Vehicle Simulation Research Articles

Simulation and electrification of a conventional vehicle into a battery electric vehicle for sustainability mobility

ABSTRACT This work explores converting a conventional 1996 Honda Civic Coupe into a battery-electric vehicle (BEV). A simulation model is developed to analyze the vehicle’s dynamic behavior under various conditions, including changes in vehicle mass and road slope. The system specifications are detailed based on a 50 kW AC induction motor, controller, battery, and battery management system. The battery comprises 2,376 NCR18650B cells with a capacity of 23 kWh based on inverter requirements. Energy consumption is simulated using the New European Driving Cycle (NEDC), resulting in 1.69 kWh to travel 10.8 km. Autonomy is estimated at 147 km. The environmental impact of 19.2 g CO2/km is calculated. The results illustrate the change in maximum speed, acceleration, and distance traveled based on changes in mass and slope. It is concluded that mass variation has a lesser impact on performance than road slope. The work demonstrates the feasibility of converting a conventional vehicle to a BEV with comparable dynamic capabilities using currently available technology. Future development plans include constructing a battery prototype, electrifying the vehicle, and incorporating instrumentation for validation.

An Alternate Search Method and An Extended Trace Line Method for Wheel‐Rail Contact Patch Centers Detection

AbstractWheel‐rail contact is a link between the vehicle system and the rail system, and the contact processing approach affects accuracy of vehicle dynamic simulation. Contact detection is usually the start of wheel‐rail contact calculation, and detecting precision of contact patch center affect accuracy of contact force calculating results, therefore it is significant to improve detecting precision of contact patch center. An alternate search method (ASM) and an extended trace line method are proposed to improve precision and efficiency of contact patch centers detection. The proposed alternate search method applies Golden Ratio Search Method and variable intervals between grids to detect contact patch centers. To make the detected contact patch centers match rail tangential directions better, an extended trace line method is constructed based on geometrical constraints between contact points and rail tangential directions. Dynamic simulations of a bogie vehicle formed by two wheelsets and a bogie frame negotiating a rail curve are conducted to evaluate effects of the proposed methods. Calculating times of the proposed methods for finishing the simulations are compared to explain computational efficiencies of the different methods. The proposed approaches are real‐time capable and can be used for vehicle dynamics simulations.

Design of Static Output Feedback Suspension Controllers for Ride Comfort Improvement and Motion Sickness Reduction

This paper presents a method to design a static output feedback active suspension controller for ride comfort improvement and motion sickness reduction in a real vehicle system. Full-state feedback controller has shown good performance for active suspension control. However, it requires a lot of states to be measured, which is very difficult in real vehicles. To avoid this problem, a static output feedback (SOF) controller is adopted in this paper. This controller requires only three sensor outputs, vertical velocity, roll and pitch rates, which are relatively easy to measure in real vehicles. Three types of SOF controller are proposed and optimized with linear quadratic optimal control and the simulation optimization method. Two of these controllers have only three gains to be tuned, which are much smaller than those of full-state feedback. To validate the performance of the proposed SOF controllers, a simulation is carried out on a vehicle simulation package. From the results, the proposed SOF controllers are quite good at improving ride comfort and reducing motion sickness.

Numerical prediction for vertical dynamic anti-sinking performance of underwater vehicle

Computational fluid dynamics (CFD) simulations of an underwater vehicle (UV) during self-propulsion and dynamic anti-sinking maneuvers are presented. A generic submarine, Joubert BB2, was selected as the test model, which was modified by the Maritime Research Institute, Netherlands (MARIN). Different recovery maneuvers for improving the anti-sinking ability were performed when the UV bow cabin was broken. The overset mesh method was used to simulate the deflection of the rudder and stern planes, and a body-force propeller represented by an actuator disk incorporating predetermined propulsion properties provided the thrust and torque for self-propulsion. Extra vector forces of inflow water forces and recovery buoyancy forces were proposed to simulate the flooding of a bow cabin and recover buoyancy by emptying the ballast. Numerical simulations were performed for 20 different combinations of vertical anti-sinking maneuvers, including stern control surfaces, increasing ship speed, and emptying the ballast water. The pitch angle and depth characteristics of UV self-propelled motion were analyzed based on the pre-set anti-sinking criterions. The results indicated stern control surfaces combined with accelerating could effectively maintain the UV's depth and pitch angle, and discharge ballast water could always make it surface quickly. We hope that this study will provide ideas for further research on anti-sinking abilities.

A new control method for leader–follower consensus problem of uncertain constrained nonlinear multi-agent systems

The design of Adaptive Fuzzy Sliding-Mode Control (AFSMC) is discussed in this study for the distributed leader–follower consensus problem of nonlinear uncertain multi-agent systems with input and state constraints. Avoiding the critical circumstances related to the violation of input and state constraints is an important issue in the design of consensus control systems. The limitations of existing adaptive consensus controllers developed for full-state constrained systems motivated the present novel method without restrictive structural assumptions. To convert the original problem to an unconstrained consensus control problem, the proposed control system uses a barrier function-based state transformation method and the input-state linearization methodology. As a result, each agent’s adaptive fuzzy sliding-mode control input, which consists of a fuzzy system and a robust term, is constructed based on the derived representation of the system dynamics. The fuzzy system is used to approximate the dynamic equations of the agent, its neighbors, and maybe the leader in each local feedback control law structure, and the robust term is designed to compensate for the fuzzy approximation error. The magnitudes of the robust terms and the output vectors of the fuzzy systems are also determined using the proposed adaptation laws. The second Lyapunov theorem and Barbalat’s lemma are used to verify the closed-loop system’s asymptotic stability. The effectuality of the proposed methodology is confirmed by numerical simulations for several Autonomous Underwater Vehicles (AUVs). Simulation results show that by means of the proposed AFSMC, the leader–follower consensus is achieved without state constraint violation and performance degradation.

Parameter identification and NVH characteristic analysis of automotive suspension rubber bushing

Accurate prediction of the dynamic stiffness of rubber bushings is crucial for optimizing vehicle vibration and noise performance. However, this task is highly challenging due to various influencing factors such as frequency and amplitude. Consequently, there has been limited research conducted in this area thus far. This paper presents a novel approach for predicting the dynamic stiffness of rubber bushings. The frequency and amplitude dependencies of rubber bushings were thoroughly investigated through dynamic loading tests. A comprehensive model for rubber bushings, incorporating parallel connections of elastic elements, friction elements, and higher-order fractional derivative viscous elements, was established. The model parameters were accurately identified using the GA-BP method. The results demonstrate that the proposed model exhibits a high level of precision in estimating dynamic stiffness across a frequency range of 0–200 Hz. In comparison to the conventional model, the proposed approach enables more precise computation of interior sound pressure response and enhances vehicle simulation accuracy.

Bidirectional neural network for trajectory planning: An application to medical emergency vehicle

This paper focuses on the autonomous trajectory optimization of in-hospital emergency vehicles, aiming to plan a fast, safe, and comfortable trajectory for real time navigation to reach the emergency room. This has direct and significant practical implications for saving patients’ time and reducing the burden on medical staff. To address it, we propose a novel real-time trajectory planning algorithm and introduce a training framework that contributes to planner’s transferability. In the dataset preparation phase, we employ an optimization-based method to solve the in-hospital trajectory planning problem, generating substantial trajectories. In the training phase, we design a neural network-based planner to establish logical connections between states and control commands. To enhance interaction with the vehicle itself, we design a novel network training framework that incorporates a neural network-based vehicle simulator to learn the vehicle’s self-information, facilitating training the planner. During the inference phase, our planner can plan a collision-free, time-efficient, and comfortable trajectory. Furthermore, our algorithm demonstrates ease of transferability to different vehicle models. Finally, extensive simulations experiments are conducted to validate the safety, speed, and comfortability of the algorithm’s trajectory planning, as well as its excellent transferability.

Development, Simulation and Visualization of Autonomous Farm Vehicles

Abstract: A Research in Autonomous Agricultural Vehicle Technology is presented. With the coming of the Smart Farming Revolution the development of more complicated machines has begun. Agricultural machines are made with the capability of driving themselves, using GPS maps and electronic sensors, an approach that will lead to the development of futuristic Precise Autonomous Farming Systems. The basic issues to be addressed are autonomous path planning, obstacle detection and avoidance for which various sensors will be employed such as GPS, laser-based sensors, ultrasonic sensors, IMU sensors and Camera sensors whose data has to be constantly monitored using Dead Reckoning algorithm and constant Pose Estimation. In these sensors, Camera, Ultrasonic, Laser-Based Sensors are used as Detection sensors while the IMU and GPS sensors are used as Localization sensors. Global Avoidance Subsystem which examines the whole environment of an autonomous vehicle mission-level path planner that pre-plans paths around all known obstacles. While Local Avoidance Subsystem/Reactive planner consists of an obstacle filter and an obstacle avoidance algorithm and re-plans a short way for the vehicle to navigate. With the ROS framework and all sensors data, a vehicle can guide itself through an unknown environment. This will help the farmer to undercut the labour shortage and improve the precision in farming which in turn improves the output to meet the growing demands

A test rig for the validation of CFD simulations of a passenger vehicle under hood environment

As part of global efforts to reduce greenhouse gas emissions, the automotive industry is moving towards the electrification of its fleet – including full electric and hybrid vehicles. Considering hybrid vehicles, the energy efficiency and thermal management of powertrains including IC engines remains an important contribution. In this regard, engineers need accurate tools to understand heat-transfer in engine bays. This work presents a flexible test rig design to be used for the validation of CFD simulations of an underhood environment. The test rig and measurement equipment are introduced in detail with experimental data (and CAD) being available.A possible test scenario presented in this research is when an engine is subjected to heavy loads (i.e., constant uphill driving) and the vehicle is subsequently stopped. The experimental results are analysed and, furthermore, the data in terms of flow and temperature fields is compared against the results of the numerical simulations. This sort of comparison is the main usage scenario for the constructed rig, and it demonstrates the value of this facility for research. The rig's geometry and the experimental data being available can additionally be used to facilitate development and validation of various CAE methodology as well as simulation techniques.

The Impact of Vehicle Technology, Size Class, and Driving Style on the GHG and Pollutant Emissions of Passenger Cars

Although technical improvements to engines and aftertreatment systems have the greatest impact on pollutant emissions, there is also potential for reducing emissions through driver behavior. This potential can be realized in the very short term, while better emission-control technologies only take effect once they have penetrated the market. In addition to a change in driving style, the vehicle owner’s choice of vehicle technology and size class will also have an impact on the future emissions of the vehicle fleet. The effects of different driving styles, the tire choice, the vehicle size class, and propulsion technologies on energy consumption and tailpipe and non-exhaust emissions are analyzed in this paper for different traffic situations and start temperatures for cars with petrol and diesel combustion engines and for battery electric vehicles. The analysis is completed with the corresponding upstream emissions from fuel and electricity production. The analysis is based on a vehicle simulation using the Passenger car and Heavy-duty Emission Model (PHEM), which is based on a large database of vehicles created using measurements of real driving conditions. For the assessment of the driving style, a novel method was developed in an H2020 project, which reproduces a measured trip with a virtual eco-driver. Carbon dioxide equivalent emissions (CO2eq) increase with increasing vehicle size, but can be reduced by around 20% for conventional vehicles and 17% for battery electric vehicles (BEVs) through an environmentally conscious driving style. On average, BEVs have around 50% lower CO2eq emissions than conventional vehicles, if the emissions from vehicle production are also taken into account. On an average journey of 35 km, the cold start of modern diesel vehicles accounts for around half of the total NOx emissions, while the proportion of cold starts for petrol vehicles is around 25%. Tire and brake wear together generate a similar amount of PN23 emissions as the exhaust gases from new cars.

A novel suppressing Kalman filter divergence method for the state of charge estimation of lithium-ion batteries under complex conditions

The estimation method for state of charge (SOC) of lithium-ion batteries based on the Extended Kalman Filter (EKF) is prone to losing the ability to track sudden changes under complex driving conditions. Thus, this method cannot guarantee estimation accuracy and robustness. To overcome this deficiency, fading factors are introduced into the error covariance to enhance the effect of newly generated measurement data on the current filtering estimation. A Suboptimal Multiple Fading EKF (SMFEKF) SOC estimation method is proposed herein. The fading factors are solved by forcing the new error sequence of the predicted and actual terminal voltage values to remain orthogonal. The new error sequence that remains orthogonal undergoes transitions from strong autocorrelation in the original stable state to weak autocorrelation. Through real-time adjustment of the Kalman gain matrix, the effective tracking ability of the system in the event of sudden changes gets enhanced. To verify the effectiveness of the proposed method, first, a battery dynamic condition test library covering multiple driving scenarios (such as cities, suburbs, and highways) is established through ADVISOR advanced vehicle simulator driving condition tests. Second, a second-order equivalent circuit model is established and the recursive least squares method is used for parameter identification. Finally, a detailed comparison is made between SMFEKF and EKF in terms of SOC estimation accuracy and robustness under driving conditions with different levels of complexity. It is found that four operating conditions are the main reasons for filtering divergence in EKF. The results show that the root mean square errors of SMFEKF are 63.72 %, 54.20 %, and 21.30 % those of EKF under the three complex conditions, respectively. The new method effectively reduces SOC estimation error and solves the problem of EKF filtering divergence under complex conditions.

Tire–road contact modelling for multibody simulations with regularised road and enhanced UA tire models

AbstractMultibody simulations play an important role in the study of the dynamic behaviour of road vehicles, being one of the key aspects the modelling of the tire-road interaction. The tire-road contact modelling is required to detect the contact between the tire and road and to obtain the tire contact forces from the tire-road relative motion. The first part of the problem is solved by a contact detection algorithm, whereas the second part is handled by a tire model. In both cases, the trade-off between accuracy and computational cost needs to be addressed in the road dynamics scenario. For the contact detection problem, this work proposes a methodology in which the road is described by a mesh of triangles while the tire is described by a toroid. The triangular mesh road input can be directly extracted from any CAD model. To avoid the geometric discontinuities due to the transition between triangular patches, a set of auxiliary points is used to obtain a continuous variation of the road surface normal, based on the method proposed by Rill (Multibody Syst. Dyn. 45:131–153, 2019. In the process of contact detection, all kinematic quantities required by the tire-road contact model are evaluated. The tire–road forces are evaluated using the enhanced UA tire model, which is a numerically stable and efficient development of the original tire model proposed by Gim and Nikravesh (Int. J. Veh. Des. 12, 1991; Int. J. Veh. Des. 12:19–39, 1991; Int. J. Veh. Des. 12:217–228, 1991). Both the contact and tire models have smooth transitions between their internal transitions, thereby reducing the need for the variable-step ODE integrators to unnecessarily decrease time steps. The advances on contact detection and the tire–road contact models, proposed here, are demonstrated with a set of road vehicle simulation scenarios in which the influence of various modelling parameters on the stability and efficiency of the simulations is addressed. The novelties of the work consist not only in the demonstration of the enhanced UA tire model in practical situations but also in the description of the new tire–road contact methodology that employs a simple description of the road geometry, to achieve smooth tire–road contact forces.

Safety in context: routines and the effect of a balanced safety and operations focus on worker perceptions and performance

PurposeTo investigate the influence of logistics and transportation workers’ perceptions of their management’s simultaneous safety and operations focus (or lack thereof) on related worker safety and operational perceptions and behaviors.Design/methodology/approachThis multi-method research consisted of two studies. Study 1 aimed to establish correlational relationships by evaluating the impact of individual-level worker perceptions of operationally focused routines (as a moderator) on the relationship between worker perceptions of safety-related routines and workers’ self-reported safety and in-role operational behaviors using a survey. Study 2 aimed to establish causal relationships by evaluating the same conceptual relationships in a behavioral-type experiment utilizing vehicle simulators. After receiving one of four pre-task briefings, participants completed a driving task scenario in a driving simulator.FindingsIn Study 1, the relationship between perceived safety focus and safety behavior/in-role operational behavior was strengthened at higher levels of perceived operations focus. In Study 2, participants who received the balanced pre-task briefing committed significantly fewer safety violations than the other 3 treatment groups. However, in-role driving deviations were not impacted as hypothesized.Originality/valueThis research is conducted at the individual (worker) level of analysis to capture the little-known perspectives of logistics and transportation workers and explore the influence of balanced safety and operational routines from a more micro perspective, thus contributing to a deeper understanding of how balanced routines might influence worker behavior when conducting dynamic tasks to ensure safe, effective outcomes.

Pairing in-vehicle intelligent agents with different levels of automation: implications from driver attitudes, cognition, and behaviors in automated vehicles

ABSTRACT In-vehicle intelligent agents (IVIAs) have been developed to improve user experience in autonomous vehicles. Yet, the impact of the automation system on driver behavior and perception toward IVIAs is unclear. In this study, we conducted three experiments with 73 participants in a driving simulator to examine how automation system parameters (the level of automation system and IVIA features) influence driver attitudes, cognition, and behaviors when driving or riding in a simulated vehicle. We focused on subjective evaluations of driver-agent interaction and driver trust toward IVIAs to assess driver attitudes, driver situation awareness, and visual distraction to capture their cognition, and their driving performance to understand their behaviors. Our results show that the level of automation system affects drivers’ attitudes toward agent capabilities (e.g. perceived intelligence). Embodiment benefits are more pronounced with Level 5 systems, while speech style, in general, is more influential in determining affective aspects of user attitudes (e.g. Warmth, Likability). As the level of automation increases, drivers engage in more visual distractions. In addition, conversational speech style in general encouraged safer driving behaviors indicated by more stable lateral control under lower levels of automation. Our findings uncover the path of how system parameters affect driver behaviors through system evaluation and trust in agents. These findings have important implications for the development of cohesive user experiences in future transportation systems.

Physical Modeling and Simulation of Reusable Rockets for GNC Verification and Validation

Reusable rockets must rely on well-designed Guidance, Navigation and Control (GNC) algorithms. Because they are tested and verified in closed-loop, high-fidelity simulators, emphasizing the strategy to achieve such advanced models is of paramount importance. A wide spectrum of complex dynamic behaviors and their cross-couplings must be captured to achieve sufficiently representative simulations, hence a better assessment of the GNC performance and robustness. This paper focuses on of the main aspects related to the physical (acausal) modeling of reusable rockets, and the integration of these models into a suitable simulation framework oriented towards GNC Validation and Verification (V&V). Firstly, the modeling challenges and the need for physical multibody models are explained. Then, the Vertical Landing Vehicles Library (VLVLib), a Modelica-based library for the physical modeling and simulation of reusable rocket dynamics, is introduced. The VLVLib is built on specific principles that enable quick adaptations to vehicle changes and the introduction of new features during the design process, thereby enhancing project efficiency and reducing costs. Throughout the paper, we explain how these features allow for the rapid development of complex vehicle simulation models by adjusting the selected dynamic effects or changing their fidelity levels. Since the GNC algorithms are normally tested in Simulink®, we show how simulation models with a desired fidelity level can be developed, embedded and simulated within the Simulink® environment. Secondly, this work details the modeling aspects of four relevant vehicle dynamics: propellant sloshing, Thrust Vector Control (TVC), landing legs deployment and touchdown. The CALLISTO reusable rocket is taken as study case: representative simulation results are shown and analyzed to highlight the impact of the higher-fidelity models in comparison with a rigid-body model assumption.

High-fidelity aero-acoustic evaluations of a heavy-lift eVTOL in hover

Electric Vertical Take-off Landing (eVTOL) vehicles can transform aviation, but the unconventional aircraft development currently requires an extensive knowledge base. This paper presents a high-fidelity aerodynamic and acoustic evaluation of a heavy-lift multi-rotor eVTOL design in hover. The design, known as Skybus, has an MTOW of 14,000 kg, and six tiltable rotors attached at the tip of three sets of tandem wings with flaps. High-fidelity scale-adaptive CFD simulations of the full-scale Skybus vehicle in hover, with three design variations, were conducted using the HMB3 framework. The aerodynamic performance and interactions were analysed in detail. The baseline airframe caused a blockage force of about 13% of the MTOW, and the compact hovering rotors induced a sinusoidal single-blade loading variation, blended with small local interactions. The near-field acoustics were then directly extracted from the high-fidelity CFD results, and far-field acoustic propagation was computed using the acoustic analogy, following reference acoustic measurements for eVTOLs proposed by EASA. Different aerodynamic interactions and propeller acoustic characteristics showed strong impacts on the vehicle acoustics. The EPNL metric was also computed and analysed assuming various hover duration, and restrictions on maximum EPNL and hover duration are recommended for future rotorcraft hover noise certification. These novel results provide valuable guidance for eVTOL acoustic design, infrastructure planning, and policy-making.

Adaptive MATLAB GUI for Hydraulic Brake Stopping Distance Simulation

The crucial segment of any vehicle is the braking unit which decides the vehicle’s performance and passenger safety on a large quotient. It is highly essential to decelerate the vehicle within a reduced distance to avoid any collision. The stopping distance is also dependent on many characteristics apart from the vehicle itself which are GVW(Gross Vehicle Weight), brake sizing, Road Friction, and other vehicle design parameters. Hereby, the result of any brake system is the Stopping Distance. Therefore, for every vehicle, it is inevitable to design in such a way that meets all requirements to pass the safety standards. In this article, we provide an interactive GUI of Mathematical modelling using MATLAB that depicts the running vehicle simulation while the brakes are applied and highlights the Stopping distance for any given inputs. Here, we have used a novel approach to calculate the stopping distance, given we have all vehicle properties. Importantly, cuts the design phase to a large extent saving an ample amount of time with all ease of access and dexterity. The core features of GUI: One will be able to generate and compare stopping distance and other brake-related results for any two given different design scenarios. GUI is automated so that it can simulate the stopping distance just by providing the brake/vehicle parameters inputs to the interface. Additional functionalities to compare design results for various standards (FMVSS, IS, ECE) can also be done effectively. Keywords: Braking Systems, MATLAB Application, Vehicle Safety, Stopping Distance

Braking system analysis with the aid of a dynamic vehicle simulator with nine degrees of freedom

ABSTRACT The braking system is one of the most important vehicle systems, therefore, its optimised operation is essential in the dynamic performance of the automotive project; in addition to ensuring the necessary security of the object of study. Consecutively, the in-depth study on the sizing and performance of the braking system becomes necessary and, using software to assist in the manipulation of the parameters that influence this system, the automotive industry has presented different solutions in recent years, especially due to the use of central electronics such as the brake system module (BSM). In this context, the present work simulates, via a multibody virtual model and the aid of software such as VI-CarRealTime, the dynamic behaviour of a vehicle according to the braking system efficiency loss. With this goal, the virtual model is correlated with a physical test car and the conditions proposed in the AMuS test are executed, where the friction between the pad and the brake disc is changed. Using driver-in-the-loop simulation in a vehicle dynamics simulator with nine degrees of freedom, it was noted, qualitatively, that the loss of brake efficiency through g-force in each braking results in a long time for the vehicle to react according to the driver’s assessment and by telemetry.

Constructing Pavement Roughness of a Three-Dimensional Bridge in Abaqus for Vehicle Scanning Method

In this study, a procedure for constructing pavement roughness in a three-dimensional bridge subjected to a vehicle using Abaqus is proposed for numerical simulation of vehicle–bridge interaction problems. As the plug-in RufGen, a graphical user interface in Abaqus/CAE for surface interaction, creates a hexahedron with a rough surface and attaches it to the bridge deck, the resulting weight of a bridge model can be much larger than the original one. In the vehicle scanning method, it is essential to keep a finite element bridge model with an unchanged weight after imposing a rough surface on it in consideration of the roughness effect for bridge health monitoring. As such, the proposed procedure is aimed at resolving this issue, with the following two highlights: First, the Toeplitz matrix is introduced to generate a rough surface in three dimensions to ensure the randomness of roughness in the two perpendicular directions under a given roughness profile. Second, a shell-type rough pavement is established for directly using as the bridge pavement or attaching to the bridge deck, which minimizes weight increment in the bridge.

Design of Active Suspension Controller for Ride Comfort Enhancement and Motion Sickness Mitigation

This paper presents a method for designing an active suspension controller for ride comfort enhancement and motion sickness mitigation. For this, it is necessary to design an active suspension controller, which aims to reduce the vertical acceleration and pitch rate of a sprung mass in a vehicle. A half-car vehicle model was selected. For the controller design, a static output feedback (SOF) control was selected instead of a full-state feedback control because it is hard to measure all state variables in real vehicles. With the available signals, three types of SOF controller were proposed. To determine the gains of the SOF controllers, a linear quadratic optimal control methodology and a simulation-based optimization method were adopted. To validate the proposed method, a simulation was carried out using vehicle simulation software. The simulation results show that the proposed method is quite effective for ride comfort enhancement and motion sickness mitigation.