Showing 21–24 of 150 results
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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 qualityPresenter
Blake Hawley – Sr. Battery Engineer at VoltaiqBlake 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 operationPresenter
Dr. Vikas Tomar – Professor at Purdue UniversityProf. 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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Caution – You Might NOT Have Freedom to Infringe Expired Patents!
A patent term is generally limited to twenty-years from its filing date. Since the battery industry is more than twenty-years mature, certain seminal battery patents covering active materials, electrolytes, and separators are going offline. Does that mean anyone can practice what these patents claim? Or have the patent owners found ways to evergreen and extend their effective patent terms? This webinar will discuss freedom-to-operate in view of expired patents and the second-generation patents that followed.
This webinar will focus on the following key topics:
• Seminal battery patents that were filed more than twenty-years ago are going offline
• Does that mean you can infringe those patents with impunity?
• It depends on whether the patent owners have found means to extend their patent monopoly
• How can one evergreen a battery patent portfolio and what does that mean for competitors that want to practice expired patents?Presenter
Todd Ostomel – Partner at Squire Patton BoggsTodd focuses on patent prosecution and portfolio management, patent opinions, due diligence, utility and design patent applications, and trade secret counseling. Todd has extensive experience preparing and prosecuting US and international patent applications for energy storage devices, rechargeable battery materials, small and large molecules, ceramics, polymorphs, biofuels, diagnostics, chemical processes, cryptocurrency, LEDs, photovoltaics, and machine learning technology. Todd also has extensive experience with trade secret enforcement.
His clients appreciate his ability to understand the technical details of their inventions as well as the legal issues relevant to their business goals.
PlugVolt is a proud sponsor of this event.
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Detecting Large Particles and Contaminants Using Dynamic Imaging and Adaptive Diffraction on Mastersizer 3000+
Large particles and contaminants may cause local hotspots in battery cells severely impacting safety and performance, making early detection of oversized particles an important analytical requirement. We will discuss how a combination of dynamic imaging and adaptive Laser diffraction can be used to detect oversized particles down to a few particles per million level.
This webinar will focus on the following key topics:
• Large particles and contaminants in electrode materials
• Laser diffraction for particle sizing
• Dynamic imaging for particle shape analysis and large particle detection
• Adaptive diffraction for large particle detectionPresenter
Umesh Tiwari – Market Development Manager, Advanced Battery at Malvern PanalyticalDr. Umesh Tiwari is the Market Development Manager, Advanced Battery at Malvern Panalytical. He has a Ph.D. in physics and has 17 years of experience working with academia and industries for lab and online solutions for advanced research and enhanced productivity. His expertise is in the structural, elemental, and morphological characterization of powder, slurry, and finished materials including online monitoring for process and quality controls. He has been closely associated with many development projects within Malvern Panalytical to bring technological enhancement in characterization tools to benefit battery research and manufacturing.
Malvern Panalytical is a proud sponsor of this event.
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