Vehicle Simulation Research Articles

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.

Modeling and Simulation of Hybrid Electric Vehicles for Sustainable Transportation: Insights into Fuel Savings and Emissions Reduction

Most motor vehicles have historically utilized Internal Combustion Engines powered by a fossil fuel. Despite the technological advancements in fuel-efficient engines, further improvements are required to reduce the effect of fuel costs and global warming. This study models and simulates Hybrid Electric Vehicles (HEVs) to evaluate their potential for fuel cost savings and emissions reduction compared to traditional vehicles. Using MATLAB® version R2023a Update 5 (9.14.0.2337262) Simulink version 10.7 (R2023a) and the Advanced Vehicle Simulator (ADVISOR) version 2003-00-r0116, the research examines Series and Parallel HEV configurations. The simulation explores various battery configurations, engine capacities, and power unit models to analyze their impact on energy utilization. The data, collected from the simulations, show significant fuel savings and emissions reduction with HEVs. The Parallel HEV configuration consistently saves fuel with fewer battery modules, while the Series HEV configuration performs better but requires more modules to maintain the system’s State of Charge.

Driving Control Strategy and Specification Optimization for All-Wheel-Drive Electric Vehicle System with a Two-Speed Transmission

Equipping electric vehicles with a two-speed gearbox allows for achieving high torque and maximum speed through appropriate gear ratio adjustments. Additionally, tuning motor operating points to efficient zones, considering energy efficiency, significantly enhances the vehicle’s overall performance. This paper presents an AWD system configuration method, integrating a two-speed transmission to improve energy efficiency and driving performance through front and rear motor torque distribution and powertrain specification optimization. Based on vehicle simulations conducted using MATLAB/Simulink, a strategy for torque distribution between the front/rear axles was established using fuzzy logic, considering energy efficiency and driving stability. Furthermore, a multi-objective optimization was performed using a surrogate model trained through MATLAB parallel simulations. When the optimization results were applied to various vehicle specifications, it was observed that energy efficiency was improved, and acceleration performance was increased compared to a baseline vehicle without optimization.

Model predictive control for autonomous vehicle path tracking through optimized kinematics

Tracking performance and stability of path tracking is crucial for unmanned vehicles' navigational tasks. Researches on vehicle path tracking controllers primarily rely on dynamic models. In contrast, there are less designs and researches that focus on path tracking controller based on kinematic model. This scarcity may stem from the perceived inadequacy in the accuracy of vehicle kinematic models. This research introduces a novel method for modifying the traditional vehicle kinematic model by incorporating a front wheel steering angle modification function to enhance tracking performance of path tracking controllers based on kinematic models. In terms of control strategy selection, the study opted for nonlinear model predictive control with terminal cost. A controller which is founded on the modified model was used on a simulated vehicle on CarSim-Simulink platform to assess the tracking performance. The simulation was designed with a double-shift line reference trajectory and the initial speeds of the simulated vehicle are 5 m/s and 10 m/s, respectively. The results of the simulations indicate that employing a controller based on the modified model could reduce the peak tracking error by 76.45 % and 43.06 %, and the root mean square of the tracking error was reduced by 73.29 % and 36.06 %, respectively.

A Finite-Time Path Tracking Control Scheme for Unmanned Vehicles Based on Multidimensional Taylor Network and Adaptive Filtering

Aiming at the path tracking control problem of unmanned vehicles under the conditions of model uncertainty and measurement noise, a finite-time path tracking control scheme based on multidimensional Taylor network (MTN) is proposed. The MTN model is used to characterize the uncertainty of the unmanned vehicle model, and the improved back propagation (BP) algorithm is used as its learning algorithm; an adaptive MTN filter is designed to filter out the measurement noise, MTN is used as a filter, and the least mean square (LMS) algorithm is used as its learning algorithm; an MTN finite-time controller is designed to perform precise path tracking control on the unmanned vehicle, which can quickly and accurately track the reference path; according to the finite-time control theory, the convergence proof verifies the effectiveness of the proposed method through unmanned vehicle simulation experiments.

Holistic Sensitivity Analysis for Long-Term Energy Demand Prediction of Battery Electric Vehicles

Accurate and robust range estimation algorithms for battery electric vehicles have the potential to reduce range anxiety, increase the acceptance of lower-range vehicles, and improve the overall driving experience. However, developing such algorithms faces challenges due to the complexity of the driver-vehicle-environment system and the multitude of factors influencing a vehicle's energy demand. To address these challenges, this paper introduces a sensitivity analysis focused on driver- and environment-related factors, which are notably difficult to predict. Employing a global sensitivity analysis for factor prioritization, this study delineates and assesses the parameters and their value distributions using a validated vehicle simulation model. The co-simulation of a powertrain and an auxiliaries model enables the parameter-specific investigation of parameters related to the thermal system. The results are scenario-individual parameter rankings that show the importance of the considered factors in prediction algorithms and guide the strategy for the development of these algorithms. The acceleration behavior of the driver, often emphasized in literature, is shown to be of secondary importance to energy consumption. Moreover, factors such as air density and wind speed are identified as crucial in highway driving scenarios, whereas outside temperature and the probability of stopping at traffic lights are critical in urban settings. For validation purposes, the resulting rankings of the sensitivity study are validated by means of a convergence analysis.

A framework for the acoustic simulation of passing vehicles using variable length delay lines

The sound produced by vehicles driving on roadways constitutes one of the dominant noise sources in urban areas. The impact of traffic noise on human activities and the related investigation on modeling, assessment, and abatement strategies fueled the research on the simulation of the sound produced by individual passing vehicles. Simulators enable in fact to promote a perceptual assessment of the nature of traffic noise and of the impact of single road agents on the overall soundscape. In this work, we present TrafficSoundSim, an open-source framework for the acoustic simulation of vehicles transiting on a road. We first discuss the generation of the sound signal produced by a vehicle, represented as a combination of road/tire interaction noise and engine noise. We then introduce a propagation model based on the use of variable length delay lines, allowing to simulate acoustic propagation and Doppler effect. The proposed simulator incorporates the effect of air absorption and ground reflection, modeled via complex-valued reflection coefficients dependent on the road surface impedance, as well as a model of the directivity of sound sources representing the passing vehicles. The source signal generation and the propagation stages are decoupled, and all effects are implemented using finite impulse response filters. Moreover, no recorded data is required to run the simulation, making the framework flexible and independent on data availability. Finally, to validate the framework capability to accurately simulate passing vehicles, a comparison between synthetic and recorded pass-by events is presented. The validation shows that sounds generated with the proposed method achieve a good match with recorded events in terms of power spectral density and psychoacoustics metrics as well as a perceptually plausible result.

Comprehensive Evaluation of the Massively Parallel Direct Simulation Monte Carlo Kernel “Stochastic Parallel Rarefied-Gas Time-Accurate Analyzer” in Rarefied Hypersonic Flows—Part A: Fundamentals

The Direct Simulation Monte Carlo (DSMC) method, introduced by Graeme Bird over five decades ago, has become a crucial statistical particle-based technique for simulating low-density gas flows. Its widespread acceptance stems from rigorous validation against experimental data. This study focuses on four validation test cases known for their complex shock–boundary and shock–shock interactions: (a) a flat plate in hypersonic flow, (b) a Mach 20.2 flow over a 70-degree interplanetary probe, (c) a hypersonic flow around a flared cylinder, and (d) a hypersonic flow around a biconic. Part A of this paper covers the first two cases, while Part B will discuss the remaining cases. These scenarios have been extensively used by researchers to validate prominent parallel DSMC solvers, due to the challenging nature of the flow features involved. The validation requires meticulous selection of simulation parameters, including particle count, grid density, and time steps. This work evaluates the SPARTA (Stochastic Parallel Rarefied-gas Time-Accurate Analyzer) kernel’s accuracy against these test cases, highlighting its parallel processing capability via domain decomposition and MPI communication. This method promises substantial improvements in computational efficiency and accuracy for complex hypersonic vehicle simulations.

Virtual Analysis and Optimization of Fuel Consumption for Diesel-Powered Buses

In this study, fuel consumption test cycles and commonly used SORT cycles for buses were discussed. An exemplary bus model was extensively modeled in terms of power systems. In ad-dition to main components such as the engine, transmission, torque converter, and axle, the drags of engine-powered components such as alternators, engine cooling fan, and air conditioning compressor were integrated into the powertrain model to ensure that the calculated consumption comes as close as possible to the real-life values. The tire model, which determines the quality standard in vehicle simulation, was also discussed in detail. During the modeling of these components, necessary parameters were obtained through analyses and tests conducted on the sample vehicle using the CAN bus and added sensors. After the completion of the model, real-life tests were conducted to validate the virtual analysis results. Once the model was validated, virtual studies continued to reduce the vehicle's consumption. Particularly during these studies, reducing consumption and enhancing performance through automatic transmission optimization were emphasized. When virtual models validated with SORT simulations are optimized with route-specific virtual analysis, public vehicles will minimize environmental impacts by reducing carbon emissions and at the same time perform more efficiently.

AERODYNAMIC AND ROLLING RESISTANCES OF HEAVY DUTY VEHICLE. SIMULATION OF ENERGY CONSUMPTION

The main objective of the work was to develop a comprehensive model of energy consumption simulation of heavy duty vehicles using the VECTO simulation tool. The research issue was the impact of aerodynamic drag and rolling resistance on fuel consumption and emissions under various driving conditions described in four driving cycles: Urban Delivery, Regional Delivery, Urban, and Suburban. Each cycle differed in driving time, distance and average speed to represent different operational scenarios. The methodology involved defining vehicle parameters such as weight, aerodynamic coefficients and tyre rolling resistance. The main findings show that the impact of both aerodynamic drag and rolling resistance on fuel consumption can be efficiently modelled. It has been proven that the proposed modifications to aerodynamic drag and rolling resistance can reduce fuel consumption by more than 8%. The lowest fuel consumption was achieved in the Regional Delivery cycle, while the Urban cycle had the highest fuel consumption due to frequent vehicle stops. The results show that optimization of vehicle design and its performance can significantly improve energy efficiency and reduce emissions. A computational modelling tool such as VECTO can contribute to sustainable transport solutions and improve the efficiency of heavy duty vehicle.

Evaluation of the effect of binder type and layer thickness on the performnce of open graded friction courses

To improve the long-term durability and functionality of open graded friction courses (OGFC), the Florida Department of Transportation (FDOT) investigated the use of a highly modified asphalt binder and increased thickness using Accelerated Pavement Testing (APT). A total of six test sections with combinations of two modified binder types (PG 76–22 and PG 82–22) and three lift thicknesses of 0.75 (19.05 mm), 1.25 (31.75 mm) and 2 (50.8) inches were constructed at FDOT’s APT facility. Accelerated loading was performed using a Heavy Vehicle Simulator (HVS) to evaluate the relative rutting performance of the test sections. Supplementary field and laboratory tests to evaluate tensile strength, Cantabro loss, field permeability, surface characteristics, and asphalt binder properties were also conducted. Test results indicated that the use of PG 82–22 polymer modified asphalt (PMA) binder may be beneficial to improve long-term durability of the OGFC. Thicker layers of OGFC were found to have considerably lower durability. It is recommended that the use of a highly modified PMA asphalt binder in OGFC layers be considered when raveling and other durability issues are of concern.

A randomized algorithm based approach to fault detection for uncertain systems subject to deception attack

This paper presents a fault detection strategy for discrete uncertain systems subject to deception attack based on the probabilistic framework. Probabilistic parameter models are utilized for characterizing model uncertainties, multiplicative faults and additive faults. An observer-based fault detection filter is constructed to generate fault information under deception attack. A randomized algorithm based approach is developed to determine the observer gain and threshold for attaining a trade-off between the false alarm rate (FAR) and fault detection rate (FDR). Simulation of an unmanned surface vehicle (USV) system and comparison against existing work are performed to illustrate the effectiveness of the proposed scheme.

Integrated Dynamic Modeling and Simulation of Wheeled Vehicle with Outer-Rotor In-Wheel Motors and Key Units

The purpose of this paper is to accurately establish a model of a special vehicle driven by in-wheel motors (IWMs) and investigate its dynamic characteristics. This study proposes a novel integrated vehicle modeling strategy, focusing on the IWM and other key subsystems. Based on this strategy, the vehicle model is developed and simulated in ADAMS. The natural frequencies of the vehicle and transmission characteristics of key components are compared with MATLAB simulation results to validate the accuracy of the model. In the time-domain analysis, the vehicle system’s time-domain characteristics are obtained by using random road spectra of different road grades as excitations. Our simulation results demonstrate that vibration input can be reduced by between 97% and 99% after multi-stage vibration reduction. This work will provide relevant parameters for the design of special vehicles driven by IWMs.

Research regarding the simulation of the autonomy for the electric vehicles in the case of the WLTC test cycle

Abstract In current mainstream research, energy consumption and autonomy are just a few of the the major objectives anticipated for electric road vehicles. The production of electric vehicles is continuously expanding, offering to the market a large variety of constructive variants in order to satisfy the requirements for their exploitation. Also, in the current energy context, it is expected from the electric car manufacturers, to deliver high quality models which can be characterized as the actual alternatives to actual existing solutions equipped with an internal combustion engine. Under these circumstances, the continuous research it is a high priority for everyone electric vehicle producers, where simulation on the computer is basically not absent as a go-between step prior to the validation of the final draft proposals. According to these assumptions, in the first part of the article you will find the AMESim simulation platform points, where it describes the model made with it and the simulated test cycle. In the second part of the article, as a consequence of the simulation of the experimental electric vehicle, are highlighted some aspects of the autonomy behaviour in the case of simulation on the World-wide harmonized Light duty Test Cycle (WLTC), for all four speed zones specific to it.

Advances in terminal management: simulation of vehicle traffic in container terminals

AbstractControlling and managing traffic flows on internal roads in container terminals are crucial in achieving expected productivity levels and reducing negative externalities caused by congestion inside and outside the terminal areas. This paper proposes a simulation approach which terminal operators can use as a decision-support tool to assess the effects of their management strategies and improve terminal performance, resilience, and sustainability. A microscopic traffic simulation approach models key operations of a typical container terminal affecting road traffic flows. In particular, to estimate quantitative indicators, an import truck process is reproduced, considering the overlapping of the external truck and internal trailer flows. To measure environmental impacts, the model is extended with an instantaneous emissions model linked directly to the step-by-step traffic data. The proposed method is tested on a sector of the PSA Genova Pra’, the main Italian container gateway terminal. Performance indicators related to the terminal’s efficiency and sustainability are estimated, to compare alternative scenarios considering possible operational configurations and disturbance events, such as the closure of a part of the yard. By focusing on the interactions between vehicle flows and terminal equipment operations, this approach offers a new perspective on terminal operations, oriented both towards container terminal operators and stakeholders, such as road hauliers.

Design and Assessment of Fighter Pilot Assistance Systems for Air-to-Air Refuelling with Probe-and-Drogue-Equipment

Air-to-air refuelling (AAR) with a probe-and-drogue system is a highly demanding process for fighter pilots. In the frame of the project “Future Air-to-Air Refuelling” (F(AI)2R) the German Aerospace Center (DLR) is designing concepts for assistance systems to support fighter pilots during this manoeuvre. The AAR process was examined using a hierarchical task analysis (HTA) based on a literature review and semi-structured interviews with Eurofighter-, Tornado- and test pilots. “Situation Awareness Requirements” (SAR), which represent relevant parameters for the situation awareness of the fighter pilot were derived from the HTA and rated by pilots according to their relevance. The collected data was then used to design different concepts of pilot assistance systems using a morphological box and a decision matrix. The concepts were visualised as storyboards, assessed by test pilots of the “Wehrtechnische Dienststelle 61” (WTD 61) and optimized based on their feedback. In this paper, the used methodology as well as the results of the HTA and SAR analysis are presented. Besides that, storyboards for pilot assistance systems displaying the overtake speed “German Patent No. 10.2022.110.505” and the “Contact Assistance, Status and Warning System” (CASWS) “German Patent No. 10.2022.123.653” are described. Additionally, the planned implementation using a HoloLens 2 is explained and the strategy for the subsequent simulator study on the DLR fighter aircraft simulator “Military AiR vehicle Simulator – Fast Integration Testbed” (MARS-FIT) is described.

Unveiling the impact of date-specific analytics on vehicle fuel consumption and emissions: A case study of Shiraz city

Understanding the factors affecting internal combustion engine performance is crucial for improving emissions and fuel efficiency in real traffic. This paper investigates the influence of date-specific factors, such as working days and holidays on fuel consumption and emissions of a representative internal combustion engine in the context of Shiraz city. The data was collected by measuring the speed of vehicle on a specific route during peak traffic times on both working days and holidays. The determined route was calculated by a qualitative and quantitative method. K-means clustering and principal component analysis are employed to design representative driving cycles, then combining micro-trips and smoothing them to develop driving cycles. Characteristics of both driving cycles, emissions such as HC, NOx, CO, and fuel consumption were specified under a simulated vehicle model of Peugeot 206 and placed through both real driving cycles by using advanced vehicle simulation software. It has been observed that variations in fuel consumption and emissions between holidays and working days can be attributed to distinct driving patterns and characteristic parameters, such as acceleration, speed, and driving time. Contrary to common assumptions, the study found that fuel consumption was approximately 8 % higher on holidays compared to working days, primarily due to increased driving time and higher average speeds. Moreover, higher acceleration and speed on holidays led to a significant increase in CO emissions (about 36 %) and NOx emissions (about 4 %) compared to working days. However, HC emissions was found to be 27.03 % higher on working days according to lower speed. This study helps in reducing fuel consumption and emissions by uncovering internal combustion engine factors, optimizing driving patterns, and promoting sustainable transport on working days and holidays.

Aerodynamic simulation on roof-mounted intelligent awareness system of intelligent vehicle

With the rapid development of intelligent driving technology, it is urgent to conduct research on the distribution and motion characteristics of snow particles in the area of the intelligent vehicle awareness system under harsh snow and wind conditions. In this paper, the Unsteady Reynolds-Averaged Navier-Stokes (URANS) was first used to conduct 1:12 scaled aerodynamic simulation of the intelligent vehicle. It is found that the error of aerodynamic drag coefficient between simulation and test is 5.6%, indicating that the current numerical simulation model can predict the aerodynamic characteristics of intelligent vehicle accurately. Based on this, URANS was used coupled with Lagrangian Multiphase Model (LMP) to investigate the snow accumulation near the roof-mounted intelligent awareness system under different vehicle speeds and snowfall intensities. The study found that with the increase of snowfall intensity from moderate to heavy and vehicle speed at 40 km/h, the incidence of snow particles on various components significantly increases, and the number of adhered particles also increased accordingly. The particle adhesion rate of the awareness system has decreased by 1.24%, while the number of adhered snow particles increased significantly at the back of the roof-mounted intelligent awareness system and the wake window. Under moderate snow condition, as the vehicle speed increased from 40 to 60 km/h, the number of incident particles and adhered particles in the awareness system have significantly decreased by 24,592 and 317,019, respectively, but the particle adhesion rate has increased by a significant 2.88%. Compared to the same snowfall intensity but low speed, the range of particle distribution is significantly reduced and more scattered.

Enhancing mutual understanding of e-scooter user’s perspective in overtaking maneuver through replaying own driving trajectory

The global adoption of e-scooters as a convenient mode of micro-mobility transportation is on the rise, offering a flexible solution for covering first- and last-mile journeys. However, this surge in usage brings challenges, particularly concerning road safety, as e-scooter riders often share road space with other vehicles, heightening the risk of serious accidents. While numerous studies have explored safe overtaking behaviors and safety perceptions from drivers’ viewpoints, limited attention has been given to understanding the varying safety perceptions of both drivers and e-scooter riders, particularly after riding an e-scooter and being overtaken by their own vehicles. This research aims to bridge this gap by examining variations in safety perceptions and assessing behavioral changes before and after experiencing overtaking scenarios. Specifically, the study focuses on scenarios where an e-scooter rider experiences being overtaken by a vehicle they had previously driven. A Unity-based sequential simulation process is employed to replay scenarios obtained from a vehicle simulator during an e-scooter experiment involving the same participant without their awareness. This innovative approach allows e-scooter rider participants to relive their own prior vehicle overtaking maneuvers while riding an e-scooter. The findings reveal that most participants (64%) felt less safe as e-scooter riders, influenced by factors like relative speed and acceleration of overtaking vehicles. After experiencing being overtaken by their own pre-driven vehicles, a noteworthy positive correlation emerged between safety perception and lateral distance, indicating that greater distance is derived from a better understanding of e-scooter safety. The study demonstrates the effectiveness of the sequential simulation strategy in fostering safe driving behavior and raising road safety awareness. Experiencing overtaking behaviors firsthand as an e-scooter rider, previously behind the wheel of the overtaking vehicle, encourages a heightened awareness of road safety. These findings have significant implications for road safety authorities, suggesting the potential application of this approach in driver education programs. By incorporating such interventions tailored to improve the safety of vulnerable road users, authorities can take proactive steps towards mitigating risks associated with micro-mobility transportation.

Real-Time Estimation of Origin–Destination Matrices Using a Deep Neural Network for Digital Twins

The digital twin, a real-time replica of physical systems, has garnered attention as a promising tool to strategize and evaluate solutions for complex real-world issues. However, developing digital twins in the field of transportation faces significant challenges related to the real-time estimation of dynamic origin–destination (OD) matrices constrained by computation time. To bridge this gap, microscopic traffic simulations with real-time synchronization are being researched. Nonetheless, the computational issue persists, emphasizing the need for more efficient OD estimation methods. In this regard, our objective is to reduce computation time in simulation-based methods by developing a data-driven metamodel using a deep neural network. The proposed model serves to map the correlation between the OD matrix and detector data. This model simplifies the computational process using hidden layers, rather than calculating complex interactions between vehicles in the traffic simulation. Compared to conventional methods, we evaluate the capability to estimate a reasonable OD matrix within a restricted time and validate our approach using detector data from Daejeon City, South Korea. The findings indicate that by combining our data-driven metamodel with the simultaneous perturbation stochastic approximation, it becomes feasible to estimate a reasonable OD matrix within a stipulated time frame, compared to the conventional method. Given a 1-min time frame, the proposed method outperforms the conventional simulation-based method by improving the calibration performance of traffic flow by 44.5 percentage points. This paper proposes a practical and versatile approach for real-time OD estimation, laying the foundation for extending microscopic traffic simulation into the digital twin.