Vehicle Simulation Research Articles

Evaluation of automated driving safety in urban mixed traffic environments

AbstractConflicting driving behaviours between automated vehicles and manually driven vehicles may compromise driving safety. The aim of this study is to analyse the safety of mixed traffic on urban roads. The driving simulation tests were conducted using a multi‐agent driving simulator, which allows real‐time synchronization of multiple simulators. These data were further processed to derive the driving behaviour parameters of manually driven vehicles in VISSIM traffic simulations. Driving safety evaluation indicators included conflict‐related indicators, as well as individual safety indicators. The safety evaluation indicators were normalized through min–max normalization, and the risk scores were summed to evaluate the urban roads. The analysis revealed that driving safety was poor at unsignalized intersections with a market penetration rate of 10% and 50% and at signalized intersections with traffic islands and a market penetration rate of 100%, where conflicts arise from the deceleration of leading vehicles and lane changes. This finding is about the driving behaviour of automated vehicles, which maintain a greater distance from the leading vehicle than manually driven vehicles, resulting in poorer driving safety due to lane changes rather than deceleration. Using the findings of this study, criteria for assessing the safety of mixed traffic situations in existing road infrastructures can be established.

Neural Network-Based Descent Control for Landers with Sloshing and Mass Variation: A Cascade and Adaptive PID Strategy

Autonomous control of lunar landers is essential for successful space missions, where precision and efficiency are crucial. This study presents a novel control strategy that leverages proportional, integral, and derivative (PID) controllers to manage the altitude, attitude, and position of a lunar lander, considering time-varying mass and sloshing behavior. Additionally, neural network models are developed, to approximate the lander’s mass properties as they change during descent. The challenge lies in the significant mass variations due to fuel, oxidizer, and pressurant consumption, which affect the lander’s inertia and sloshing behavior and complicate control efforts. We have developed a control-oriented model incorporating these mass dynamics, employing multiple PID controllers to linearize the system and enhance control precision. Altitude is maintained by one PID controller, while two others adjust the thrust vector control (TVC) gimbal angles to manage pitch and roll, with a fourth controller governing yaw via a reaction control system (RCS). A cascade PD controller further manages position by feeding commands to the attitude controllers, ensuring the lander reaches its target location. The lander’s TVC mechanism, equipped with a spherical gimbal, provides thrust in the desired direction, with control angles α and β regulated by the PID controllers. To improve the model’s accuracy, we have introduced time delays caused by fluid dynamics and actuator response, modeled via computational fluid dynamics (CFD). Fluid sloshing effects are also simulated as external forces acting on the lander. The neural networks are trained using data derived from computer-aided design (CAD) simulations of the lander vehicle, specifically the inertia tensor and the center of mass (COM) based on the varying mass levels in the tanks. The trained neural networks (NNs) can then use lander tank levels and orientation to inform and accurately predict the lander’s COM and inertia tensor in real time during the mission. The implications of this research are significant for future lunar missions, offering enhanced safety and efficiency in vehicle descent and landing operations. Our approach allows for real-time estimation of the lander’s state and for precise execution of maneuvers, verified through complex numerical simulations of the descent, hover, and landing phases.

System to Assist the Driver During a Single Lane Change Maneuver, in the Conditions of Danger Arising from a Change in the Condition of the Road Surface

The article describes the application of a vehicle simulation model to assess the driving hazards that arise due to changes in the tyre–road adhesion during a lane change. An experimentally verified vehicle dynamics model was used. The typical single lane change maneuver, performed on dry concrete, wet concrete, and ice-covered roads, was simulated for three vehicle speeds. The disturbance was introduced as a changed tyre–road adhesion on the target lane. Typical sinusoidal (one-period) input was applied to the steering wheel. Adhesion change may lead to an accident when considering the driver’s reaction time. An original control algorithm has been built. It is the driver assistance system with a double PID controller of the steering wheel angle. The system parameters were determined based on the principles recommended in the automatic control engineering monographs. The controller fulfils its tasks for motion at very high speeds on a homogeneous road surface and for the case where the tyre–road adhesion is changed during a maneuver. Thanks to this, the threat can be avoided.

Development of a Mechanical Vehicle Battery Module Simulation Model Combined with Short Circuit Detection

In recent years, electric vehicles (EVs) have gained significant traction within the automotive industry, driven by the societal push towards climate neutrality. These vehicles predominantly utilize lithium-ion batteries (LIBs) for storing electric traction energy, posing new challenges in crash safety. This paper presents the development of a mechanically validated LIB module simulation model specifically for crash applications, augmented with virtual short circuit detection. A pouch cell simulation model is created and validated using mechanical test data from two distinct out-of-plane load cases. Additionally, a method for virtual short circuit prediction is devised and successfully demonstrated. The model is then extended to the battery module level. Full-scale mechanical testing of the battery modules is performed, and the simulation data are compared with the empirical data, demonstrating the model’s validity in the out-of-plane direction. Key metrics such as force-displacement characteristics, force, deformation, and displacement during short circuit events are accurately replicated. It is the first mechanically valid model of a LIB pouch cell module incorporating short circuit prediction with hot spot location, that can be used in full vehicle crash simulations for EVs. The upscaling to full vehicle simulation is enabled by a macro-mechanical simulation approach which creates a computationally efficient model.

Modeling of Three-Dimensional Ocean Current Based on Ocean Current Big Data for Underwater Vehicles

This paper proposes a real-time and high-resolution current system for underwater vehicle simulation and testing based on global ocean current data. The goal was to address the issue of the existing systems for underwater vehicle simulation, whose tests cannot provide real-time and continuous current velocity data. Thus, a three-dimensional ocean current model (3D-OCM) was built for depths of 0~4000 m via the reconstruction of raw current data, fast-access information retrieval, and three-dimensional interpolation. The three interpolation algorithms’ data smoothness and computational times were contrasted. The three-dimensional spline and bilinear algorithm performed the best, taking about 22 milliseconds to acquire the current information anywhere underwater. The comparative analysis revealed that the constructed current system performed strongly in real time and had good velocity data consistency compared with the current data from the National Marine Data Center (NMDC). Furthermore, the running trajectories of the profiling float without interpolation and with three interpolations were contrasted, where the trajectories were more consistent between the three-dimensional spline and bilinear and the three-dimensional Newton and bilinear interpolations. The system can support various marine phenomena for the underwater vehicle’s hardware-in-the-loop (HIL) simulation and testing, and it is meaningful and valuable for increasing the effectiveness of the underwater vehicle’s research and development.

Integrated electric vehicle design and simulation: Combining a mechanical and electrical model using Simscape/Multibody

This paper presents a novel methodology for modeling and implementing an electric vehicle (EV) simulation model using the Multibody approach. The proposed method provides a robust solution for studying EV performance, taking into consideration the dynamic behavior of its complex mechanical system. The mechanical design was carried out using SOLIDWORKS software and then exported to the Simscape/Multibody platform. This process was performed to combine the obtained mechanical design with an electrical model, developed using the Simscape and MATLAB/Simulink toolbox, in the same simulation platform. The proposed EV is powered by an electromechanical PowerDrive based on a permanent magnet synchronous machine (PMSM), supplied by a hybrid energy storage system (HESS). The implemented HESS, operating in a fully active topology, is controlled by a deterministic rule-based energy management system (EMS). To evaluate the proposed model performance, simulations were conducted under ECE-15.

Neural network controller for hybrid energy management system applied to electric vehicles

Hybrid energy storage systems based on batteries and supercapacitors can mitigate the aging of electric vehicle batteries aging by avoiding high currents and rapid discharge cycles. This system requires energy management systems that efficiently split the power during real driving cycles. This paper outlines a design methodology for creating a Multilayer Perceptron neural controller that governs the power distribution between the storage system components. In parallel, a Proportional-Integral-Derivative controller was implemented to ensure voltage regulation in the primary DC bus, ensuring stable operation of the entire system and providing flexibility to the neural design. The controller was tuned using particle swarm optimization, hippopotamus optimization, and differential evolution algorithms, designed to minimize the battery root-mean-square current in a comprehensive vehicle simulation. The training was structured into layers to approach the physical problem and controller optimization facets. The controller’s primary goal is to minimize the battery strain, mitigating stress events and prolonging its lifespan. The energy management neural controller was designed using a simple drive cycle and later validated with a realistic cycle. The findings demonstrate the feasibility of achieving a significant reduction in current, both in peak value and on average, especially when compared to basic energy management strategies. The process uncovered a strategy that prioritizes the use of the supercapacitor in power balance, with this effect being more pronounced during critical load events. The main bus voltage remained stable, with no deviations exceeding 5%, owing to the voltage regulator’s stability. Additionally, the particle swarm optimization and the hippopotamus optimization techniques exhibited notable performance compared to the differential evolution algorithm for this problem. Subsequently, the design was evaluated in a more complex cycle, revealing a slight decrease in average battery current performance. However, it still maintained a significant reduction in peak battery current compared to traditional controllers. Nevertheless, this methodology demonstrated power management optimization efficacy and can be extended to more complex scenarios.

Impact load characterization for security barrier performance assessment through simulations using generic vehicle models

The threat stemming from the use of vehicles as a weapon in urban environments may be mitigated by employing properly designed protective structures such as bollards, street furniture or landscaping options. In order to assess the performance of a barrier resistance to a vehicle impact, the initial step involves characterizing the load on the barrier. To this aim, two recently developed generic vehicle models are utilized to conduct numerical simulations of vehicle impacts on a security barrier. Various impact configurations are examined and compared based on force-time functions. In addition to comparing the impact loadings in terms of peak forces, comparisons are also done in terms of equivalent static loads, determined by computing the dynamic load factors (DLF). The study provides new insights into the characterization of vehicle impact loads on security barriers, which could improve current engineering practices in the field.

Investigating a Toolchain from Trajectory Recording to Resimulation

The growing variety of transportation options and increasing traffic congestion pose new challenges for road safety. As a result, there is an intensified focus on developing automated driving features and assistance systems aimed at minimizing accidents caused by human errors. The creation of these systems requires a substantial amount of testing kilometers, with estimates suggesting that around 2.1 billion kilometers would be necessary to ensure that each situation pertinent to the driving function is encountered at least once with a probability of 50%. This paper advances the microscopic simulation of traffic scenarios beyond linear patterns, utilizing the open-source environment openPASS. It addresses the research question of whether existing microscopic simulations are able to realistically represent non-linear traffic scenarios. A comprehensive toolchain integrates simulation with video recordings and laser scans. The study compares recorded traffic flow data with simulations at a T-junction, assessing the realism of vehicle models and trajectory representation. Three scenarios are analyzed, considering vehicles and pedestrians. The 3D geometry of the scene was captured with a laser scanner, enabling the mapping of recorded video data onto a geo-referenced environment. Object trajectories were extracted using an ’Regions with Convolutional Neural Networks features’ object detector. While openPASS simulated vehicle and pedestrian behaviors effectively, limitations in trajectory variability and reaction times were observed. These findings highlight the need for more realistic behavior models. This research emphasizes the necessity for improvements to accommodate complex driving behaviors and pedestrian dynamics.

Laboratory test and design methods of horizontal-ellipse corrugated-steel-plate–utility-tunnel with varying top–side plate ratios

Corrugated steel-plate culverts, particularly in horizontal ellipse form, are commonly used in large-span projects. Despite the guidelines on plate radius ratios, the impact of these ratios on mechanical properties remains unexplored. This gap highlights the need for research to guide utility tunnel design because existing studies mainly focus on round culverts compressed into elliptical shapes. Therefore, this study conducted backfill, simulated vehicle live load, and ultimate-load tests on two horizontal-ellipse corrugated steel utility tunnel structures with different top-side plate ratios to examine their response characteristics under various load conditions. Moreover, they were compared with those of existing design methods to offer new insights for the design analysis of soil–steel structures. The results demonstrated that the ratio significantly influenced bending moment distribution, and the critical section was concentrated beneath the loading pad for live loads. The ultimate capacity varied with the ratio, with the higher ratio specimen reaching approximately 92.5 % of the capacity of its counterpart. Both specimens failed via tri-plastic hinge mechanisms, with reduced capacity as corrugations flattened. The Canadian Highway Bridge Design Code, which considers thrust force and bending moment, accurately predicted bearing capacity than the other methods in this study. These findings are vital for optimising design and ensuring safety in horizontal-ellipse corrugated steel utility tunnels.

Fast calculation of vehicle-road coupled response based on moving frequency response function

Vehicle–road coupled system is inherently time–varying, and its responses are traditionally calculated using time–domain methods which involves significant computational effort. Aiming to improve the efficiency of response calculation for the coupled system, this paper proposes a fast calculation method in frequency domain, based on the newly developed moving frequency response function (FRF). Firstly, considering the vibration characteristics of an infinitely long road, the road response is straightforwardly expressed using the road impulse response function (IRF). Subsequently, the concept of the road moving IRF is proposed and derived with respect to the moving observation points. The moving FRF is then obtained by applying Fourier transform, which allows the responses of the road moving observation points to be established in frequency domain for fast calculation under moving loads. Furthermore, by analyzing the vehicle–road coupled vibrations, based on the vehicle FRF and road moving FRF, a formula for the vehicle–road coupling force is derived in frequency domain, along with an expression for the responses at the vehicle–road contact points. Finally, the approach is illustrated in numerical simulations of vehicle–road coupled systems, and its computational efficiency and accuracy are verified through comparison with currently popular methods.

Optimizing Hybrid Electric Microcar Design: A Simulation-Based Approach to Fuel-Cell Powertrains Analysis

Abstract The pressing need to reduce emissions and pollutants requires a significant diversification of mobility solutions and energy sources used to this aim. Light quadricycles with electric powertrains are increasingly recognized in research and industrial sectors as viable alternatives for achieving urban sustainable mobility solutions. Their lightweight design, compact dimensions, simplified prototyping and manufacturing processes, high efficiency, and lower emissions across the entire life cycle contribute to their growing importance. In line with this trend, integrating a hydrogen fuel cells stack with a battery pack is a promising powertrain solution, leveraging both technologies’ strengths. However, the design and development of such hybrid systems necessitate advanced methodologies that can address the problem of significant prototyping costs and time commitments while still ensuring optimal configurations. The availability of detailed simulation tools integrated with hardware components (vehicle simulation platforms) plays a crucial role in this endeavor, enabling insights on local phenomena which otherwise would be neglected, as such the purge phase, strongly impacting on the global consumption. These test platforms encompass all the main subsystems to account for all the vehicle demands while ensuring very good reliability for the predictions of performance and allowing the validation of the components. This enables the possibility of testing the vehicle under a wide set of operating conditions (e.g., weather conditions and driving cycles) within a simulated environment, thus reducing the costs related to the component sizing issues. In this context, the work presented in this paper, develops the fuel cell stack sizing analysis of a hybrid electric microcar, proposing a methodological approach for the powertrain design process. A detailed simulation platform is introduced for a hybrid electric microcar. The two energy sources, namely the Li-ion battery pack and the fuel cell stack, have been experimentally characterized and implemented into the model, also considering the experimental performances of the balance of plant elements as of the DC-DC converter. A linear scale-up method is implemented on the fuel cell stack to consider different configurations, and a deep analysis of the hydrogen consumption, comprising the scale-up of the purge phase, is presented, proposing two different approaches to the problem. From a technological standpoint, different results regarding the choice of fuel cell size have been found. As an example, the role of purging and its relationship with the size of the fuel cell stack may lead to a 14 % increase in the vehicle’s estimated range with respect to the baseline. Cost analyses are then needed to reach a trade-off between the costs and the technological standpoints.

Comparative Analysis of Electric Vehicle Simulator for Accurate Battery Pack Internal Signal Generation

Comparative Analysis of Electric Vehicle Simulator for Accurate Battery Pack Internal Signal Generation

High-Efficiency e-Powertrain Topology by Integrating Open-End Winding and Winding Changeover for Improving Fuel Economy of Electric Vehicles

The fuel economy of electric vehicles (EVs) is an important factor in determining the competitiveness of EVs. Since the fuel economy is affected by the efficiency of an e-powertrain composed of a motor and inverter, it is necessary to select a high-efficiency topology for the e-powertrain. In this paper, a novel topology of e-powertrains to improve the fuel economy of EVs is proposed. The proposed topology aims to improve the system efficiency by integrating open-end winding (OEW) and winding changeover (WC). The proposed OEW-PMSM with WC enables to drive a permanent magnet synchronous motor (PMSM) in four different modes. Each mode can increase inverter efficiency and motor efficiency by changing motor parameters and maximum modulation index. In this paper, the system efficiency of the proposed topology was evaluated using electromagnetic finite element analysis and a loss model of power semiconductors. In addition, the vehicle simulations were performed to evaluate the fuel economy of the proposed topology, thereby proving the superiority of the proposed topology compared with the conventional PMSM.

Identifying vehicle characteristic target based on subjective ratings from drivers

In virtual tire development, defining vehicle characteristic target is crucial to achieving the desired vehicle performance with the equipped tires. Vehicle performance is generally evaluated through subjective ratings by skilled drivers following SAE J1441 guidelines, as well as the objective performance metrics obtained via vehicle dynamics analysis. However, due to the scarcity and bias in real-world testing dataset, the vehicle characteristic target cannot be determined definitively. To address this issue, we propose a recommendation system that searches the unexplored vehicle characteristic domains. This suggested method provides virtual subjective ratings to enrich the limited real-world dataset from the skilled drivers. Moreover, we highlight the correlation between subjective and objective data using vehicle and tire dynamics simulation software. Based on this augmented data, we predict the vehicle characteristic target according to the subjective rating requirements. The proposed approach was validated through various experiments involving skilled drivers and various vehicle-tire combinations, showing 91.26% to 96.2% consistency in generated steering parameter domains. Domains of stability parameters show that one fewer domain was generated by a conservative driver compared to more lenient driver, which confirms the robustness of the proposed approach in accommodating individual driver preferences.

Design of a Path-Tracking Controller with an Adaptive Preview Distance Scheme for Autonomous Vehicles

This paper presents a method to design a path-tracking controller with an adaptive preview distance scheme for autonomous vehicles. Generally, the performance of a path-tracking controller depends on tire–road friction and is severely deteriorated on low-friction roads. To cope with the problem, it is necessary to design a path-tracking controller that is robust against variations in tire–road friction. In this paper, a preview function is introduced into the state-space model built for better path-tracking performance. With the preview function, an adaptive preview distance scheme is proposed to adaptively adjust the preview distance according to the variations in tire–road friction. Front-wheel steering (FWS) and four-wheel steering (4WS) are adopted as actuators for path tracking. With the state-space model, a linear quadratic regulator (LQR) is adopted as a controller design methodology. In the adaptive preview distance scheme, the best preview distance is obtained from simulation for several tire–road friction conditions. Curve fitting with an exponential function is applied to those preview distances with respect to the tire–road friction. To verify the performance of the adaptive preview distance scheme under variations in tire–road friction, a simulation is conducted on vehicle simulation software. From the simulation results, it was shown that the path-tracking controller with an adaptive preview distance scheme presented in this paper was effective for path tracking against variations in tire–road friction in the peak’s center offset, and the settling delays were reduced by 60% and 23%, respectively.

Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios

Autonomous vehicles (AVs) must fulfill adequate safety requirements before formal application, and performing an effective functional evaluation to verify vehicle safety requires extensive testing in different scenarios. However, it is crucial to rationalize the application of different scenarios to support different testing needs; thus, one of the current challenges limiting the development of AVs is the critical evaluation of scenarios, i.e., the lack of quantitative criteria for scenario design. This study introduces a method using the Spherical Fuzzy-Analytical Network Process (SF-ANP) to evaluate these scenarios, addressing their inherent risks and complexities. The method involves constructing a five-layer model to decompose scenario elements and using SF-ANP to calculate weights based on element interactions. The study evaluates 700 scenarios from the China In-depth Traffic Safety Study–Traffic Accident (CIMSS-TA) database, incorporating fuzzy factors and element weights. Virtual simulation of vehicles in the scenarios was performed using Baidu Apollo, and the performance of the scenarios was assessed by collecting the vehicle test results. The correlation between the obtained alternative safety indicators and the quantitative values confirms the validity and scientific validity of this approach. This will provide valuable guidance for categorizing audiovisual test scenarios and selecting corresponding scenarios to challenge different levels of vehicle functionality. At the same time, it can be used as a design basis to generate a large number of effective scenarios to accelerate the construction of scenario libraries and promote commercialization of AVs.

Development and Validation of a Driving Simulator for Comfort Assessment

The passenger’s comfort is of vital importance in today’s car, even more so in the future autonomous car. Its thorough analysis requires the use of objective measures, but the subjectivity of the passenger must also be considered. The relationship between both aspects is an uncommon research avenue, and could greatly benefit from the use of a vehicle simulator. This study use a simulator to replicate a journey with diverse characteristics that numerous passengers may experience (including engaging in different activities). The appropriate variables for a proper comfort analysis are determined in this study, resulting in a comprehensive database for the same purpose. Additionally, a passenger comfort based questionnaire is developed and applied to obtain the subjective assessment of different passengers. The results of these questionnaires help not only in the identification and study of the self perception of comfort by the passengers but also in the use of the simulator for comfort experiments. The detailed comparison between both the objective approach of signal analysis and the subjective approach of questionnaires that is performed thanks to the simulator creates a foundation for future research and narrows the existing research gap.

Dynamic Analysis of a Vehicle–Bridge System Under Excitation of Random Road Irregularities

This paper presents a comprehensive study of the dynamic response analysis of vehicle–bridge coupled systems, with detailed simulation methods for the vehicles, bridges, and wheel–road coupling relationships. The simulation of the entire vehicle–bridge coupling system is carried out using the open-source finite element analysis platform OpenSees. A novel three-dimensional wheel–road coupling element is introduced to model the interactions between the wheel and road nodes. This element facilitates precise computation of the dynamic responses within the vehicle–bridge coupled system, including both vehicle and bridge behaviors, along with the interaction forces between the wheels and the bridge surface. The coupling element consists of a wheel node and all potential road nodes on the bridge surface that the wheel may traverse. This configuration preserves the finite element model of the entire vehicle–bridge coupled system throughout the vehicle’s movement, thereby improving the efficiency of numerical simulations of vehicle–road interactions. The study accounts for the impact of random road irregularities on the dynamic responses of both the vehicle and the bridge. These irregularities are treated as input parameters for the wheel–road coupling element rather than being accounted for through the wheel–road interaction constraint equations, thereby improving the convenience of simulating random road irregularities.

Data-Driven Approaches for State-of-Charge Estimation in Battery Electric Vehicles Using Machine and Deep Learning Techniques

One of the most important functions of the battery management system (BMS) in battery electric vehicle (BEV) applications is to estimate the state of charge (SOC). In this study, several machine and deep learning techniques, such as linear regression, support vector regressors (SVRs), k-nearest neighbor, random forest, extra trees regressor, extreme gradient boosting, random forest combined with gradient boosting, artificial neural networks (ANNs), convolutional neural networks, and long short-term memory (LSTM) networks, are investigated to develop a modeling framework for SOC estimation. The purpose of this study is to improve overall battery performance by examining how BEV operation affects battery deterioration. By using dynamic response simulation of lithium battery electric vehicles (BEVs) and lithium battery packs (LIBs), the proposed research provides realistic training data, enabling more accurate prediction of SOC using data-driven methods, which will have a crucial and effective impact on the safe operation of electric vehicles. The paper evaluates the performance of machine and deep learning algorithms using various metrics, including the R2 Score, median absolute error, mean square error, mean absolute error, and max error. All the simulation tests were performed using MATLAB 2023, Anaconda platform, and COMSOL Multiphysics.