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SAE International Digitization and AI Policy
SAE International prohibits the entry of SAE Content into any form of Artificial Intelligence (AI) tools, such as ChatGPT, OpenAI, or Microsoft Copilot. Additionally, creating derivatives of SAE Content using AI is also prohibited without express written permission from SAE International. In the case of such use, SAE International will suspend access to SAE Content, and further legal action will be considered.
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Welcome to the new SAE Mobilus®
Welcome to the new era of SAE Mobilus®, redesigned with a modern, intuitive web interface featuring enhanced search capabilities to help you stay informed and easily discover content. Login today: To access content within your subscription and make the most of the platform, please log into your subscription and your personal My Library (sign up is free) located in the top right corner of the screen. Key Features Intuitive User Interface: We've listened to our customers and tied this to the best practices in the industry to offer an improved, intuitive, and desirable website experience for our users. Improved In-app Discoverability: Enhanced search and expanded menu featuring search suggestions that allow for quick discovery of content. Announcements: Stay up to date on new features through a dedicated announcements page. Most recent announcements also get featured on the homepage. Enhanced Browse capabilities: Browse through over 200,000 publications that are now classified into categories commonly used across the industry. Deep-dive further into any topic or sub-topic of interest to make the most for your corporate projects or research. Smart Automation: "Meta Tags" on each result will help you find content related to your search. My Library: My dashboard is rebranded as My Library with powerful features to help you make the most out of the platform Mobile Friendly: Upgraded navigation on mobile devices Training Available SAE’s Customer Success team is dedicated to delivering the best platform experience possible. Through monthly trainings and short, on-demand video tutorials, find answers to your questions here: SAE Mobilus Spotlight training - Available monthly, hosted live by SAE’s Customer Success team Mobilus Minute – 2-minute videos, available on-demand Need some troubleshooting help? Access our FAQs by visiting our Help section: https://saemobilus.sae.org/help/home Resources to help you navigate the new platform are available here, where you can find links to quick start guides and video tutorials on features like My Library, Announcements, Saved Search, Login and new features. Have feedback that you'd like to share with us? Please submit your feedback to the Mobilus team through the blue Knowledge bot in the bottom right hand corner of the new platform.
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Numerical Approach for the Characterization of the Venting Process of Cylindrical Cells under Thermal Runaway Conditions

2024-01-2900

05/06/2024

Increasing awareness of the harmful effects on the environment of traditional Internal Combustion Engines (ICE) drives the industry toward cleaner powertrain technologies such as battery-driven Electric Vehicles (EV). Nonetheless, the high energy density of Li-Ion batteries can cause strong exothermic reactions under certain conditions that can lead to catastrophic results, called Thermal Runaway (TR). Hence, a strong effort is being made to understand this phenomenon and increase battery safety. Specifically, the vented gases and their ignition can cause the propagation of this phenomenon to adjacent batteries in a pack. In this work, Computational Fluid Dynamics (CFD) is employed to predict this venting process in an LG18650 cylindrical battery. The shape of the venting cap deformation obtained from experimental results was introduced in the computational model. The ejection of the generated gases was considered to analyze its dispersion in the surrounding volume through a Reynolds

Gil, Antonio, Micó, Carlos, Marco-Gimeno, Javier, Castro Espín, Mar

Simulation and Evaluation of Battery Aging in Electric Hybrid Storage Systems

2024-01-2903

05/06/2024

The extension of traction batteries from electric vehicles with supercapacitors is regularly discussed as a possibility to increase the lifetime of lithium-ion batteries as well as the performance of the vehicle drive. The objective of this work was to validate these assumptions by developing a simulation model. In addition, an economic analysis is performed to qualitatively classify the simulation results. Initially, a hybrid energy storage system consisting of battery and supercapacitor was developed. A semi-active hybrid energy storage topology was selected. Subsequently, the selection of use cases as well as the application-specific definition of load cycles took place. In addition, the control strategy was further developed so that a simulation on lifetime was made possible. The end-of-life of the battery cells was defined, according to the USABC guideline values. Based on the data of the respective use case, the control strategy parameter optimization was carried out according to

Mödl, Roman, Braun, Andreas, Kallis, Lena

Model-Based Knowledge Management in HV Battery Development

2024-01-2902

05/06/2024

In the dynamic landscape of battery development, the quest for improved energy storage and efficiency has become paramount. The contemporary energy transition, coupled with growing demands for electric vehicles, renewable energy sources, and portable electronic devices, has underscored the critical role that batteries play in our modern world. To navigate this challenging terrain and harness the full potential of battery technology, a well-defined and comprehensive data strategy resp. knowledge management strategy are indispensable. Conversely, the imminent and rapid progression of artificial intelligence (AI) is poised to have a substantial impact on the forthcoming landscape of work and the methodologies organizations employ for the management of their knowledge management (KM) procedures. Conventional KM endeavors encompass a spectrum of activities such as the creation, transmission, retention, and evaluation of an enterprise’s knowledge over the entire knowledge lifecycle. However

Badi, Ibtihal, Braun, Andreas, Kallis, Lena

A Comparative Analysis of Thermal Runaway Propagation in Different Modular Lithium-Ion Battery Configuration

2024-01-2901

05/06/2024

Thermal runaway is a critical safety concern in lithium-ion battery systems, emphasising the necessity to comprehend its behaviour in various modular setups. This research compares thermal runaway propagation in different modular configurations of lithium-ion batteries by analysing parameters such as cell spacing and applying phase change materials (PCMs) and Silica Aerogel. The study at the module level includes experimental validation and employs a comprehensive model considering heat transfer due to thermal runaway phenomena. It aims to identify the most effective modular configuration for mitigating thermal runaway risks and enhancing battery safety. The findings provide valuable insights into the design and operation of modular lithium-ion battery systems, guiding engineers and researchers in implementing best practices to improve safety and performance across various applications

Garcia, Antonio, Monsalve-Serrano, Javier, Dreif, Amin, Guaraco-Figueira, Carlos

Effects of Hard-to-Measure Material Parameters on Clinching Joint Geometries Using Combined Finite Element Method and Machine Learning

05-17-03-0018

05/06/2024

In this article, we investigated the effects of material parameters on the clinching joint geometry using finite element model (FEM) simulation and machine learning-based metamodels. The FEM described in this study was first developed to reproduce the shape of clinching joints between two AA5052 aluminum alloy sheets. Neural network metamodels were then used to investigate the relation between material parameters and joint geometry as predicted by FEM. By interpreting the data-driven metamodels using explainable machine learning techniques, the effects of the hard-to-measure material parameters during the clinching are studied. It is demonstrated that the friction between the two metal sheets and the flow stress of the material at high (up to 100%) plastic strain are the most influential factors on the interlock and the neck thickness of the clinching joints. However, their dependence on the material parameters is found to be opposite. First, while the friction between the two metal

Nguyen, Duc Vinh, Tran, Van-Xuan, Lin, Pai-Chen, Nguyen, Minh Chien, Wu, Yan-Jiu

Statistical Analysis on Wear Behavior of Aluminum Alloy2024–Silicon Carbide–Fly Ash Metal Matrix Composites

2024-01-5058

05/06/2024

Aluminum and its alloys entered a main role in the engineering sectors because of their applicable characteristics for indispensable applications. To enhance requisite belongings for the components, the composition of variant metal/nonmetal with light metal alloys is essential in the manufacturing industries. To enhance the wear resistance with significant strength property of the aluminum alloy 2024, the reinforcement SiC and fly ash (FA) were added with the designation Al2024 + 10% SiC; Al2024 + 5% SiC + 5% FA; and Al2024 + 10% FA via stir-casting technique. The wear resistance property of the composites was tested in pin-on-disc with a dry-sliding wear test procedure. The experiment trials were designed in Box–Behnken design (BBD) by differing the wear test parameters like % of reinforcement, sliding distance (m), and load (N). The wear tests on casted samples were carried out at the constant velocity of 2 m/sec, such that the corresponding wear rate for the experiment trials was

Sivakumar, N., Sireesha, S. C., Raja, S., Ravichandran, P., Sivanesh, A. R., Aravind Kumar, R.

Legal Issues and Policy: Vol. 1

EPRCOMPV122023

05/03/2024

Eastman, Brittany, Dukarski, Jennifer

Safety, ADAS, and Cybersecurity: Vol. 1

EPRCOMPV152023

05/03/2024

Chen, Guang, Chalmers, Seth, Zheng, Ling

Vehicle Experience and Human Interaction: Vol. 2

EPRCOMPV182023

05/03/2024

Fang, Chen, Razdan, rahul, Beiker, Sven, Taleb-Bendiab, Amine

SAE TOMORROW TODAY: How AI is Redefining the Aerospace Industry

1345205/03/2024

Simulation optimization. Generative design. Predictive algorithms. These AI-powered activities can "automate the mundane" and transform the way engineers work, for the better. As a forerunner in digitalization and automation, Siemens co-creates with customers across all industries to address highly complex challenges. By harnessing data and connecting the real and digital worlds, Siemens is using AI to increase operational efficiencies and accelerate the development of aerospace and defense applications. To learn more, we sat down with Todd Tuthill, Vice President, Aerospace & Defense Industry, Siemens, to discuss how the company is integrating AI and other advanced technologies to take the aerospace industry to the next level of efficiency--and how that will multiply the impact of engineers by freeing them to focus on what they do best

Hineman, Marcie

Automated Vehicles: Development, Testing, and Validation: Vol. 1

EPRCOMPV072023

05/03/2024

Razdan, Rahul, Khalighi, Yaser, Khalkhali, Mohsen, Alonso da Silva, Fabio

Optimizing Fuel Injection Timing for Multiple Injection Using Reinforcement Learning and Functional Mock-up Unit for a Small-bore Diesel Engine

03-17-06-0041

05/03/2024

Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. The difference from other computational approaches is the emphasis on learning by an agent from direct interaction with its environment to achieve long-term goals [1]. In this work, the RL algorithm was implemented using Python. This then enables the RL algorithm to make decisions to optimize the output from the system and provide real-time adaptation to changes and their retention for future usage. A diesel engine is a complex system where a RL algorithm can address the NOx–soot emissions trade-off by controlling fuel injection quantity and timing. This study used RL to optimize the fuel injection timing to get a better NO–soot trade-off for a common rail diesel engine. The diesel engine utilizes a pilot–main and a pilot–main–post-fuel injection strategy. Change of fuel injection quantity was not attempted in this study as the main objective was to

Vaze, Abhijeet, Mehta, Pramod S., Krishnasamy, Anand