Pre-Recorded Webinars

Showing 37–40 of 147 results

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  Low Data Machine Learning for Predicting Lithium-ion Battery Aging

  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 on a battery management system, showcasing its potential impact on power usage efficiency. 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:

  • Unveiling Battery Aging: identifying key aging features
  • Data-Driven Insights: machine learning for battery state of health assessment
  • Cycle Life Precision: machine learning in Lithium-Ion battery predictions
  • Addressing Data Gaps: synthetic data for enhanced prediction accuracy
  • Real-World Impact: practical deployment of ML on battery management systems

  Presenter
  Meghana Sudarshan – Ph.D. Candidate at Purdue University

  Meghana Sudarshan is currently pursuing a Ph.D. from the School of Aeronautics and Astronautics at Purdue University. Her research focuses on developing data-driven models agnostic battery management systems in UAVs and electric vehicles for predicting degradation of COTS (Commercial Off-The-Shelf) Li-ion Batteries as a function of operation parameters.

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  Electrode Damage Characterization in Li-Ion Batteries Using Raman Spectroscopy

  While Li-Ion battery technology has continually advanced to provide cells that are smaller and more powerful, compromised safety concerns due to physical damage are always present. Physical damage to a Li-Ion battery can significantly affect its operational performance, causing accelerated degradation and capacity fade. Damage to electrodes and removal of active material lead to microstructural changes in electrode material and unbalanced current distribution, causing polarization in cells. This work focuses on characterizing the effects of partial nail penetrations on electrodes in cells that continue cycling after being damaged by using Raman spectroscopy and incremental capacity analysis. This helps to determine the type and extent of damage to the electrodes over the course of their abbreviated lifetime.

  This webinar will focus on the following key topics:

  • Dynamic impact testing of prismatic Li-Ion cells
  • Raman spectroscopy analysis for anode damage characterization
  • Increased polarization due to unbalanced current distribution
  • Accelerated degradation caused by physical damage
  • Incremental capacity analysis to determine mechanisms of aging

  Presenter
  Casey Jones – Ph.D. Candidate at Purdue University

  Casey Jones is a PhD student in the School of Aeronautics and Astronautics at Purdue University, where he works in the Interfacial Multiphysics Laboratory for Dr. Vikas Tomar. His research focuses on destructive testing of Li-ion batteries and the characterization of the effects on cell operation and is funded by the Office of Naval Research. Prior to studying at Purdue he served in the US Navy as a nuclear electronics technician aboard a fast-attack submarine based in Pearl Harbor, and received his BS in Mechanical Engineering from the University of Hawai’i at Manoa.

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  De-risking ESS Projects by Improving Safety & Availability

  Battery energy storage systems (BESS) ensure grid stability and align supply with demand by storing surplus energy during periods of abundance and discharging it only when needed.

  But they require a lot of monitoring: individual battery cells vary in quality and age, and these cell imbalances increase stress on the system. This leads to wasted energy and increased downtime. At the same time, regulators are paying attention and developing safety standards that apply to battery storage and the risks involved. In this webinar, you’ll gain a deeper understanding of how analytics can help you identify and mitigate battery performance and safety risks along the entire lifecycle, from design over commissioning & operation to de-commissioning.

  This webinar will focus on the following key topics:

  • De-risk deployment and operators of BESS from design over commissioning & operation to de-commissioning
  • Why monitoring battery systems with advanced analytics is so important
  • How to identify issues with your system early on
  • How to optimize BESS performance
  • Industry best practices and emerging trends

  Presenters
  Dr. Matthias Simolka – Product Manager for Energy Solutions at TWAICE
  Ryan Franks – Senior Technical Solution Engineer at TWAICE

  Dr. Matthias Simolka is Product Manager for Energy Solutions at TWAICE. Prior to joining TWAICE, Matthias was working several years in academic research focusing on the aging mechanisms of modern Li-ion batteries. His research combined material analysis down to the nanometer scale with system level observations to link the battery behavior to actual degradation mechanisms.

  Ryan bridges the gap between sales, product, and technology, working with all teams to ensure that maximum value and the optimal solution are delivered to customers. He holds a BS in Engineering Mechanics from the University of Illinois and an MBA from John Carroll University.

  TWAICE is a proud sponsor of this event.

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  XRF Analysis in Process Control of Battery Cathode Manufacturing

  To optimize the chemical composition of the final cathode materials, it is first essential to control the chemical composition of the precursor and raw materials. X-ray fluorescence (XRF) analysis, which can characterize chemical composition and impurities from just a few ppm all the way up to 100%, is the best technique for controlling this parameter.

  Specifically, XRF provides a simpler and more accurate way of measuring elemental composition than inductively coupled plasma (ICP) mass spectrometry, as it does not require any sample dilution or acid digestion.

  Malvern Panalytical specialists have developed a turn-key solution, including certified reference materials (CRMs) and calibration templates, for the analysis of both precursor and cathode material composition with the benchtop Epsilon 4 EDXRF or floor-standing Zetium WDXRF spectrometers.

  This webinar will focus on the following key topics:

  • On-line and at-line XRF analysis of solutions containing Ni, Co and Mn
  • NCM-certified reference materials for XRF calibration purposes
  • Turn-key solution for the XRF analysis of NCM precursors and cathodes

  Presenter
  Alexander Komelkov – XRF Application Specialist at Malvern Panalytical

  Back in 1996 Alexander obtained a diploma of Engineer-Physicist followed by Master of Science degree in Physics in 2000. Then he worked in a metallurgical and a mining industries as a chemical analysist and R&D specialist.

  In 2008 Alexander joined (Malvern) Panalytical as an Application Specialist for X-Ray Fluorescence analysis. Currently, Alexander provides XRF expertise consultancy to customers, develops advanced XRF applications and solutions, participates in XRF R&D projects. The main areas of expertise are geological and mining applications, as well as borate fusion for XRF analysis. He is co-creator of the methodology for combined WD/ED XRF analysis.

  Malvern Panalytical is a proud sponsor of this event.

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