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To describe the microtrajectory change behavior of two-wheeled vehicles when encountering nonrigid obstacles, this paper proposes an obstacle avoidance model of two-wheeled vehicles for nonrigid obstacles based on the improved social force method. In the model, the obstacle avoidance characteristics of two-wheeled vehicles for nonrigid obstacles and the safety operation requirements of two-wheeled vehicles are considered. The calculation methods of the minimum boundary distance of avoidance and the boundary avoidance force range edge function are proposed based on the ultimate turning angle and boundary avoidance characteristics, respectively. The speed control and direction control behavior of the rider to the vehicle is abstracted as the form of social force, and the two-wheeled vehicle/rider individual is subjected to the avoidance force and correction force perpendicular to the direction of the longitudinal axis of the vehicle body. The centrifugal trajectory obstacle avoidance motion obeying the Newtonian mechanics is studied based on the characteristics of the vehicle body turning angle change. Different traffic environments are constructed by MATLAB software and simulated according to the model running logic, and sensitivity analysis is performed based on the actual data collected in the survey. The simulation results show that, under the same conditions, compared with μ i = 50N, when μ i = 200N, the X-axis displacement of the simulated vehicle in the avoidance process is shortened by 26.31%, and the body deflection angle increases by 50.75% at the end moment of the avoidance process. Compared with electric motorcycles, the displacement of the X-axis in the avoidance process of the electric bicycle is shortened by 16.92%, and the body deflection angle at the end moment of the avoidance process is increased by 13.68%. The simulation results show that the model can well describe the trajectory change behavior characteristics of two-wheeled vehicles/riders encountering nonrigid obstacles in real situations, and the obstacle avoidance model of two-wheeled vehicles proposed in this paper is considered to be traffic justified. [ABSTRACT FROM AUTHOR]

The high carbon emissions of vehicles traveling on horizontal curve road sections cannot be ignored. Facing the difficulty of accurately quantifying the carbon emission of driving on horizontal curves and the unknown causes of high carbon emission, this study proposes to construct a carbon emission prediction model applicable to road sections with different planar geometries. The direct and indirect effects of horizontal curve alignment on vehicle carbon emissions are represented in the model interms of travel stabilization and speed changes, respectively. A lateral force coefficient parameter was introduced into the model to integrate the carbon emission quantification problem for different planar geometry sections. Meanwhile, field tests were conducted to assess the reliability of the model and the research findings. The model reveals that the geometric parameters of horizontal curves that affect carbon emissions are the radius of the circular curve, the superelevation, and the length of the gentle curve. The root causes of high carbon emissions on horizontal curve road sections are curve driving resistance and speed fluctuations. Under the free-flow driving condition of the highway, the maximum curve radius affecting the carbon emissions of passenger cars and trucks is 400 m and 550 m, respectively. The research results can realize the carbon emission quantification of vehicles on the road sections with different plane geometries. Also, it is helpful to control the high carbon emission of vehicles traveling on horizontal curve road sections. [ABSTRACT FROM AUTHOR]

This paper proposed a novel direct yaw moment control (DYC) system to enhance vehicle stability and handling performance in various driving conditions and overcome the chattering problem of traditional sliding mode control. Accordingly, a DYC strategy is developed for eight-wheeled DDEVs by utilizing a super-twisting sliding mode (STSM) algorithm. Initially, a three-degrees-of-freedom model, nonlinear tire model, and motor model are established for vehicles. Subsequently, the reference yaw rate is obtained based on the reference model of the vehicle to serve as a control target. The DYC strategy is then established using the error between the actual yaw rate and the reference yaw rate as the input. Moreover, a traditional sliding mode (SM) controller is developed to enhance vehicle stability. A second-order SM controller is designed by incorporating a STSM control algorithm to address the chattering problem associated with traditional SM controllers. The algorithm adaptively adjusts the sliding surface and controls the gains based on the dynamic state of the vehicle. The effectiveness of the proposed control strategy is validated via hardware-in-the-loop simulations. [ABSTRACT FROM AUTHOR]

The topology optimization of crash structures in automotive and aeronautical applications is challenging. Purely mathematical methods struggle due to the complexity of determining the sensitivities of the relevant objective functions and restrictions according to the design variables. For this reason, the Graph- and Heuristic-based Topology optimization (GHT) was developed, which controls the optimization process with rules derived from expert knowledge. In order to extend the collected expert rules, the use of reinforcement learning (RL) agents for deriving a new optimization rule is proposed in this paper. This heuristic is designed in such a way that it can be applied to many different models and load cases. An environment is introduced in which agents interact with a randomized graph to improve cells of the graph by inserting edges. The graph is derived from a structural frame model. Cells represent localized parts of the graph and delineate the areas where agents can insert edges. A newly developed shape preservation metric is presented to evaluate the performance of topology changes made by agents. This metric evaluates how much a cell has deformed by comparing its shape in the deformed and undeformed state. The training process of the agents is described and their performance is evaluated in the training environment. It is shown how the agents and the environment can be integrated as a new heuristic into the GHT. An optimization of the frame model and a vehicle rocker model with the enhanced GHT is carried out to assess its performance in practical optimizations. [ABSTRACT FROM AUTHOR]

Vehicle detection is crucial for traffic surveillance and assisted driving. To overcome the loss of efficiency, accuracy, and stability in low-light conditions, we propose a lightweight "You Only Look Once" (YOLO) detection model. A polarized self-attention-enhanced aggregation feature pyramid network is used to improve feature extraction and fusion in low-light scenarios, and enhanced "Swift" spatial pyramid pooling is used to reduce model parameters and enhance real-time nighttime detection. To address imbalanced low-light samples, we integrate an anchor mechanism with a focal loss to improve network stability and accuracy. Ablation experiments show the superior accuracy and real-time performance of our Light-YOLO model. Compared with EfficientNetv2-YOLOv5, Light-YOLO boosts mAP@0.5 and mAP@0.5:0.95 by 4.03 and 2.36%, respectively, cuts parameters by 44.37%, and increases recognition speed by 20.42%. Light-YOLO competes effectively with advanced lightweight networks and offers a solution for efficient nighttime vehicle-detection. [ABSTRACT FROM AUTHOR]

The large amounts of greenhouse gases generated in the logistics and transportation industry worsen global climate deterioration, jeopardizing the human living environment. A multi-objective nonlinear planning model for vehicle routing problems (VRPs) was established by considering VRPs with carbon emission constraints to realize the shortest vehicle mileage and the minimum carbon emission. An improved simulated annealing algorithm was proposed to solve the model. The farthest insertion heuristic method, combined with a relocation operator, was applied in the reinsertion operation to determine the preferred insertion back to the customer point and the optimal insertion point. The customer point with the largest minimum target increment was removed from the removed customer points and inserted into the optimal insertion point until all of them were inserted. Results demonstrate that the proposed algorithm outperforms the common genetic algorithms and forbidden search algorithms in similar studies with strong global optimization search capability. The improved algorithm could solve low-carbon VRP with high sensitivity and effectiveness. [ABSTRACT FROM AUTHOR]

This paper presents a new type of monorail rapid transit vehicle, which can operate in formation. In order to optimize the dynamic performance of the formation vehicles when passing through the small radius curve, this paper first analyzes the structure of the new monorail rapid transit vehicle, and establishes the vehicle dynamic model and multibody dynamics simulation model. Then the improved artificial potential field algorithm is used to build the formation vehicle operation controller. Finally, the dynamic performance evaluation index of the new monorail rapid transit formation vehicle is formulated, and the formation vehicle passing through the small radius curve scene is simulated. The simulation results show that the controller of the formation vehicle with dynamic optimization control can effectively optimize the load transfer coefficient of running wheels, unbalanced centrifugal acceleration and roll angle of vehicle body when passing through the curve. Through this control method, the dynamic performance of formation vehicles passing through small radius curve lines can be optimized. [ABSTRACT FROM AUTHOR]

At work, three tasks were presented: road lane detection and trajectory estimation, environment mapping, and the application of a neural network. All these tasks are based on the results of the lane detection method. The presented lane detection method stands out due to the execution of an interpolation transformation for all previously detected edge points. This transformation transfers these points to a "bird's-eye" coordinate system and distributes them on a grid. Road lanes are identified by a lane feature filter based on the analysis of the distances between unique points. This allows lane views to be obtained in a coordinate system while preserving the distance condition. The road environment map is constructed from the obtained images using a probabilistic algorithm called Distributed Particle-SLAM (DP-SLAM). Based on the map result, a method for representing characteristic points describing the path of road lanes in each incoming camera image has been developed. These points are then used for training the neural network. The neural network solves a regression task for the coordinates of the points on the road lanes, enabling the identification of coefficients for parabolic fitting. Validation has been performed. [ABSTRACT FROM AUTHOR]

One of the main problems of modern big city is the global crisis of normal urban environment operation as a result of structural growing of automobilization level, oversaturation of road-street network by the transport streams. Buses of the especially large size class occupy, at the least, 20% in the structure of a modern bus park. The buses of especially large size classes, with the aim of necessary maneuverability, as a rule are made articulated. One of the ways of improving the maneuverability of articulated buses is the usage of guided towed sections. Solving the mentioned problem usually needs the mathematic models for predicting the vehicle behavior. The aim of introducing the trailer direction is to increase the overall traffic lane of an articulated bus that ensures a necessary maneuverability. The aim is gained by a choice of a rational transmitting ratio of trailer axle gear and necessary value of braking moment on one of its wheels. The solving of the problem concerning the stability of an articulated bus with a guided trailer axle was made by the way of analyzing the linear approaching of stationary vehicle movement. An influence of the construction parameters of the bus and the trailer, a transmitting ratio of control gear and a value of braking moment on the parameters of bus maneuverability were analyzed. It was proved that for an articulated bus, the normal maneuverability parameters can be ensured at the trailer direction by a way of braking of one of the wheels. [ABSTRACT FROM AUTHOR]

Introduction/purpose: The study of vehicle suspension is a challenge for researchers in the field of vehicles regarding the impact of the suspension system on vehicle performances such as ride comfort, road holding, and working space. This paper presents the simulation of the Land Rover Defender 110 vehicle in the roll plane (half vehicle) in Simulink/MATLAB. The obtained results were compared with the results obtained in the ADAMS/CAR software package of the Land Rover Defender 110 simulation model previously experimentally validated. The Defender 110 vehicle has a dependent suspension system in both axles and a passive suspension type with four degrees of freedom (4 DOF). Methods: The equations of the system can be solved mathematically with a scheme in Simulink/MATLAB while half-vehicle modeling has been done in ADAMS/CAR. Results: The comparison of the vehicle characteristics obtained by the two simulation methods was done for three different scenarios, and it was noticed that there is a good correlation between them. Conclusion: It was concluded that the Defender 110 vehicle simulation model in Simulink/MATLAB is validated. The validated model can be used to perform suspension system optimization in future work [ABSTRACT FROM AUTHOR]

Finding possible solutions where there are multiple conflicting objectives to be simultaneously satisfied is a challenging situation. Multi-objective optimisation of a rear spoiler on a generic road vehicle model is carried out by using adjoint-based optimisation coupled with Computational Fluid Dynamics. The study aims to reduce the vehicle drag and increase vehicle downforce simultaneously by optimising the shape of the spoiler, by allowing the deformation to achieve the most optimised shape assuming no manufacturing constraint. The OpenFOAM software was used for the solver. A strategy for multi-objective optimisation was proposed by assigning appropriate objective function weight, leading to some possible solutions and Pareto front of the proposed design family. Five optimisation solutions of the non-dominated solution Pareto front resulting from the spoiler shape optimisation are presented, explaining the trade-off between conflicting drag and downforce objectives on the vehicle model. The baseline geometry of the simulation is in good agreement with the experimental measurement. The analysis of the shape changes in the proposed optimisation is deeply investigated in terms of the optimised geometry deformation, velocity contour comparison, recirculating region on the base, pressure coefficient comparison and stream-wise velocity component at the slant region of the model. The adjoint-based optimisation method in the presence study can handle multiple objective optimisations and generate possible optimised spoiler shapes to reduce drag and increase downforce. Free deformation of the shape yields in the unique shapes of the spoiler, enabling to manipulate of the base flow at the rear of the vehicle model. [ABSTRACT FROM AUTHOR]

Advanced driving algorithms, control strategies, and their optimization in self-driving vehicles in various scenarios are hotspots in current research; 4WS-4WD (four-wheel steering and four-wheel drive) is another hotspot in the study of new concept models; and the nonlinear dynamic characteristics of self-driving vehicles (AVs) are prominent in the fast cornering mode, which leads to a significant reduction in the accuracy and stability of trajectory tracking. Based on these research backgrounds, this paper proposes a control strategy optimization idea based on the 4WS4WD vehicle and its optimization model. The main content includes the establishment of a 3D vehicle model that takes into account vehicle load transfer and position change, and the establishment of a hierarchical control strategy based on the optimized NMPC and 4WS4WD models. The controller consists of two parts: an upper tracking controller based on the new vehicle model and NMPC, and a lower decoupled controller. The tracking control effect of the algorithmic control strategy based on the model and controller is validated in the high-speed serpentine motion mode and double-shift linear motion mode on the joint simulation platform of Car Sim and Simulink. [ABSTRACT FROM AUTHOR]

The trend in the global automotive industry is moving towards electric vehicles that do not emit exhaust gases and use eco-friendly fuel. Electric vehicles are more eco-friendly compared to internal combustion engine vehicles, as they emit less carbon dioxide and pollutants. Research and development are actively underway to produce new electric vehicle models in the rapidly growing electric car market. In this study, a 2-speed transmission for electric vehicles, applicable to 300 Nm-class electric cars, has been developed. The 2-speed transmission structure enables efficient energy use and utilizes a planetary gear set and wet multi-plate clutch, which are effective in the power transmission process. The 2-speed transmission developed through the research results of this paper has a compact structure optimized for electric vehicles. The design feasibility of the transmission was verified through performance tests of the prototype, contributing to fuel efficiency improvement and environmental enhancement. [ABSTRACT FROM AUTHOR]

The present work deals with the design of a traction system for a hybrid vehicle using a PMSM-type motor, including an energy regeneration stage, using a Neutral Point Piloted (NPP) inverter. Optimal operation allows the motor to be supplied in the traction stage with the correct energy in such a way that it operates properly; on the other hand, during braking, it facilitates the transfer of energy in regenerative mode to the DC bus, storing the greatest amount of energy in the storage elements, which, in this case, refers to a battery bank. It also includes a DC-DC bidirectional boost interleaved converter to regulate the DC voltage levels both in traction and in braking. Its fundamental characteristic is that with a reduced number of switching devices, it allows for the reduction in DC voltage of the DC bus with adequate characteristics, constant and stable, regardless of abrupt changes in the output voltage reference value. Also, other features include oscillation control, that is, reducing or increasing oscillations according to operating conditions. Its transient operation is excellent, since the settling time is considerably reduced, which implies that the voltage of the DC bus does not cause mishaps in the operation of the motor. In its operation as a boost converter, in the event of any voltage value of the DC bus, the converter raises the voltage value according to the reference conditions. Similarly, control allows the voltage to be held at a stable value regardless of changes in the set point. This work presents a novel energy management scheme for permanent magnet electric drive based on a Model Predictive Control strategy, thus contributing to the effective energy management of a standard hybrid electric vehicle driving cycle. Results obtained using a real-time Hardware-in-the-Loop (HIL) platform showed the effectiveness of Model Predictive Control in dealing with power flow management while ensuring control of electric drive in all the required speed–torque profiles. [ABSTRACT FROM AUTHOR]

How to improve the driving performance of the vehicle while carrying out path tracking control has become a hot issue in current research. In this paper, an MPC (Model predictive control) path tracking control algorithm incorporating differential braking control is proposed. By establishing a vehicle dynamics model of a semi-trailer train, the model predictive control theory is adopted for path tracking. Then, the vehicle dynamics model, considering the additional yaw moment, is established to design the differential braking control strategy. Under low-speed working conditions, the PID (Proportional Integral Derivative) algorithm is used to solve the additional yaw moment with the yaw rate of the tractor traveling alone as the desired value. Under high-speed working conditions, the Fuzzy PID algorithm is used to solve the additional yaw moment with the control objective of reducing the articulation angle. Simulation models are built using MATLAB/Simulink, and TruckSim for numerical experimental validation. The numerical experimental results show that the differential braking control method proposed in this paper can improve the maneuverability of vehicles driving in low-speed conditions and the stability of vehicles driving in high-speed conditions without decreasing the precision of path tracking control. [ABSTRACT FROM AUTHOR]

The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. The data obtained from the simulation of target algorithms and vehicle models cannot be reasonably estimated. This problem affects subsequent vehicle improvement and parameter calibration. The authors relied on the simulation results of the AEB algorithm. We selected the BP Neural Network as the basis and improved it with a genetic algorithm optimized via a roulette algorithm. The regression evaluation indicators of the prediction results show that the GA-BP neural network has better prediction accuracy and generalization ability than the original BP neural network and other optimized BP neural networks. This GA-BP neural network also fills the Gap in Evaluation and Prediction Systems. [ABSTRACT FROM AUTHOR]

Amidst the rising prevalence of electric vehicles (EV) and the corresponding need for adequate charging infrastructures, this study presents an innovative application of deep learning models to analyze urban traffic. Utilizing the YOLO v3 model, our research focuses on developing a prototype for counting vehicles at an urban roundabout connecting three main roads, providing essential data for planning EV charging infrastructures. This work involves adapting and optimizing the YOLO v3 model to accurately identify and count vehicles passing through the roundabout. The aim is to measure traffic volume and identify circulation trends, which are crucial for understanding the spatial and temporal distribution of vehicles. The collected data not only estimate the number of EVs likely to traverse this area but also predict the needs for charging infrastructure in and around this specific roundabout. Our prototype demonstrated high accuracy in vehicle counting, allowing detailed analysis of traffic patterns at different times of the day. This analysis reveals significant variations in traffic flows, providing valuable insights into the most suitable times and locations for installing EV charging stations. While this study focuses on a single roundabout, it establishes a precedent for using advanced deep learning techniques in assessing EV charging infrastructure needs in broader urban contexts. The results suggest that such methods can be extended to other urban areas for more comprehensive and effective planning of EV charging infrastructures, thus contributing to more sustainable and intelligent urban mobility management. [ABSTRACT FROM AUTHOR]

This paper deals with the integrated control of trajectory tracking and yaw stability for autonomous vehicles. Firstly, a nonlinear vehicle dynamics model is established. The MPC algorithm was used to determine the best front wheel angle. The PID control algorithm is used to ensure the accuracy of longitudinal speed tracking. The sliding mode control algorithm is used to generate additional yaw moment and optimize the distribution of longitudinal tire force. In order to ensure the most effective distribution of the driving torque of the four wheels of the vehicle, the PID algorithm is used to track and manage the longitudinal slip rate of each tire on the bottom layer. Simulation tools such as MATLAB/Simulink and Carsim are used to verify the effectiveness of the multi-closed-loop integrated control technology. Compared with the general control strategy (no slip rate control), the trajectory error of this control algorithm is reduced by 55.6%, which indicates that it has advantages in obtaining a high tracking accuracy and ensuring the stability of autonomous vehicles. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle models while addressing data privacy and trust challenges. We deploy resilient countermeasures, incentivized mechanisms, and a secure gradient distribution to combat single-point failure verification attacks. Additionally, we integrate the Byzantine fault-tolerant algorithm (BFT) into the node verification component of the delegated proof of stake (DPoS) to minimize verification delays. We validate our approach through experiments on the MNIST, SVHN, and CIFAR-10 datasets, showing convergence rates and prediction accuracy comparable to traditional OppCL approaches. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles are certainly one of the key solutions for improving fuel efficiency and reducing emissions, especially in terms of special vehicles and with the use of CO2-neutral fuels. Determining the energy management strategy and finding the optimal solution with regard to the aforementioned goals remains one of the main challenges in the design of HEVs. This paper presents a new vehicle modeling method, with an emphasis on HEVs, which is based on the frequency analysis of emissions and consumption according to the current specific traction power of the vehicle. An evaluation of the newly introduced model in the RDE, NEDC and WLTP cycle was performed, and the results were compared with the standard verified vehicle model that was created in AVL's CruiseM R2021.2 software package. Positive traction energies have positive deviations of between 0.35% and 2.85%. The largest deviation in CO2 emissions was recorded for the HEV model in the RDE cycle and in the non-hybrid model in the WLTP cycle and were 3.79% and 4.4%, respectively. All other combinations of cycle and vehicles had deviations of up to about 1%. As expected, the largest relative deviations were recorded for NOx emissions and ranged from 0.13% to 9.62% for HEVs in the WLTP cycle. [ABSTRACT FROM AUTHOR]

The realistic simulation of the dynamic responses of a moving articulated vehicle has attracted considerable attention in various disciplines, with the identification of the vehicle model being the prerequisite. To this end, a double-sensor hump calibration method (DHCM) was developed to identify both unladen and laden vehicle models, consisting of a sensor layout optimization step and a system identification step. The first step was to optimize the number and position of sensors via parameter sensitivity analysis; the second was to inversely identify the vehicle system based on sensor responses. For comparison, the DHCM and the existing single-sensor hump calibration method (SHCM) were used to calibrate a small-sized vehicle model and a multi-axle articulated vehicle model. Vertical accelerations of the vehicle models were then simulated and characterized by power spectral densities (PSDs). Validation against experimental measurements indicated that the PSDs of the models identified with the DHCM matched the measured PSDs better than those of the SHCM, i.e., the DHCM-identified model accurately simulated the dynamic response of an articulated vehicle with relative errors below 16% in the low-frequency range. Therefore, the DHCM could identify models of small-sized vehicles and multi-axle articulated vehicles, while the SHCM was only suitable for the former. [ABSTRACT FROM AUTHOR]

A microscopic traffic flow model is developed that incorporates vehicle vibrations due to pavement condition. The Intelligent Driver (ID) model employs a fixed exponent so traffic behavior is the same regardless of the road condition. Thus, it ignores the underlying physics. To address this limitation, the proposed model employs the Pavement Condition Index (PCI) in describing traffic behavior. The performance of both models is evaluated on a 3000 m circular road using the Euler numerical discretization technique. The results show that the performance of the proposed model varies with the pavement condition (PCI), as expected. Furthermore, the traffic flow increases with vehicle speed. The oscillations in speed and density with the proposed model decrease as the PCI increases, and are larger when the speed is higher. Consequently, the results with the proposed model align more closely with reality as they are based on the PCI, and so are a more accurate representation of traffic behavior. [ABSTRACT FROM AUTHOR]

A pre-emptive braking control method is proposed to improve the stability of autonomous vehicles during emergency collision avoidance, aiming to imitate the realistic human driving experience. A linear model predictive control is used to derive the front wheel steering angle to track a predefined fifth-degree polynomial trajectory. Based on a two-degrees-of-freedom (DOF) vehicle dynamics model, the maximum stable vehicle speed during collision avoidance can be determined. If the actual vehicle speed exceeds the maximum stable vehicle speed, braking action will be applied to the vehicle. Furthermore, four-wheel steering (4WS) control and direct yaw moment control (DYC) are employed to further improve the stability of the vehicle during collision avoidance. Simulation results under a double lane change scenario demonstrate that the control system incorporating pre-emptive braking, 4WS, and DYC can enhance the vehicle stability effectively during collision avoidance. Compared to the 2WS system without pre-emptive braking control, the maximum stable vehicle speed of the integrated control system can be increased by at least 56.9%. The proposed integrated control strategy has a positive impact on the safety of autonomous vehicles, and it can also provide reference for the research and development of autonomous driving systems. [ABSTRACT FROM AUTHOR]

Accurately quantitative evaluation of the response capability of electric vehicle aggregator (EVA), i.e., the adjustable range of power, is the premise and basis of its participation in the demand response (DR). In response to the current EVA's response capability evaluation, ignoring the autonomy of EV decision-making behavior may lead to overly idealized evaluation results, as well as the problem of evaluating only from a single time scale. To this end, this paper proposes a response capability evaluation model of EVA based on evolutionary game and response anticipation. Based on the physical constraints of EVs and their willingness to discharge, response capability evaluation models are constructed for individual EVs of different response types. Considering the autonomy of decision-making behavior of EVs, an evolutionary game model with multistrategy sets of EVs is constructed. Based on the idea of response anticipation, a regulation strategy is proposed for EVA to achieve the dynamic update of EVA's response capability. The proposed model is verified to be able to accurately evaluate the EVA's response capability through simulation examples, and the EVA response capability evaluation is extended from a single time scale to multiple time scales. The evaluation results can be used as the basis for EVA to participate in the electricity market bidding. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid growth in the number of electric vehicles (EVs), the large-scale grid connection of EVs has had a profound impact on the power grid. As a flexible energy storage resource, EVs can participate in auxiliary services of the power grid via vehicle-to-grid (V2G) technology. Due to the uncertainty of EVs accessing the grid, it is difficult to accurately control their charging and charging behaviors at both the day-ahead and real-time stages. Aiming at this problem, this paper proposes a two-stage scheduling strategy framework for EVs. In the presented framework, according to historical driving data, a day-ahead scheduling model based on distributionally robust optimization (DRO) is first established to determine the total power plan. In the real-time scheduling stage, a real-time scheduling model based on model predictive control (MPC) is established to track the day-ahead power plan. It can reduce the impact of EVs' uncertainties. This strategy can ensure the charging demand of users is under the control of the charging and discharging behaviors of EVs, which can improve the accuracy of controlling EVs. The case study shows that the scheduling strategy can achieve accurate and fast control of charging and discharging. At the same time, it can effectively contribute to the security and stability of grid operations. [ABSTRACT FROM AUTHOR]

In this paper the results of a simulation of a sports vehicle's braking maneuver in the adopted road conditions has been presented. The aim of this paper was to analyze the results and compare them with the ones obtained in the previous paper by the author, also prepared for the acceleration maneuver. At the same time considerations on the damaging effect of the road irregularities have been presented as this time no straightforward steering was included, so the vehicle could move freely. The simulated braking of the vehicle's model started at the speed of 100 km/h and its duration was 10 s. As in the previous paper the random road irregularities had three different amplitudes which enabled analysis of vehicle's deceleration on a different road conditions each time. Almost different profiles were also assumed for the left and the right wheels of the vehicle's model which moved on the dry and the icy road surface. [ABSTRACT FROM AUTHOR]

The recent widespread installation of renewable energy sources in place of fossil fuel, along with the substitution of electric vehicles, has given rise to several integration difficulties. Due to their intermittent nature, these systems are generally supplied through variable power generators. The control architecture of such connected systems focuses especially on minimizing frequency deviations and tie-line power fluctuations. In this paper, a cascaded proportional integral-proportional derivative (PI-PD) controller is proposed based on a chaotic butterfly optimization algorithm (CBOA) for parametric determination. The suggested controller and optimization approach is tested on a two-area power network, consisting of multiple renewable energy systems and electric vehicles while considering different load and frequency fluctuations. The proposed algorithm is evaluated based on four performance features. Its superiority is demonstrated by comparing it with recent optimization-algorithms-tuned controllers such as artificial rabbit algorithm 1 + PD, chaos game algorithm 1 + PI, dragonfly search algorithm, fractional order PID, and firefly algorithm PID. The acquired results validate the efficient performance of the proposed methodology by mitigating the frequency and tie-line power variations. Moreover, it exhibits robustness and enhanced stability of the system throughout a broad range of parameters. [ABSTRACT FROM AUTHOR]

The high construction and operation costs of urban rail transit have prompted scholars to explore new urban rail transit systems and operating modes. Based on the new straddle-type rapid transit system proposed by our team, the collaborative control of intelligent formation vehicles is discussed in this paper. The mathematical formulation of straddle-type rapid transit vehicle (SRTV) operating in intelligent formation operation mode (IFOM) was first constructed, and then the formation vehicle safety protection strategy based on limited speed difference (LSD) was analyzed. Subsequently, based on the Model Predictive Control (MPC) algorithm, a formation collaborative operation controller was established for IFOM operation. Based on the typical operating scenarios of formation vehicles exiting, cruising, and entering the station, formation performance evaluation indicators were adopted, with a focus on optimizing the time for formation vehicles to exit and enter the station. The simulation results show that the formation operates well and the vehicles in the formation can safely and stably travel in formation, proving that the use of LSD speed limitation strategy and MPC formation controller can ensure the safety and stability of SRTV vehicle formation. [ABSTRACT FROM AUTHOR]

Constantly tightening vehicle emission standards leads to the creation of new technologies for reducing the content of toxic substances in exhaust gases while maintaining the highest possible engine performance. At the moment, however, it is clear that with regard to future emission standards, despite the technologically highly refined system of catalytic converters, the reduction of emissions with the help of catalytic converters alone will be insufficient. That is why a variable exhaust manifold/system was developed and patented, which has a significant impact on the reduction of vehicle emissions. This innovation enables three working modes of the engine, depending on the demand and output power requirements. The presented article presents the complex architecture of the proposed system, including the description and simulation of individual operating modes. The first working mode is the most ecological and economical mode, but it has a relatively lower performance. The second working mode is characterized by the production of higher power, and the third mode can be used at high engine loads. [ABSTRACT FROM AUTHOR]

To tackle the challenges associated with low recognition rates and demanding computational requirements in vehicle detection, we suggest a MAGS-YOLOv5s based vehicle detection model that builds on an upgraded version of YOLOv5s. Initial efforts are focused on applying data augmentation, using mosaic augmentation and integrating mix-up augmentation to enhance the model's generalisation and robustness, thus increasing sample diversity. Secondly, the loss function for predicting bounding boxes is enhanced by including an α coefficient in addition to CIoU, facilitating adjustment to varying data scales and hastening the model's convergence speed. Moreover, Ghost Shuffle Convolution (GSC) is employed to substitute the convolutional layers in the neck of the initial network model. This replacement decreases the computational load and network parameters, whilst enhancing the feature extraction capability and robustness of the network model. Our proposed SGC block, which is built on the GC block, has been integrated to the C3 module to specifically improve the feature extraction capability of the model for occluded object detection. Experimental results indicate that the enhanced network delivers a boost of 2.9% and 5.3% in mAP (mean average precision) on the Pascal VOC and MS COCO vehicle datasets, respectively, compared to the original model. The improved network displays enhanced multi-scale representation and generalisation capabilities. [ABSTRACT FROM AUTHOR]

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs. [ABSTRACT FROM AUTHOR]

This article addresses a common issue in the design of battery electric vehicles (BEVs) by introducing a comprehensive methodology for the modeling and simulation of BEVs, referred to as the "PerfECT Design Tool". The primary objective of this study is to provide engineers and researchers with a robust and streamlined approach for the early stages of electric vehicle (EV) design, offering valuable insights into the performance, energy consumption, current flow, and thermal behavior of these advanced automotive systems. Recognizing the complex nature of contemporary EVs, the study highlights the need for efficient design tools that facilitate decision-making during the conceptual phases of development. The PerfECT Design Tool is presented as a multi-level framework, divided into four logically sequential modules: Performance, Energy, Currents, and Temperature. These modules are underpinned by sound theoretical foundations and are implemented using a combination of MATLAB/Simulink and the vehicle dynamics software VI-CRT. The research culminates in the validation of the model through a series of experimental maneuvers conducted with a Tesla Model 3, establishing its accuracy in representing the mechanical, electrical, and thermal behavior of BEVs. The study's main findings underscore the viability of the design tool as an asset in the initial phases of BEV design. Beyond its primary application, the tool holds promise for broader utilization, including the development of active control systems, advanced driver assistance systems (ADAS), and solutions for autonomous driving within the domain of electric vehicles. [ABSTRACT FROM AUTHOR]

Worldwide, in many cities, electric vehicles (EVs) have started to spread as a green alternative in transportation. Several well-known automakers have announced their plans to switch to all-electric engines very soon, although for EV drivers, battery range is still a significant concern—especially when driving on long-distance trips and driving EVs with limited battery ranges. Cities have made plans to serve this new form of transportation by providing adequate coverage of EV charging stations in the same way as traditional fuel ones. However, such plans may take a while to be fully deployed and provide the required coverage as appropriate. In addition to the coverage of charging stations, cities need to consider the potential loads over their power grids not only to serve EVs but also to avoid any shortages that may affect existing clients at their various locations. This may take a decade or so. Consequently, in this work, we propose a novel city-friendly navigation model that is oriented to serve EVs in particular. The methodology of this model involves reading real-time power loads at the grid's transformer nodes and accordingly choosing the routes for EVs to their destinations. Our methodology follows a real-time pricing model to prioritize routes that pass through less-loaded city zones. The model is developed to be self-aware and adaptive to dynamic price changes, and hence, it nominates the shortest least-loaded routes in an automatic and autonomous way. Moreover, the drivers have further routing preferences that are modeled by a preference function with multiple weight variables that vary according to a route's distance, cost, time, and services. Different from other models in the literature, this is the first work to address the dynamic loads of the electricity grids among various city zones for load-balanced EV routing in an automatic way. This allows for the easy integration of EVs through a city-friendly and anxiety-free navigation model. [ABSTRACT FROM AUTHOR]

In order to simultaneously improve the fuel economy and overall performance of plug-in hybrid electric vehicles (PHEVs), this study selected the P1 + P3 configuration as its research object. Through a configuration analysis of hybrid vehicles, it confirmed the feasibility of P1 + P3 configuration-PHEV operating modes. Based on this, a rule-based control strategy was developed, and simulation models for the entire vehicle and control strategy were constructed in both Cruise and MATLAB/Simulink software. The study conducted simulation analysis by combining three sets of Worldwide Harmonized Light vehicles Test Cycle (WLTC) driving cycles to assess the fuel-saving potential of the dual-motor P1 + P3 configuration. The simulation results showed that the vehicle model was reasonably constructed and the proposed control strategy had good control effects on the entire vehicle. Compared to conventional gasoline vehicles, the P1 + P3 configuration PHEV achieved a 67.4% fuel economy improvement, demonstrating a significant enhancement in fuel efficiency with the introduction of electric motors. [ABSTRACT FROM AUTHOR]

This paper proposes a control strategy for active lateral secondary suspension that uses preview data. Based on a derived analytical model, a model predictive controller (MPC) is implemented. The influence of the track irregularities upon carbody lateral dynamics is considered explicitly. The controller developed is applied to a full-scale rail vehicle model. Ride comfort is evaluated according to EN 12299. Multibody simulations show that there is a significant increase in continuous ride comfort on poor-quality tracks. [ABSTRACT FROM AUTHOR]

E-commerce has become the mainstream consumption mode for people, and with the continuous increase of online shopping business volume, controlling logistics costs has become an urgent problem to be solved. In order to solve the challenges in vehicle scheduling and achieve a scientific and reasonable vehicle scheduling scheme, this research is based on Hadoop Big data platform, introduces the concept of time axis, and builds a vehicle scheduling model based on Hadoop intelligent platform. Based on quantum genetic algorithm and combined with MapReduce model, a quantum improved genetic algorithm is constructed to solve vehicle scheduling optimization problems. The results show that the traditional quantum genetic algorithm converges after 20 iterations, achieving an optimal value of 575 and taking 360 seconds. The improved quantum genetic algorithm converged after 10 iterations and achieved an optimal value of 675, taking 200 seconds. Compared with quantum genetic algorithm, the improved quantum genetic algorithm reduces the time spent by 44.4%. Selecting customer data from a certain logistics company for testing, the improved algorithm shortens the delivery time and achieves the design of the optimal path scheduling plan. This study optimized transportation routes and resource scheduling, reduced transportation costs, and played an important role in optimizing vehicle scheduling in the logistics industry. [ABSTRACT FROM AUTHOR]

An effective path-following controller is a guarantee for stable sailing of underactuated unmanned surface vehicles (USVs). This paper proposes an event-triggered robust control approach considering an unknown model nonlinearity, external disturbance, and event-triggered mechanism. The proposed method consists of guidance and dynamic control subsystems. Based on the tracking error dynamics equations, the guidance subsystem is designed to achieve the guidance law. For the dynamic control subsystem, the radial basis function neural networks (RBFNNs) are designed to approximate the unknown model nonlinearity and external disturbances to improve the robustness of the proposed method. In addition, an event-triggered mechanism is constructed to reduce the triggering times. The closed-loop system is proven to be stable, and the effectiveness of the proposed method is illustrated through simulation results. [ABSTRACT FROM AUTHOR]

Sharp steering-braking at a high speed exposes sport utility vehicles with high gravity centers and narrow wheel tracks to the risks of tire locking, sideslip and rollover. To avoid these risks and ensure braking safety, yaw stability and roll stability upon steering-braking, a braking-yaw-roll stability integrated control strategy was proposed, which consists of a supervisor, an upper and a lower controller for the front and rear axle independent drive electric vehicle. In the supervisor, a nonlinear vehicle predictive model was constructed and four control modes were proposed according to the vehicle status and rollover indexes. The weight coefficients between braking force, yaw stability and roll stability are determined dynamically by the control mode and output to the upper controller. The upper controller used a nonlinear model predictive control to determine the longitudinal braking force distribution of the four wheels. And in the lower controller, the regenerative braking torque and friction braking torque of each wheel were distributed. Finally, simulation verifications were carried out on the high and low adhesion roads. The results show that the control strategy proposed in this study can effectively prevent the vehicle from rollover while ensuring braking safety and yaw stability. [ABSTRACT FROM AUTHOR]

As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle's fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results. [ABSTRACT FROM AUTHOR]

A significant resonance pattern at high frequencies around 400 Hz to 1000 Hz has been well observed from measured rail accelerations. Such resonances are key issues for rail noise and corrugation. Although this pattern has been noted and discussed in various papers in terms of wave reflections among multiple wheels, the aim of this study is to develop novel dynamic model and reveal generation mechanism of this resonance phenomenon with the train-track coupled system. An infinite Timoshenko beam with continuous supports is adopted for modeling the track system, and the point and transfer receptances of the rail for a moving excitation are explicitly deduced by the residue theorem together with a Fourier transform-based method. A frequency-domain method is then established to calculate the power spectral densities (PSD) of the train-track coupled system responses to stochastic irregularities with the moving vehicle model with multiple wheels. It is found that the high-frequency resonances occur when the reflected waves generated from multiple wheels on the rail are exactly in phase with the initial track irregularity. The load speed has negligible influence on the wheel–rail interaction in the lower frequency range and a small influence in the upper frequency range only for very high train speeds. [ABSTRACT FROM AUTHOR]

Vehicle and crew scheduling is vital in public transit planning. Conventionally, the issues are handled sequentially as the vehicle scheduling problem (VSP) and crew scheduling problem (CSP). However, integrating these planning steps offers additional flexibility, resulting in improved efficiency compared with sequential planning. Given the ever-growing market share of electric buses, this paper introduces a new model for integrated electric VSP and CSP, called EVCSPM. This model employs the minimum cost flow formulations for electric VSP, set partitioning for CSP, and linking constraints. Due to the nonlinear integer property of EVCSPM, we propose a method that hybrids a matching-based heuristic and integer linear programming solver, GUROBI. The numerical results demonstrate the efficiency of our methodology, and the integrated model outperforms the sequential model in real-life scenarios. [ABSTRACT FROM AUTHOR]

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Accurate control of front wheel angle is essential in intelligent vehicle electric‐hydraulic power steering (EHPS) system, because it has a significant impact on the safety, comfort, and reliability of vehicle steering. However, the accurate control of front wheel angle is a challenging problem due to the non‐linearity of hydraulic power in steering system and the uncertainty of steering resistance. In this paper, a model predictive control (MPC) method for front wheel angle based on steering resistance estimation is proposed. Firstly, through theoretical analysis and practical experiment of EHPS system model, the relationship between front wheel angle and steering wheel angle and the non‐linear characteristics of hydraulic power are obtained. Secondly, a sliding mode observer is established to estimate the front axle lateral tyre force and steering resistance, which is verified by MATLAB/TruckSim co‐simulation. Then a controller based on the MPC theory is designed and applied to the accurate control unit of front wheel angle in the EHPS system for real‐time control. Finally, the simulation and test platform results show that the control algorithm proposed in this paper can precisely control the vehicle's front wheel turning angle within a certain frequency range. [ABSTRACT FROM AUTHOR]

For the problem of insufficient small target detection ability of the existing network model, a vehicle target detection method based on the improved YOLO V3 network model is proposed in the article. The improvement of the algorithm model can effectively improve the detection ability of small target vehicles in aerial photography. The optimization and adjustment of the anchor box and the improvement of the network residual module have improved the small target detection effect of the algorithm. Furthermore, the introduction of the rectangular prediction frame with orientation angles into the model of this article can improve the vehicle positioning efficiency of the algorithm, greatly reduce the problem of wrong detection and missed detection of vehicles in the model, and provide ideas for solving related problems. Experiments show that the accuracy rate of the improved algorithm model is 89.3%. Compared to the YOLO V3 algorithm, it is improved by 15.9%. The recall rate is improved by 16%, and the F1 value is also improved by 15.9%, which greatly increased the detection efficiency of aerial vehicles. [ABSTRACT FROM AUTHOR]

Vehicle load estimation and health monitoring of bridges are of great importance for the health monitoring of bridge structure under vehicle loads. Traditional methods for the estimation of vehicle load require the positions of the vehicles. The vehicle position tracking is generally conducted in offline manner and requires the installation of additional sensors. To resolve these problems, we developed a Bayesian probabilistic approach for the online estimation of vehicle loads, vehicle positions, and structural parameters for bridges. The crux is to model the vehicle load vector as a modulated filtered Gaussian white noise due to the fact that the vehicle-bridge interaction forces are in essence the responses of the vehicle-bridge coupled system under the excitation of the road roughness described by Gaussian random field and the constant vehicle weights. Furthermore, the vehicle speed vector is introduced to track the unknown positions of vehicles. There are three appealing features in this approach. First, it allows the simultaneous estimation of vehicle loads, vehicle positions, and structural parameters in an online manner. Second, this method allows for time-varying vehicle speed tracking. Third, the proposed method is applicable to the case with multiple vehicles. Examples for the case where single/multiple vehicles pass across bridges with uniform speeds/variable speeds are presented to demonstrate the feasibility of the proposed method for vehicle load estimation, vehicle position tracking, and bridge structural identification using only strain measurements. [ABSTRACT FROM AUTHOR]

This paper presents a novel differential with lateral torque vectoring activated by an auxiliary motor, called motor-modulated lateral torque vectoring differential, or briefly, MLTD. Its architecture and kinematic characteristics are described, and the optimal cornering performance due to torque vectoring is evaluated using a steady-state vehicle dynamic model. Then, a conceptual design case of MLTD installed in a front-wheel-drive electric vehicle is conducted to assess its feasibility in terms of cornering, driving performances, and energy efficiency performance. The calculations show that an optimal distribution ratio between left and right torques for maximum lateral acceleration can be obtained for a specific cornering condition, further used for DM sizing in the preliminary stage. The simulations for energy consumption at constant-speed turning reveal that energy efficiencies of MLTD are lower than those of conventional differentials with evenly distributed torques. However, this deficiency may be paid off by moderately cutting down the rolling resistance of tire while the superior cornering performances brought by torque vectoring is still preserved. Accordingly, the newly proposed MLTD possesses greater flexibility to improve energy efficiency and driving range of electric vehicles without trading off against its desirable cornering performance and safe handling, and is thus worthy of further development. [ABSTRACT FROM AUTHOR]

Model-based approaches to navigation, control, and fault detection that utilize precise nonlinear models of vehicle plant dynamics will enable more accurate control and navigation, assured autonomy, and more complex missions for such vehicles. This paper reports novel theoretical and experimental results addressing the problem of parameter estimation of plant and actuator models for underactuated underwater vehicles operating in 6 degrees-of-freedom (DOF) whose dynamics are modeled by finite-dimensional Newton-Euler equations. This paper reports the first theoretical approach and experimental validation to identify simultaneously plant-model parameters (parameters such as mass, added mass, hydrodynamic drag, and buoyancy) and control-actuator parameters (control-surface models and thruster models) in 6-DOF. Most previously reported studies on parameter identification assume that the control-actuator parameters are known a priori. Moreover, this paper reports the first proof of convergence of the parameter estimates to the true set of parameters for this class of vehicles under a persistence of excitation condition. The reported adaptive identification (AID) algorithm does not require instrumentation of 6-DOF vehicle acceleration, which is required by conventional approaches to parameter estimation such as least squares. Additionally, the reported AID algorithm is applicable under any arbitrary open-loop or closed-loop control law. We report simulation and experimental results for identifying the plant-model and control-actuator parameters for an L3 OceanServer Iver3 autonomous underwater vehicle. We believe this general approach to AID could be extended to apply to other classes of machines and other classes of marine, land, aerial, and space vehicles. [ABSTRACT FROM AUTHOR]

Current emission models primarily focus on traditional combustion vehicles and may not accurately represent emissions from the increasingly diverse vehicle fleet. The growing presence of hybrid and electric vehicles requires the development of accurate emission models to measure the emissions and energy consumption of these vehicles. This issue is particularly relevant for low-emission zones within cities, where effective mobility planning relies on simulation models using continuously updated databases. This research presents a two-dimensional emission model for hybrid vehicles, employing artificial neural networks for low-emission zones. The key outcome is the methodology developed to create a CO2 emission model tailored for hybrid vehicles, which can be used to simulate various road solutions. The CO2 emission model achieved an R2 coefficient of 0.73 and an MSE of 0.91, offering valuable information for further advancements in emission modelling. [ABSTRACT FROM AUTHOR]

This work proposes a centralized data-based orderly charging strategy that considers the user's charging choices. Three charging choices for different types of users are described. Then, a scheduling model of electric vehicles based on the time dimension is established. In this strategy, the optimization model not only considers the demand of the grid side and the user side, but also takes the driving data of electric vehicles as the driver. The grid-side optimization involves minimizing the equivalent load fluctuation, and the user-side is optimized to minimize the charging cost and maximize the charging electric quantity. The scheduling capabilities of the three charging strategies are analyzed based on a series of driving data of electric vehicles. The results show that the peak-valley difference and equivalent load fluctuation of the power grid in the data-based orderly charging strategy reduced by 22.2% and 22.7%, respectively, and the charging cost of users also reduced much more than the other two charging strategies. Additionally, the effect of users' charging choices on the charging strategy is analyzed, and the results show that the orderly charging strategy that considers users' charging choices can effectively decrease the scheduling deviation caused by users' charging choices. It greatly improves the security and economy of the grid. [ABSTRACT FROM AUTHOR]