Webinars

(FREE) SQUIRE PATTON BOGGS: Intellectual Property Clash of Copper Current Collectors

High voltage cathode materials, dendrite-preventing electrolytes, and new anode architectures often grab headlines. But what may send shockwaves through the battery industry is the winner of the patent and trade secret battle being waged by SK Nexilis Co., Ltd. and Solus Advanced Materials Co., Ltd. The spoils of war being the right to make and sell copper foils used for current collectors in electric vehicle batteries. With parallel litigation occurring in the United States, South Korea, and Europe, and patent invalidation proceedings before the US Patent Office, this case is one to watch for battery manufacturers worldwide. This talk will highlight the procedural posture and the intellectual property tools being brought to bear by both sides in this on-going dispute.

This webinar will focus on the following key topics:

• Building patent thickets around single battery components
• Trade secret rights complementary to battery manufacturing patent rights
• Strategically acquiring foreign patent rights with high investment returns
• Value of patents given the increased institutional denial of proceedings to invalidate patents

A PDF copy of the presentation will be sent to all attendees after the event.

Presenter
Todd Ostomel – Partner at Squire Patton Boggs

Todd focuses on patent prosecution and portfolio management, patent opinions, due diligence, utility and design patent applications, and trade secret counseling. He has extensive experience preparing and prosecuting US and international patent applications for inventions related to energy storage, energy production, fuel cells, rechargeable batteries, battery recycling, AI diagnostics, chemical processes, small and large molecules, polymorphs, ceramics, biofuels, cryptocurrency, and LEDs.

Todd also has extensive experience with trade secret enforcement. He helps entrepreneurs develop and manage global patent and trade secret portfolios.

PlugVolt is a proud sponsor of this event.

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PURDUE UNIVERSITY: 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

A PDF copy of the presentation will be sent to all attendees after the event.

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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UCLA: 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

A PDF copy of the presentation will be sent to all attendees after the event.

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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