Lightweight materials, such as polymers and composites, are increasingly used in the automotive and aerospace industries. Recently, there has been an increase in the use of these materials, especially in electric vehicles. However, these materials cannot shield sensitive electronics from electromagnetic interference (EMI). The current work investigates the EMI performance of these lightweight materials using an experimental setup based on the ASTM D4935-99 standard and EMI simulation using the ANSYS HFSS. This work studies how metal coating from zinc and aluminum bronze can improve the shielding performance of polymer-based materials, such as polyphenylene sulfide (PPS), polyetheretherketone (PEEK), and polyphthalamide (PPA). Based on the findings of this study, a thin coating (50 μm) of Zn on the surface of PPS and a thin coating of 5 μm and 10 μm of Al-Bronze, respectively, on the surface of PEEK and PPA have indicated an increase in the shielding effectiveness (SE) when subjected to EMI. The shielding effectiveness significantly increased from 7 dB for the uncoated polymer to approximately 40 dB at low frequencies and up to approximately 60 dB at high frequencies for coated polymers. Finally, various approaches are recommended for improving the SE of polymeric materials under the influence of EMI.

As part of the recent electrification of the transportation industry, internal combustion engines are being coupled with or replaced by electric motors. This movement towards an electrified drivetrain poses new noise, vibration, and harshness (NVH) challenges related to electric motors. In this paper, the acoustic signature of an electric motor was analyzed to obtain a better understanding of the sound generated by these motors. This work provides an insight into an acoustic measurement technique that can be used to identify certain frequency bands that significantly contribute to the perceived sound. In the first part, the structural response of the motor was correlated with its acoustic spectra. Furthermore, data from acoustic and structural measurements were used to analyze the order content of the signal and identify critical contributors to the overall perceived sound. The differences between data captured by microphones in different positions around the motor helped to localize components of the overall sound. The results provide some discussion about techniques to decrease the overall sound. The technique described in this paper can be extended to fan-cooled motors that are used in vehicles such as golf carts or as auxiliary motors in electric/hybrid vehicles, as well as across a wide range of industrial applications.

There are some constraints such as external electrodes, a failure to capture most paroxysmal atrial fibrillation (AFib), low power transfer efficiency (PTE) for 24/7 charging technology, a short period of monitoring, and automatic detection of AFib in conventional electrocardiogram (ECG) sensors. To overcome these constraints, an implantable ECG sensor with a 2-coil inductive link with maximum power transfer efficiency (PTE) is designed to continuously monitor patients and efficiently detect AFib using global covering rule discovery and the minimum description length (MDL) algorithm. Among different combinations of ECG coils, the square spiral-square spiral coil demonstrates the maximum PTE, 56.23%, at the resonant frequency of 13.56 MHz and it is used in the implantable ECG sensor. The QRS complex from ECG signals of twenty-nine AFib patients is detected using different operation methods (DOM). The MDL algorithm is used to group 12 features of heart rate variability (HRV) parameters. The global covering rule discovery is proposed as a novel classification technique of AFib in ECG data. The average classification accuracy was 96.67 ± 7.03, and then the average recall, precision, F1-measures, and an average number of generated rules were 97.08 ± 6.23, 97.08 ± 6.23, 96.57 ± 7.23, and 7.9 ± 0.32, respectively. We found that the NN50, pNN50, and LF parameters can distinguish the AFib patient better than a healthy one. Among these parameters, pNN50 showed that it is greater than 34.75 in 41.38% of patients. The optimized implantable ECG sensor with a maximum PTE of 56.23% along with novel AFib detection and classification methods is suitable for its implementation in future implantable ECG sensors.

The automotive and aerospace industries increasingly use lightweight materials to improve performance while reducing fuel consumption. Lightweight materials are frequently used in electric vehicles (EVs). However, using these materials can increase airborne and structure-borne noise. Furthermore, EV noise occurs at high frequencies, and conventional materials have small damping. Thus, there is an increasing need for procedures that help design new materials and coatings to reduce the transferred and radiated noise at desired frequencies. This study pioneered new techniques for microstructure modeling of coated and uncoated materials with improved noise, vibration, and harshness (NVH) performance. This work uses the microstructure of materials to study their vibration-damping capacity. Images from an environmental scanning electron microscope (ESEM) show the microstructure of a sample polymer and its coating. Tensile tests and experimental modal analysis were used to obtain the material properties of the polymer for microstructure modeling. The current work investigates how different microstructure parameters, such as fiberglass volume fraction and orientation, can change the vibration performance of materials. The damping ratio in the study was found to be affected by changes in both the direction and volume ratio of fiberglass. Furthermore, the effects of the coating are investigated in this work. Through modal analysis, it was observed that increasing the thickness of aluminum and aluminum bronze coatings caused a rightward shift in resonance frequency. Coatings with a thickness of 2 mm were found to perform better than those with lower thicknesses. Furthermore, the aluminum coating resulted in a greater shift in frequency than the aluminum bronze coating. Additionally, the coating with a higher damping ratio (i.e., aluminum bronze) significantly reduced the amplitude of surface velocity due to excitation, particularly at higher frequencies. This study provides engineers with an understanding of the effects of layer coating on the NVH performance of components and a modeling approach that can be used to design vehicles with enhanced noise and vibration performance.

Data-driven modeling can help improve understanding of the governing equations for systems that are challenging to model. In the current work, the Sparse Identification of Nonlinear Dynamical systems (SINDy) is used to predict the dynamic behavior of dynamic problems for NVH applications. To show the merit of the approach, the paper demonstrates how the equations of motions for linear and nonlinear multi-degree of freedom systems can be obtained. First, the SINDy method is utilized to capture the dynamic behavior of linear systems. Second, the accuracy of the SINDy algorithm is investigated with nonlinear dynamic systems. SINDy can output differential equations that correspond to the data. This method can be used to find equations for dynamical systems that have not yet been discovered or to study current systems to compare with our current understanding of the dynamical system. With this amount of flexibility, SINDy can be used for NVH applications to help analyze vibration-related datasets as the study shows that SINDy results are consistent with ODE solutions. This study demonstrates how SINDy can accurately replicate mature known dynamical system models to highlight its potential to extract equations for more complex systems whose dynamic equations are challenging or impossible to obtain.