Pre-Recorded Webinars

Showing 1–4 of 155 results

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  From Mobility to Infrastructure – High Performance Materials Engineered to Extend Battery System Lifetime

  With battery technologies extending beyond automotive applications into grid-scale energy storage systems (ESS) and data centers, the performance requirements for battery packs are evolving toward longer service life, higher reliability, and highly predictable behavior over 15–20 years of operation. In this presentation, we discuss how high-performance Norseal® foam materials, originally developed for EV battery packs, can be adapted to meet the mechanical and durability requirements of adjacent segments such as ESS and data centers. Key battery system design challenges – including cell swelling, compression cycling, and pack sealing – are addressed, supported by experimental testing validation for Norseal® foam products.

  We further demonstrate how tailored material formulations, guided by design tools such as MAXIO, enable optimization of stiffness, resilience, and long-term stability across diverse pack architectures. Finally, we provide insights into emerging battery system technologies, such as immersion cooling, and outline the performance of Norseal® foam materials for these architectures.

  This webinar will focus on the following key topics:

  • EV battery material learnings applied to ESS and data center batteries
  • High-performance Norseal® foams to manage mechanical challenges: cell swelling, optimal cell pressure, sealing and materials resilience overtime
  • Customized formulation and simulation modeling for optimized cell-to-cell (C2C) pad designs
  • Emerging trends: ESS & data center convergence and immersion cooling

  Presenters
  Patricia Adame – Senior Applications Engineer at Saint Gobain
  Weilin Deng – Senior Research Engineer II at Saint Gobain

  Saint Gobain is a proud sponsor of this event.

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  Adjacent Market Compliance for EV Cells and Batteries

  EV manufacturers and their major battery suppliers have invested $-billions in large format Lithium Ion cell and battery pack manufacturing capacity. With the sometimes volatile EV demand market, these manufacturers must look to adjacent markets to utilize and monetize this capacity. New industries, applications and global markets each bring their own unique challenges when working to comply with all relevant regulatory and industry standards and regulations. This webinar addresses some of the key challenges faced when developing new battery systems for compliance within these adjacent markets. Key markets include micromobility, industrial trucks, eVTOL, product handling equipment, as well as stationary storage. Challenges include: understanding market regulations and regulators, the role of certification bodies and other third parties, as well as common misconceptions when applying EV expertise to other industries.

  This webinar will focus on the following key topics:

  • Identifying adjacent markets for EV cell and battery capacity
  • Regulatory drivers
  • Third party certification options
  • Common missteps applying EV designs to new markets
  • Second life vs. new construction applications

  Presenter
  Rich Byczek – Global Technical Director at Intertek

  Rich Byczek is the Global Chief Engineer, Batteries at Intertek. He has 30+ years of experience in automotive product development and validation testing, over 20 of which have been spent at Intertek. Rich sits on several SAE, IEC, UL, UN and ANSI standards panels, focusing on electric vehicle, battery, and emerging vehicle technologies. He holds a Bachelor of Science in Electrical Engineering from Lawrence Technological University in Southfield, Michigan, and is based at the Intertek facility located in Plymouth, Michigan.

  Intertek is a proud sponsor of this event.

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• 

  Intelligent Battery Operation Via AI-Powered Virtual Sensing

  Battery management systems (BMS) rely on measurable parameters such as voltage and current to define safe operating limits, yet many catastrophic failure modes – including lithium metal plating – remain undetected. In this webinar, we will introduce the concept of a machine learning–powered virtual reference electrode, which enables real-time prediction of anode voltage without requiring physical reference electrodes or changes in battery hardware. Trained on large datasets from three-electrode cells, this approach allows physically meaningful internal parameters to be inferred using only standard two-electrode measurements. We will show how these virtual sensors enable smart charging protocols that dynamically avoid failure while extending cycle life, and discuss how this framework can generalize to other hard-to-measure battery health indicators.

  This webinar will focus on the following key topics:

  • Predicting anode voltage without physical sensors
  • Enabling smart charging protocols in real time
  • Implications for battery safety and lifetime

  Presenter
  Dr. Yuzhang Li – Associate Professor at UCLA

  Yuzhang Li is an Associate Professor of Chemical and Biomolecular Engineering at UCLA. His research focuses on understanding and controlling battery interfaces through advanced characterization and data-driven approaches. His group develops new experimental and computational tools (including cryogenic electron microscopy and machine learning) to reveal degradation mechanisms and enable safer, higher-performance batteries. Prof. Li’s work spans lithium-ion, lithium-metal, and multivalent battery systems, with an emphasis on translating fundamental insights into practical battery management strategies.

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  Low Data AI for Energy Orchestration in Autonomy

  This work presents a set of hierarchical machine learning (ML) models for precise cycle life prediction in lithium-ion batteries (LIB), LIB packs, and system-level LIB packs. To address data limitations, synthetic data generation is employed across different scales, enhancing prediction accuracy. The presentation concludes by demonstrating the practical deployment of these ML models for accelerated degradation prediction—useful in battery cell development and manufacturing feedback—and the onboard implementation of low-data AI for energy management during operation. Discussions include key topics like battery aging, data-driven health assessment, and the model’s capacity to handle unexpected effects during use.

  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 on topics related to developing data-driven models for agnostic BMS in UxVs and EVs, predicting degradation of COTS Li-ion batteries. The technology is now part of a startup, Primordis Inc., focused on launching small language models for autonomous systems within the framework of autonomous energy intelligence.

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