This paper provides an overview of current Noise Vibration and Harshness (NVH) challenges for the design of Permanent Magnet Synchronous Motors (PMSMs) for automotive applications. Particular attention is devoted to analytical and numerical methods utilized in NVH modelling. Electromagnetic force computation, motor structural modelling and acoustic emission calculation are reviewed focusing on NVH emissions. The literature reveals, as expected, that analytical methods are simpler than numerical ones, and are usually employed during the preliminary design stage. Numerical methods are adopted in the detailed design stage as the computational cost increases. PMSMs are selected for high performance and high efficiency motor drives, they are widely adopted in the actual transition to vehicle electrification. The paper provides guidelines for facing NVH design issues of PMSMs.

Design of electric motors, especially for automotive applications where early design choices have significant impacts on the final results, in terms of both cost and performances, is a matter of primary importance, with direct consequences on the entire vehicle. Common practice lacks of simple, yet reliable, analytical method to evaluate vibration and noise emission of permanent magnet synchronous motors (PMSMs), and this is the gap that the work here proposed aims to fill. In particular, electric vehicles noise requirements have added a design opportunity to manufacturers, that, depending on the type of costumers they are selling their products to, need to comply with different preferences. Accurately estimate noise emissions of such complex machines is a difficult matter, that can be fully exploited only at the end of the design process. The paper aims to provide a simple, yet accurate enough, tool to estimate the noise emission of electrical motors at the early stage of the design, giving engineers and researchers a tool to drive their choices. An equivalent curved beam model is employed for modelling the structural vibration of the stator when subjected to electromagnetic forces. The sound pressure amplitudes are analytically derived based on the acoustic solution for infinitely long cylindrical radiators. Part of the innovation lays on the fact that the contributions of both radial and tangential forces are taken into account, and a discussion on the effect and influence of the latter on the stator acoustic emission is presented. The sound pressure radiated from the outer surface of the stator is calculated, and a unique indicator of sound emission, named Sound Pressure Level (SPL), is determined. The proposed method is validated against data of two PMSMs from literature, showing good agreement with experiments, proving the method is a reliable tool for electric motor designers to be used especially during the early stage of design.

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.