Vehicle Simulation Research Articles

Rail-wheel friction quantification and its variability under lab and field trial conditions

Friction forces (often referred to as adhesion or traction forces) at the wheel/rail interface can vary dramatically due to changing environmental and contact conditions. The causes of this variance are partially documented, but it is not fully understood. Friction forces affect wheel and rail wear, traction energy usage, vehicle dynamics and safety through braking performance. A range of different portable railhead tribometers are used in the field to measure friction, but until recently have been limited in their performance, being unable to measure low friction situations or have made use of an unrealistic contact geometry. Recent developments have improved this situation but there is currently a lack of published field data which is required for validation, benchmarking and comparison between other studies and test rigs, as well as for input to multi-body dynamics simulations of railway vehicles. Friction studies in general are often undertaken for a specific period of time or under closely controlled conditions which makes it difficult to understand the true range of conditions occurring in the wheel/rail contact. In this paper an extensive dataset of railhead measurements is presented, using two types of measuring devices and three railhead conditions throughout a 4-week test period. Confidence in tribometer results was gained by comparing between established laboratory friction test rigs and methodologies. The results provide an insight into the friction variance and transient conditions that would occur on the railhead during operational use.

Optimized deep learning-based intrusion detection framework for vehicular network

The Internet’s evolution resulted in a massive amount of data. As a result, the internet has become more sophisticated and vulnerable to massive attacks. The attack detection system is a key feature for system security in modern networks. The IDS might be signature-based or detect anomalous behavior. Researchers recently created several detection algorithms for identifying network intrusions in vehicular network security, but they failed to detect intrusions effectively. For this reason, the optimal Deep Learning approach, namely Political Fractional Dingo Optimizer (PFDOX)-based Deep belief network is introduced for attack detection in network security for vehicles. The Internet of Vehicle simulation is done initially, and then the input data is passed into the pre-processing phase, which removes noise present in the data. Then, the feature extraction module receives the pre-processed data. The Deep Maxout Network is trained using the Fractional Dingo optimizer (FDOX)is utilized to detect normal and abnormal behavior. Fractional calculus and Dingo optimizer (DOX) are combined to create the proposed FDOX. Finally, intruder/attack types are classified using the Deep Belief Network, which is tuned using the PFDOX. The PFDOX is created by the assimilation of the DOX, Fractional Calculus, and Political Optimizer (PO). The experimental result shows that the designed PFDOX_DBN for attack type classification offers a better result based on f-measure, precision, and recall with the values of 0.924, 0.916, and 0.932, for the CIC-IDS2017 dataset.

Simulation of motor vehicle brake pressure monitoring system

The braking system is a system that is always evolving to improve safety and security in driving so as to minimize the number of accidents. The braking system aims to reduce speed and stop the vehicle. This study uses a simulation on the MATLAB Simulink. The purpose of this research is to find out how the braking system works to improve safety for riders. The principle of the hydraulic braking system is to work according to Pascal's law where fluid is used to continue the braking force of the brake pedal being pressed towards the master cylinder to produce brake pressure. This research begins by making equations in hydraulic braking followed by making a Simulink block braking system. Based on the research, data on the comparison of the number of vehicle loads on the braking system and comparison data on the variation of road conditions on the braking system show that if the vehicle load is heavier, the time needed to stop is longer, the braking pressure is greater, and the distance needed to stop is greater. If the road adhesion coefficient is smaller, the time needed to stop is longer and the distance needed to stop is greater.

Comparative Study on Coordinated Control of Path Tracking and Vehicle Stability for Autonomous Vehicles on Low-Friction Roads

This paper presents a comparative study on coordinated control of path tracking and vehicle stability for autonomous vehicles on low-friction roads. Generally, a path-tracking controller designed on high-friction roads cannot provide good performance under low-friction conditions. To cope with the problem, a coordinated control between path tracking and vehicle stability has been proposed to date. In this paper, three types of coordinated controllers are classified according to the controller structure. As an actuator, front-wheel steering, four-wheel steering, and four-wheel independent braking and driving are adopted. A common feature of these types of controllers is that front steering and yaw moment control are adopted as control inputs. To convert the yaw moment control into tire forces generated by combinations of multiple actuators, a control allocation method is applied. For each type, a controller is designed and simulated using vehicle simulation software. From the simulation results, a performance comparison among those controller types is carried out. Through comparison, it is shown that there are small differences among those types of controllers in terms of path tracking.

An electric road system or big batteries: Implications for UK road freight

An Electric Road System (ERS)—comprising a network of overhead cables to charge Heavy Goods Vehicles (HGVs) via a pantograph pick-up—is a cost-competitive solution to rapidly decarbonise the UK road freight sector. A major benefit over conventional battery electric HGVs is the reduction in battery capacities needed to fulfil logistics needs. In this study, we develop a detailed vehicle simulation model and use it to calculate the battery capacity requirements of real UK logistics journeys against a range of ERS network sizes and on-route static charging options. The results show that, averaged over all static charging scenarios, ERS reduces battery sizes by 41 %, 62 %, and 75 % for the ‘Light’ (2,750 km), ‘Medium’ (5,500 km) and ‘Heavy’ (8,500 km) ERS scenarios. Of the static charging scenarios, drop-off charging is shown to be more effective than rest stop charging at reducing battery sizes.

Simultaneous Localization and Mapping and Tag-Based Navigation for Unmanned Aerial Vehicles

This paper presents navigation techniques for an Unmanned Aerial Vehicle (UAV) in a virtual simulation of an indoor environment using Simultaneous Localization and Mapping (SLAM) and April Tag markers to reach a target destination. In many cases, UAVs can access locations that are inaccessible to people or regular vehicles in indoor environments, making them valuable for surveillance purposes. This study employs the Robot Operating System (ROS) to simulate SLAM techniques using LIDAR and GMapping packages for UAV navigation in two different environments. In the Tag-based simulation, the input topic for April Tag in ROS is camera images, and the calibration of position with a tag is done through assigning a message to each ID and its marker image. On the other hand, navigation in SLAM was achieved using a global and local planner algorithm. For localization, an Adaptive Monte-Carlo Localization (AMCL) technique has been used to identify factors contributing to inconsistent mapping results, such as heavy computational load, grid mapping accuracy, and inadequate UAV localization. Furthermore, this study analyzed the April Tag-based navigation algorithm, which showed satisfactory outcomes due to its lighter computing requirements. It can be ascertained that by using ROS packages, the simulation of SLAM and Tag-based UAV navigation inside a building can be achieved.

Dynamic mass estimation framework for autonomous vehicle system via bidirectional gated recurrent unit

AbstractThe precise estimation of vehicle mass is crucial for the optimal performance of electronic control systems in autonomous vehicles. However, the nonlinear nature of vehicle dynamics makes it a challenging task to estimate the mass accurately. In response to this concern, this paper proposes a dynamic vehicle mass estimation framework underpinned by a bidirectional gated recurrent unit, developed using deep neural networks. The bidirectional mechanism and gated recurrent unit network are adopted to elevate the precision of the neural network estimator. The dataset used for training and validation is collected from heavy‐duty vehicle simulations and real vehicle road tests. The average root mean square error, mean absolute percentage error, and mean absolute error evaluated over simulation tests are 92.66 kg, 0.93%, and 79.67 kg, respectively, and those in real vehicle data tests are 16.61 kg, 0.13%, and 16.61 kg, respectively. The outcomes manifest that the method put forth surpasses the contrasted approaches in relation to accuracy in the conducted experiments.

Ultracapacitor as selectable energy buffer in electric vehicle application

A method of ultracapacitor integration with a regenerative braking system for use in electric drive trains is presented in this paper. An ultracapacitor (UC) is an intermediary to store and provide energy on the DC bus in certain scenarios, such as during acceleration and regenerative braking. UC is used to avoid direct inrush loading on the battery, which the electric drive system may cause. The energy from UC is injected into the DC bus in compliment to the battery, for which a dedicated power source switch called the “Main Charge Controller” (MCC) is responsible. The charge discharge of UC is done through a bidirectional buck-boost converter (BDBBC). The use of UC to offload high energy demand from the battery and store energy recovered during regenerative braking helps improve the vehicle's life and per-charge range. The stored energy in UC can be obtained from the battery at the start. UC can also be charged at the time of charging. The design and the presented control are ideal for lightweight electric vehicles. The system uses a 39.9 kJ ultracapacitor, formed from market-available 50F 2.7 V units in a 52S configuration, storage, and a 6kWh battery. The simulated vehicle in battery only case provides a minimum of 154 km range in rough conditions, in best case scenario it provides a 200 km range. The proposed design provides an increased extended range compared to simple battery-based regenerative braking, in the case of this work the hybrid solution provides an addition of more than 30 km of additional range compared to a battery-only solution which is simulated for all the same conditions except without the addition of the UC and its control and handling system, resulting in a total range of 230 km. The speed control response achieved by the user is also very fast. Electric Vehicles have range limitations due to storage technology which is a high entry barrier for consumers looking for reliable vehicles without range anxiety, as storage technology progresses adoption will become easier. This work is an attempt at using hybrid storage and selective use of it, for improved range and sharper speed response. However, this work does not explore the aging of the storage components due to increased operational load and thermal stresses or application to alternate battery chemistries. This work is primarily focused on the vehicles that use lithium-based batteries.

Models, algorithms, and equilibrium conditions for the simulation of autonomous vehicles in exclusive and mixed traffic

This paper proposes models and algorithms for the estimation of traffic flows generated by the centralised management of autonomous vehicles under exclusive and mixed traffic conditions. It is assumed that the autonomous vehicle manager wants to obtain the system optimum that minimises the total travel time on the network, of autonomous vehicles in the case of exclusive traffic, and of all flows in the case of mixed traffic. In the case of mixed traffic, the drivers of the human-guided vehicles choose their routes from a user-optimal perspective; in this case, a fixed-point problem has to be solved, which represents a state of equilibrium between the flows of human-guided vehicles, the flows of autonomous vehicles, which follow paths aimed at the system optimum, and the link costs. The proposed models and algorithms are tested on a small network with a single origin-destination pair, and on the Sioux Falls network, with different levels of congestion and different percentages of autonomous vehicles. The results show the validity of the proposed methods and the impact of autonomous vehicles on network performance. The proposed static/equilibrium approach is necessary to evaluate transport planning, design or policy interventions that include the presence of autonomous vehicles in traffic flows.

DEASA studies and applications to space physics and muon tomography

The high energy cosmic rays entering the Earth’s atmosphere throw light upon many aspects of Astroparticle and Particle Physics. This work outlines investigative learning about these high energy primaries based on a mini array DEASA in Agra,India. DEASA (Dayalbagh Educational Air Shower Array) consists of eight plastic scintillators each with an area of 1 square meter. This array covers an area of 260 square meters and is the first array in the northern part of our country. A real-life application of these highly energetic particles has been to find the best material to protect the astronaut from them in form of galactic cosmic rays (GCR). Geant4 based hadronic binary model was used in simulation of phantom, vehicle, SEP (Solar Energetic Particles) and GCR shield. The SEP shielding material was fixed as water and GCR shield was varied among aluminum, Polystyrene and Polyethylene. The poly materials were found to be the best due to large amount of hydrogen (H) and low atomic number (Z). In this work the equivalent dose deposited in the phantom with Polystyrene material for GCR shield was calculated to be minimum (107 sievert) as compared to the other materials.In the second application, the high energy muons have been studied to image nuclear caskets through muon tomography.In this Monte Carlo based simulation, a dry cask container containing different number of the UO2 rods have been bombarded with definite energy muons to measure the muon scattering .The parameters computed in this work are energy loss,radiation length and scattering angle which can calibrate these containers for correct identification of nuclear wastage.

Possibilities of using commercial electronics to measure driver physiological functions in a vehicle simulator

The aim of this paper is to explore the potential of using inexpensive, commercial electronics such as a smartwatch or chest belt to measure human physiological functions while driving in vehicle simulator, both to improve safety and to use these devices in other experiments to facilitate their conduct and replace more complex laboratory equipment. In the first part of the paper, the signs of stress and cognitive load through physiological functions are researched, then the possibilities of measuring these signs are explored. This is followed by an experiment in which two commonly available devices are compared in terms of accuracy and usefulness for measurement during the experiment.

Dynamic Testing of In-Wheel Motor Based Electric Vehicle in Longitudinal Direction

This paper presents an investigation into the performance of in-wheel motor (IWM) based electric vehicles (IWM-EV) in the longitudinal direction. The design of IWM-EV is an innovation of the conventional go-kart vehicle with slightly modifications in steering, suspension, and braking system, which then makes use three-phase permanent magnet synchronous in-wheel motor (PMSM-IWM) at both of the rear axle wheels. An extension of that is a simulation of IWM-EV vehicle using a 5-degree-of-freedom vehicle longitudinal model that has been developed by incorporating PMSM-IWM as a drive wheel located at the rear axles. Using the simulation, vehicle dynamic control in the longitudinal direction-based Proportional-Integral-Derivative (PID) controller has also been strategized. As the intention to confirm the capability of the IWM-EV, experimental studies-based real IWM-EV hardware have been conducted. Three dynamic tests that generalized from SAE standard SAE J866-199908, namely acceleration performance at the level pavement (include acceleration tests and acceleration then braking tests) and road gradient tests at constant speeds of 10, 15 and 20 km/h, were used as the testing method. The performance areas evaluated were vehicle body speed, wheel speed, distance travel experienced by the vehicle, IWMs current, drive torque as well as the battery voltage capacity used by the vehicle. The finding indicate that the simulation results and experimental data are similar with less than 5 % error. The outcomes from this study will be considered in the design optimization of a torque vectoring control in the next research study.

A Novel Asynchronous Brain Signals-Based Driver-Vehicle Interface for Brain-Controlled Vehicles.

Directly applying brain signals to operate a mobile manned platform, such as a vehicle, may help people with neuromuscular disorders regain their driving ability. In this paper, we developed a novel electroencephalogram (EEG) signal-based driver-vehicle interface (DVI) for the continuous and asynchronous control of brain-controlled vehicles. The proposed DVI consists of the user interface, the command decoding algorithm, and the control model. The user interface is designed to present the control commands and induce the corresponding brain patterns. The command decoding algorithm is developed to decode the control command. The control model is built to convert the decoded commands to control signals. Offline experimental results show that the developed DVI can generate a motion control command with an accuracy of 83.59% and a detection time of about 2 s, while it has a recognition accuracy of 90.06% in idle states. A real-time brain-controlled simulated vehicle based on the DVI was developed and tested on a U-turn road. Experimental results show the feasibility of the DVI for continuously and asynchronously controlling a vehicle. This work not only advances the research on brain-controlled vehicles but also provides valuable insights into driver-vehicle interfaces, multimodal interaction, and intelligent vehicles.

Evaluation of Brain Rotational Injury Criteria (BrIC) in vehicle frontal crashes

Objective The objective of this study was to estimate strains in the human brain in regulatory, research, and due care frontal crashes by simulating those impacts. In addition, brain strain simulations were estimated for belted human volunteer tests and in impacts between two players in National Football League (NFL), some with no injury and some with mild Traumatic Brain Injuries (mTBI). Methods The brain strain responses were determined using version 5 of the Global Human Body Modeling Consortium (GHBMC) 50th percentile human brain model. One hundred and sixty simulations with the brain model were conducted using rotational velocities and accelerations of Anthropomorphic Test Devices (ATD’s) or those of human volunteers in sled or crash tests, as inputs to the model and strain related responses like Maximum Principal Strains (MPS) and Cumulative Strain Damage Measure (CSDM) in various regions of the brain were monitored. The simulated vehicle tests ranged from sled tests at 24 and 32 kph delta-V with three-point belts without airbags to full scale crash and sled tests at 56 kph and a series of Research Mobile Deformable Barrier (RMDB) tests described in Prasad et al. Results The severity of rotational input into the model as represented by BrIC, averaged between 0.5 and 1.2 for the various test conditions, and as high as 1.5 for an individual case. The MPS responses for the various test conditions averaged between 0.28 and 0.86 and as high as 1.3 in one test condition. The MPS responses in the brain for volunteers, low velocity sled, and NCAP tests were similar to those in the no-mTBI group in the NFL cases and consistent with real world accident data. The MPS responses of the brain in angular crash and sled tests were similar to those in the mTBI group. Conclusions The brain strain estimations do not indicate the likelihood of severe-to-fatal brain injuries in the crash environments studied in this paper. However, using the risk functions associated with BrIC, severe-to-fatal brain injuries (AIS4+) are predicted in several environments in which they are not observed or expected.

The Design and Simulation of Autonomous Agricultural Vehicle for Orchards

In recent years, agricultural producers have faced challenges due to the uncertainty of labor force access, growing demand for safe, accessible, and high-quality agricultural products, high competition with international producers, and the need to reduce their carbon footprint. To continue their competitive and profitable production, producers must invest in technology and increase efficiency. Autonomous agricultural vehicles are crucial for autonomous processes in orchards, increasing productivity, collecting data for decision-making, reducing operating costs, and carbon footprint. This study focuses on the design and simulation of an autonomous vehicle for orchards. The autonomous vehicle can map the orchards using data from odometry and light detection and ranging (LIDAR) sensors by utilizing Simultaneous Localization and Mapping (SLAM) algorithm, accurately determine its position using the Adaptive Monte Carlo Localization method, and avoid obstacles using the dynamic window approach algorithm. The autonomous vehicle is an original design for netted orchards where GPS cannot work properly and is fully autonomous, requiring no external GPS data. It is expected to provide higher efficiency by reducing environmental pollution, operating expenses, and labor force in practice. The success of the mapping and localization application depends on the update frequency of the position and the number of particles used for localization. A path-planning application was developed to reach the desired position from the autonomous agricultural vehicle's current position on the map. The Dijkstra Algorithm was used for path planning, with the Dynamic Window Approach allowing the robot to escape obstacle. The simulation studies achieved the lowest position error when the vehicle's position was updated at intervals of 2 cm, and a minimum of 500 and a maximum of 2000 particles were used. While the vehicle was moving on a straight obstacle row in the simulation environment, an average localization error of 2.1 cm was obtained. This error is convenient as it enables seamless navigation between tree rows without any collision.

General modeling and energy management optimization for the fuel cell electric tractor with mechanical shunt type

The fuel cell electric tractor has emerged as a new development direction in modern agriculture due to its energy-controllable and emission-reducing advantages. This paper focuses on improving the universality of basic research by studying the fuel cell electric tractor with mechanical shunt type (FCETMST). The bond graph model has the benefits of cross-physical system integration. To fill the gap in general modeling of electric tractors, an electromechanical hydraulic integrated machine model by utilizing the bond graph model, which includes energy system, drive system, lifting system and power take-off (PTO) system, is developed. The general power flow model of the electric tractor with mechanical shunt type is also derived. Lastly, the accuracy of the model is validated by analyzing the tractor’s traction characteristics. To further enhance energy efficiency, this paper proposes an energy management strategy (EMS) utilizing a multi-population genetic algorithm (MPGA) and compares it with a power following control strategy (PFCS) for a fuel cell/lithium battery composite energy system. The results base on the co-simulation by using AVL-Cruise and Matlab/Simulink to build a vehicle simulation model and EMS models, respectively, show that under ploughing and transportation conditions, compared with the traditional PFCS, using MPGA leads to reduce state of charge (SOC) fluctuations and the remaining SOC is 11.22 % and 6.1 % higher, respectively. The theoretical hydrogen fuel consumption is decreased by about 26.49 % and 36.21 %, respectively. This study can provide theoretical support for the modeling and energy management optimization of the electric tractor system.

Event-Triggered Underactuated Control for Networked Coupled PDE-ODE Systems With Singular Perturbations

An improved fuzzy event-triggered underactuated control strategy for networked coupled partial differential equation-ordinary differential equation (PDE-ODE) systems subject to singular perturbations is established. First, the networked coupled PDE-ODE systems are combined into an integrated system by representing the ODE and PDE states as a new state variable. Subsequently, a singular perturbed matrix is introduced to describe the multiple time-scale phenomenon of the system. In addition, an improved fuzzy controller is proposed to ensure the stability of the closed-loop coupled PDE-ODE systems based on boundary measurements. The novelties of the designed control strategy pertain to the following aspects: 1) The dynamic event-triggered mechanism is optimized; 2) an underactuated controller is proposed; and 3) the controller is constructed in a unified form. Finally, a hypersonic rocket vehicle's simulation is performed to demonstrate the method's effectiveness.

Energy, Exergy, and Emissions Analyses of Internal Combustion Engines and Battery Electric Vehicles for the Brazilian Energy Mix

Exergy is a thermodynamic concept that ponders the quality of energy. It evaluates the irreversibilities of a machine, demonstrating its capacity to perform work associated with energy conversion. This article focuses on directing public policies and vehicle development toward their most proper usage worldwide. In the urban mobility scenario, there is an obvious demand to decrease greenhouse gas (GHG) emissions. In addition, the internal combustion engine (ICE) experiences considerable energy losses through heat exchange through the radiator and exhaust flow gases, which are not considerable in battery electric vehicles (BEVs) since there are no exhaust gases subsequent to combustion, nor combustion itself. This work presents longitudinal dynamics simulations of passenger vehicles to understand the magnitude of exergy destruction in ICEVs and BEVs, considering the Brazilian and European Union electric energy mix. Overall, the method can be applied to any other country. The simulation and model parameters were configured to match production road vehicles commercialized in the Brazilian market based on different versions of the same model. Two vehicle dynamic duty cycles were used, one relating to urban usage and another to highway usage, resulting in an overall exergy efficiency of around 50–51% for BEVs considering the exergy destruction in power plants. In contrast, ICE has an average efficiency of 20% in the urban cycle and around 30% in the highway cycle. By comparing the overall equivalent CO2 emissions, it is possible to conclude that EVs in the European energy matrix produce more GHG than ICE vehicles running on ethanol in Brazil. Nevertheless, there are increasing uses of coal, natural gas, and oil thermal electric power plants, raising the question of how the transition may occur with a general increase in electrification since there is an increasing electric expenditure in all sectors of society, and the renewable energy plants may not meet all of the demand.

A Vehicle Crash Simulator Using Digital Twin Technology for Synthesizing Simulation and Graphical Models

Computer vehicle simulators are used to model real-world situations to overcome time and cost limitations. The vehicle simulators provide virtual scenarios for real-world driving. Although the existing simulators precisely observe movement on the basis of good-quality graphics, they focus on a few driving vehicles instead of accident simulation. In addition, it is difficult to represent vehicle collisions. We propose a vehicle crash simulator with simulation and animation components. The proposed simulator synthesizes and simulates models of vehicles and environments. The simulator animates corresponding to the simulation through the execution results. The simulation results validate that the proposed simulator provides collision and non-collision results according to the speed of two vehicles at an intersection.

A hybrid search mode-based differential evolution algorithm for auto design of the interval type-2 fuzzy logic system

This article proposed a hybrid search mode-based differential evolution (HSM-DE) algorithm to overcome the complex design of an interval type-2 fuzzy logic system (IT2FLS). First, transform complex design processes of the IT2FLS into optimization problems for our proposed evolutionary algorithm by decoding the whole system into design parameters regarded as search objectives. Then, the DE algorithm with a rapid convergence characteristic is adopted as a global search method for exploration according to a memetic algorithm (MA) structure. Considering the conventional DE algorithm is lack of escaping ability for the local optimal solution, a local search method combined with a simulated binary crossover (SBX) and polynomial mutation (PM) is utilized to enhance the exploitation behavior of our proposed method. In addition, simple constraints on the design parameters of the membership function are added to balance the searching freedom of the HSM-DE algorithm and the readability of the optimal IT2FLS. Finally, a simulation of a mobile two-wheel vehicle (MTWV) under a goal search task is performed to verify the effectiveness of the optimal IT2FLS auto designed by HSM-DE. All results demonstrate that the HSM-DE realizes auto design for the IT2FLS with high-performance control and outperforms the comparative methods. Moreover, four comparison methods are performed to validate the high-performance of the proposed HSM-DE on the search ability of the optimal solution.