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.

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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 the stiffness, safety and dynamic eigenfrequencies of the chassis. A Finite Element model of the entire chassis and body was developed. The manufacturing process of the entire chassis and body is described in the paper and some data obtained during on-track tests of the vehicle are presented. The vehicle reached the 4th place at the 2019 edition of the Shell Eco-marathon competition, with an equivalent energy consumption of 184 km/kWh.

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.