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A global survey in December 2020 revealed a preference for surface and air disinfection in automobiles which may have been accelerated by the COVID-19 pandemic. The observed trend towards healthy cars may remain well after the current pandemic. Additionally, new safety features like CO2 gas sensors, antimicrobial fabrics, and enhanced air purifiers have emerged. While automobile air purifiers trap contaminants using cartridge filters, they are not particularly efficient at removing viral particles and create large pressure drops, which must be compensated with larger fans, increasing power requirements and noise in the vehicle cabin. A HVAC system with integrated UVC-LEDs can inactivating viruses, bacteria, and mold. UVC LEDs are desirable because unlike mercury lamps, they do not pose electrical, glass, and chemical hazards.

The author has been conducting research on UV based photocatalytic air purifier systems for the past 5 years to eliminate living organic germs, bacteria, pathogens, etc. from the cabin air. An HVAC system has been developed by using a filter impregnated by titanium di-oxide (TiO2) with UV lights to improve and maintain cabin air quality. The designed system can be used for conventional vehicles, EVs, ride sharing and for autonomous vehicles. The author has designed and constructed a 3rd generation HVAC unit for cabin air purification for automobiles that is based on UV photocatalytic process by using UV-C LEDs to eliminate viruses that typically exist in conditioned space.

A comfortable thermal environment can alleviate fatigue, reduce irritability, and improve driving safety. However, it is rather a challenge to evaluate thermal comfort inside a vehicle due to multifarious geometric and environmental factors as well as human differences. This study conducted a series of field experiments both in summer and winter conditions, measuring the thermal environment parameters inside the compartment and the skin temperature of experimental personnel, and carrying out subjective thermal sensation and comfort questionnaires. The experimental results showed that head and trunk are the most relevant parts of all human body parts to the overall thermal sensation/comfort. For overall thermal sensation, the value of regression R2 referring to head/trunk is 0.691/0.721, while those corresponding to overall thermal comfort is 0.802/0.773.

Recently, automobile manufacturers are interested in the development of battery electric vehicle (BEV) having a longer mileage to satisfy customer needs. The BEV with high efficiency depends on the temperature of the electric components. Hence it is important to study the effect of the cooling system in electric vehicle in order to optimize efficiency and performance. In this study, we present a 1-D vehicle thermal management (VTM) simulation model. The individual vehicle subsystems were modeled including cooling, power electric (PE), mechanical, and control components. Each component was integrated into a single VTM model and it would be used to calculate energy transfer among electrical, thermal, and mechanical energy. As a result, this simulation model predicts a plenty of information including the state of each component such as temperature, energy consumption, and operating point about electric vehicle depending on driving cycles and environmental conditions.

Model portability, model fidelity and Real-Time capability are becoming critical requirements in the new era of virtual engineering. These characteristics provide the foundation to ensure continuity, reliability, and scalability both for physical and control model representations, along all the product development phases. The adoption of the digital twin model design is key to enhancing the pre-concept phase, to anticipating possible issues, to being competitive in the time-to-market especially for high performance BEV vehicles. Novel simulation methodologies have been developed in a joint effort between Maserati and Gamma Technologies with the aim of reducing computation time and of preserving model fidelity.

Slot liners are commonly used in electric motors to electrically insulate the motor windings from the laminated core. However, thermal conductivity of materials commonly used as slot liners is very low compared to other components in the motor thus creating a barrier for heat transfer. This thermal barrier affects overall motor performance and efficiency. Also, slot liners typically lack intimate contact with the laminated core resulting in air gaps which further increase thermal resistance in the system. Slot liners are traditionally made from high temperature films/papers that are cut and slid into slots of motors. The current work proposes developing an injection moldable slot liner to minimize air gaps. Additionally, use of TCEI materials further lowers thermal resistance. A thermal finite element model has been developed to evaluate effects of slot liner thermal properties and air gaps on temperature distribution within the motor.

This paper presents the design, implementation, and performance evaluation of a reduced complexity algorithm for a predictive control which is based on our previously published SAE paper (2021-01-0225) titled, “Model Predictive Control for Engine Thermal Management System.” That paper presented a model predictive control (MPC) design concept and demonstrated energy efficiency improvements by enabling engine pre-cooling based on GPS/Navigation data to recognize future vehicle speed limit and road grade in anticipation of high engine load demand. When compared to conventional control, the predictive control demonstrated considerable energy and fuel savings due to delayed timing of both knock mitigation and activation of radiator cooling fan during high engine load demand. However, this predictive control strategy is much more complicated due to its highly coupled nonlinear behavior.

Interest in electric vehicles (EVs) has significantly increased from the last decade, as the whole world is concerned about the reduction of emission of greenhouse gas by reducing the use of fossil fuel in transportation. The primary issue for electric vehicles is to develop an energy storage system i.e battery that can enable high mileage, rapid charging, and high-performance driving. Hence, battery management is required to get maximum, safe, and consistent performance of electric vehicles when running in a variety of conditions. To get the most out of a battery, it's important to keep an eye on its operating conditions, especially temperature, which has been shown to have a direct impact on battery performance and life. So, a battery thermal management system (BTMS) is crucial in the control of the thermal behavior of the battery. A good system simulation tool can minimize the time and cost of designing such a complicated thermal management system.

Thermal management of battery packs is essential to keep the cell temperatures within safe operating limits at all times and, hence, ensure the healthy functioning of an EV. The life cycle of a cell is largely influenced by its operating temperature, maintaining the cell temperature in its optimum range improves its longevity by decreasing its capacity fade rate and in turn extending the life of an EV. The battery thermal management solution being presented employs a tabbed type liquid cooling technology that achieves low-temperature differentials for an in-house designed battery pack consisting of 320 LFP cells (Size: 32700) with a total voltage and capacity of 27V and 240Ah respectively. Thermal design of the battery pack considers maximum dissipation when continuously operating at 1C-rate conditions. Furthermore, an intelligent methodology was adopted for higher reliability - cooling the entire battery pack from 50°C to 25°C within 30 min.

This paper reports on an analytical study of the heat transfer and fluid flow in an electric vehicle e-Motor cooled by twenty five sprays/jets of oil. A three-dimensional, quasi-steady state, multi-phase, computational fluid dynamics (CFD) and conjugate heat transfer (CHT) model was created using a commercial CFD software. The transport equations of mass, momentum, energy and volume fraction were solved together with models for turbulence and wall treatment. An explicit formulation of the volume of fluid (VOF) technique was used to simulate the sprays, a time-implicit formulation was used for the flow-field and three dimensional conduction heat transfer with non-isotropic thermal conductivities was used to simulate the heat transfer in the windings.

Designing an efficient vehicle coolant system depends on meeting target coolant flow rate to different components with minimum energy consumption by coolant pump. The flow resistance across different components and hoses dictates the flow supplied to that branch which can affect the effectiveness of the coolant system. Hydraulic tests are conducted to understand the system design for component flow delivery and pressure drops and assess necessary changes to better distribute the coolant flow from the pump. The current study highlights the ability of a complete 3D Computational Fluid Dynamics (CFD) simulation to effectively mimic a hydraulic test. The coolant circuit modeled in this simulation consists of an engine water-jacket, a thermostat valve, bypass valve, a coolant pump, a radiator, and flow path to certain auxiliary components like turbo charger, rear transmission oil cooler etc.

Aluminium alloy material cylinder head is a popular choice for any air-cooled internal combustion engine. But when it is exposed to higher temperature, it is vulnerable for its loss in strength. It becomes imperative to maintain cylinder head temperature well below acceptable temperature limit. Efficient cooling system play a vital role to achieve this objective. In the present work, an air-cooled diesel engine is converted into compressed natural gas (CNG) engine configuration for 25kVA genset configuration. A 1D gas-exchange model is created to generate the thermal boundary conditions required for Computational Fluid Dynamics (CFD) analysis. A steady-state 3D Conjugate Heat Transfer (CHT) model, that uses the predicted in-cylinder temperatures as a spatially varying boundary condition, is created to predict the convective heat transfer between engine fins and cooling air. A Blower Fan is modelled using the Moving Reference Frame (MRF) approach.

Plane strain tension is the critical stress state for sheet metal forming because it represents the extremum of the yield function and minima of the forming limit curve and fracture locus. Despite its important role, the stress response in plane strain deformation is routinely overlooked in the calibration of anisotropic plasticity models due to challenges and uncertainty in its characterization. Plane strain tension test specimens used for constitutive characterization typically employ large gage width-to-thickness ratios to promote a homogeneous plane strain stress state. Unfortunately, the specimens are limited to small strain levels due to fracture initiating at the edges in uniaxial tension. In contrast, notched plane strain tension coupons designed for fracture characterization have become common in the automotive industry to calibrate stress-state dependent fracture models. These coupons have significant stress and strain gradients across the gage width to avoid edge fracture.

In sheet metal testing, in-situ crack detection is either performed manually by purely visual inspection by the machine operator or automatically by a crack detection system. The automatic crack detection method, commonly integrated in sheet metal testing machines, evaluates the drawing force during forming. However, friction, vibration, and machine noise prevent reliable crack detection in thin sheets and foils. The same disturbance variables also prevent robust crack identification in thin sheets and foils by systems that analyze structure-borne sound. Crack detection systems that use reflected light methods, on the other hand, necessitate homogeneous illumination and are interfered by highly reflective as well as inhomogeneous sheet surfaces. In order to avoid the above-mentioned disadvantages of the currently existing crack detection methods, a procedure based on transmission-illumination was developed.

The automotive industry applies Laser Welded Blanks (LWB) to increase the material utilization and light-weighting of the vehicle structure. This paper introduces a novel tensile testing method to characterize the hardening behavior of the weld material with a digital image correlation (DIC) and apply it as a constitutive hardening model in forming simulations with the LWBs of GEN3 steel. Formability tests under biaxial conditions were performed with LWB of GEN3 steel. Experimental results were correlated with finite element analysis (FEA) predictions that were conducted with and without the weld material model. The results show the weld material model for the LWB improves the accuracy of FEA predictions of both necking failures on the base metal as well as cracking on the weld.

Limited room temperature formability hinders the wide-spread use of high strength aluminum alloys in body parts. Forming at warm temperatures or from softer tempers are the current solutions. In this work, our approach is to start with age-hardened sheets from 7xxx and 6xxx family of alloys and improve their formability using local thermomechanical processing only in the regions demanding highest ductility in the forming processes. We achieved local formability improvements with friction stir processing and introduce another process named roller bending-unbending as a concept and showed its feasibility through finite element simulations. Initial results from FSP indicated significant deformation in the processed zones with minimal sheet distortion. FSP also resulted in dynamically recrystallized, fine grained (d < 5 μm) microstructures in the processed regions with textures significantly different from the base material.

Soft magnetic lamination core is a major component of electric motors. The magnetic quality of the lamination has a significant effect on the energy efficiency of the motor. The magnetic properties of electrical steel sheets, which are important design parameters for the manufacturing of electric motors, are normally measured on cut steel strips by standard Epstein frame method, which is destructive and not suitable for the evaluation of magnetic anisotropy. In this paper, magnetic Barkhausen noise (MBN) analysis is used to evaluate the magnetic quality of electrical steel sheets. This method is featured by non-destructive, simple measurement, short measuring time, and offline/online measurements, etc. In addition, it can be readily used to estimate the magnetic anisotropy in all directions of the electrical steel sheet.

During deep drawing processes, the metal blank is radially drawn by mechanical action of the punch forcing the metal into a forming die. As a result, the workpiece goes through some work hardening where some residual energy is released in final stage which results in further deformation of the part (so called “springback”). This research paper is focused on development of a real-time control strategy to reduce springback effects in deep drawing. It reports on the results of the experimental study of springback in drawing and the parameters involved. In this regard, an experimental setup is designed and developed that is used for design of experiment study and simulation validation of the process. Design of experiment technique is utilized to systematically analyze the effects of the process parameters and their relative contribution in springback phenomenon.

This study focuses on variable amplitude loadings applied to automotive chassis parts experiencing carmaker’s specific proving grounds. They are measured with respect to time at the wheel centres and composed of the six forces and torques at each wheel, within the standard vehicle reference frame. In the scope of high cycle fatigue, the loadings considered are supposedly acting under the structure yield stress. Among the loadings encountered during the vehicle lifetime, two classes stand out: Driven Road: loads measured during the vehicle manoeuvre; Random Road: loads mainly coming from the road asperity. To separate both effects, a frequency decomposition method is proposed before applying any lifetime assessment methods. The usual Rainflow counting method is applied to the Driven Road signal. These loadings, depending on the vehicle dynamics, are time-correlated. Thus, the load spectra is set only thanks to the vehicle accelerations time-measurement.

As the international community strengthens the control of carbon dioxide emissions, electric vehicles have gradually become a substitute for internal combustion engine vehicles. The battery pack is one of the most important components of electric vehicles. The strength and fatigue performance of the battery support plate not only affect the performance of the vehicle but also concern the safety of the driver. In the present study, the finite element model of a battery pack for fatigue analysis is completely established. The random vibration stress response analysis and acceleration power spectral density response analysis of the support plate for the battery pack are carried out, and the accuracy of the finite element model is verified by a random vibration test.