A detailed analysis of the power demand of an ultraefficient lightweight-battery electric vehicle is performed. The aim is to overcome the problem of lightweight electric vehicles that may have a relatively bad environmental impact if their power demand is not extremely reduced. In particular, electric vehicles have a higher environmental impact during the production phase, which should be balanced by a lower impact during the service life by means of a lightweight design. As an example of an ultraefficient electric vehicle, a prototype for the Shell Eco-marathon competition is considered. A “tank-to-wheel” multiphysics model (thermo-electro-mechanical) of the vehicle was developed in “Matlab-Simscape”. The model includes the battery, the DC motors, the motor controller and the vehicle drag forces. A preliminary model validation was performed by considering experimental data acquisitions completed during the 2019 Shell Eco-marathon European competition at the Brooklands Circuit (UK). Numerical simulations are employed to assess the sharing of the energy consumption among the main dissipation sources. From the analysis, we found that the main sources of mechanical dissipation (i.e., rolling resistance, gravitational/inertial force and aerodynamic drag) have the same role in the defining the power consumption of such kind of vehicles. Moreover, the effect of the main vehicle parameters (i.e., mass, aerodynamic coefficient and tire rolling resistance coefficient) on the energy consumption was analyzed through a sensitivity analysis. Results showed a linear correlation between the variation of the parameters and the power demand, with mass exhibiting the highest influence. The results of this study provide fundamental information to address critical decisions for designing new and more efficient lightweight vehicles, as they allow the designer to clearly identify which are the main parameters to keep under control during the design phase and which are the most promising areas of action.

This paper aims to provide a quantitative assessment of the effect of driver action and road traffic conditions in the real implementation of eco-driving strategies. The study specifically refers to an ultra-efficient battery-powered electric vehicle designed for energy-efficiency competitions. The method is based on the definition of digital twins of vehicle and driving scenario. The models are used in a driving simulator to accurately evaluate the power demand. The vehicle digital twin is built in a co-simulation environment between VI-CarRealTime and Simulink. A digital twin of the Brooklands Circuit (UK) is created leveraging the software RoadRunner. After validation with actual telemetry acquisitions, the model is employed offline to find the optimal driving strategy, namely, the optimal input throttle profile, which minimizes the energy consumption over an entire lap. The obtained reference driving strategy is used during real-time driving sessions at the dynamic driving simulator installed at Politecnico di Milano (DriSMi) to include the effects of human driver and road traffic conditions. Results assess that, in a realistic driving scenario, the energy demand could increase more than 20% with respect to the theoretical value. Such a reduction in performance can be mitigated by adopting eco-driving assistance systems.

The paper proposes an optimization procedure to be adopted by wheels design engineers for the identification of preliminary solutions to design lightweight and safe wheels. In particular, decisions related to the sizing of the wheel rim, to the number and type of spokes and to the spokes structural layout are addressed. The process relies on the combination of a simplified finite element model of the tire/wheel assembly and artificial neural networks used for global approximation, within a multi-objective optimization framework. Mass and compliance of the wheel are minimized at the same time, with constraints on structural safety and manufacturing. The method is applied to the preliminary design of the wheel of a lightweight electric vehicle specifically designed for energy efficiency competitions and allows to derive simple and general design guidelines for developing efficient products.

In this paper, the bending behavior of microbeams resting on elastic foundations is analyzed. Due to the widespread use of Micro-Electro-Mechanical Systems (MEMSs) in sensing and actuation applications, various approaches have been developed for modeling such beams. Numerous specialized analytical and numerical models exist for specific configurations of beams and elastic foundations. This work proposes a novel approach. Separate models for the beam and the elastic foundation are developed using the finite element method. These models are then coupled using a variable reduction technique, in which only the degrees of freedom of the beam are retained in the solving system. This approach enables the coupling of any beam and foundation model and allows for independent refinement of the foundation mesh without increasing the size of the solving system. This method is particularly effective for analyzing configurations where the substrate exhibits nonlinear or non-homogeneous characteristics, or where gaps are present between the beam and the substrate. The nonlocal effects due to the small scale of the beam are also considered. This paper focuses on both the static deformation and frequency response of the microbeam. The proposed approach is validated against previously published models. Compared with existing models, the method presented here offers a simpler and more flexible formulation, while allowing the inclusion of nonlinearities in both the beam and foundation, as well as the modeling of gaps.

The paper presents a fully new chassis of a high efficiency vehicle for the Shell Eco-marathon competition. The chassis is realized by a sandwich structure with an expanded polypropylene (EPP) core and carbon fiber reinforced plastic (CFRP) external skins. The chassis is connected to the body to realize a safe and stiff structure. Numerical analyses have been performed to assess