Showing 13–16 of 30 results
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Battery Selection Tutorial Course 1/3: Selecting your Cell and Cell Manufacturer(s)
After designing your product, you need to ensure the battery with which you are operating it will ensure the right performance and lifetime. When deciding this, narrowing down which chemistry (e.g. Li-ion, lithium primary, NiMH, etc.) best fits your product and which form factor are some of the first steps. Choosing a cell design (high-power vs. high-energy, for example) is another step and finally, finding the right cell manufacturer to fabricate your cells and packs. This webinar is the first in a three-part series on designing the right battery for your product. It will cover many of the key differences in chemistries, form factors, and cell designs and other best practices.
This webinar will focus on the following key topics:
• Choosing the right chemistry for your application
• Choosing the right form factor
• Choosing cell designs (e.g. high power vs. high-energy)Presenter
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Exponent – a multidisciplinary engineering and scientific consulting firm with significant experience in various aspects of battery design, safety testing and failure analysis. -
Advanced Battery EIS Tutorial Course 2/2: Monitoring Battery Resistance Parameters as a Function of SoH and SoC
There is a need for indicators that can accurately reflect the State of Charge (SoC) and State of Health (SoH) of a battery as a single cell or in packs. Battery parameters related to resistance are excellent candidates for such indicators. AC and DC methods can be used to determine resistance. The DC method is fast and simple, but it gives a composite parameter, whereas the AC method is more advanced and gives complete parameters. After giving examples of data on single cells, we will present three-electrode, battery packs, and modules measurements.
This webinar will focus on the following key topics:
• Obtaining battery parameters via DC and AC methods
• The pros and cons of each method
• How these parameters can be used to indicate SoC and SoH
• Data examples
• Three-electrode single cell and measurements on battery pack or modulePresenter
Dr. Nicolas Murer – Product Manager and Applications Engineer at Bio-Logic SAS, FranceDr. Nicolas Murer is a Product Manager and Applications Engineer at Bio-Logic SAS, France, which designs and manufactures high performance research grade instrumentation and software : potentiostats/galvanostats with built-in Electrochemical Impedance Spectroscopy (EIS), Battery Cyclers, Frequency Response Analyzers for materials analysis, and scanning probe electrochemical workstations. Nicolas received his engineering diploma from Polytechnic Institute of Grenoble in electrochemistry and materials in 2003. He then received his Ph.D. at Université de Bourgogne in 2008. Prior to joining Bio-Logic, he was a post-doctorate at the Ohio State University, Columbus, Ohio (USA).
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Advanced Battery EIS Tutorial Course 1/2: How to Ensure the Quality of Your EIS Measurements
Electrochemical Impedance Spectroscopy (EIS) is a technique that can be performed by controlling the potential or current. Controlling the current can limit the effect of the time variance of the system, but may also more easily lead the system to its non-linear behavior. We will show in this case how an adaptive EIS method can be used to circumvent this effect, and how we can only take the best from current control.
This webinar will focus on the following key topics:
• How to choose between PEIS and GEIS ?
• Non-linearity: the problems it brings and how to avoid them
• A new groundbreaking technique to control amplitude in GEIS
• EIS quality indicatorsPresenter
Dr. Nicolas Murer – Product Manager and Applications Engineer at Bio-Logic SAS, FranceDr. Nicolas Murer is a Product Manager and Applications Engineer at Bio-Logic SAS, France, which designs and manufactures high performance research grade instrumentation and software : potentiostats/galvanostats with built-in Electrochemical Impedance Spectroscopy (EIS), Battery Cyclers, Frequency Response Analyzers for materials analysis, and scanning probe electrochemical workstations. Nicolas received his engineering diploma from Polytechnic Institute of Grenoble in electrochemistry and materials in 2003. He then received his Ph.D. at Université de Bourgogne in 2008. Prior to joining Bio-Logic, he was a post-doctorate at the Ohio State University, Columbus, Ohio (USA).
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Battery Analytics Tutorial Course 3/3: Predictive Modelling, Machine Learning, and AI
This one-hour webinar is Part 3 of a 3-part series. It moves from a discussion of data capture and trend reporting explored in Part 2 to predictive modeling, machine learning, and artificial intelligence as the next levels of battery analytics.
We will examine how machine learning and artificial intelligence can be implemented to identify hidden correlations between disparate data and energy storage system performance, and also independently take pre-emptive action to increase ESS reliability and battery life.
Real-life examples will be shared where predictive models could have flagged anomalous behaviors that were experienced in the field, and led to corrective actions to mitigate unplanned costs and labor.
This webinar will focus on the following key topics:
• Coming to Terms – Understanding the differences between machine learning, artificial intelligence, deep learning, and rule-based systems
• Predictive Modeling Approaches – using data mining and probability to forecast outcomes
• What’s Next – How AI and Machine Learning will impact large-scale battery energy storagePresenter
Michael Worry – CEO at Nuvation EnergyMichael Worry founded Nuvation in 1997 and has grown the company over 21 years into a thriving electronic products and engineering services firm with offices in Sunnyvale, California and Waterloo, Ontario Canada. He is the CEO of Nuvation Energy, a provider of battery management systems and engineering services for large-scale energy storage systems.
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