Farzad Jaliliantabar

Professional Research Associate

University of Saskatchewan

Canada

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Farzad Jaliliantabar is a Professional Research Associate at the University of Saskatchewan, Canada. His research focuses on advanced technologies for battery thermal management systems, including artificial intelligence and sustainability. His recent work also investigates the performance and emissions of alternative fuels in diesel engines, as well as the thermal stability and conductivity of nano-enhanced phase change materials.

Articles (10)

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2025

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2024

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2024

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2023

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2023

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2022

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2022

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2022

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2021

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2020

Collaborators (2)

Antonio Paolo Carlucci

Assistant Professor

University of Salerno

ITALY

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Luciano Strafella

University of Salerno

ITALY

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Farzad Jaliliantabar

Research Interests

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Articles (10)

Future of battery thermal management systems (BTMS): Role of advanced technologies, artificial intelligence and sustainability

Experimental study of quaternary blends with diesel/palm-oil biodiesel/ethanol/diethyl ether for optimum performance and emissions in a light-duty diesel engine using response surface methodology

Parametric investigation of battery thermal management system with phase change material, metal foam, and fins; utilizing CFD and ANN models

Exploring the temperature-dependent thermal stability of nano-enhanced phase change materials: An experimental study using eicosane as the base material

Improving the cooling performance of cylindrical lithium-ion battery using three passive methods in a battery thermal management system

The effect of different enclosure materials and NePCMs on performance of battery thermal management system

Effects of dispersed multiwalled carbon nanotubes on the micro-explosion and combustion characteristics of 2-methylfuran – diesel mixture droplets

Prediction of lithium-ion battery temperature in different operating conditions equipped with passive battery thermal management system by artificial neural networks

Thermal conductivity prediction of nano enhanced phase change materials: A comparative machine learning approach

A comprehensive study on the effect of pilot injection, EGR rate, IMEP and biodiesel characteristics on a CRDI diesel engine

Collaborators (2)

Antonio Paolo Carlucci

Luciano Strafella

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