Mousa Marzband

Aug 22, 2024 | mdpi.com | Amir Imam |Abdullah M Abusorrah |Mousa Marzband |Mustafa M. A

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Oct 17, 2023 | mdpi.com | Seyed Saeed Madani |Carlos Ziebert |Mousa Marzband |Seyed Saeed

1. IntroductionLithium battery systems encompass a diverse array of configurations, as illustrated in Figure 1. Among these configurations, lithium-ion batteries emerge as a highly promising storage technology for reducing greenhouse gas emissions within the transportation sector. With their exceptional power and energy densities, lithium-ion batteries offer a well-suited solution for an extensive range of renewable energy storage applications.

Aug 19, 2023 | mdpi.com | Niall Smith |Mousa Marzband |Mahmoud Shahbazi |Niall Andrew

3. Neural Network Regression of Lifetime ConsumptionNeural networks are mathematical abstractions that mimic the human brain. Their structure can be adjusted to optimize various computational tasks, such as numerical prediction or classification. Regression neural networks, commonly used for making numerical predictions, are prevalent tools in handling non-linear functions. In the context of similar lifetime predictions, neural networks have been successfully applied [21].

Aug 17, 2023 | mdpi.com | Seyed Saeed Madani |Mousa Marzband |Seyed Saeed |Carlos Ziebert

4. Thermal Modelling of Lithium-Ion BatteriesThe thermal modeling of lithium-ion batteries involves considering various parameters and factors that influence their temperature behavior and performance. Some of the key parameters that are typically included in thermal models are heat capacity, density, along-plane and through-plane thermal conductivity, electric conductivity, and equivalent circuit model parameters.

Jun 24, 2023 | mdpi.com | Mousa Marzband |Mansour Selseleh Jonban |Luis Romeral |Elyas Rakhshani

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