Automotive Design Research Articles

A review of automotive intelligent and adaptive headlight beams intensity control approaches

The automotive headlight stands out as a critical vehicle component, particularly emphasized during nighttime driving. The high beam, designed for optimal driver visibility on long-distance roads, traditionally relies on manual control by the driver. However, this manual control poses challenges, particularly when the high beam light temporarily blinds oncoming drivers. The resultant dazzle for drivers of opposing vehicles is a significant concern. In response to these issues, there is a growing demand for adaptive and intelligent headlights that can autonomously adjust beam intensity. The intelligent headlight system takes on the responsibility of modifying the beam intensities without requiring explicit input from the drivers. This study aims to systematically review various approaches to controlling intelligent headlight beam intensity. The paper identifies four prominent approaches to intelligent headlight beam intensity control, recognized as widely used techniques. Furthermore, the study uncovers intriguing connections between some of these intensity control approaches. A survey on utilization rates indicates that sensor-based and machine learning (ML)-based intensity control approaches are the most commonly employed methods by automotive headlight designers. The paper concludes by providing insights into the future prospects of intelligent headlight technology, offering guidance for future researchers in this field.

Navigating the Future: AI’s Revolutionary Drive in the Auto Industry

The dawn of the 21st century has witnessed the automotive industry at the threshold of a paradigm shift, propelled by the integration of Artificial Intelligence (AI). This comprehensive exploration delves into the multifaceted role of AI as a harbinger of change, reshaping the contours of automotive design, manufacturing, and user experience. The paper commences with a historical overview, charting the ascent of AI from conceptual frameworks to its pervasive application across the automotive sector. A thorough literature review synthesizes seminal research, underscoring the operational efficiencies achieved, the impetus for innovation, and the challenges encountered in data stewardship and ethical governance. AI’s imprint on the industry is indelible, enhancing operational efficiency in production, quality control, and assembly, while simultaneously serving as the crucible for the development of connected and autonomous vehicles. The narrative, however, is nuanced by the challenges that accompany this technological march forward. The discourse addresses the intricacies related to data availability, quality, and system integration, alongside the ethical quandaries and regulatory conundrums that accompany the increased autonomy of AI-driven vehicles. Peering into the future, the paper proffers informed predictions about the trajectory of AI within the automotive industry, positing its potential to redefine mobility. Recommendations for future research and development are articulated, emphasizing the need to bridge identified literature gaps, particularly in the ethical deployment of AI and the pursuit of sustainable practices. In summation, the paper reaffirms the pivotal role of AI in the automotive industry, not solely as a catalyst for technological advancement but as a beacon for safety, efficiency, and environmental stewardship. The prospective landscape of AI in automotive paints a portrait of an interconnected, intelligent transportation ecosystem that promises to redefine the human experience of mobility. The conclusion encapsulates the essence of the discourse, advocating for a responsible embrace of AI that aligns with societal values and enhances the collective well- being

Fast Finite-Time Composite Controller for Vehicle Steer-by-Wire Systems with Communication Delays

The modern steer-by-wire (SBW) systems represent a revolutionary departure from traditional automotive designs, replacing mechanical linkages with electronic control mechanisms. However, the integration of such cutting-edge technologies is not without its challenges, and one critical aspect that demands thorough consideration is the presence of nonlinear dynamics and communication network time delays. Therefore, to handle the tracking error caused by the challenge of time delays and to overcome the parameter uncertainties and external perturbations, a robust fast finite-time composite controller (FFTCC) is proposed for improving the performance and safety of the SBW systems in the present article. By lumping the uncertainties, parameter variations, and exterior disturbance with input and output time delays as the generalized state, a scaling finite-time extended state observer (SFTESO) is constructed with a scaling gain for quickly estimating the unmeasured velocity and the generalized disturbances within a finite time. With the aid of the SFTESO, the robust FFTCC with the scaling gain is designed not only for ensuring finite-time convergence and strong robustness against time delays and disturbances but also for improving the speed of the convergence as a main novelty. Based on the Lyapunov theorem, the closed-loop stability of the overall SBW system is proven as a global uniform finite-time. Through examination across three specific scenarios, a comprehensive evaluation is aimed to assess the efficiency of the suggested controller strategy, compared with active disturbance rejection control (ADRC) and scaling ADRC (SADRC) methods across these three distinct driving scenarios. The simulated results have confirmed the merits of the proposed control in terms of a fast-tracking rate, small tracking error, and strong system robustness.

A fsQCA-Based Framework for Cybersecurity of Connected and Automated Vehicles: Implications for Sustainable Development Goals

Connected and automated vehicles (CAV) are increasingly recognized as a critical component of intelligent transportation systems (ITS), contributing to advances in transportation safety and mobility. However, the implementation of CAV in a real-world environment comes with various threats, and cybersecurity is among the most vulnerable. As the technology becomes more advanced and complex, it is essential to develop a comprehensive cybersecurity framework that can address these concerns. This research proposes a novel framework based on complexity theory and employs the fuzzy set qualitative comparative analysis (fsQCA) technique to identify combinations of security attacks that lead to achieving cybersecurity in CAV. Compared to structural equation modelling (SEM), the fsQCA method offers the advantage of demonstrating all possible ways to achieve the outcome. The study’s findings suggest that in-vehicle networks and data storage security are the most crucial factors in ensuring the cybersecurity of CAV. The results can be useful for automotive designers in reducing the potential for attacks while developing secure networks.

How to Design Human-Vehicle Cooperation for Automated Driving: A Review of Use Cases, Concepts, and Interfaces

Currently, a significant gap exists between academic and industrial research in automated driving development. Despite this, there is common sense that cooperative control approaches in automated vehicles will surpass the previously favored takeover paradigm in most driving situations due to enhanced driving performance and user experience. Yet, the application of these concepts in real driving situations remains unclear, and a holistic approach to driving cooperation is missing. Existing research has primarily focused on testing specific interaction scenarios and implementations. To address this gap and offer a contemporary perspective on designing human–vehicle cooperation in automated driving, we have developed a three-part taxonomy with the help of an extensive literature review. The taxonomy broadens the notion of driving cooperation towards a holistic and application-oriented view by encompassing (1) the “Cooperation Use Case”, (2) the “Cooperation Frame”, and (3) the “Human–Machine Interface”. We validate the taxonomy by categorizing related literature and providing a detailed analysis of an exemplar paper. The proposed taxonomy offers designers and researchers a concise overview of the current state of driver cooperation and insights for future work. Further, the taxonomy can guide automotive HMI designers in ideation, communication, comparison, and reflection of cooperative driving interfaces.

Current study on interaction design in automotive intelligent cockpit

In recent years, with the rapid development of Internet communication digital technology represented by artificial intelligence, Internet of Things, big data, the human-computer interaction of automobiles has also been further developed, showing a new look. As the main part of automotive interaction design, the digital, intelligent and Internet-oriented characteristics of intelligent cockpit are becoming increasingly obvious, especially in recent years, the intelligent cockpit design of new energy vehicles has broken the traditional interaction mode of automobile cockpit in the era of industrial machinery, and more intelligent and immersive interaction design is adopted. And the cockpit of the car not only has a single function for driving, but also provides a space for leisure and entertainment for the driver. Key technologies and design cases of cockpits and its interaction design were sorted out and summarized, and then the problems that cockpits interaction design faces and its future development were analyzed in this paper.

Effect of Weight Percent Mg and Heat Treatment on the Mechanical Properties of Al10Si Aluminum Alloy Casting Products Car Chassis Materials

Innovation in the automotive field is now growing rapidly. New materials are considered to be incorporated in automotive design if they have economic and vehicle performance benefits. This research investigates the change of the Magnesium (Mg) addition and heat treatment to the mechanical properties and microstructure of the car chassis prototype with Al10Si Aluminum alloy base material. The process of casting using the High-Pressure Die Casting method. Variation of Mg (3, 4, 5 wt%) to increase the strength of mechanical properties of Al10Si aluminum alloy material. In the casting process, the first Al10Si heated up to 690°C. Mg is incorporated into the heating furnace, then stirred by a mechanical stirrer. Stirring speed of 90 rpm and stirring time of 120 seconds. After it has poured into the mold, the casting temperature is 740°C. Then cools the room to room temperature 39°C. Then performed, heat treatment, using the method of age hardening and artificial aging. The test results prove that the hardening heat treatment makes the grain size smaller. Small grain size, then increase the strength of the material with the addition of Mg elements.

The Application Value of Human-Vehicle Interaction Theory in Intelligent Cockpit Design

With the rapid development of intelligent cockpit, the frontier research of human-vehicle interaction issues has gradually become the focus of the automobile industry. This paper introduces the development history of intelligent cockpit and its design principles and outlook also be proposed. Given the crucial role of multimodal interaction on the intelligent cockpit usability, its definition, common forms, challenges and solutions are introduced and discussed. Four conclusions are drawn from this paper. First of all, the intelligent cockpit design is combined with artificial intelligence technology to improve the convenience and safety of driving. Secondly, the multi-modal interaction provides drivers with a more natural and intuitive interaction mode, and increases the fun of driving. Moreover, the emotional design makes the car not only a means of transportation, but also a kind of emotional sustenance, which meets the emotional needs of users. Finally, the personalized experience allows users to customize the various functions of the car according to their own preferences and habits, which improves the driving comfort. These cutting-edge research not only promotes the technological progress of the automotive industry, but also provides new ideas and directions for the future automotive design.

Product design evaluation based on improved CRITIC and Comprehensive Cloud-TOPSIS – Applied to automotive styling design evaluation

To tackle the challenge of achieving more reasonably calculation of evaluation indicator weights and a more scientifically rigorous scoring system for ranking in product design evaluation, a novel evaluation model that leverages improved CRITIC (Criteria Improved Criteria Importance) and CC-TOPSIS (Comprehensive Cloud-Technique for Order Preference by Similarity to Ideal Solution) is proposed. Firstly, the process commences with the collection of qualitative product evaluations from respondents through questionnaires, presented in the form of fuzzy confidence intervals. Subsequently, these evaluations are transformed into generalized trapezoidal fuzzy numbers, and their center of gravity values are calculated. Secondly, after the weight for each evaluation indicator is calculated employing an enhanced CRITIC approach, a weight cloud model utilizing the backward cloud methodology is constructed. Subsequently, the scoring cloud model is generated as a backward cloud, followed by the application of an enhanced synthesis operator to amalgamate the weight cloud model with the scoring cloud model, culminating in the formation of a comprehensive cloud model. Finally, the product design schemes are ranked using the Cloud-TOPSIS method, and a cloud chart analysis provides insights into optimization directions. The utilization of this methodology in assessing automotive styling design serves as a compelling showcase of its practicality and efficiency, in addition to, the method's resilience is further bolstered through a sensitivity analysis of the evaluation indicators. This method adeptly computes the weights of evaluation indicators, while also addressing the inherent variability in weight values and incorporates the benefits of Cloud Model and TOPSIS in the evaluation of product design alternatives, ultimately elevating the rationality and the scientific underpinnings of the product design evaluation process.

Investigation of manufacturing process effects on microstructure and fatigue prediction in composite automotive tailgate design

Investigation of manufacturing process effects on microstructure and fatigue prediction in composite automotive tailgate design

In automotive design - the effect of MPG on vehicle fuel consumption

Fuel efficiency, measured in miles per gallon (MPG), and engine power, represented by horsepower, are two critical metrics that gauge a vehicle’s performance. Historically, a negative correlation has been perceived between these two variables: cars with higher horsepower were deemed to have a lower MPG. This study comprehensively explores this correlation using a dataset obtained from the UCI repository. Three regression models were applied: linear, quadratic, and cubic. Each was meticulously analyzed and evaluated using mean squared error (MSE) and residual plot to understand the data’s fit and the models’ predictive capabilities. Our research found that while the linear model provides initial insights, polynomial regression models, especially the quadratic one, can capture the relationship more succinctly, which revealed a relationship expressed as fx x x ( )= -+ 1.66 7.87 21.94 2 . The findings indicate that as horsepower increases, MPG decreases, but with diminishing intensity, suggesting an intricate balance between power and efficiency in automotive design.

Analyzing the impact of geographic variability on emissions reduction and cost efficiency in automotive lightweighting strategies

Lightweight design is crucial in automotive design, especially for battery electric vehicles (BEVs), as it can extend driving range or minimize battery size for a set distance. However, the choice of materials like high-strength steels, while beneficial for vehicle weight, often carries greater environmental costs during production. A comprehensive analysis considering the entire lifecycle and electricity sources is essential to identify truly eco-friendly materials. The market introduction of innovative materials like green steel and recycled products further complicates the selection process due to increased competition. Current assessments of lightweight strategies are complex and challenging to compare, highlighting a research gap in easy-to-understand and actionable evaluations. This study aims to investigate automotive lightweighting across various propulsion systems and regions using prospective Life Cycle Assessment (LCA). It introduces a novel approach by examining how the mix of electricity generation affects the environmental benefits of lightweight materials. A new Key Performance Indicator (KPI) is proposed to help those with limited LCA expertise evaluate the environmental impact of different structural materials. The research also provides visual tools and analyzes cost savings from lower lifecycle greenhouse gas emissions, presenting a rounded view of the economic and environmental advantages of diverse lightweight strategies.

Lightweight Design of Automotive Drive Axle Housing under Multiple Operating Conditions Analysis

Lightweight Design of Automotive Drive Axle Housing under Multiple Operating Conditions Analysis

The Future of Transportation Design: Balancing Aesthetics and Functionality in Autonomous Vehicles

This study examines the rapidly changing and competitive area of automobile design, with a particular focus on the introduction of autonomous vehicles. The study moves the focus of the conversation from conventional automotive design approaches to new, usercentric techniques which make use of the opportunities of autonomous technology. Moreover, it assesses the potential effects of different design choices on the experience of users, protection, and transportation within cities. Also, the research offers realistic viewpoints on the coming shifts and alterations in mobility design, predicting the significant effect of autonomous vehicles on both personal and social transportation models. When it comes to aesthetics, the recommendations for designing autonomous vehicles highlight finding an equilibrium between practicality and efficacy without compromising aesthetic appeal. Incorporating adaptable design characteristics that may be changed according to different user needs is advised, while also giving importance to ergonomics and simple designs that improve user interaction. Vehicle aerodynamics should be enhanced, and sustainable materials should be used if possible because these actions can help cut reduce emissions and energy use. In addition, the design process must take into account the integration of cutting-edge technology, including artificial intelligence and machine learning, to improve the vehicle's functionalities while verifying that aesthetic improvements have no impact on the vehicle's efficiency.

Harnessing CFD Modeling Techniques for Optimizing Automotive Streamlines and Reducing Aerodynamic Drag

Computational fluid dynamics (CFD) has emerged as an indispensable tool in automotive design, significantly enhancing vehicle performance through aerodynamic optimization. This paper offers a comprehensive exploration of the application of CFD modeling techniques in refining automotive streamline designs, with the overarching goal of mitigating aerodynamic drag and amplifying vehicle efficiency. To contextualize the discussion, the author commences with a historical overview of fluid dynamics, segueing into a detailed exposition of core CFD algorithms, numerical methodologies, and advanced modeling approaches encompassing boundary layer dynamics, turbulence modeling, meshing strategies, and coupling techniques. Through illustrative case studies, the author elucidates the transformative impact of CFD in streamlining the design process and achieving tangible drag reduction outcomes. The discourse extends to the implications of CFD on fuel economy, vehicular speed, stability, and holistic design considerations. In conclusion, the author addresses the challenges inherent in transposing CFD insights to tangible automotive applications and charts prospective trajectories for CFD's continued evolution in automotive engineering, underscoring its pivotal role in steering the future of vehicular design.

Comprehensive Analysis of Internal Flow Dynamics in Pipeline Systems

This research delves into the intricate flow dynamics within a gas pipeline, leveraging the small perturbation equations to elucidate the flow scenario. Utilizing the Python programming language, the study derives the numerical solutions for the governing equations of the pipeline's flow field. This is achieved through the successive over-relaxation (SOR) iterative method in tandem with the Neumann boundary conditions. The flow dynamics are subsequently visualized and analyzed using streamlined diagrams and Mach number cloud representations, facilitating the identification of stress concentration points on the pipeline's inner wall. Such insights significantly affect the pipeline's optimal design and strategic reinforcement. Beyond its immediate application, the methodology presented herein offers a robust framework for addressing diverse fluid mechanics simulation challenges. Its versatility extends to realms such as automotive contour design, pipeline leakage simulations, and engine compressor configurations. When confronted with the need to dissect fluid flow and stress dynamics, professionals can harness the Python-based SOR iteration code to derive governing fluid equations, enabling a graphical interpretation through streamlined and Mach number cloud diagrams. This study, thus, underscores a pivotal toolset for fluid mechanics challenges across various engineering domains.

Enhancing Driver Comfort and Control: A Proposal for Customizable Gas and Brake Pedal Sensitivity

Despite spectacular advancements in automotive industry, a significant gap exists within the domain of driver-vehicle interaction like the fixed response force of the accelerator and brake pedals. Nowadays, although there are studies on safety especially for pedals, the issue of customizing the pedal hardness has not become widespread. In this study, a method for customizing the accelerator and brake pedals is suggested. The proposed system improves driving comfort and experience by allowing drivers to customize their preferred pedal level. Utilizing an accessible tuner for stepwise adjustments, drivers can fine-tune the pedal response force until it matches their comfort and driving style. Moreover, our system includes numbered preset buttons, which enable drivers to save their preferred force profiles. This feature not only accommodates the unique preferences of individual drivers but also eases transitions between different drivers sharing the same vehicle. By just a push of a button, drivers can recall their saved profiles, ensuring consistent comfort and control across different driving sessions. By incorporating this system to the car, we aim to revolutionize driver-vehicle interactions, moving away from the traditional standardized approach towards a personalized driving experience. This novel solution holds promise in expanding driving comfort, improving vehicle control, and potentially enhancing driving safety. Furthermore, it creates new avenues for research into the effects of tailored pedal forces on driving efficiency and fatigue, thus shaping the future trajectory of automotive design.

Large Eddy Simulation of Low Reynolds Number Flow Around a NACA0015 Airfoil with Modified Trailing Edges

The flow field of a low Reynolds number regime is three-dimensional and exceedingly complicated due to numerous forms of vortical phenomena, which has triggered the interest of many researchers. This study aims to investigate the influence of various trailing edge configurations on the aerodynamic characteristics and flow structure of airfoils. Specifically, five different configurations, namely baseline, serration, comb, and comb-serration are analyzed in detail. The study seeks to identify the configuration that provides optimal aerodynamic performance, which can then inform the design of more efficient airfoils for a range of applications, including unmanned aerial vehicle (UAV), wind energy, and automotive design. A Large-eddy simulation numerical model was developed to effectively evaluate unsteady pressure fluctuations and turbulence parameters at the source. The pressure coefficient and skin friction coefficient figures reveal that modifications result in an early negative pressure zone and uneven flow along the airfoil surfaces. This study presents numerical findings on the NACA0015 airfoil at a zero angle of attack. The pressure coefficient distribution along the airfoil surface exhibits a symmetrical pattern, with the highest pressure observed at the leading edge due to flow deceleration. Analysis of the skin friction coefficient confirms the absence of separation zones on the airfoil surface, except for the tip of the trailing edge. The velocity profile demonstrates a consistent and smooth flow, indicating stable and symmetrical conditions across the airfoil. Moreover, the velocity profiles of the baseline airfoil at zero angle of attack do not indicate any signs of flow separation. Notably, the serrated, combed and comb-serrated trailing edge configurations each yield distinctive effects on fluid flow. The serrated trailing edge promotes increased fluid velocity and pressure drop, while the combed configuration induces separation at the root. Meanwhile, the comb-serrated design sustains a continuous fluid flow over the airfoil surface. Overall, these results contribute to a comprehensive understanding of the aerodynamic behavior and flow characteristics influenced by various trailing edge configurations.

Tribological properties of DLC and GLC coating for automotive engine components application under lubrication

Modern automotive designs are needed to increase mechanical and thermal loads that have longer lifespans and are lighter. The power transmissions and motors often use low-friction hard coatings to prevent wear and reduce friction. The Cr-doped graphite-like carbon method is employed for evaluating coating friction and responses to chromium-doped graphite-like carbon (Cr-GLC) under lubrication. Cr-GLC coatings and chromium-doped diamond-like carbon (Cr-DLC) coatings are arranged using physical vapor deposition (PVD) and plasma-enhanced chemical vapor deposition (PECVD), respectively. The results have demonstrated in comparison to the dry friction coefficient, the friction coefficient under lubrication conditions has been reduced by 40%. Due to its excellent frictional physicochemical properties and compact microstructure, Cr-DLC has an optimum tribological resistance that is significantly higher than that of Cr-GLC. Viscosity, corrosivity, and coating microstructure are used to measure the impact of composite elements. The most ideal characteristics of the Cr-GLC coating are attributed to the non-reaction of additives in oil with friction surfaces.

Multidisciplinary optimization of automotive mega-castings merging classical structural optimization with response-surface-based optimization enhanced by machine learning

Large high pressure die castings (HPDC), recently referred to as mega-castings, can replace plenty of steel metal sheets usually employed for body-in-white (BIW) structures. They can save manufacturing expense and unleash additional lightweight potential thanks to additional design freedom and material properties. The BIW plays a major role in automotive design since it must fulfill numerous structural targets ranging from stiffness for vehicle dynamics, dynamic responses for NVH (noise, vibration, harshness), driving comfort standards and several passive safety requirements. The use of mega-casting structures leads to additional requirements with respect to castability and material quality. Achieving a lightweight design considering requirements related to crash or castability is a challenge on its own, due to the high computational cost of related simulation techniques. Considering multiple requirements simultaneously, therefore often leads to non-weight-optimal structures. To exploit the full lightweight potential, we present a generative multidisciplinary optimization pipeline for the structural design of automotive mega-casting parts in this paper. The approach combines established methods in automotive industry such as topology optimization and response-surface-based (RSM) optimization and enhances the latter by machine learning (ML) based clustering and classification. In a first step topology optimization is employed to derive optimal load-paths for multidisciplinary loading conditions. For this purpose, casting manufacturing constraints as well as more than hundred linearized loads are used to incorporate NVH and passive safety requirements. In a next step the optimal thickness distribution and rib orientation of the structure is achieved using RSM optimization algorithms for the computationally expensive nonlinear crash and casting simulations. Performance indicators are treated by unsupervised learning based on clustering. This enables classification constraints based on simulation field results from hundreds of samples to be included into RSM optimization. It resolves a typical risk of pure scalar, regression-type targets, where supposed optimal results fail when domain experts examine the full field result of the corresponding simulation. It is shown how this approach is superior in achieving a weight-optimal design and turnaround time compared to a design workflow classically used for BIW structures.