Pre-Recorded Webinars

Showing 17–20 of 147 results

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  Advances in Battery Performance and Safety Testing using Calorimetry

  This presentation describes two main types of calorimetric techniques that can be used to carry out performance and safety testing on high-energy batteries.

  The first half of the presentation introduces isothermal calorimetry; focused on the new IBCx system from Thermal Hazard Technology (THT). Method of operation, hardware overview and examples of data will be presented.

  The second half of the presentation covers battery testing methods for the ARC adiabatic calorimeter system. The theoretical background of the test method will be described, and new developments to address blade-type batteries and high ampere-hour cells will be presented.

  The presentation also mentions complementary test methods and optional modules that can be integrated with calorimetry to provide more useful analysis. For example; fast-tracking heaters, online gas analysis etc.

  This webinar will focus on the following key topics:

  • Principles of isothermal and adiabatic calorimetry testing for high-energy batteries
  • Advantages and limitations of these two methods
  • New product developments from THT to address market test requirements
  • Discussion of THT lab testing results

  Presenter
  Matthew Stewart – Application Scientist at THT

  Matthew Stewart joined Thermal Hazard Technology UK in 2021 following his graduation from Swansea University with a master’s degree in chemical engineering. In two years he has accrued a wealth of experience in battery testing and instrumentation. In his role as Application Scientist, he helps to manage THT’s test lab and carries out cutting-edge testing on the latest energy-dense cell designs. Matt has worked with several of the UK’s leading motorsports, aviation and performance vehicle manufacturers.

  THT is a proud sponsor of this event.

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  Modeling Mechanical Abuse and Short Circuit of EV Batteries

  As electric vehicles (EVs) become more widespread, ensuring lithium-ion battery safety during collisions is increasingly important. Mechanical impacts can cause internal damage, leading to short circuits, thermal runaway, or explosions. Protective enclosures help reduce deformation, but effective design demands accurate failure predictions. This webinar presents a comprehensive approach for modeling mechanical abusive loads on EV batteries, incorporating experiments, material characterization and the Sahraei Failure Criterion—a universal failure model based on microstructural simulations of the electrode-separator assembly. Model validations will be presented across various cell types and loading scenarios in commercial software such as Ansys LS-Dyna and Altair Radioss. Combined with multi-scale simulations, this framework supports the development of safer, more resilient battery systems for EVs.

  This webinar will focus on the following key topics:

  • Experimental Methods for Material Characterization
  • Multiscale Modeling from Components to Cells and Battery Packs
  • Short Circuit Prediction with Sahraei Failure
  • Applicability to Pouch, Cylindrical and Prismatic Cells

  Presenter
  Elham Sahraei – Associate Professor at Temple University

  Elham Sahraei is an Associate Professor and Director of the Electric Vehicle Safety Lab at Temple University. Her research focuses on lithium-ion battery safety under extreme mechanical loading. She is the founder of the Center for Battery Safety, advancing experimental and simulation methods for battery modeling. Her work is supported by the automotive industry, software companies, state agencies, and the U.S. Navy. Previously, she was a Research Scientist and Co-Director of the MIT Battery Consortium. Dr. Sahraei holds a Ph.D. from George Washington University. She has received multiple awards for her research and contributes extensively to conferences on battery safety and crashworthiness.

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  Accelerating Factory Ramp-up and Quality Through Advanced In-line Electrochemical Techniques

  Battery quality issues in production — more specifically, slow identification of issues — severely hamper both new factory ramp-up and in-field device performance and reliability.

  By leveraging electrochemical techniques and the fundamental signatures of batteries during the end-of-line process, we can: 1) identify poor-performing cells before they would be otherwise be identified, 2) quickly correlate performance issues to upstream root cause, and 3) identify which electrochemical metrics are best correlated with long-term performance.

  In the webinar, we will delve into strategies to leverage end-of-line electrochemical characteristics, encompassing thermodynamics, kinetics, and transport phenomena. The analysis of these fundamental metrics enables the identification of quality issues early to accelerate new factory ramp-up and ensure the performance and reliability of shipped devices.

  This webinar will focus on the following key topics:

  • The multi-year, multi-billion-dollar battery factory scale-up challenge
  • The impact of battery quality variation on devices in the field
  • Techniques to understand the fundamental electrochemical signatures of batteries
  • Use of these techniques to accelerate factory ramp-up and improve shipped production quality

  Presenter
  Blake Hawley – Sr. Battery Engineer at Voltaiq

  Blake obtained a PhD from the University of Tennessee in Energy Science and Engineering and performed his dissertation research at Oak Ridge National Laboratory. In his career, he has developed next-generation electrode processing methods, including water-processed cathodes and dual-layered electrodes. He also has industrial experience with materials quality assurance, cell testing, and cobalt-free cathode technology.

  Voltaiq is a proud sponsor of this event.

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  Low Data Machine Learning for Accelerated Degradation Prediction of Lithium-ion Batteries

  Meeting the demand for reliable energy storage, this work presents a machine-learning model for precise cycle life prediction in lithium-ion batteries (LIB). It explores battery aging features, utilizes data-driven methods for health assessment, and applies machine learning to predict cycle life. To address data limitations, synthetic data generation is employed, enhancing prediction accuracy. The presentation concludes by demonstrating the practical deployment of the proposed ML model for accelerated degradation prediction (for battery cell development and manufacturing feedback) and onboard deployment of low data AI on in-operation energy management. Discussions cover crucial aspects such as battery aging, data-driven health measurement, and the model’s versatility in handling accidental effects during operation.

  This webinar will focus on the following key topics:

  • Accelerated degradation based on low data AI for battery development for targeted applications
  • Data-driven insights: machine learning for battery state of health assessment
  • Prediction of rejection thresholds during cell manufacturing for application oriented cell development
  • Prediction of targeted C-Rates for specific device applications
  • Real-world impact: practical deployment of low data ML during real time device operation

  Presenter
  Dr. Vikas Tomar – Professor at Purdue University

  Prof. Tomar’s interests lie in directed cell development using low data AI and vertical integration of targeted cells in c-rate and energy density specific devices. His research group has published extensively in topics related to developing data-driven models for agnostic BMS in UAVs and EVs, predicting degradation of COTS Li-ion batteries. The technology is now part of a startup, Primordis Inc., focused on launching vertically integrated Li-ion cells in autonomous systems within the framework of autonomous energy intelligence using an ASIC technology.

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