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Batteries are the key elements for the massive electrification of the transport sector. With the rapidly growing popularity of electric vehicles, it is becoming increasingly important to characterize the behavior of battery packs through fast and accurate numerical models, in order to support experimental activities. A coupled electro-thermal simulation framework is required, as it is the only way to realistically represent the interactions between real world battery pack performances and the vehicle-level thermal management strategies. The purpose of this work is to pave the way for a comprehensive methodology for the development of a supporting modeling framework, to efficiently complement experiments in the optimal design and integration of battery packs.

Vehicle electrification is one of the most important emerging trends in the transportation sector and a necessary step towards the reduction of polluting substances and greenhouse gas (GHG) emissions. However, electric vehicles still present some environmental criticalities, such as indirect emissions related to the electricity used for charging the traction battery, which depends on the considered national electricity generation mix. The leading approach for quantifying the potential environmental impacts is the Life Cycle Assessment (LCA), a standardized methodology that takes into account the whole life cycle of a product, including production, use phase, and end-of-life.

In European Union (EU), transport causes about a quarter of the total greenhouse gases (GHG) emissions and road vehicles are the biggest contributors, with nearly three-quarters of the overall GHG emissions. In this context, many governments are adopting different strategies to achieve a sustainable mobility, including the electrification of public transport, such as full electric taxis (e-taxis). Indeed, battery electric vehicles (BEVs) represent a promising solution towards the achievement of sustainability since they involve zero emissions during the use phase, despite indirect emissions are generated during the charging of the traction battery according to the specific national electricity mix. However, a proper choice of the vehicle segment for the e-taxi and its battery capacity can represent a crucial factor in reducing the overall environmental impacts.

The LES hybridization of standard two-equation turbulence closures is often achieved leaving formally unchanged the turbulent viscosity expression in the URANS and LES modes of operation. Although generally convenient in terms of ease of implementation, this choice leaves some theoretical consistency questions unanswered, the most obvious being the actual meaning of the two transported turbulent scalars and their exact role in the modeled viscosity build-up. A possible remedy to this is represented by the simultaneous modification of one or both the turbulent transport equations and of the turbulent viscosity formula, for which a standard LES behavior is enforced whenever needed. The present work compares a conventional DES-based hybrid model with a consistency-enforcing modified variant for turbulent fuel spray simulation. In our case, LES-mode consistency is accomplished by excluding the second turbulent scalar quantity from the viscosity calculation.

Due to their ability to store higher electrical energy, lithium ion batteries are the most promising candidates for electric and hybrid electric vehicles, whose market share is growing fast. Heat generation during charge and discharge processes, frequently undergone by these batteries, causes temperature increase and thermal management is indispensable to keep temperature in an appropriate level. In this paper, a coupled Lattice Boltzmann-Finite Volume model for the three-dimensional transient thermal analysis of an air-cooled Li-ion battery module is presented. As it has already been successfully used to deal with several fluid-dynamics problems, the Lattice Boltzmann method is selected for its simpler boundary condition implementation and complete parallel computing, which make this approach promising for such applications.

Scale-resolving turbulence modeling for engine flow simulation has constantly increased its popularity in the last decade. In contrast to classical RANS modeling, LES-like approaches are able to resolve a larger number of unsteady flow features. In principle, this capability allows to accurately predict some of the key parameters involved in the development and optimization of modern engines such as cycle-to-cycle variations in a DI engine. However, since multiple simulated engine cycles are required to extract reliable flow statistics, the spatial and temporal resolution requirements of pure LES still represent a severe limit for its wider application on realistic engine geometries. In this context, Hybrid URANS-LES methodologies can therefore become a potentially attractive option. In fact, their task is to preserve the turbulence scale-resolving in the flow core regions but at a significantly lower computational cost compared to standard LES.