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Siamak Farhad

Researcher at University of Akron

Publications -  87
Citations -  2077

Siamak Farhad is an academic researcher from University of Akron. The author has contributed to research in topics: Battery (electricity) & Lithium-ion battery. The author has an hindex of 24, co-authored 75 publications receiving 1430 citations. Previous affiliations of Siamak Farhad include University of Waterloo & Amirkabir University of Technology.

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Performance Evaluation of Different Configurations of Biogas-Fuelled SOFC Micro-CHP Systems for Residential Applications

TL;DR: In this article, three configurations of solid oxide fuel cell (SOFC) micro-combined heat and power (micro-CHP) systems are studied with a particular emphasis on the application for single-family detached dwellings.
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Heat generation in lithium-ion batteries with different nominal capacities and chemistries

TL;DR: In this article, a multiphysics modeling and computer simulation of heat generation in lithium-ion batteries (LIBs), different in nominal battery capacity and electrode materials (battery chemistry), is studied at various charge and discharge rates.
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A novel electrochemical DNA biosensor for Ebola virus detection.

TL;DR: The aim of this study was to fabricate a novel electrochemical-based DNA-sensing device for Ebola virus DNA diagnostic by an enzyme-amplified detection, which improves the sensitivity and selectivity of the sensor.
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Multiscale modeling of lithium-ion battery electrodes based on nano-scale X-ray computed tomography

TL;DR: In this article, a multiscale platform has been developed to model lithium ion battery (LIB) electrodes based on the real microstructure morphology, which can predict the experimental performance of LiFePO4 (LFP) cathode at different discharge rates.
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Empirical Modeling of Lithium-ion Batteries Based on Electrochemical Impedance Spectroscopy Tests

TL;DR: In this article, an empirical model for commercial lithium-ion batteries is developed based on electrochemical impedance spectroscopy (EIS) tests, which can predict the battery operating output potential difference in battery electric and plug-in hybrid vehicles at various operating conditions.