Pre-Recorded Webinars

Showing 25–28 of 142 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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  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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  Passive Mitigation of Thermal Runaway Propagation in Dense 18650 Lithium Ion Cell Assemblies

  Utilization of lithium ion batteries (LIBs) in various applications has been growing exponentially. LIBs offer some distinct advantages including high energy density, outstanding efficiency, long lifespan, and fast charging capability. Probably, the main disadvantage of LIBs is that a small deviation from normal operating condition may result in rapid self-heating accompanied by ejection of large quantities of flammable materials, which can cause fire or explosion. The failure process becomes more dramatic when many cells are arranged in large arrays in order to fulfill the power requirements by most of applications. Failure of a single cell can release sufficient energy to trigger failure into adjacent cell, which subsequently propagates throughout the entire array. In this webinar, a set of passive strategies to mitigate failure propagation will be presented. The dynamics, heating rates, gaseous emissions, and energetics of thermally induced thermal runaway propagation in dense arrays consisting of 12-15 fully charged 18650 lithium ion cells have been quantified to determine the effectiveness of these passive mitigation strategies.

  This webinar will focus on the following key topics:

  • Thermal runaway in lithium ion batteries
  • Thermal runaway propagation in lithium ion battery packs
  • Hazards associated with failure propagation
  • Passive mitigation strategies

  Presenter
  Ahmed Said – Postdoc Fellow, Worcester Polytechnic Institute

  Ahmed Said is a post-doctoral fellow in the Department of Fire Protection Engineering at Worcester Polytechnic Institute. He Obtained his PhD from the Department of Mechanical Engineering at the University of Maryland, College Park, in 2020. He is broadly interested in fire and combustion science problems. More specifically, his research is centered on thermal and fire safety of energy storage systems, material flammability, fire spread on façade systems, and wildland fires.

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