Battery electrical vehicle (BEV) ownership has increased in recent years. There is a general concern over the life cycle of the batteries used in such vehicles. This study provides a comprehensive overview of electric vehicles, encompassing their technical evolution, autonomy, and ownership. The analysis delved into the various types of batteries utilized in these vehicles, examining the composition of their constituent materials and the mechanisms underlying their operation. Additionally, it assessed their performance in terms of energy density storage, recharge capabilities, autonomy, and prospects. A critical evaluation of electric vehicles and their internal combustion engine vehicle (ICEV) counterparts, considering the Life Cycle Assessment (LCA) criterion, was conducted. The LCA criterion encompasses emissions during the entire lifecycle, from the “cradle” to the “tank” (WTT) and the “tank” until the end of its cycle (TTW). The findings of this study indicate that BEVs consistently outperformed ICEVs in terms of greenhouse gas (GHG) emissions in all the sizes of vehicles studied.

The evolution of viscoelastic turbulent models, in the last years, has been significant due to the direct numeric simulation (DNS) advances, which allowed us to capture in detail the evolution of the viscoelastic effects and the development of viscoelastic closures. New viscoelastic closures are proposed for viscoelastic fluids described by the finitely extensible nonlinear elastic-Peterlin constitutive model. One of the viscoelastic closure developed in the context of isotropic turbulent models, consists in a modification of the turbulent viscosity to include an elastic effect, capable of predicting, with good accuracy, the behaviour for different drag reductions. Another viscoelastic closure essential to predict drag reduction relates the viscoelastic term involving velocity and the tensor conformation fluctuations. The DNS data show the high impact of this term to predict correctly the drag reduction, and for this reason is proposed a simpler closure capable of predicting the viscoelastic behaviour with good performance. In addition, a new relation is developed to predict the drag reduction, quantity based on the trace of the tensor conformation at the wall, eliminating the need of the typically parameters of Weissenberg and Reynolds numbers, which depend on the friction velocity. This allows future developments for complex geometries.