Vehicle Load Research Articles

Legal Study on the Oversight of Overloaded Motor Vehicles: Efforts to Protect Road Safety and Infrastructure

The oversight of motor vehicles exceeding weight limits is critical in safeguarding road safety and protecting infrastructure from premature damage. Overloaded vehicles are a common issue, contributing significantly to accidents and accelerated road deterioration, which incurs high repair costs and disrupts public transport and logistics. This study examines the legal frameworks and regulatory practices aimed at controlling vehicle overloading in Indonesia. Utilizing a normative juridical approach, the research reviews pertinent laws, regulations, and real-world cases involving load limit violations to gain insights into the effectiveness of existing policies. Findings reveal that while stringent load regulations are in place, enforcement remains inconsistent due to various limitations, including outdated monitoring technology, insufficient human resources, and challenges in coordinating between regulatory agencies. The research identifies critical obstacles in the current oversight mechanisms, such as a lack of real-time data collection and limited roadside weighing facilities, which hinder comprehensive monitoring. Furthermore, penalties for overloading are often inadequate to deter repeat violations. Based on these findings, this study recommends a multi-faceted approach to strengthen vehicle load regulation enforcement. Suggested improvements include upgrading monitoring technology, such as installing automatic weigh-in-motion systems, increasing the frequency and coverage of roadside inspections, and implementing more severe penalties to enhance deterrence. Additionally, the study emphasizes the need for inter-agency coordination and public awareness campaigns to foster compliance among vehicle operators. Through these initiatives, Indonesia can better protect its roadways, ensuring both road safety for all users and the long-term sustainability of its infrastructure.

Influence of Horizontal Distance Between Earthmoving Vehicle Load and Deep Excavation on Support Structure Response

The objective of this paper is to investigate the influence of earthmoving vehicle load position on the deformation and internal force characteristics of a deep excavation (DE) support structure. The position of the earthmoving vehicle load near a DE is described by the horizontal distance between the earthmoving vehicle load and the DE. A two-dimensional finite element model is established for simulating DE engineering under the earthmoving vehicle load. The load of the earthmoving vehicle is treated as the static load, and the influence of the earthmoving vehicle load on the excavation support structure is considered from the static point of view. The numerical results of the finite element model agree well with the measured data from the field, which verifies the validity of the model. On the basis of this model, multiple models are established by changing the horizontal distance (D) between the earthmoving vehicle and the DE. The influence of D on the support structure and its critical magnitude for ensuring safety were studied. The results show that the underground diaphragm wall (UDW) is the main component for which horizontal displacement occurs under the earthmoving vehicle load. The horizontal displacements of the support structure exhibit an asymmetric distribution. When D decreases from 20 m to 0.5 m, the horizontal displacement of the UDW near the loading side increases, and the maximum horizontal displacement occurs at the top of the excavation support structure. The critical magnitude of D for ensuring safety is found to be 1 m. When D is less than 1 m, the DE is in an unsafe state. The UDW is the main component subject to the bending component. The bending moment distribution exhibits an “S” shape. The maximum bending moment increases with the decrease in D, and it occurs at the intersection of the second support and the UDW. As D decreases, the axial force in the first internal support changes from pressure to tension. The axial forces in the second and third internal supports are both pressures. The axial force in the third internal support is the largest. The research results have a positive effect on the design and optimization of DE support structures under the earthmoving vehicle load.

Micro-Scale Fracture Characteristics of Emulsified Asphalt Cold Recycled Mixture Based on Discrete Element Method

Asphalt pavement often experiences structural failure due to repeated vehicle loading. The discrete element method (DEM) model was established based on the semicircle bending test (SCB) to investigate the fracture damage mechanism of emulsified asphalt cold recycled mixture (CRME) under loading. The micro-mechanical parameters of CRME were determined through a reliable validation process using the uniaxial compression static creep test. The microscopic fracture characteristics of CRME were investigated through the load-displacement curve, stress distribution, and force chain distribution. The fracture energy was used as the evaluation index to analyze the influence of prefabricated notch length and aggregate gradation on the fracture performance of CRME. The results indicate that the emulsified asphalt mortar-aggregate interface was the critical weak position of the mixture fracture; the failure of the tension chain was the main destructional form of the SCB test. The development of cracks affected the stress concentration phenomenon and stress concentration level of the mixture. Fine-grained mixture exhibited crack resistance. The number and length of cracks were affected by gradation. As the prefabricated notch length increased, the influence gradually diminished. The research results could provide theoretical and data support for the design of CRME.

Field Weakening Operation of Pentagon Connected FPIM Under Open Phase Fault With Electric Vehicle Load

Field Weakening Operation of Pentagon Connected FPIM Under Open Phase Fault With Electric Vehicle Load

Failure Analysis of Girth Weld Cracking in Gas Transmission Pipelines Subjected to Ground Subsidence and Traffic Loads

Girth welds are weak points in pipelines, and failures occur frequently. In a gas transmission pipeline, a girth weld experienced cracking, prompting a failure analysis using experimental methods and finite element analysis (FEA). Experimental results showed that X-ray non-destructive testing (NDT) revealed cracks, porosity, and lack of fusion in the girth weld. However, the hardness and microstructure of the material showed no abnormalities. During operation, the pipeline experienced an increase in soil cover and was subjected to ground subsidence and vehicle loads. Finite element analysis was conducted on the defective girth weld under different conditions, including varying soil cover depths, different levels of subsidence, and varying vehicle loads, to examine the pipeline’s stress response. The results indicated that the combination of soil cover, subsidence, and vehicle loads led to pipeline failure, whereas none of these factors alone was sufficient to cause girth weld failure. To prevent such failures from occurring again, the following measures are recommended: strengthen on-site welding quality control of girth welds, conduct inspections for defects in girth welds of in-service pipelines, and promptly address any defects that exceed acceptable limits.

Influence depth of highway subgrade under heavy vehicle loads based on a theoretical model

A three-dimensional dynamic model of vehicle-road-unsaturated subgrade coupling is established. The semi-analytical and numerical solutions of three-dimensional dynamic response of highway subgrade under vehicle load are presented for the first time. Then, the accuracy of the theoretical model is verified by the field measurement data. Finally, the spatial distribution of dynamic stress and depth of working zone under different influencing factors are studied, and the calculation model of depth of working zone of highway subgrade is proposed. The results show that the dynamic stresses, σx, σy, σz, and τxz increase significantly with the increase of vehicle axle load and decrease significantly with the increase of road thickness under the same subgrade depth. In addition, with the increase of axle load and the decrease of road thickness, the peak values of dynamic stress σx, σy, σz, and τxz decrease more obviously along the depth of roadbed. In the process of vehicle movement, the traditional subgrade working area can be divided into a sensitive area and an influence area of dynamic load. The depth of these two areas is positively correlated with the axle load of the vehicle and negatively correlated with the thickness of the road surface. The advantage of the model in this paper is that the semi-analytical solution of the total stress component of the soil element in the course of traffic load is given.

Research on Energy Management Technology of Photovoltaic-FESS-EV Load Microgrid System

This study focuses on the development and implementation of coordinated control and energy management strategies for a photovoltaic–flywheel energy storage system (PV-FESS)-electric vehicle (EV) load microgrid with direct current (DC). A comprehensive PV-FESS microgrid system is constructed, comprising PV power generation, a flywheel energy storage array, and electric vehicle loads. The research delves into the control strategies for each subsystem within the microgrid, investigating both steady-state operations and transitions between different states. A novel energy management strategy, centered on event-driven mode switching, is proposed to ensure the coordinated control and stable operation of the entire system. Based on the simulation results, the PV system cannot cope with the load demand power when it is increased to a maximum of 2800 W, the effectiveness of the individual control strategies, the coordinated control of the subsystems, and the overall energy management approach are confirmed. The main contribution of this research is the development of a coordinated control mechanism that integrates PV generation with FESS and EV loads, ensuring synchronized operation and enhanced stability of the microgrid. This work provides significant insights into optimizing energy distribution and minimizing losses within microgrid systems, thereby advancing the field of energy management in DC microgrids.

Vehicle flow indication and identification using FBG sensors

Abstract This paper proposes a vehicle flow monitoring system utilizing fiber Bragg grating (FBG) sensor technology. The system comprises a prototype road model along with a car with varying loads. The FBG sensor is embedded in the road surface for sensing the strain induced by the vehicle movement. The laboratory experiments were conducted using an embankment model equipped with FBG sensor to identify vehicle movement under static and movable loads. Under static condition, different loads were applied on the car and the corresponding wavelength shifts have been measured using FBG-interrogator. Under dynamic condition, the vehicle was moved with certain speed and the corresponding wavelength shifts were recorded over time. The sensitivity of FBG for vehicle load sensing has been obtained as 0.3 pm/N. The proposed vehicle flow sensor system offers good accuracy and sensitivity and has potential applications for real-time vehicle flow monitoring. The proposed sensor system can be used to analyze road surface irregularities, crucial for assessing heavy-vehicle fatigue.

A Novel Enhanced Fire Hawks Optimization Approach for Improving the Efficiency of Converter‐Based Controllers in Switched Reluctance Motors

ABSTRACTSwitched reluctance motors (SRMs) have gained popularity in various industrial applications due to their advantages, including structural simplicity, high reliability, low cost, and operational stability over a wide speed range without relying on rare‐earth permanent magnet materials. However, these motors exhibit drawbacks such as weak torque density, low efficiency, and significant torque ripple, particularly in high‐speed operation. An efficient converter‐based control approach is proposed to manage speed and torque variations in SRM motors, addressing these issues. A multilevel converter (MC) is introduced as a fundamental component. The novel multilevel converter (NMC) accommodates SRMs with varying numbers of phases and exhibits quick demagnetization and excitation behaviors, enabling independent operation of each phase even during conduction overlaps. Subsequently, an effective controller is developed for the SRM motor, combining proportional integral derivative (PID) control with enhanced fire hawks optimization (EFHO). The EFHO optimizes the PID gain values to enhance controller performance. The converter operation minimizes torque ripple and facilitates speed management. The EFHO technique is a fusion of fire hawks optimization (FHO) and the sine cosine algorithm (SCA). This amalgamation improves the updating process of FHO by incorporating SCA. The proposed methodology is implemented in MATLAB and evaluated through various metrics, including SRM motor current, voltage, speed, and torque, under electric vehicle (EV) load conditions. Performance comparisons are conducted with traditional optimization algorithms such as the whale optimization algorithm (WOA) and ant colony optimization (ACO). The results validate the effectiveness of the proposed methodology in achieving improved SRM motor control and performance.

Automatic control algorithm for V2G frequency response mode of electric vehicle charging station

Abstract To solve the problem of excessive fluctuation of the V2G load of the electric vehicle charging pile, this paper proposes an automatic control algorithm for the V2G frequency response mode of the electric vehicle charging pile. The FPGA is used as the core processing unit to collect and process the V2G frequency data of the charging pile; the multiple linear regression model is used to predict the charging and discharging loads, and the automated two-layer control model is established; the sparrow search algorithm is used to optimize the weights and thresholds of the BP neural network to realize the control of the V2G frequency response mode. The experimental results show that the load prediction error of this method is small. After the automatic control, the load fluctuation of the charging pile is small, and the electric vehicle charging pile’s V2G frequency response control effect is better.

Effect of Vehicle Load on the Fatigue Cracks of the Center-stay for a Steel Girder Deck in a Suspension Bridge

Steel deck systems are widely used in cable-stayed and suspension bridges because of their various advantages. However, the direct effect of vehicle loads often leads to fatigue cracks in steel decks, particularly at the intersections of the longitudinal and transverse ribs, which have been the focus of most studies. In the target bridge of this study, unlike the location where fatigue cracking generally occurs, cracks were detected in the vertical stiffener inside the stiffening girder just below the center-stay and that adjacent to the center-stay. A precise analysis was performed to determine the cause of fatigue cracking, and the analysis model was verified by comparing it with the results of an existing load test. This study examined the effects of heavy vehicle loads by adjusting the vehicle weight and changing the position of the vehicle. This study confirms that as the vehicle load increases, fatigue cracing may occur in the internal vertical stiffener of the center-stay stiffening girder, leading to subseuent cracs in the external center-stay.

Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions

Logistics, as a significant field for achieving energy-saving and carbon reduction goals, is recognized as a crucial direction for realizing the global “double carbon” objective, while vehicle path optimization is an effective method for promoting energy efficiency and reducing carbon emissions. In this paper, an improved genetic algorithm is proposed for optimizing logistics and distribution paths concerning the carbon emissions of fuel vehicles throughout the logistics and distribution process, and a low-carbon logistics and distribution path model is constructed based on time windows, vehicle loading, and carbon emissions. The INNC method is adopted to initialize the population, and an enhanced genetic algorithm (GA-LNS) is designed to solve the model in conjunction with a large-scale neighborhood vector search algorithm. The results indicate that the initialization of the population using the INNC method produces a higher-quality initial solution. Compared to traditional genetic algorithms and particle swarm optimization, the GA-LNS algorithm exhibits superior robustness, effectively addressing the limitations of traditional genetic algorithms that rely on initial solutions and are prone to local optima. By comparing the computational results of the low-carbon logistics distribution path model constructed in this study with those of traditional optimization objective models, it is demonstrated that this model effectively balances the trade-offs between objectives and benefits, achieving the lowest total logistics distribution cost while promoting sustainable low-carbon logistics. The research findings provide a theoretical foundation for optimizing logistics vehicle paths and formulating energy-saving and carbon reduction implementation plans in China.

A Novel Method of Bridge Deflection Prediction Using Probabilistic Deep Learning and Measured Data.

The deflection control of the main girder in suspension bridges, as flexible structures, is critically important during their operation. To predict the vertical deflection of existing suspension bridge girders under the combined effects of stochastic traffic loads and environmental temperature, this paper proposes an integrated deflection interval prediction method based on a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a probability density estimation layer, and bridge monitoring data. A time-series training dataset consisting of environmental temperature, vehicle load, and deflection monitoring data was built based on bridge health monitoring data. The CNN-LSTM combined layer is used to capture both local features and long-term dependencies in the time series. A Gaussian distribution (GD) is adopted as the probability density function, and its parameters are estimated using the maximum likelihood method, which outputs the optimal deflection prediction and probability intervals. Furthermore, this paper proposes a method for identifying abnormal deflections of the main girder in existing suspension bridges and establishes warning thresholds. This study indicates that, for short time scales, the CNN-LSTM-GD model achieves a 47.22% improvement in Root Mean Squared Error (RMSE) and a 12.37% increase in the coefficient of determination (R2) compared to the LSTM model. When compared to the CNN-LSTM model, it shows an improvement of 28.30% in RMSE and 6.55% in R2. For long time scales, the CNN-LSTM-GD model shows a 54.40% improvement in RMSE and a 10.22% increase in R2 compared to the LSTM model. Compared to the CNN-LSTM model, it improves RMSE by 38.43% and R2 by 5.31%. This model is instrumental in more accurately identifying abnormal deflections and determining deflection thresholds, making it applicable to bridge deflection early-warning systems.

Accurate identification of moving vehicle loads on beam-like bridge structures integrating novel PCA-based dictionary with grouping and weighting strategy

Most moving load identification (MLI) methods incorporate the dictionary theory as a regularization technique to address the ill-posedness of the problem. However, the selection of atoms in the dictionary often inadequately represents the actual vehicle loads in existing methods, and the prior information from these atoms is disregarded in the subsequent MLI computations. Therefore, a novel MLI framework is proposed for beam-like bridge structures based on a novel dictionary derived from principal component analysis (PCA) and newly grouping and weighting strategy in this study. At first, a vehicle-bridge coupling system (VBCS) is established to obtain the interaction forces between the moving vehicles and bridge deck. The system matrices between the interaction forces and structural responses are derived in the time domain. The PCA technique is then employed to extract information from these interaction forces and to subsequently construct a PCA-based dictionary. Based on the eigenvalues of each principal component in the dictionary, a weighted group sparse model, incorporating the newly grouping and weighting strategy, is defined to obtain the coefficients of each atom. The solution to this model is obtained using the alternating direction method (ADM). Finally, the proposed method is validated in numerical simulations comparing with some existing methods. The effects of noise levels, road surface roughness, number of training data, response combinations and the tolerances in ADM were also studied. Similarity law for the VBCS is conducted in experimental verifications to construct the PCA-based dictionary, allowing for a reasonable identification of gross vehicle weights. The results indicate that the MLI accuracy has been enhanced by the proposed method, and the similarity law employed in experiments is reasonable. Settingthe tolerance in ADM as ε = 1 × 10−5 can lead to a higher accuracy and reduce the computation cost in both numerical simulations and experimental verifications.

Performance evaluation of a racetrack high temperature superconductor winding in an HTS levitation platform

Abstract High temperature superconducting (HTS) is a key technology of next generation high-speed railway systems, which guarantees high current and highly efficient traction power supply. HTS devices can reduce system weight and thus improve loads of vehicles. On high-speed maglev trains, HTS windings wound by second-generation (2G) HTS tapes play an important role in suspension and traction. However, in complex ferromagnetic environments, the performances of windings are notably influenced by the varying magnetic fields. In this paper, a new maglev platform is designed and comprised of a winding of six racetrack HTS coils, an E shape iron core and a magnetic guide rail. The air core structure of the winding is modeled，tested and the electromagnetic characteristics are analyzed for three dimensional (3D) finite element analysis of the platform. An A formulation is used to calculate magnetic vectors A and infer magnetic flux densities and current densities for the structures. After the winding is installed on the iron core and the magnetic guide rail is suspended above the iron core, the air gap between the iron core and the rail is varied from 0 mm to 20 mm in models and experiments. The magnetic fields, critical currents and losses of the winding in cases of the air core and the iron core conditions are used for analysis. The comparison between the air core and the iron core conditions confirms the reliability of the proposed models. This study provides means for analysis of complex superconducting maglev systems.

Renewable Energy-Based Electric Drive with a Novel Control Technique for Smooth Power-Sharing

Energy management between different power sources, which are used to feed Electric vehicles (EVs) is one of the complex scenarios. Normally, power management is done based on the Electric vehicle load requirements. In this work, three different energy sources are considered, in that one is an active energy source and two are passive energy storage elements. The Photo Voltaic (PV) based energy source is considered an active element, on the other hand, the Battery sand supercapacitor (SCap) is treated as a passive type of element. To manage the power flow between two energy sources, a novel control technique is adopted by considering the speed and current of the electric motor as major input parameters known as the Measurement of the parameter-based controller (MPBC). The Measurement of a parameter-based controller is used to control the pulse signals of the Bidirectional converters connected at the battery and supercapacitor ends, and the required pulse signals can be generated by the Conventional Proportional Integral (PI) controller. The main circuit is implemented with a novel hybrid controller which is used to provide controlled pulse signals to the bidirectional converters connected to the battery and supercapacitor. Finally, the MATLAB/Simulink model was developed with the novel hybrid controller and examined the performance at different load conditions of the motor by considering three different states of power delivered by the PV array.

Multi-objective-based economic dispatch and loss reduction in the presence of electric vehicles considering different optimization techniques

Currently, electric vehicles (EVs) are the most liked mode for green transportation. However, the vehicle-to-grid (V2G) technology can reduce the peak demand on the power grid, which is an efficient way to encourage the integration of EVs. This paper proposes a multi-objective-based economic dispatch management including EVs to minimize the generator cost and active power loss. The entire system is retained for keeping in mind the economic operation of the whole system. Then, EVs are introduced to the system, taking into account vehicle requirements and load demands and considering EV constraints. The target of the proposed work is to demonstrate how effectively large-scale EVs can participate in valley filling and peak load shaving along with multi-objective-based cost and loss reduction. The proposed optimization problem is employed in an IEEE 30-bus system. The multi-objective grasshopper optimization algorithm and the ant-lion optimization are compared to observe the minimum cost and total loss of the system. The results show that the total generation cost and power loss of the system decrease due to the V2G mode of operation. In addition, EVs provide an alternative method for dealing with peak load, while filling the off-peak hours effectively. The total generation cost and power loss for 24 h using MOGOA without implementation of EVs are 8,757.128 $/hr and 65.28509 MW, respectively, and with EVs, the total generation cost and power loss for 24 h are 8,617.077 $/hr and 55.65349 MW, respectively. Thus, with the implementation of EVs, the total generation cost reduced by 1.59% and the total power loss reduced by 14.75%, and with MOALO, the total generation cost and power loss for 24 h without EVs are 8,977.077 $/hr and 44.20877 MW, respectively, and with EVs, the total generation cost and power loss for 24 h are 8,923.529 $/hr and 41.69524 MW, respectively. Thus, with the implementation of EVs, the total generation cost reduced by 0.59% and the total power loss reduced by 5.68%. The analysis of the results demonstrates how effectively EVs in the V2G mode can reduce the dependency over the grid power during the time of peak load demand.

Research and development of automation subroutine for forecasting the number and load of cargo vehicles depending on the stochastic formation of dangerous goods collection centers

The issues of forecasting the load of vehicles, route networks, formation of collection centers for cargo flows, including dangerous goods, in territorial planning systems determine the need for transition from deterministic models to dynamic models with the possibility of taking into account the influence of the external environment. Existing information transportation systems in the sphere of work with dangerous goods are based on deterministic models and allow to study the problems of forecasting changes to a limited extent. Models of routing networks construction on cargo vehicles are limitedly applicable when there is a task of dangerous goods transportation taking into account the presence of a distributed system of accumulation centers both in the city and, for example, within a large transport hub, seaport with different terminals. The paper presents routing models adapted to the conditions of dangerous goods transportation. Determining transport efficiency and the main costs of transport enterprises is the scheme of traffic flows, fixed in the territorial scheme. The task of the study is to develop a separate automation toolkit for decision making on the number and type of specialized transport (depending on the load capacity) to work with dangerous goods and large-sized objects within the existing flow scheme. On the basis of the research of the group of companies the conclusion is presented that today the task of choosing the optimal amount of transport by carriers is solved, as a rule, not systematically, but by operative construction of a set of routes, and does not involve primary analysis of the territory and parameters of accumulation sites. The presented study considers a new approach to the analysis of the system of transportation of hazardous waste, associated large cargoes, in terms of assessing the necessary list of universal structured input data, which are primary principled and characterize the complexity of work in the selected facility, terminal. To solve the problem of forecasting the load of vehicles is offered developed a new automation subprogram based on the organization of client-server access to data, and implemented in the internal programming language “1C. Management of motor transport” (version ММT 8.2). The presented automation subprogram provides data connectivity on volumes of dangerous goods cargo flows, cargo flow collection centers, available data on vehicles and planned data on removal schedules. Work of the subprogram provides integration of work with other databases of transport companies and allows to carry out modeling of various variants of placement of centers of collection of dangerous goods, centers of cargo flows with high accuracy. The obtained results allow to determine the required number of vehicles, their load taking into account different types and technical capabilities, as well as to determine the required number of vehicle trips in the serviced area taking into account the dynamic influence of the external environment. The presented methodology of working with data, performed in a separate automation subprogram, presented routing models, adapted to the transportation of dangerous goods, can be applied to various transport systems, including sea cargo ports and terminals, which determines the universality of the presented approach.

Electric delivery vehicle routing problem optimization model with time-varying traffic congestion

In order to plan a reasonable scheme for the fleet travel and minimize the energy consumption of electric vehicles during traffic congestion periods, while relieving the mileage anxiety of delivery personnel, the pure electric delivery vehicle routing problem considering time-varying traffic congestion was studied. Firstly , a novel mixed integer programming model (TD-EVRP) is established to step for its extended model of EVRP, which especially characterizes the time-varying traffic congestion state and measures the energy consumption of electric delivery vehicles. This model also refines its constraints including customer time window, vehicle load, and vehicle charging requirements. Secondly, a traffic congestion avoidance strategy is introduced into the model, which allows vehicles to park and charge at customer points during peak traffic periods in order to actively avoid unnecessary energy consumption caused by traffic congestion. Finally, the improved ant colony algorithm is designed to acquire the optimal solution.

Predicting Curb Side Parking Availability for Commercial Vehicle Loading Zones

Predicting Curb Side Parking Availability for Commercial Vehicle Loading Zones