Trajectory tracking control is a core technology in intelligent vehicle autonomous driving systems, directly influencing both driving safety and control accuracy. To overcome the limitations of traditional model predictive control (MPC) in real-time performance under complex operating conditions, as well as the limited robustness of linear quadratic regulators (LQR) against system uncertainties, this article proposes a hybrid iterative LQR–MPC (ILQR-MPC) control strategy. First, a dynamic model of the intelligent vehicle is developed to capture its behavior during high-speed driving and cornering. Next, an ILQR-MPC hybrid framework is designed. By exploiting the rapid iterative optimization capabilities of the ILQR algorithm, an initial control sequence is generated for the MPC, thereby reducing the computational load during MPC’s online rolling-horizon optimization. This approach preserves MPC’s advantages in handling constraints and maintaining robustness against parameter variations and external disturbances. Finally, joint simulations using MATLAB/Simulink and CarSim are conducted to evaluate the proposed approach against conventional MPC under standard road conditions, curved sections, and sudden changes in road friction. The results show that the ILQR-MPC strategy reduces trajectory tracking errors, shortens computational time, and maintains excellent stability and robustness under complex operating conditions.
In this article, the aerodynamic features of two configurations of Lotus EMEYA are introduced. The first configuration includes a fixed air dam and an active rear spoiler (ARS) assembly, which has two active blades in order to obtain the aerodynamic drag and lift performance required. The second configuration includes an Active Air Dam (AAD) assembly and a gurney flap mounted on the ARS in order to achieve more aggressive aerodynamic performance. The aerodynamic bandwidths and the lift balances of both configurations are demonstrated, and the strategies of active aero components of the two configurations are also introduced. Through active aerodynamics and control strategies, the two configurations of Lotus EMEYA can meet the performance requirements of users in different scenarios.
Occupant protection has been at the forefront of risk evaluation regarding vehicle crashworthiness design. However, the vehicle is a member of a larger transportation system with varied stakeholders. This article identifies an opportunity for assessing risk in a crash event through emerging safety science paradigms. Conventional Safety I and Safety II frameworks handle well-defined hazards but falter with uncertainty, variability, and emergent behaviors in real crashes. A comprehensive literature review was performed on peer-reviewed research to situate automotive crash safety risk within the Safety III paradigms. The review addresses two questions: (1) How is “risk” defined across the crash safety literature and adjacent safety science domains? and (2) What limitations arise from these definitions in practice? Findings show a dominant probabilistic framing alongside a minority of system-oriented interpretations. Current crash safety practice lacks a coherent, system-level definition of risk that integrates uncertainty and knowledge strength, leading to fragmented methods and limited alignment with modern safety science. Based on this synthesis, the article proposes guiding principles for Safety III-aligned guidelines and recommendations that integrate consequences, uncertainty, and knowledge strength to improve transparency, traceability, and adaptability in crash safety decision-making.
Passenger vehicles experience severe packaging constraints around the instrument panel, rendering glove-box operation a critical yet ergonomically underexplored interaction. Although glove-box interaction occurs frequently during routine vehicle use, its potential implications for ergonomic risk remain largely unexamined in existing automotive research. To isolate the influence of driver-side packaging constraints from component-level design effects, this study adopts a comparative evaluation of driver and co-driver glove-box interaction as a built-in control condition. This study introduces a discomfort-based evaluation framework that integrates Digital Human Modeling with India-specific anthropometric datasets. A composite loss-function scoring model is developed to quantify functional usability differences across four glove-box configurations, defined by variations in latch placement (center or side) and storage-bin mechanisms (fixed or rotating). Indians are utilized to assess reachability and visibility during glove-box interaction. Ergonomic performance is analyzed through reach and visibility metrics for both latch actuation and storage-access tasks. For the co-driver, all configurations exhibit 0% loss, confirming that usability remains unaffected. In contrast, the driver assessment reveals pronounced limitations. Center-mounted latches prove inaccessible from a neutral seated posture, reflecting an approximate loss function of 55%. Among the side-latch alternatives, the rotating-bin configuration achieves the lowest discomfort score (41%), supported by more favorable access posture and smoother hand-entry alignment. The findings specify that ergonomic limitations stem primarily from driver-side packaging constraints rather than inherent flaws in the glove box unit. Based on the reach and visibility loss values obtained through the developed framework, the Side-Latch + Rotating-Bin configuration emerges as the most suitable design option for passenger-vehicle layout. The proposed methodology offers a practical decision-support tool for early stage ergonomic evaluation of glove-box configurations in passenger vehicles.
An accurate air spring model is essential for the design and optimization of air suspension systems to achieve superior performance. This article presents a novel stiffness model for a rolling lobe air spring (RLAS), formulated using stiffness characteristic parameters. Prediction models for these parameters, including effective area and its change rate, as well as effective volume and its change rate, are derived through geometric analysis, based on polynomial fitting of the irregular piston contour. The local contour cone angle of the piston is determined by differentiating the polynomial function, capturing the geometry-dependent variation across the profile. Additionally, a nonlinear hysteresis model for the rubber bellows is integrated, combining a Berg friction component and a Kelvin-Voigt fractional derivative viscoelastic model to represent the amplitude- and frequency-dependent behavior of the RLAS. The proposed model is parameterized through quasi-static and dynamic bench tests under varying amplitudes and frequencies and is validated against both experimental data and an existing modeling approach. Comparative results demonstrate that the proposed model effectively and accurately predicts the static and dynamic responses of the RLAS.
In recent times, energy conservation and environmental protection have attracted more and more attention. This research presents a comparative study on the quantitative analysis and comprehensive ranking of the cradle-to-grave environmental benefits of a multi-material body shell across 18 countries. For quantitative analysis of the cradle-to-grave environmental impact of the body shell, life cycle assessment (LCA) was adopted to assess the process of interactions between the environment and human activity. For a comprehensive ranking of the environmental impacts across 18 nations, two modified techniques were used for order preferences by similarity to the ideal solution (TOPSIS) methods, which are improved by the fuzzy analytic hierarchy process (FAHP) and entropy method (EM). The outcomes from these three methodologies; FAHP&EM-TOPSIS, FAHPTOPSIS, and conventional TOPSIS revealed that the comprehensive environmental benefit rankings of TOPSIS are highly different from the two improved TOPSIS methods, which shows the superiority of modified TOPSIS. The common results of the three measurement methodologies were that New Zealand has the best environmental benefit and Mexico's environmental performance is the worst. Based on the two modified TOPSIS methods used in this study, the comprehensive environmental benefit resulting from the multi-material body shell in various countries can be compared and analyzed accurately and subjectively. Lastly, the obtained results underscore the illumination, usefulness, and practicality of the modified TOPSIS.
The current work analyzes the effect of time-step size on the predictive capability and computational cost of the Sliding Mesh (SM) method for modeling flows around the rotating wheels of a mass production luxury sport utility vehicle (SUV). Two unsteady turbulence models [Unsteady Reynolds Averaged Navier-Stokes (URANS) and Delayed Detached Eddy Simulations (DDES)] were tested using time-step sizes ranging from the current recommended time-step size of 1 degree of rotation per time-step (1 D/TS) up to 50 degrees of rotation per time-step (50 D/TS). The flow field predictions compare favorably to the 1 D/TS case for a time-step size as large as 5 D/TS. Using this time-step size leads to a reduction in computational cost of approximately 80% for both unsteady methods. At a time-step of 5 D/TS, the computational cost of the SM method is comparable to the more commonly used Moving Reference Frame (MRF) method. However, drag and flow field predictions by the SM method at this larger time-step compare far more favorably to the 1 D/TS SM case than the standard MRF method. Thus, increasing the time-step size is an effective way to implement the more accurate SM rotation model without increasing the cost over the MRF.
In this work, the complex wake flow from a double-slanted Ahmed body with an upper slant of alpha = 25 degrees and a standard single-slanted Ahmed body with a slant angle of 40 degrees were used to evaluate vortex identification methods for automotive wake flows. Multiple three-dimensional (3D) vortex identification methods including Q-, 22-, Omega- criteria, and Liutex method and the two-dimensional (2D) Gamma 1-criterion were evaluated against the streamline topology as a pseudo-truth model. Of the 3D methods analyzed, none were found to produce wholly satisfactory results. The Q-and 22-criteria were plagued by high threshold sensitivity and a failure to separate shear from rotation which led to inconsistent identification of the weak, lower-rotation vortices. While the Omega-criterion was able to mitigate the issues related to threshold sensitivity and separation of shear and rotation by consistently identifying the weak vortices, the identified structure did not align well with the streamline topology, producing mismatches at least three times greater than any other method analyzed. This phenomenon was found to be partially caused by geometry-induced solid body rotation (GISBR), in which the complex geometry interacting with the flow results in streamline curvature that produced local regions of solid body rotation without the presence of a vortex. Additionally, the localized non-dimensionalization of the Omega-criterion was found to exacerbate the effects of GISBR in mischaracterizing the weak, lower-rotational flow structures. Of the methods tested, the Liutex method offered the best compromise for 3D flows. Although the Gamma 1-criterion was found to most consistently align with the streamline topology and was not impacted noticeably by GISBR, the method is currently relegated to 2D flows due to the need of assuming the axis of rotation. A possible extension of the Gamma 1-criterion to 3D flows has also been proposed.
Meta-wheels-non-pneumatic wheels whose performance is governed by structural geometry rather than internal pressure-offer new opportunities for directional stiffness control. Yet achieving independent tuning of longitudinal, lateral, and vertical stiffness within a single wheel architecture has remained challenging due to the inherent coupling in conventional radial and planar curved spokes. In this study, we introduce a three-dimensional (3D) discrete curved-spoke design that provides explicit geometric control through two independent parameters: the in-plane curvature angle (alpha) and the out-of-plane inclination angle (beta). Using spoke-level and full-wheel finite-element (FE) simulations, supported by a simplified cantilever-beam analytical model, we show that these two geometric parameters govern stiffness in fundamentally different ways. The curvature angle alpha serves primarily as a geometric softener, reducing stiffness in all directions while maintaining a high top-loading ratio (TLR) (>92%). In contrast, the inclination angle beta enables true directional stiffness decoupling: increasing beta substantially raises longitudinal stiffness and decreases lateral stiffness, while leaving vertical stiffness nearly unchanged (approximate to 1.4% variation). Compared with conventional two-dimensional (2D) spoke designs, the proposed 3D architecture achieves stiffness characteristics approaching those of pneumatic tires, particularly higher longitudinal stiffness and lower lateral stiffness, without sacrificing vertical load-bearing capacity. Moreover, the combined simulation-analysis framework provides an efficient early-stage screening tool by mapping desired stiffness ratios directly to geometric parameters, narrowing the feasible design space before full-wheel FE verification. Overall, this work demonstrates that 3D discrete curved spokes present a practical and interpretable route toward stiffness-decoupled, directionally programmable meta-wheels for next-generation mobility platforms.
Automotive wooden interiors are increasingly popular among consumers for their excellent appearance and texture. However, low light transmittance limits their application in automotive interior smart surfaces. This study explores light transmission technology for wood veneer in automotive interiors, proposing two solutions based on the properties of wood veneer: the light-transmitting veneer solution and the laser-engraved beacon solution. Both solutions were tested through production experiments to evaluate the light transmission effects and process feasibility. Experimental results show that the light-transmitting veneer solution significantly improves the light transmittance of wood veneers through material modification, but instability in structure and materials leads to the difficulty of presenting a better light transmission effect. In contrast, the laser-engraved beacon solution achieves clear and stable light transmission effects by directly processing light-transmitting beacons on the veneer, effectively avoiding interference from surface paint. To further address the issue of veneer fragility around the beacon areas, the solution was optimized, and the laser-engraved micro-perforated beacon solution was proposed. After testing, the optimal micro-perforation diameter (0.25 mm) and micro-perforation spacing (0.25 mm) were finally determined for the laser-engraved micro-perforated beacon solution. This research provides innovative solutions for the application of wood veneer in automotive interiors, promoting the personalization and intelligence of automotive interior design.
The thermal characteristics of brakes significantly influence the braking performance of passenger vehicles. During braking, most of the vehicle's kinetic energy is converted into internal energy in the brake disk through friction, leading to complex coupled thermomechanical issues. This article focuses on the analysis of a disk brake from a specific vehicle model. Using STAR-CCM+, a virtual disk brake bench simulation model was established. Based on the multi-timescale and multi-field coupled simulation method, the analysis of the brake disk temperature and field distributions under cyclic braking conditions was carried out. Subsequently, this work investigated the effects of factors such as thermal conduction, thermal radiation, and the shape of ventilation ribs on the heat generation and dissipation characteristics of the brake disk. Finally, a thermal deformation simulation and optimization method was developed using STAR-CCM+, ABAQUS, and ALTAIR OPTISTRUCT software. In comparison with the test measurements, the accuracy of the thermal deformation simulation for the brake disk reached 94.5%, and the optimized brake disk's thermal deformation was further reduced by 36.1%. This work provides a design verification method for brake disk optimization.
Electromechanical brakes (EMB) are currently coming into focus in the automotive industry. This trend was confirmed in 2022, when a first automotive supplier [1] announced the series production of EMB systems. One major driver is safety, especially if EMB systems are implemented with smart actuators that install redundant electronic control units (ECU) and distributed software [1]. Earlier, the authors have addressed safety mechanisms in EMB actuators [2]. In this article the authors extend their investigation to address safety mechanisms in future EMB central control systems (CCS). Impact of different brake system topologies (X-, H-, centralized) vis-& agrave;-vis potential safety mechanisms within communication buses and ECUs is analyzed.
In this article, a three-dimensional transient CFD simulation method is used to simulate the wind noise of a vehicle model's external Flow field. The transient noise excitation of external noise sources outside each window glass are analyzed, and the statistical energy analysis method is used to calculate the articulation index of the front and rear passenger inside the vehicle. Then, the variation range of the thickness of each window glass is set, and the side window glass is also divided into two types: single-layer glass and laminated glass. After the design parameters are defined, the design space is established. The articulation index of the front and rear passengers and the total weight of the glass are the three design objectives for multi-objective optimization simulation, based on the results of optimization simulation, the change trend of each design parameter and design objective is analyzed; the sensitivity of the design objective to each design parameter is studied; the limit value of the design objective is calculated; the relationship between each design objective is analyzed; and the optimal design proposal that can achieve multiple design objectives at the same time is obtained.
Engine performance is affected by cooling airflow onto the engine cooling module. During initial design, frontal openings, grills, cooling module size, placement, and location are optimized to ensure sufficient airflow onto the cooling module. Currently, design concepts are validated using 3D computational fluid dynamics (CFD) simulations performed iteratively on full vehicle models to predict and optimize cooling airflow onto cooling modules. Each design concept iteration consumes significant time and resources. This study introduces a machine learning (ML) model to streamline underhood airflow prediction, reducing reliance on iterative CFD. Previous CFD simulation data is used to create a training dataset, which calibrates the ML model, describing underhood airflow as a function of input parameters. The relevant ML algorithm is used to calibrate the model, perform data fitting of the training values, after which a testing dataset is created to validate the model for a range of design parameters and vehicle conditions. Upon achieving the target testing accuracy (90% accuracy target in this particular case), the ML model is ready for implementation. The ML model is used to predict initial estimates of airflow and refine the design iterations, while CFD simulations are performed for the finalized concepts. This eliminates the need for expensive and lengthy design analysis iteration loops, effectively replacing them with a highly flexible model capable of predicting underhood airflow for even minor design changes quickly. Use of this model can decrease the time required per iteration by more than 90% compared to conventional CFD, thus enabling analysis of more designs in a given time frame.
A DRL (deep reinforcement learning) algorithm, DDPG (deep deterministic policy gradient), is proposed to address the problems of slow response speed and nonlinear feature of electro-hydrostatic actuator (EHA), a new type of actuation method for active suspension. The model-free RL (reinforcement learning) and the flexibility of optimizing general reward functions are combined with the ability of neural networks to deal with complex temporal problems through the introduction of a new framework called “actor-critic”. A EHA active suspension model is developed and incorporated into a 7-degrees-of-freedom dynamics model of the vehicle, with a reward function consisting of the vehicle dynamics parameters and the EHA pump–valve control signals. The simulation results show that the strategy proposed in this article can be highly adapted to the nonlinear hydraulic system. Compared with iLQR (iterative linear quadratic regulator), DDPG controller exhibits better control performance, achieves the EHA control objective at faster speed, and notably improves the ride comfort and handling stability of the car. Moreover, DDPG’s optimized valve–pump joint control strategy can reduce the energy consumption of the EHA system and improve the life of the hydraulic components while ensuring the control accuracy, solving the problem of low reliability of the active suspension system.
Hydroplaning contributes to approximately 20% of traffic accidents during adverse weather conditions, with factors such as velocity, water film thickness, tire inflation, and vehicle weight playing significant roles. This study aims to simulate the hydroplaning phenomenon using a fluid–structure interaction model based on the coupled Eulerian–Lagrangian (CEL) capabilities of ABAQUS. Results reveal that vehicle linear velocity is a key determinant of hydroplaning risk, with a positive correlation observed. The findings suggest maintaining speeds under 50 km/h to mitigate hydroplaning risk, contingent on well-maintained, properly inflated tires. Multiple linear regression analysis further demonstrates correlations among velocity, tire inflation, quarter vehicle load, and water film thickness in predicting the reaction force between the tire and roadway. The proposed scheme provides a predictive mechanism for hydroplaning risk under varying conditions, offering valuable insights into prevention strategies. The proposed scheme offers a valuable predictive mechanism for understanding and mitigating hydroplaning risk by analyzing key environmental and vehicle parameters. It identifies the critical factors influencing hydroplaning, including velocity, tire inflation, water film thickness, and vehicle load, while offering actionable insights to reduce risk. By employing advanced simulation techniques, specifically ABAQUS with CEL capabilities, the model provides a realistic and accurate representation of the hydroplaning phenomenon. Furthermore, the correlation analysis offers a comprehensive understanding of the relationship between multiple variables, enabling risk assessment under varying conditions. This approach not only highlights the underlying physics of hydroplaning but also supports evidence-based strategies for risk reduction and improved vehicle safety.
In this article, a finite element analysis for the passenger car tire size 235/55R19 is performed to investigate the effect of temperature-dependent properties of the tire tread compound on the tire–road interaction characteristics for four seasons (all-season, winter, summer, and fall). The rubber-like parts of the tire were modeled using the hyperelastic Mooney–Rivlin material model and were meshed with the three-dimensional hybrid solid elements. The road is modeled using the rigid body dry hard surface and the contact between the tire and road is modeled using the non-symmetric node-to-segment contact with edge treatment. At first, the tire was verified based on the tire manufacturer’s data using numerical finite element analysis based on the static and dynamic domains. Then, the finite element analysis for the rolling resistance analysis was performed at three different longitudinal velocities (10 km/h, 40 km/h, and 80 km/h) under nominal loading conditions. Second, the steady-state traction analysis with the corresponding angular velocities of the mentioned longitudinal velocities range was carried out. In addition, a series of transient traction analyses were performed under 40 rad/s angular velocity (corresponding with the 50 km/h longitudinal velocity). The results show that the temperature plays a key role in the final value of the rolling resistance coefficient. Moreover, the longitudinal stiffness of the tire during the traction performance was investigated based on the various ambient temperatures, and it was observed that tire traction is very sensitive to the temperature-dependent properties of the tread compound.
The effectiveness of the negative suspension structure (NSS) in isolating the driver’s seat vibrations has been demonstrated based on the seat’s model or vehicle’s one-dimensional dynamic model. To fully assess the effectiveness and stability of the seat’s NSS (S-NSS) on different models of vehicles, the three-dimensional models of the vibratory rollers (VR), heavy trucks (HT), and passenger cars (PC) have been built to assess the effectiveness of S-NSS compared to the seat’s passive suspension (S-PC) and seat’s control suspension (S-CS). The effectiveness of S-NSS is then investigated under all operating conditions of vehicles. The investigation results indicate that under a same simulation condition, S-NSS improves the ride comfort and health of the driver better than both S-PS and S-CS on all VR, HT, and PC. However, the effectiveness of S-NSS on PC is lower than on both VR and HT while the effectiveness of S-CS on PC is better than on both VR and HT. Besides, the effectiveness of S-NSS with VR moving on the poor class of the ground surface is better than on the good class of the ground surface. In addition, under the change of the velocity and seat mass, the effectiveness of S-NSS on VR is not only higher than that on HT and PC but also very stable, conversely, the effectiveness of S-CS on PC is better than that on VR and HT. These results imply that S-NSS should be applied on the seat suspension of VR, HT, and PC to improve the comfort and health of the driver, especially on VR, while S-CS should be applied to PC to achieve its best isolation effectiveness.
This computational fluid dynamics (CFD) study examines the comfort parameters of an innovative air vent concept for car cabin interiors using a reduced order model (ROM) and proper orthogonal decomposition (POD). The focus is on the analysis of the influence of geometric and fluid mechanical parameters on the resulting jet, in particular on the deflection angle of the airflow and the total pressure difference along the outlet geometry. Different parameters of the investigated system, such as the surface orientation, the outlet height, the separator distance, and the separator height, lead to different effects on the airflow structure. The results show that changes in the air vent surface orientation are always accompanied by an increase in the deflection angle and the total pressure difference. In contrast, the variation of the outlet height ratio positively influences the deflection angle and the total pressure difference in terms of the requirements for air vent geometries. The study also examines the interaction of the geometric parameters and reveals complex correlations that influence the resulting air jet. A comprehensive understanding of these influences makes it possible to adapt the design and implementation of new and innovative air vent concepts to meet specific requirements. By balancing design considerations and technical requirements, optimized solutions are characterized by a high deflection angle and a reduced overall pressure difference for improved system performance and efficiency. Therefore, this evaluation provides a final framework for the design and implementation of an innovative air vent concept based on the volume flow vectoring that is tailored to specific application requirements.
