Full Vehicle Research Articles

Fatigue Life Analysis of Automotive Cast Iron Knuckle under Constant and Variable Amplitude Loading Conditions

The main aim of the present paper is to assess the fatigue lifetime of ductile cast iron knuckles as one of the critical components of an automotive steering system. To this end, a real driving path, including various maneuvers, such as acceleration, braking, cornering, and moving on various types of road roughness, was considered. Different load histories, which are applied on various joints of the component (i.e., lower control arm, steering linkage, and Macpherson strut), were extracted through Multi-Body Dynamics (MBD) analysis of a full vehicle model. The achievements of previous studies have proved that the steering knuckle fails from the steering linkage and due to the rotational motion. Therefore, only this destructive load history was considered in future analyses of the present study. The CAD model was created using Coordinate Measuring Machine (CMM) data and some corrections in CATIA software. Furthermore, transient dynamic analysis was performed, and the time history of von Misses equivalent stress was obtained at the root of the steering linkage (which is exactly the location of failure based on the laboratory data as well as finite element simulations validated by the author in the previous studies). To predict the fatigue life of a component, two different methodologies were considered. Firstly, some well-known criteria were employed for equalization of load spectrum to a Constant Amplitude Loading (CAL). Then, fatigue analysis under sinusoidal loading was performed. Secondly, the fatigue life of the component considering Variable Amplitude Loading (VAL) was predicted using the Critical Plane Method (CPM), employing the Rain-flow cycle counting technique, and utilizing the Miner–Palmgren damage accumulation rule. Eventually, to evaluate the prediction accuracy of different methodologies, the obtained results were compared with the full-scale axial variable amplitude fatigue test which was performed by the corresponding author. The results indicated that the prediction of variable amplitude fatigue lifetime by Finite Element (FE) analysis in the time domain has about 21% error compared to reality. Additionally, the relative error between the results obtained from two different methodologies is about 20%, which is acceptable due to the scattering of the fatigue phenomenon results, the complex geometry of the part, and the complicated loading.

Improving aero-thermal environment in vehicle under-hood through small modification upstream the cooling module

The control of vehicle cooling airflow is one of the key issues of under-hood thermal management as well as the reduction of cooling drag. Aerodynamic analysis using full vehicle CFD method was conducted to further improve the cooling efficiency of a production vehicle equipped with an air-guide. Existing under-hood airflow problem of the original vehicle was first analyzed, and then several modified schemes using various deflectors in the area between the front bumper and the condenser were proposed. The comparison of the under-hood airflow characteristics of each modified scheme and the original model was carried out. The simulation results revealed that sealing the cavity between the front bumper and the anti-collision beam could improve the airflow rate through the cooling module with a reduction in the total air intake volume from the grilles, while blocking the channel from the upper grille opening to the cavity could increase the airflow evenness through the cooling module. The aerodynamic and thermal tests in wind tunnels demonstrated that, for the best scheme with three deflectors at 40 km/h, the temperature of the coolant and the exhaust pipe were reduced by 6.6°C and 15°C, respectively, with a minor reduction in Cd,grilleopen, compared to those of the original vehicle. With less cooling airflow rate, the scheme with a single deflector blocking the upper channel of the cavity achieved similar cooling effect as the best scheme, the reductions of the coolant temperature and the exhaust temperature at 40 km/h were around 6.6°C and 13°C, respectively, with a similar value of Cd,grilleopen, compared to those of the original vehicle.

Engine-in-the-Loop in practical application: A sensitivity study toward the influence of test bench parameters

The complexity of automobile powertrains continues to increase, leading to increased development time and effort. One method to address this challenge is to shift vehicle calibration and testing tasks to simulations with Engine-in-the-Loop (EiL) test setups. This increases the efficiency of the development process through high flexibility, low prototype costs, an early starting point, and a high degree of automation. EiL emulates the dynamic interactions between the internal combustion engine and other powertrain components by closed-loop coupling to real-time simulation models. The coupling quality strongly depends on the interface properties, the hardware layout, and the control functionality of the dynamometer test bench. This paper analyzes an existing test bench and presents the impact of altered test bench parameters on different EiL quality aspects. Thereto, the setup is analyzed and a Simulink based grey box test bench model is derived. The latter is parametrized with a full factorial approach in comparison to real test bench measurements. The test bench model is then used for simulative investigations considering different test bench parameters: Two different use cases of Model-in-the-Loop (MiL) supported EiL are investigated. First, a simulative improvement of the test bench control quality by altering the PI based dyno speed controller parameters is investigated. The virtually calibrated controller parameters are applied to the real EiL system and compared to vehicle measurements. Second, sensitivity studies are shown with respect to dynamometer inertia and system dead time in terms of control quality and energy. As a major outcome, a compromise of control accuracy and stability for different speed controller parameters, dyno inertia, and system dead time is shown. Furthermore, it is demonstrated that EiL has only a small deviation in terms of energy compared to a full vehicle, making it a valid approach when considering CO2 emissions.

Modeling and Analysis of Active Full Vehicle Suspension Model Optimized Using the Advanced Fuzzy Logic Controller

The suspension system plays a major role in automobiles to improve passenger comfort, passenger safety and road handling. It isolates the body of a vehicle from road disturbances. The full vehicle would be subjected to disturbances from all four wheels or a full suspension model of the vehicle and, thus, a full-suspension model of the vehicle should be added to the idea of an enhanced control preview. The input data for the fuzzy logic controller (FLC) is the velocity and acceleration of the front and rear wheels. Controller outputs are considered to be active forces that improve driver comfort, safety and road handling characteristics. The objective of this work is to model and analyse an active full vehicle suspension. The model is optimized using advanced FLC to improve driver comfort, safety and road handling. The mathematical model for the active full vehicle suspension model has been derived. The necessary background for the Simulink fuzzy logic and FLC has been presented. All the simulations are carried out using MATLAB/SIMULINK, a high-performance numeric computation and visualization software package. The fuzzy logic-controlled active values have been compared with ordinary passive simulated values for road profiles. The result of the simulation show that the designed advanced FLC has improved ride comfort by effectively reducing the vehicle body displacement. There is also an appreciable reduction in velocity and acceleration with no increase in suspension travel.

EVs on Road Ground-Impact Energy Evaluation Methodology: Dynamic of a Finite Element (FE)-Based Battery Pack Integrated into Full Vehicle Multi-Body Model on Abaqus

<div class="section abstract"><div class="htmlview paragraph">The present paper documents a comprehensive study on the ground-impact of battery packs in Electric Vehicles (EVs), a paramount concern for vehicle manufacturers. During an accidental crash, battery packs are usually exposed dangerously to possible intrusion of foreign objects (e.g., road debris). With the purpose of developing a generic methodology to evaluate the released energy content and due to high nonlinearities involved during the ground-impact maneuvers, a hybrid full vehicle model is needed. Hence, both FE-based battery case and virtual energy sensors have been integrated into the current Abaqus multi-body (MB) full vehicle model (suspensions, steering system, engine and a trimmed body). Energy peaks are estimated simulating a vehicle going over obstacles that a typical customer can encounter in daily-life (no off-normal situations) performed at different approaching speeds and obstacle sizes.</div><div class="htmlview paragraph">To evaluate the energy, the battery plate is divided as if it were a spaced grid of identical square-sized modules. Plate surface modularization is useful to install properly a certain amount of virtual sensors that store and measure the energy released during the impact. In addition, this strategic layout promotes the accuracy of the contact conditions between obstacles and the battery case.</div><div class="htmlview paragraph">The concept underlying the proposed multidisciplinary sequential procedure, based on three different phases (on road-impact energy evaluation, load cases definition and battery case/mechanical structure strength), ensures both to define specification for subsequent analyses (making iterations on the structure faster) and to possibly complete an effective design validation of the specific vehicle project under test. This methodology has been successfully applied transversally to several vehicle architectures (e.g., light commercial vehicles, sport/compact utility vehicles, supercars) and it shows the benefits of choosing the integration strategy over a typical co-simulation approach.</div></div>

Characterization of high-strength bolts and the numerical representation method for an efficient crash analysis

This paper presents novel insights of a numerical bolt representation approach taking into account damage and failure, suitable for large-scale models as in full vehicle crash simulations. Custom-built experimental equipment combined with digital image correlation enables observation of bolt failure behavior and its physical characteristics under tension, shear as well as combined loading. A fine-meshed model demonstrates the transferability of the physical properties to numerics including the associated restrictions. The experimental and numerical findings are used to develop an efficient substitute bolt model with high prediction accuracy that takes into account the bolt kinematics due to the thread via volume elements.An isotropic constitutive law with linear elastic and multilinear plastic progression supplemented by a phenomenological damage model depicts the bolt material behavior numerically.The observation of the bolt models under different tension and shear ratios provides new insights to the models accuracy which is assessed by an analytically failure envelope.

Optimization of damping pad distribution on body-in-white car structure

Structure-born noise is one of the most important contributors to the interior noise in a vehicle cabin. This can be effectively reduced by placing damping pads with special material properties along the chassis and on the large, radiating panels. This paper proposes a new methodology for the optimization of placing these pads as a part of a full vehicle acoustic simulation in Actran Virtual SEA. With this new strategy, it was possible to reduce the among of damping pads on a real car structure by 33 % while keeping the same acoustic performance. This was validated by investigating the mean velocity of the floor panel in two load cases and compared to the original damping pad layout to the optimized one.

Experimental Tests on a Spoke-Type Permanent Magnets Synchronous Machine for Light Electric Vehicle Application

In an attempt to limit the effects of global warming, virtually all car manufacturers have introduced in the last years Hybrid or full Electric Vehicles. The current study shows the experimental testing of a spoke-type PMSM that was developed based on the requirements of the L6e European light vehicle class. A test bench was developed for this purpose, using a DC machine fed by a bidirectional DC Power Supply that allowed the testing of the PMSM prototype both in motor and generator/brake regimes. The Worldwide Harmonized Light-Duty Vehicles Test Procedure (WLTP) was implemented on the control stage of the testing set-up, allowing an accurate estimation of the PMSM-based E-drivetrain performance. The test results validated the FEM-simulated results and provided an insight on the efficiency of the entire drive system (battery–inverter–PMSM) and the autonomy of the L6e light EV. The electric drive system was integrated and tested on a prototype vehicle in normal operating conditions, validating the results obtained on the developed test bench.

Reduced Order Modeling of Deformable Tire-Soil Interaction With Proper Orthogonal Decomposition

AbstractIn this study, model order reduction of high-fidelity off-road mobility models is explored to address the computational intensity of nonlinear finite element deformable tire–soil interaction models. To this end, a model order reduction procedure for the tire–soil interaction model is developed with the proper orthogonal decomposition (POD), and it is integrated into the off-road mobility simulation framework, leveraging high-performance computing. The POD is, however, limited in that the modes are dependent on snapshot data collected during the running of a full order model, limiting the modes to being accurate only for the specific scenario from which they were collected. Due to this limitation, a method of mode adaptation through interpolation on a tangent space of the Grassmann manifold is investigated to allow modes to be predicted for cases in which a full order model has not been run. It is demonstrated by several numerical examples that the POD modes are effective at retaining predictive accuracy while reducing computational time. The results show that adapted POD modes are more capable of characterizing the behavior of the model than modes produced at a different value of the simulation parameter. The POD-based reduced order modeling approach is further extended to the full vehicle simulation on deformable terrain through the co-simulation coupling algorithm by leveraging the high-performance computing technique.

A Novel 10-Parameter Motor Efficiency Model Based on I-SA and Its Comparative Application of Energy Utilization Efficiency in Different Driving Modes for Electric Tractor

To build a more accurate motor efficiency model with a strong generalization ability in order to evaluate and improve the efficiency characteristics of electric vehicles, this paper researches motor efficiency modeling based on the bench tests of two motor efficiencies with differently rated powers. This paper compares and analyzes three motor efficiency modeling methods and finds that, when the measured values in motor efficiency tests are insufficient, the bilinear interpolation method and radial basis kernel function neural networks have poor generalization abilities in full working conditions, and the precision of polynomial regression is limited. On this basis, this paper proposes a new modeling method combining correlation analysis, polynomial regression, and an improved simulated annealing (I-SA) algorithm. Using the mean and the standard deviation of the mean absolute percentage error of the 5-fold Cross Validation (CV) of 100 random tests as the evaluation indices of the precision of the motor efficiency model, and based on the motor efficiency models with verified precision, this paper makes a comparative analysis on the full vehicle efficiency of electric tractors of three types of drive in five working conditions. Research results show that the proposed novel method has a high modeling precision of motor efficiency; tractors with a dual motor coupling drive system have optimal economic performance.

Microphone Array for Speaker Localization and Identification in Shared Autonomous Vehicles

With the current technological transformation in the automotive industry, autonomous vehicles are getting closer to the Society of Automative Engineers (SAE) automation level 5. This level corresponds to the full vehicle automation, where the driving system autonomously monitors and navigates the environment. With SAE-level 5, the concept of a Shared Autonomous Vehicle (SAV) will soon become a reality and mainstream. The main purpose of an SAV is to allow unrelated passengers to share an autonomous vehicle without a driver/moderator inside the shared space. However, to ensure their safety and well-being until they reach their final destination, active monitoring of all passengers is required. In this context, this article presents a microphone-based sensor system that is able to localize sound events inside an SAV. The solution is composed of a Micro-Electro-Mechanical System (MEMS) microphone array with a circular geometry connected to an embedded processing platform that resorts to Field-Programmable Gate Array (FPGA) technology to successfully process in the hardware the sound localization algorithms.

Hybrid model for exhaust systems in vehicle thermal management simulations

AbstractUsing Vehicle Thermal Management (VTM) simulations to predict the thermal load experienced by components is a popular method within the automotive industry. The VTM simulation approach is fast becoming equivalent to conducting thermal load tests with prototypes for vehicles powered by internal combustion engines. This is especially true in the early development phase of the vehicle. The accuracy of the VTM simulations plays a pivotal role at them being accepted as an eventual replacement for physical testing. The correct prediction of thermal loads in VTM simulations depends on a multitude of different parameters, but the modelling of the exhaust system plays a central role in it. This is because the exhaust gas, and with it the exhaust system, is the primary source of heat in a vehicle powered by an internal combustion engine. The developed approach not only needs to be accurate but also modular enough to allow for different exhaust configurations to be tested. It also needs to be capable of integration into any VTM simulation workflow while maintaining an industrially acceptable turnaround time. This paper explores a new methodology to achieve these requirements. A 1D/3D hybrid approach to exhaust system modelling is presented. In this, the components that have an enthalpy change of the exhaust gas, such as the turbocharger, have been modelled as 1D and simple components such as pipes have been modelled in 3D. This has the advantage of combining the speed of 1D simulations with the spatial accuracy of 3D simulations. The method uses a unique three-code co-simulation technique for full vehicle VTM simulations. The coupling is between a 3D CFD software, a 1D simulation tool, and a Finite Element based thermal solver. The methodology was validated against experimental data for multiple loadcases. The results show good agreement with experiment within acceptable tolerances.

Full Vehicle Trajectory Planning Model for Urban Traffic Control Based on Imitation Learning

Connected and automated vehicles (CAVs) extend urban traffic control from temporal to spatiotemporal by enabling the control of CAV trajectories. Most of the existing studies on CAV trajectory planning only consider longitudinal behaviors (i.e., in-lane driving), or assume that the lane changing can be done instantaneously. The resultant CAV trajectories are not realistic and cannot be executed at the vehicle level. The aim of this paper is to propose a full trajectory planning model that considers both in-lane driving and lane changing maneuvers. The trajectory generation problem is modeled as an optimization problem and the cost function considers multiple driving features including safety, efficiency, and comfort. Ten features are selected in the cost function to capture both in-lane driving and lane changing behaviors. One major challenge in generating a trajectory that reflects certain driving policies is to balance the weights of different features in the cost function. To address this challenge, it is proposed to optimize the weights of the cost function by imitation learning. Maximum entropy inverse reinforcement learning is applied to obtain the optimal weight for each feature and then CAV trajectories are generated with the learned weights. Experiments using the Next Generation Simulation (NGSIM) dataset show that the generated trajectory is very close to the original trajectory with regard to the Euclidean distance displacement, with a mean average error of less than 1 m. Meanwhile, the generated trajectories can maintain safety gaps with surrounding vehicles and have comparable fuel consumption.

Fuzzy logic and proportional integral derivative based multi-objective optimization of active suspension system of a 4×4 in-wheel motor driven electrical vehicle

This paper considers fuzzy logic and proportional integral derivative based multi-objective optimization of a non-linear active suspension system of a 4 × 4 in-wheel motor-driven electric vehicle by using the non-dominated sorting genetic algorithm II. The active suspension system of the electric vehicle and its controllers are optimized to achieve International Organization for Standardization2631-1 ride comfort and health criteria, also providing the actual working conditions such as roll angle and tire load transfers simultaneously for driving safety. In this regard, a non-linear full electrical vehicle model with quadratic tire and cubic suspension stiffnesses with 11 degrees of freedom and a seat-driver model with 5 degrees of freedom are implemented and optimized regarding seven objective functions determined from the root mean square of head and seat accelerations, crest factor , vibration dose value , the ratio of head and seat accelerations, the ratio of the upper torso and seat acceleration, root mean square upper torso acceleration, and root mean square of suspension, tire, and in-wheel motor displacements. The design variables of the optimization problem are chosen as the stiffnesses and damping coefficients of the suspension, in-wheel motors, and seat, as well as the parameters of the proportional derivative and fuzzy logic controllers. The obtained results demonstrate that significant improvements can be achieved by using a controller over the passive systems. It is also noted that the fuzzy logic controller improves ride comfort and the health criterion over proportional derivative system up to 13%, while the load transfer ratio index showed no adverse change between models concerning the rollover condition. The outcomes of this work clearly state that significant improvements, in terms of vibration exposure, can be achieved with the help of reducing the vibration amplitude of an in-wheel motor-driven electrical vehicle active suspension system by using multi-objective optimization considering a non-linear full vehicle model and realistic working conditions such as tire load transfer and vehicle body roll during cornering circumstances. Thus, obtained results are of utmost importance for manufacturers about the active suspension design process providing both a safe and comfortable driving of in-wheel driven electric vehicles.

Study on the influence of seating orientation during frontal impacts by child occupant human body model response analysis

Objective Autonomous driving cars must be developed to ensure that children will have the highest level of protection in case of collision. Changes to the vehicle cabin design (different seat orientations, fully reclining seats, etc.) may significantly impact child occupant safety. Understanding child occupant responses under these new conditions is necessary to decrease risk and enhance child safety. In this study, child occupant response in different seating orientations exposed to frontal impacts with a focus on the head injuries and kinematics was analyzed. Methods Finite elements simulations were performed using the PIPER 6-year-old human body model (HBM). All simulations were carried out in a generic full vehicle environment. The child model was positioned in an adequate generic car restraint system (CRS) in the left rear vehicle seat in 4 seating orientations: 0° (forward-facing position), 30°, 60°, and 90° (living room position). Two scenarios were evaluated for all seating orientations according to the left front seat backrest position: reclined position nominal upright and rest position (55°). All seat configurations were subjected to the mobile progressive deformable barrier frontal impact (European New Car Assessment Programme [Euro NCAP] frontal impact testing protocol). A total of 8 scenarios were simulated in LS-DYNA. Results Based on the Euro NCAP injury risk rate, 90° seating orientation (living room position) was the safest among all selected scenarios independent of the left front seat backrest position. The worst case was found in 60° seat rotation. The highest values for Head Injury Criterion (HIC) and head acceleration (Acc 3 ms) were noted for this case. Higher Brain Injury Criterion (BrIC) values were observed at higher seat rotation angles. Hence, a 90° seating orientation showed the highest BrIC value. Attending to the skull stress, greater head injuries were caused principally by contact with the vehicle interior (seat headrest). Maximum stress values were reached at 30° and 60° seating orientations with the front seat in rest position. In 90° seating orientation, high stress values were also identified. Conclusions These results show that attending to these new seating orientations, current child safety standards are not sufficient to ensure children the highest level of protection. Other additional criteria such as BrIC or skull stress that offer a way to capture brain injuries should be used.

Effect of Turbulence Modeling Selection Within Helios for Small Quadrotor Aerodynamics

The high-fidelity rotorcraft simulation framework HPCMP CREATE-AV Helios was used to study the impact of turbulence modeling methods on the prediction of a small quadrotor unmanned aerial vehicle. First, the vehicle’s rotor was simulated as an isolated rotor in both hover and forward flight for turbulence model assessment. Predictions using a fully turbulent turbulence model were compared with predictions from several laminar–turbulent transition models. Both comparison with experiments and computational performance were considered. The laminar–turbulent transition models predicted flow separation near the tip for all flight conditions simulated, leading to lower torque and thrust than the fully turbulent model, which predicted attached turbulent flow. The fully turbulent model was shown to provide reasonable comparison with experiments with the lowest computational cost and was used for full vehicle simulations. The full quadrotor configuration calculations were performed in hover and forward flight. The forward flight simulations were performed with and without a detached eddy simulation method, and similar interactional aerodynamics were predicted. The Helios simulation suite is shown to be well-suited for prediction of aerodynamic performance of small-scale rotorcraft.

Optimal design of a novel crash box with functional gradient negative Poisson’s ratio structure

Crashworthiness and anti-vibration performance play critical roles in the performance of passenger cars. Aiming at enhancing the crash resistance and vibration resistance of vehicles thus providing good protection for passengers and drivers, a novel crash box with three-dimensional double arrow type negative Poisson’s ratio structure with functional gradient filling inner core (FGNPR crash box) is introduced in this paper and its performance is studied in detail through the comparison with the conventional crash box and the crash box filled with the uniform gradient negative Poisson’s ratio structure (NPR crash box) in crashworthiness and vibration resistance. Furthermore, range analysis is used to screen out the design variables that have little influence on the evaluation indexes and eliminate them. Based on these, neighborhood cultivation genetic algorithm (NCGA) and non-dominated sorting genetic algorithm-ii (NSGA-II) are selected as the optimization algorithms to carry out optimization design respectively and a comparison is made between the two suboptimal results screened out based on the normal boundary intersection (NBI) method to determine the overall optimal solution. Results show that the optimized FGNPR crash box has better crashworthiness and vibration resistance over the other crash boxes and its performance is further verified based on the peak acceleration of B-pillar in full vehicle crash condition. This paper provides some theoretical reference support for the development and exploration of automobile crash box systems.

Roll stability enhancement in a full dynamic ground-tour vehicle model based on series active variable-geometry suspension

Roll stability enhancement in a full dynamic ground-tour vehicle model based on series active variable-geometry suspension

Performance-driven closed-loop model learning with Bayesian optimization for the Car-in-the-Loop test bench control with unknown input

A major control task of a full vehicle test bench is the tracking of the wheel hub speed with disturbance and unknown inputs such as the vehicle drive torque. For this control task, Internal Model Control (IMC) and Linear-Quadratic-Gaussian controller (LQG) are investigated on the quarter-vehicle prototype of a full vehicle test bench concept called Car-in-the-Loop (CiL). In a classic model-based control approach, the identification of the model and the optimization of the controller are carried out separately. The effort for system identification is huge and the sequential approach might result in a suboptimal closed-loop performance. With little prior knowledge of the complex system, we can use Bayesian optimization (BO) to learn a model that explicitly optimizes the control performance directly under closed-loop conditions. Experimental results on the CiL prototype show that similar or better control performance can be achieved with the performance-driven approach for both IMC and LQG.

Development Full Vehicle Scale Collision Test Facility Powered by Falling Weight

Development Full Vehicle Scale Collision Test Facility Powered by Falling Weight