productivity, cutting risk, and giving family farms a fighting chance to thrive for generations to come. At the forefront of this transformation is Blue River Technology, a John Deere subsidiary leading the charge in precision agriculture. From AI-driven tools to autonomous machinery, the company is helping to build the future of farming the John Deere way, meeting farmers in the field and designing solutions grounded in real-world challenges. With safety and scalability built in, farmers can start small with retrofit kits that upgrade existing tractors, making automation more accessible than ever. To learn more, we caught up with Aaron Wells, Director of Engineering at Blue River, to explore how automation is transforming agriculture and giving farmers the tools they need to stay ahead--despite labor challenges. We'd love to hear from you. Share

Hineman, Marcie

How AI Can Help the Deaf and Non-Deaf Alike

TBMG-53078

05/13/2025

While working with deaf students for more than a decade and a half, Bader Alsharif, Ph.D. candidate in the Florida Atlantic University Department of Electrical Engineering and Computer Science, saw firsthand the communication struggles that his student faced daily.

Automotive Engineering: May 2025

25AUTD0505/08/2025

All (robot) hands on deck BMW leverages advanced manufacturing, AI and machine learning to build the next generation of EVs - and vehicles of all powertrains. Driving down the high cost of overspray in automotive paint processes By designing automated paint systems with superior control behavior, OEMs can improve transfer efficiency and minimize overspray and its heavy costs for automotive companies. Connected: EV battery tech advancements and facility coatings selection New battery chemistries and the rise of automated guided vehicles in EV battery plants will redefine what's expected from floor and wall coatings. Editorial Watch this Supplier Eye Sourcing re-evaluation underway ICE and hybrid-electric propulsion show staying power Intel's Weast says China leaping ahead due to focus on innovation Nira Dynamics' sensor-free tire pressure tech eyes new opportunities Study shows safety top priority of potential vehicle-to-everything consumers The transformative power of large-format

Active Noise Control with Head Tracking System

2025-01-0012

05/05/2025

In active noise control, the control region size (same meaning as zone of control) decreases as the frequency increases, so that even a small moving of the passenger's head causes the ear position to go out of the control region. To increase the size of the control region, many speakers and microphones are generally required, but it is difficult to apply it in a vehicle cabin due to space and cost constraints. In this study, we propose moving zone of quiet active noise control technique. A 2D image-based head tracking system captured by a camera to generate the passenger's 0head coordinates in real time with deep learning algorithm. In the controller, the control position is moved to the ear position using a multi-point virtual microphone algorithm according to the generated ear position. After that, the multi-point adaptive filter training system applies the optimal control filter to the current position and maintains the control performance. Through this study, it is possible to

Oh, ChiSung, Kang, Jonggyu, Kim, Joong-Kwan

High-Fidelity NVH Model Development for Electric Motors Using Deep Learning and Machine Learning Algorithms

2025-01-0123

05/05/2025

Electric motor whine is a significant source of noise in electric vehicles (EVs). To improve the noise, vibration, and harshness (NVH) performance of electric propulsion systems, it is essential to develop a physics-based, high-fidelity stator model. In this study, a machine learning (ML) model is developed using an artificial neural network (ANN) method to accurately characterize the material properties of the copper winding, varnish, and orthotropic stator laminate structure. A design of experiments (DOE) approach using Latin hypercube sampling of parameters is implemented after evaluating alternative surrogate models. A finite element (FE) model is constructed using the nominal stator design parameters to train the ANN model using 121 DOE variables and 72,000 data points. The ML-trained ANN model is then verified to predict the driving point frequency response function (FRF) spectrum with reasonable accuracy. Subsequently, modal tests are conducted on the electric stator, and the

Rao, Bhyri Rajeswara, GSJ, Gautam, He, Song

Brake Noise CAE Enhancement with the Synergy between Design of Experiments and Machine Learning

2025-01-0124

05/05/2025

The digitalization of industrial systems has led to increased data availability. Machine learning (ML) methodologies are now commonly used for data analysis in industrial contexts. Not all contexts have abundant data; sometimes data collection can be scarce or expensive. Design of Experiments (DOE) is a technique that provides an informative dataset for ML analysis when data are limited. It involves systematically designing experiments to collect relevant data points with regression models. Disc brake noise is a challenging problem in vehicle noise, vibration, and harshness (NVH). Different noise events occur under various operating conditions and across frequencies (1-16 kHz). To enhance computer-aided engineering (CAE) techniques for brake noise, ML is used to generate additional data. Sequential experimentation in DOE aligns well with ML’s ability to continuously learn and improve as more data become available. DOE is applied in CAE to collect data for training ML models. ML helps

Song, Gavin, Sridhar, Gurupriya, Vlademar, Michael, Venugopal, Narayana

Leveraging Vision Language Models and Prompt Engineering for Bearing Defect Classification from Vibration Signals

2025-01-0127

05/05/2025

Bearings are fundamental components in automotive systems, ensuring smooth operation, efficiency, and longevity. They are widely used in various automotive systems such as wheel hubs, transmissions, engines, steering systems etc. Early detection of bearing defects during End-of-Line (EOL) testing and operational phases is crucial for preventive maintenance, thereby preventing system malfunctions. In the era of Industry 4.0, vibrational, accelerometer, and other IoT sensors are actively engaged in capturing performance data and identifying defects. These sensors generate vast amounts of data, enabling the development of advanced data-driven applications and leveraging deep learning models. While deep learning approaches have shown promising results in bearing fault diagnosis, they often require extensive data, complex model architectures, and specialized hardware. This study proposes a novel method leveraging the capabilities of Vision Language Models (VLMs) and Large Language Models

Chandrasekaran, Balaji, Cury, Rudoniel

A Comparative Analysis of Fault Diagnosis by Vibration Signals for Critical Gear Components in Electric Vehicle Motor Testing Machines Using Machine Learning Algorithms

2025-01-0040

05/05/2025

Electric vehicles (EVs) are shaping the future of mobility, with drive motors serving as a cornerstone of their efficiency and performance. Motor testing machines are essential for verifying the functionality of EV motors; however, flaws in testing equipment, such as gear-related issues, frequently cause operational challenges. This study focuses on improving motor testing processes by leveraging machine learning and vibration signal analysis for early detection of gear faults. Through statistical feature extraction and the application of classifiers like Wide Naive Bayes and Coarse Tree, the collected vibration signals were categorized as normal or faulty under both loaded (0.275 kW) and no-load conditions. A performance comparison demonstrated the superior accuracy of the wide neural networks algorithm, achieving 95.3%. This methodology provides an intelligent, preventive maintenance solution, significantly enhancing the reliability of motor testing benches.

S, Ravikumar, Sharik, N, Syed, Shaul, V, Muralidharan, D, Pradeep Kumar

Condition Monitoring of High-Speed Rotating Machinery Using Digital Twins & Machine Learning Techniques

2025-01-0128

05/05/2025

In the era of Industry 4.0, the maintenance of factory equipment is evolving with new systems using predictive or prescriptive methods. These methods leverage condition monitoring through digital twins, Artificial Intelligence, and machine learning techniques to detect early signs of faults, types of faults, locations of faults, etc. Bearings and gears are among the most common components, and cracking, misalignment, rubbing, and bowing are the most common failure modes in high-speed rotating machinery. In the present work, an end-to-end automated machine learning-based condition monitoring algorithm is developed for predicting and classifying internal gear and bearing faults using external vibration sensors. A digital twin model of the entire rotating system, consisting of the gears, bearings, shafts, and housing, was developed as a co-simulation between MSC ADAMS (dynamic simulation tool) and MATLAB (Mathematical tool). The gear and bearing models were developed mathematically, while

Rastogi, Sarthak, Singhal, Srijan, Ahirrao, Sachin, Milind, T. R.

Cabin Noise Modeling for Seat Location Variation Using Simulation and AI

2025-01-0129

05/05/2025

Analyzing acoustic performance in large and complex assemblies, such as vehicle cabins, can be a time-intensive process, especially when considering the impact of seat location variations on noise levels. This paper explores the use of Ansys simulation and AI tools to streamline this process by predicting the effects of different speaker locations and seat configurations on cabin noise, particularly at the driver’s ear level. The study begins by establishing a baseline simulation of cabin noise and generating training data for various seat location scenarios. This data is then used to train an AI model capable of predicting the noise impact of different design adjustments. These predictions are validated through detailed simulations. The paper discusses the accuracy of these predictions, the challenges encountered and provides insights into the effective use of AI models in acoustic analysis for cabin noise, with a specific emphasis on seat location as a key variable.

Kottalgi, Santosh, He, Junyan, Banerjee, Bhaskar

Designing Rubber Flaps for Resonance Management: High-Frequency Tuning in Electric Vehicles Using AI/ML Approaches

2025-01-0125

05/05/2025

High-frequency whine noise in electric vehicles (EVs) is a significant issue that impacts customer perception and alters their overall view of the vehicle. This undesirable acoustic environment arises from the interaction between motor polar resonance and the resonance of the engine mount rubber. To address this challenge, the proposal introduces an innovative approach to predicting and tuning the frequency response by precisely adjusting the shape of rubber flaps, specifically their length and width. The approach includes the cumulation of two solutions: a precise adjustment of rubber flap dimensions and the integration of ML. The ML model is trained on historical data, derived from a mixture of physical testing conducted over the years and CAE simulations, to predict the effects of different flap dimensions on frequency response, providing a data-driven basis for optimization. This predictive capability is further enhanced by a Python program that automates the optimization of flap

Hazra, Sandip, Khan, Arkadip

Data-Driven Machine Learning Approach for Rattle Risk Mitigation

2025-01-0126

05/05/2025

In the modern automotive industry, squeak and rattle issues are critical factors affecting vehicle perceived quality and customer satisfaction. Traditional approaches to predicting and mitigating these problems heavily rely on physical testing and simulation technologies, which can be time-consuming and resource-intensive, especially for larger models. In this study, a data-driven machine learning approach was proposed to mitigate rattle risks more efficiently. This study evaluated a floor console model using the traditional simulation-based E-line method to pinpoint high-risk areas. Data generation is performed by varying material properties, thickness, and flexible connection stiffness using the Hammersley sampling algorithm, creating a diverse and comprehensive dataset for generating a machine learning (ML) model. Utilizing the dataset, the top contributing variables were identified for training the ML models. Various machine-learning models were developed and evaluated, and the

Parmar, Azan, Rao, Sohan, Reddy, Hari Krishna

Mode Shape Identification Using Graph Neural Networks for Vehicle Structure Design

2025-01-0131

05/05/2025

This paper introduces a novel, automated approach for identifying and classifying full vehicle mode shapes using Graph Neural Networks (GNNs), a deep learning model for graph-structured data. Mode shape identification and naming refers to classifying deformation patterns in structures vibrating at natural frequencies with systematic naming based on the movement or deformation type. Many times, these mode shapes are named based on the type of movement or deformation involved. The systematic naming of mode shapes and their frequencies is essential for understanding structural dynamics and “Modal Alignment” or “Modal Separation” charts used in Noise, Vibration and Harshness (NVH) analysis. Current methods are manual, time-consuming, and rely on expert judgment. The integration of GNNs into mode shape classification represents a significant advancement in vehicle modal identification and structure design. Results demonstrate that GNNs offer superior accuracy and efficiency compared to

Tohmuang, Sitthichart, Swayze, James L., Fard, Mohammad, Fayek, Haytham, Marzocca, Piergiovanni, Bhide, Sanjay, Huber, John

Navigating Evolving Vehicle NVH Challenges with Application of Emerging Technologies in Artificial Intelligence and Machine Learning

2025-01-0130

05/05/2025

The multifaceted, fast-paced evolution in the automotive industry includes noise and vibration (NVH) behavior of products for regulatory requirements and ever-increasing customer preferences and expectations for comfort. There is pressing need for automotive engineers to explore new and advanced technologies to achieve a ‘First Time Right’ product development approach for NVH design and deliver high-quality products in shorter timeframes. Artificial Intelligence (AI) and Machine Learning (ML) are trending transformative technologies reshaping numerous industries. AI enables machines to replicate human cognitive functions, such as reasoning and decision-making, while ML, a branch of AI, employs algorithms that allow systems to learn and improve from data over time. The purpose of the paper is to show an approach of using machine learning techniques to analyze the impact of variations in structural design parameters on vehicle NVH responses. The study begins by executing the Design of

Miskin, Atul R., Parmar, Azan, Raj, Sonia, Himakuntla, Uma Maheswar

Leveraging User Feedback to Optimize the Tuning of EVSE Models

2025-01-0020

05/05/2025

The implementation of active sound design models in vehicles requires precise tuning of synthetic sounds to harmonize with existing interior noise, driving conditions, and driver preferences. This tuning process is often time-consuming and intricate, especially facing various driving styles and preferences of target customers. Incorporating user feedback into the tuning process of Electric Vehicle Sound Enhancement (EVSE) offers a solution. A user-focused empirical test drive approach can be assessed, providing a comprehensive understanding of the EVSE characteristics and highlighting areas for improvement. Although effective, the process includes many manual tasks, such as transcribing driver comments, classifying feedback, and identifying clusters. By integrating driving simulator technology to the test drive assessment method and employing machine learning algorithms for evaluation, the EVSE workflow can be more seamlessly integrated. But do the simulated test drive results

Hank, Stefan, Kamp, Fabian, Gomes Lobato, Thiago Henrique

Application of AI/ML in Hydrobush Tuning to Enhance Overall Value Proposition

2025-01-0132

05/05/2025

In the highly competitive automotive industry, optimizing vehicle components for superior performance and customer satisfaction is paramount. Hydrobushes play an integral role within vehicle suspension systems by absorbing vibrations and improving ride comfort. However, the traditional methods for tuning these components are time-consuming and heavily reliant on extensive empirical testing. This paper explores the advancing field of artificial intelligence (AI) and machine learning (ML) in the hydrobush tuning process, utilizing algorithms such as random forest, artificial neural networks, and logistic regression to efficiently analyze large datasets, uncover patterns, and predict optimal configurations. The study focuses on comparing these three AI/ML-based approaches to assess their effectiveness in improving the tuning process. A case study is presented, evaluating their performance and validating the most effective method through physical application, highlighting the potential

Hazra, Sandip, Khan, Arkadip Amitava

Approach to Optimize the Airplane Boarding Process by Using Image Recognition and Stochastic Simulation to Predict Overhead Bin Fill Levels

2025-01-0164

05/02/2025

In single-aisle aircraft, the available storage space for carry-on baggage is inherently limited. When the aircraft is fully booked, it often results in insufficient overhead bin space, necessitating last-minute gate-checking of carry-on items. Such disruptions contribute to delays in the boarding process and reduce operational efficiency. A promising approach to mitigate this issue involves the integration of computer vision technologies with an appropriate data storage system and stochastic simulation to enable accurate and supportive predictions that enhance planning, reduce uncertainty, and improve the overall boarding process. In this work, the YOLOv8 image recognition algorithm is used to identify and classify each passenger’s carry-on baggage into predefined categories, such as handbags, backpacks, and suitcases. This classified data is then linked to passenger information stored in a NoSQL database MongoDB, which includes seat assignments and the number of carry-on items

Bergmann, Jacqueline, Hub, Maximilian

Image Processing-Based Chip Detection by KIZKI Algorithm and Its Optimization

2025-01-0168

05/02/2025

The process of producing aircraft parts involves the drilling of aluminum alloys. This creates a large amount of chips, which are removed using air, but sometimes they still remain within the holes. This is checked by inspectors through visual inspection. However, the quality of human inspection varies based on skill level and fatigue. Thus, image-based inspection should be used to stabilize and further improve inspection quality. This study aims to build a framework for chip detection based on image processing. Taking into account on-site implementation, the system must have low installation and running costs and be standalone. Therefore, we adopt the KIZKI algorithm, which satisfies these conditions. KIZKI means awareness in Japanese. This is a model of human peripheral vision and saccades. It does not require training like AI and can achieve high-speed and high-performance detection using a low-performance computer. In other words, there is no need for a computer with an expensive

Iinuma, Marin, Sato, Junya, Tsuji, Masahiko

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