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AS13100 stipulates requirements to establish an acceptable measurement system for use on aerospace engines parts and assemblies. Measurement Systems Analysis (MSA) is used to evaluate and improve measurement systems in the workplace because it evaluates the test method, measuring instruments, and the process of acquiring measurements. The Aerospace Engine Supplier Quality (AESQ) Strategy Group published RM13003 to define the minimum requirements and acceptance limits for conducting MSA for variable attribute assessment on characteristics as defined on the drawing specification. 

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.

Increasing fuel and electricity prices create high pressure to develop efficient external aerodynamics of road cars. At the same time, development cycles are getting shorter to meet changing customer preferences while physical testing capacities remain limited, creating a pressing need for fast and accurate turbulence models to predict aerodynamic performance. This paper introduces and discusses different turbulence modelling approaches beyond the well-known and established models used today in the industry. The RANS Lag Elliptic Blending (Lag EB) k − ϵ model, which enables highly accurate steady-state RANS, was chosen as the baseline approach. As a medium fidelity approach Scale-Resolving Hybrid (SRH) model was utilized, which modifies a RANS base model to produce a smooth transition between URANS and LES behavior. The Wall-Modelled LES (WMLES) method was chosen for high fidelity simulations.

Structural Health Monitoring (SHM), especially in the field of rotary machinery diagnosis, plays a crucial role in determining the defect category as well as its intensity in a machine element. This paper proposes a new framework for real-time classification of structural defects in a roller bearing test rig using time domain-based classification algorithms. Along with the bearing defects, the effect of eccentric shaft loading has also been analyzed. The entire system comprises of three modules: sensor module – using accelerometers for data collection, data processing module – using time-domain based signal processing algorithms for feature extraction, and classification module – comprising of deep learning algorithms for classifying between different structural defects occurring within the inner and outer race of the bearing.

An automotive wiring harness is the backbone of the electrical architecture, and it runs throughout the vehicle to transmit electric power. In a virtual simulation, the mechanical properties of individual strands cannot be considered for the harness bundle (or) cable. Predicting the mechanical properties of electrical cables is a challenging task, and it has major setbacks in virtual simulation. This paper proposes an approach to find out the mechanical properties of an electrical cable and explains how the values are used in virtual simulation. Cable modelling is represented as a lumped mass (or) modelled with a 1D element in the conventional FE modelling approach. In the first part of the study, finite element modelling and material modelling procedures of high and low-voltage electrical cables routed through brackets and troughs are discussed.

The availability of DC Fast Charging Stations (DCFCs) is considered a fundamental step for the widespread adoption of electric vehicles (EVs). To mitigate the impact of high-power charging events on the grid, DCFCs are often equipped with stationary energy storage and renewable energy resources. In literature, many methods have been proposed to design, control, and optimize the performance of multi-sources DCFCs. Many of the research contributions use the averaged EV charging power consumption as input, not the real-time event-based power request. This paper aims at comparing the effects of average-based and event-based EV charging power profiles on the design and control of multi-sources DCFCs. An algorithm that generates event-based EV charging power profiles has been developed based on the data from the California Energy Commission (CEC) report and NREL's EVI-Pro I tool.

The electric vehicle (EV) is one means of realizing carbon neutrality for automobiles, but at present, EVs still faces a number of issues. The main issues include, for example, (1) cruising range, (2) installed battery capacity, (3) charging time, (4) infrastructure facilities, and (5) support for Heavy-duty commercial vehicles. Full electrification of heavy-duty trucks, in particular, requires high power output. This means that enormous installed battery capacity is necessary, and as a result, cruising range on a par with engine-driven vehicles cannot be readily achieved. Therefore a dynamic charge system was devised here that would provide electric power to EVs directly from the infrastructure so that EVs could undergo intermittent charging while driving. This is a conductive charging system that operates from the side of the vehicle (roadside), and research has been underway on the application of this approach to passenger cars and race cars.

In response to the steadily worsening impact of global warming, greater efforts are being made to achieve carbon neutrality. Toyota Motor Corporation developed an in-vehicle solar charging system that utilizes generated solar energy to drive the vehicle. While the ignition is off, energy generated from a solar panel is used to charge the main battery. Then, while the ignition is on, this energy is supplied to the 12 V system to reduce consumption of the main battery energy, thereby helping to improve the electric driving range. This 1st-generation solar charging system adopted in the Prius PHV in 2017 was the first mass-produced in-vehicle solar charging system in the world. In 2022, the 2nd-generation solar charging system was developed and adopted in the bZ4X, including performance improvements such as a newly designed solar roof and lightweight charging system.

Electrical driven compressors (EDC) are mounted on the automotive driveline to compress refrigerants in an automotive air conditioning system. The electrical power delivered by the battery is converted to mechanical power by an inverter and an electrical motor. The inverter is composed of a printed circuit board (PCB) with assembled electronic components. For the inverter part of the EDC, the vibration failures are predominant followed by thermal failures on electronic components. Hence robust methodology is necessary to improve the strength of the design under vibration environment through Finite Element Analysis. In this paper, a multi-fidelity methodology to validate automotive electronic components under harmonic loading through Finite Element Analysis is presented. This multi-fidelity methodology allows the risk of failure to be assessed at the earliest stages of product design, when changes are easy to make and have a low financial impact.

In several regions, such as Europe, California, among others, the switch to Electric Vehicles (EVs) has been heavily pushed by policymakers for their high powertrain efficiency and zero tailpipe emissions compared to conventional Internal Combustion Engine Vehicles (ICEVs). Consequently, only zero tailpipe emission vehicles will be sold in Europe from 2035 for the passenger cars and vans segment. But an EV does emit CO2 emissions across its life cycle, mainly during production, and the Well-to-Tank (WTT) phase, i.e., from the electricity generation used to charge the batteries. Nonetheless, due to the high efficiency of the electric powertrain, the energy consumption is significantly less, making the cost of operation significantly low for EVs. Thus, clean electricity grid and cheap energy costs can make EVs one of the best options for decarbonizing transportation systems.

Steering actuator lag is detrimental to the performance of lateral control systems and often leads to oscillation, reduced stability margins, and in some cases, instability. If the actuator lag is significant, compensation is required to maintain stability and meet performance specifications. Many recent works use a high-level approach to compensate for delay by utilizing model-based methods such as model predictive control (MPC). While these methods are effective when accurate models of both the vehicle and the actuator are available, they are susceptible to model errors. This work presents a low-level, adaptive control architecture to compensate for unknown or varying steering delay and dynamics. Using an inner-loop controller to regulate steer angle commands, oscillation can be reduced, and stability margins can be maintained without the need for an accurate vehicle model.

The paper describes the integration of a high-speed data acquisition and diagnostics controller used in an advanced engine platform. The controller enables ultra-low emissions and new benchmarks of engine efficiency while running a Gasoline Compression Ignition (GCI) cycle on a 2.2L, 4-cylinder engine. The system enables real-time combustion feedback and vibration analysis in engines. The paper focuses on: (1) the development of an interpolative sampling algorithm for transposition of time acquired data to the crank angle domain using a production crank sensor (60-2 tooth wheel); (2) the control unit, high-speed data acquisition, communication rates between the dedicated data acquisition and base controller to ensure cycle-to-cycle feedback; and, (3) validation exercises using cylinder pressure measurements.

To tackle the over-actuated and highly nonlinear characteristics that four-wheel-independent-steering and four-wheel-independent -driving (4WIS/4WID) vehicles exhibit when tracking aggressive trajectory, a hierarchical controller with layers of computation-intensive modules is commonly adopted. The high-level linear motion controller commands the desired state derivatives of the vehicle to meet the overall trajectory tracking objectives. Then the system dynamic is inversed by the mid-level control allocation layer and the low-level wheel control layer to map the target state derivatives to steering angle and motor torque commands. However, this type of controller is difficult to implement on the embedded hardware onboard since the nonlinear dynamic inversion is typically solved by nonlinear programming.

Vehicular odometers serve as a standard component in driver assistance system to provide continuous navigation. Odometer fraud is the disconnection, resetting, or alteration of a vehicle’s odometer with the intent to change the number of miles indicated. Odometer fraud occurs when the seller of a vehicle falsely represents the actual mileage of a vehicle to the buyer. But the Odometer readings are essential when it comes to ascertaining the fair market value of a used vehicle. Hence, there is a need to protect the odometer which resides in the instrument cluster of the digital cockpit. Any manipulation is very difficult to detect and to prove once made, even by expert technicians using specific On-Board Diagnostics (OBD) testing devices. One of the most critical issues is that currently odometers are not locked out from external access, in contrast to other vehicle components, which have higher protection levels.

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