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Submitted URL:
https://link.sghwm.com/InsideETFs01
Effective URL:
https://www.linkedin.com/posts/sghwm_detroit-activity-6628668326287556608-FOzm
Submission: On August 26 via manual (August 26th 2022, 6:05:55 pm UTC) from US — Scanned from DE

Form analysis
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Text Content

LINKEDIN NIMMT DEN SCHUTZ IHRER PRIVATSPHÄRE SEHR ERNST

LinkedIn und Drittanbieter setzen essenzielle und nicht zwingend erforderliche
Cookies ein, um die LinkedIn Dienste bereitzustellen, zu schützen, zu
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BEITRAG VON SGH WEALTH MANAGEMENT


SGH WEALTH MANAGEMENT

2 Jahre
 * Diesen Beitrag melden

The world’s largest ETF Investment Conference, Inside ETFs featuring Speaker &
#Detroit Investment Advisor Sam G. Huszczo, CFP, CFA. Research our financial
planning approach at: https://sghwm.com


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EBENFALLS ANGESEHEN


MINA NAGUIB

1 Woche · Bearbeitet
 * Diesen Beitrag melden

I am glad to share our recent article with Dr. Phil Kollmeyer and Prof. Ali
Emadi published in the IEEE Transaction on Transportation Electrification titled
"Application of Deep Neural Networks for Lithium-Ion Battery Surface Temperature
Estimation Under Driving and Fast Charge Conditions". I would like to thank
MathWorks and NXP for supporting this work. Accurate temperature estimation
models can be used as an alternative to physical sensors or can perform as a
redundant monitoring system for the existing sensors. In addition, these models
can be applied to detect battery thermal and short circuit faults. This study
discusses how neural networks (NNs) can encode the thermal behaviour of Li-ion
batteries. The proposed NN models are tested on three different batteries under
different dynamic, charging and aging conditions. The proposed models are also
deployed to an NXP S32K344 microprocessor to ensure their feasibility in running
on BMS. The article is open access, and in the early access stage, I hope you
enjoy reading it.


APPLICATION OF DEEP NEURAL NETWORKS FOR LITHIUM-ION BATTERY SURFACE TEMPERATURE…

IEEEXPLORE.IEEE.ORG

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