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The significance of thermal management performance in electric vehicles (EVs) has grown considerably, leading to increased complexity in thermal systems and a rapid rise in safety and quality-related concerns. The present real-vehicle-based development methods encounter several constraints in their approach when dealing with highly complex systems. Huge number of verification and validation work To overcome these limitations and enhance the thermal system development process, a novel virtual development environment established using the XiLS (X in the Loop Simulation) methodology. This XiLS methodology basically based on real-time coupling between physical thermal system hardware and analytical models for the other systems of vehicle. To control vehicle model and thermal system, various options were realized through hardware, software and model for VCU (Vehicle control unit) and TMS (Thermal management system) control unit.

This paper presents deep learning-based prognostics and health management (PHM) for predicting fractures of an electric propulsion (eP) drivetrain system using real-time CAN signals. The deep learning algorithm, based on autoencoders, resamples time-series signals and converts them into 2D images using recurrence plots (RP). Subsequently, through unsupervised learning of DeepSVDD, it detects anomalies in the converted 2D images and predicts the failure of the system in real-time. Also, reliability analysis based on fracture mechanics was performed using the detected signals and big data. In particular, the severity of the eP drivetrain system is proportional to the maximum shear stress (τmax) in terms of linear elastic fracture mechanics (LEFM) and can be calculated by summarizing the relationship between cracks (a) and the stress intensity factor (KIII).

Optimizing the specifications of the parts that make up the vehicle is essential to develop a high performance and quality vehicle with price competitiveness. Optimizing parts specifications for quality and affordability means optimizing various factors such as engineering design specifications and manufacturing processes of parts. This optimization process must be carried out in the early stages of development to maximize its effectiveness. Therefore, in this paper, we studied the methodology of building a database for parts of already developed vehicles and optimizing them on a data basis. A methodology for collecting, standardizing, and analyzing data was studied to define information necessary for specification optimization. In addition, AI technology was used to derive optimization specifications based on the 3D shape of the parts. Through this study, body parts specification optimization system using AI technology was developed.

This study delves into the microstructural and mechanical characteristics of AlSi10Mg alloy produced through the Laser Powder Bed Fusion (L-PBF) method. The investigation identified optimal process parameters for AlSi10Mg alloy based on Volume Energy Density (VED). Manufacturing conditions in the L-PBF process involve factors like laser power, scan speed, hatching distance, and layer thickness. Generally, high laser power may lead to spattering, while low laser power can result in lack-of-fusion areas. Similarly, high scan speeds may cause lack-of-fusion, and low scan speeds can induce spattering. Ensuring the quality of specimens and parts necessitates optimizing these process parameters. To address the low elongation properties in the as-built condition, heat treatment was employed. The initial microstructure of AlSi10Mg alloy in its as-built state comprises a cell structure with α-Al cell walls and eutectic Si.

Recently, customers' demands for future mobility have increased, such as movement in narrow spaces, increased driving freedom, and ease of parking. The key technology to meet these demands is the four-wheel independent steering system. In this study, we introduce the concept design process for a four-corner steering module and how to prove the design. In addition, we introduce a control method for basic driving modes, such as short U-turn, diagonal driving, crab driving, and zero radius turning. Finally, we propose a special driving mode using the instantaneous rotation center of the 4WS system.

Rising gas prices and increasingly stringent vehicle emissions standards have pushed automakers to increase fuel economy. Mass reduction is the most practical method to increase fuel economy of a vehicle. New materials and CAE technology allow for lightweight automotive components to be designed and manufactured, which outperform traditional component designs. Topology optimization and other design optimization techniques are widely used by designers to create lightweight structural automotive parts. Other design optimization techniques include free-size, gauge, and size optimization. These optimization techniques are typically used in sequence or independently during the design process. Performing various types of design optimization simultaneously is only practical in certain cases, where different parts of the structure have different manufacturing constraints.

The need to reduce weight and cost of battery systems for electric vehicles has led to continued interest in metal-to-plastic substitution and mixed-material designs for battery enclosures. However, the ever-increasing performance requirements of such systems pose a challenge for plastic materials to meet. In an effort to design a cost-effective, lightweight next-generation battery enclosure while meeting the latest requirements, a new thermal runaway test method was developed, and several materials were screened. The objectives of this development project were twofold. The first was to develop a small-scale test method representative of real-world thermal runaway conditions that could be used early in the design process.

Increasing fuel prices and escalating emissions standards, are leading car manufacturers to develop vehicles with higher fuel efficiency. Reducing the mass of the vehicle is one technique to improve fuel efficiency. Shifting from metals to composite materials is a promising approach for great reductions to the vehicle mass. As more composite parts are introduced into vehicles, the approach to joining components is changing and requiring more investigation. Metallic chassis components are traditionally joined with mechanical fasteners, while composites are generally joined with adhesives. In a collaboration between Queen’s University and KCarbon, an automotive composite crossmember is being developed. A variety of lap joint geometries were modeled into a the crossmember assembly for composite-composite joints. Finite element-based optimization methods were applied to reduce mass of the crossmember. The optimized masses showed a 5% difference between the three joint geometries analyzed

Recently, keen interest has been focused on the reduction of fuel consumption through the development of eco-friendly and weight-effective vehicles. This is due in part to the strengthening of regulatory standards for fuel efficiency in each country. This study will focus on the optimization of the IP (Instrument Panel) module, in particular, the cowl crossbar, which in some vehicles, can account for more than 33% of the IP module weight. The design objectives of the cowl crossbar were to use continuous fiber thermoplastic composite materials to achieve high stiffness, while optimizing the strength to weight performance as evaluated through vehicle sled and crash testing. This research will introduce the development and optimization methodology for an alternative material, which achieved about a 30% weight reduction as compared to steel.

As demand for fuel efficiency rises, an increasing number of automotive companies are replacing their existing metal designs with carbon-fiber-reinforced polymer (CFRP) redesigns. Due to the handling and manufacturing processes associated with CFRP materials, engineers have more design freedom to create complex, light-weight designs, which would be infeasible to manufacture using metal. Additionally, it is likely that by redesigning with CFRP, many steel assemblies can be consolidated to significantly fewer parts, simplifying or potentially eliminating the assembly process. When designing an automotive crossmember using CFRP materials, designers often aim for a two-piece design (top and bottom), while utilizing reinforcement material where needed. The joining of these two pieces is typically accomplished with many mechanical fasteners and adhesives, significantly increasing the part count and the manufacturing complexity.

The H-point is a critical part of vehicle design as it is the basis for many engineering dimensions within the vehicle interior. A complete design process includes comparisons of the design to competitive benchmark vehicles. However, the competitive design considerations needed to determine the common standard H-point reference are often unknown. The SAE Human Accommodation and Design Devices (HADD) technical committee recently published a new standard benchmark SgRP procedure [2]. This new standard practice needed to be tested with respect to the accuracy and repeatability for determining the unknown h-point design parameters within industry benchmarking tolerances. In 2019, the SAE HADD committee conducted a study to evaluate the reproducibility of the new procedure. This paper presents detailed results of that study and discusses opportunities for applying the new benchmark practice.

An EGR system has been significantly used in order to cope with reinforced exhaust gas regulation and enhancement of fuel efficiency. For the well-designed EGR cooler, performance analysis is basically required. Furthermore, boiling prediction of the EGR cooler is especially essential to evaluate durability failure of abnormal operating conditions in DPF. However, due to intrinsic complexity of detailed 3-dimensional heat transfer tubes in the EGR cooler, no precise technique of boiling prediction has been developed. Therefore, this research had been performed in order to fulfill 3 goals: (1) development of 3-dimentional performance prediction technique including boiling occurrence, (2) generation and validation of a new evaluation index for boiling, (3) development of an optimized EGR cooler for suppressing boiling. In order to increase analysis accuracy and reduce analysis efforts at the same time, 3-dimensional single-phase flow analysis was developed.

Active Sound Design (ASD) allows the Personalized Engine Sound System to be implemented for different types of vehicles and in different geographical regions. While this process is possible, it requires a lot of on-road tuning and therefore is very time consuming. This study presents an efficient way of tuning ASD sounds based on binaural synthesis in a lab environment instead of on-road tuning. The on-road vehicle operating sounds are reproduced by a desktop NVH simulator while the binaural ASD sounds are synthesized by convolving measured Binaural Vehicle Impulse Responses with the output of ASD multi-channel amplifier in real time. A set of binaural recordings on road are compared with the reproduced sound in the lab environment. The comparison results showed the validity of the proposed method for ASD. The main advantage of this approach is the possibility of back-to-back comparison across different ASD tunings.

Connected and Autonomous Vehicles (CAV) rely on information obtained from sensors and communication to make decisions. In a Cooperative Vehicle Safety (CVS) system, information from remote vehicles (RV) is available at the host vehicle (HV) through the wireless network. Safety applications such as crash warning algorithms use this information to estimate the RV and HV states. However, this information is uncertain and sparse due to communication losses, limitations of communication protocols in high congestion scenarios, and perception errors caused by sensor limitations. In this paper we present a novel approach to improve the robustness of the CVS systems, by proposing an architecture that divide application and information/perception subsystems and a novel prediction method based on non-parametric Bayesian inference to mitigate the detrimental effect of data loss on the performance of safety applications.

Today, many car manufacturers and their suppliers are very interested in power-operated door handles, known as auto flush door handles. These handles have a distinguishing feature in terms of the way they operate. They are hidden in door skins and deployed automatically when users need to open the door. It is obvious that it is a major exterior styling point that makes customers interested in the vehicles that apply it. To make this auto flush door handle, however, there lie difficulties. First, because there is no sufficient space inside a door, applying these handles can be a constraint in exterior design unless the structures of them are kinematic optimized. The insufficient space can also cause problems in appearance of the handles when they are deployed. The purpose of this study is to establish the kinematic system of auto flush door handle to overcome the exterior handicaps such as the excessive exposure of the internal area on the deployed position.

Limited fossil fuel resources, air pollution, and global warming all drive strengthening of fuel economy and vehicle emission standards globally. Much R&D continues to be dedicated to improve fuel efficiency of automobiles and to reduce exhaust gasses. These include improvement of engine/driveline performance for higher efficiency, development of alternative energy, and minimization of air resistance through aerodynamic design optimization. OEM weight reduction-focused research has extended into chassis components (steering knuckle, brakes, control arms, etc.) in sequence from body-in-white(BIW). Wheel bearings, one of the core components of a driveline and part of a vehicle’s unsprung mass, are also being required to reduce weight. Conventionally, wheel bearings have achieved “lightweighting” primarily through design optimization methods. They have been highly optimized today using steel based materials.