Dynamic thermo-mechanical responses of road-soft ground system under vehicle load and daily temperature variation

A complete road-soft ground model is established in this paper to study the dynamic responses caused by vehicle loads and/or daily temperature variation.A dynamic thermo-elastic model is applied to capturing the behavior of the rigid pavement,the base course,and the subgrade,while the soft ground is characterized using a dynamic thermo-poroelastic model.Solutions to the road-soft ground system are derived in the Laplace-Hankel transform domain.The time domain solutions are obtained using an integration approach.The temperature,thermal stress,pore water pressure,and displacement responses caused by the vehicle load and the daily temperature variation are presented.Results show that obvious temperature change mainly exists within 0.3 m of the road when subjected to the daily temperature variation,whereas the stress responses can still be found in deeper places because of the thermal swelling/shrinkage deformation within the upper road structures.Moreover,it is important to consider the coupling effects of the vehicle load and the daily temperature variation when calculating the dynamic responses inside the road-soft ground system.

Electric Vehicle Charging Capacity of Distribution Network Considering Conventional Load Composition

At present,the large-scale access to electric vehicles(EVs)is exerting considerable pressure on the distribution network.Hence,it is particularly important to analyze the capacity of the distribution network to accommodate EVs.To this end,we propose a method for analyzing the EV capacity of the distribution network by considering the composition of the conventional load.First,the analysis and pretreatment methods for the distribution network architecture and conventional load are proposed.Second,the charging behavior of an EVis simulated by combining the Monte Carlo method and the trip chain theory.After obtaining the temporal and spatial distribution of the EV charging load,themethod of distribution according to the proportion of the same type of conventional load among the nodes is adopted to integrate the EV charging load with the conventional load of the distribution network.By adjusting the EV ownership,the EV capacity in the distribution network is analyzed and solved on the basis of the following indices:node voltage,branch current,and transformer capacity.Finally,by considering the 10-kV distribution network in some areas of an actual city as an example,we show that the proposed analysis method can obtain a more reasonable number of EVs to be accommodated in the distribution network.

A Novel Ultra Short-Term Load Forecasting Method for Regional Electric Vehicle Charging Load Using Charging Pile Usage Degree

Electric vehicle(EV)charging load is greatly affected by many traffic factors,such as road congestion.Accurate ultra short-term load forecasting(STLF)results for regional EV charging load are important to the scheduling plan of regional charging load,which can be derived to realize the optimal vehicle to grid benefit.In this paper,a regional-level EV ultra STLF method is proposed and discussed.The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles,and then constructed by our collected EV charging transactiondata in thefield.Secondly,these usagedegrees are combinedwithhistorical charging loadvalues toform the inputmatrix for the deep learning based load predictionmodel.Finally,long short-termmemory(LSTM)neural network is used to construct EV charging load forecastingmodel,which is trained by the formed inputmatrix.The comparison experiment proves that the proposed method in this paper has higher prediction accuracy compared with traditionalmethods.In addition,load characteristic index for the fluctuation of adjacent day load and adjacent week load are proposed by us,and these fluctuation factors are used to assess the prediction accuracy of the EV charging load,together with the mean absolute percentage error(MAPE).

A Novel Load Balancing Strategy of Software-Defined Cloud/Fog Networking in the Internet of Vehicles

The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance compute,storage and networking services,the IoV still suffers with high processing latency,less mobility support and location awareness.In this paper,we integrate fog computing and software defined networking(SDN) to address those problems.Fog computing extends computing and storing to the edge of the network,which could decrease latency remarkably in addition to enable mobility support and location awareness.Meanwhile,SDN provides flexible centralized control and global knowledge to the network.In order to apply the software defined cloud/fog networking(SDCFN) architecture in the IoV effectively,we propose a novel SDN-based modified constrained optimization particle swarm optimization(MPSO-CO) algorithm which uses the reverse of the flight of mutation particles and linear decrease inertia weight to enhance the performance of constrained optimization particle swarm optimization(PSO-CO).The simulation results indicate that the SDN-based MPSO-CO algorithm could effectively decrease the latency and improve the quality of service(QoS) in the SDCFN architecture.

Test and numerical investigations on static and dynamic characteristics of extra-wide concrete self-anchored suspension bridge under vehicle loads

The present work is aimed at studying the mechanic properties of the extra-wide concrete self-anchored suspension bridge under static and dynamic vehicle loads. Based on the field test using 12 heavy trucks and finite element simulations, the static deformations of different components, stress increments and distributions of the girder, as well as the vibration characteristics and damping ratio of the Hunan Road Bridge were analyzed, which is the widest self-anchored suspension bridge in China at present. The dynamic responses were calculated using the Newmark-β integration method assisted by the simulation models of bridge and vehicles, the influences on the dynamic impact coefficient(DIC) brought by the vehicle parameters, girder width, eccentricity travel and deck flatness were also researched. The spatial effect of the girder is obvious due to the extra width, which performs as the stress increments distribute unevenly along the transverse direction, and the girder deflections and stress increments of the upper plate change as a "V" and "M" shape respectively under the symmetrical vehicle loads affected by the shear lag effect, cross slope and local effect of the wheels, the maximum of stress increments are located in the junctions with the inner webs. The obvious girder torsional deformation and the apparent unevenness of the hanger forces between the two cable planes under the eccentric vehicle loads, together with the mode shapes such as the girder transverse bending and torsion which appear relatively earlier, all reflect the weakened torsional rigidity of the extra-wide girder. The transverse displacements of towers are more obvious than the longitudinal ones. As for the influences on the DIC, the static effect of the heavier vehicles plays a major role when pass through with a higher speed and the changes of vehicle suspension stiffness generate greater impacts than the suspension damp. The values of DIC in the vehicle-running side during the eccentric travel, affected by the restricts from the static effects of the eccentric moving trucks, are significantly smaller than the vehicle-free side, the increase in the road roughness is the most sensitive one among the above influential factors. The results could provide references for the design, static and dynamic response analysis of the similar extra-wide suspension bridges.

Analysis of permanent deformations of railway embankments under repeated vehicle loadings in permafrost regions

By large-scale dynamic tests carried out on a traditional sand-gravel embankment at the Beilu River section along the Qinghai-Tibet Railroad, we collected the acceleration waveforms close to the railway tracks when trains passed. The dynamic train loading was converted into an equivalent creep stress, using an equivalent static force method. Also, the creep equation of frozen soil was introduced according to the results of frozen soil rheological triaxial tests. A coupled creep model based on a time-hardening power function rule and the Druker-Prager yield and failure criterion was estab- lished to analyze the creep effects of a plain fill embankment under repeated train loads. The temperature field of the embankment in the permafrost area was set at the current geothermal conditions. As a result, the permanent deformation of the embankment under train loading was obtained, and the permanent deformation under the train loads to the total embankment deformation was also analyzed.

Dynamic reliability analysis of supercavity vehicle with stochastic parameters under impact loads

With dynamic reliability problems of stochastic parameters,supercavity vehicle is subject to impact loads.The supercavity vehicle is modeled by using eight-node super-parametric shell elements.The tail impact loads of supercavity vehicle structures are simplified into two stationary random processes with a certain phase difference,and the random excitations are transformed into sinusoidal ones in terms of the pseudo excitation method.The stress response of stochastic structure can be obtained through combining Newmark method with pseudo excitation perturbation method,and then all required digital features for dynamic reliability of supercavity vehicle have be calculated.The expressions of the mean value and the variance of dynamic reliability of supercavity vehicle with stochastic parameters are educed on the basis of the Poisson formula of calculating dynamic reliability.Finally,the influence of the randomness of structural parameters on the dynamic reliability is analyzed.And the feasibility and availability of this method were validated by comparing with the Monte Carlo method.

Dynamic Response of Double-Sided Loess Slope under Vehicle Load

In order to verify and study the dynamic response law on the double-sided loess slope under the action of the waves generated by automobile traffic,we select a double-sided loess slope from the long section of Anzi Road as the research object.Both field investigations and on-site monitoring processes are conducted,for the purpose of providing robust basis for road protection in these conditions.In detail,vehicleinduced vibration signals are different according to different vehicle types,speeds,as well as positions,and thus are collected,respectively.Based on the statistical analysis of the signals,the vibration response law and frequency spectrum characteristics of the slope are summarized.The results show that:①The dynamic response of the doublesided loess slope increases as the vehicle load increases,and the strong vibration response area is located in the middle of the side slope;②When the vehicle load is small,the vibration wave amplification effect is obvious.On the contrary,when the vehicle load is large,the vibration wave amplification effect is weakened;③The spectrum distribution of the X-direction wave is single-peak shape,and the dominant frequency is concentrated in 30-50 Hz;the frequency spectrum distribution of the Zdirection wave shows a multi-peak shape,and the dominant frequency is concentrated in 20-180 Hz;④The vibration wave propagates in the slope.The frequency change shows little correlation with the type,speed and position of the vehicle,and instead,it is mainly determined by the slope itself.This study reveals the dynamic response on doubled-sided loess slopes and provides both theoretical and practical significance for the road protection in such situations.

Proactive Load Balancing Mechanism for Fog Computing Supported by Parked Vehicles in IoV-SDN

Internet of Vehicles(IoV)is a new style of vehicular ad hoc network that is used to connect the sensors of each vehicle with each other and with other vehicles’sensors through the internet.These sensors generate different tasks that should be analyzed and processed in some given period of time.They send the tasks to the cloud servers but these sending operations increase bandwidth consumption and latency.Fog computing is a simple cloud at the network edge that is used to process the jobs in a short period of time instead of sending them to cloud computing facilities.In some situations,fog computing cannot execute some tasks due to lack of resources.Thus,in these situations it transfers them to cloud computing that leads to an increase in latency and bandwidth occupation again.Moreover,several fog servers may be fuelled while other servers are empty.This implies an unfair distribution of jobs.In this research study,we shall merge the software defined network(SDN)with IoV and fog computing and use the parked vehicle as assistant fog computing node.This can improve the capabilities of the fog computing layer and help in decreasing the number of migrated tasks to the cloud servers.This increases the ratio of time sensitive tasks that meet the deadline.In addition,a new load balancing strategy is proposed.It works proactively to balance the load locally and globally by the local fog managers and SDN controller,respectively.The simulation experiments show that the proposed system is more efficient than VANET-Fog-Cloud and IoV-Fog-Cloud frameworks in terms of average response time and percentage of bandwidth consumption,meeting the deadline,and resource utilization.

Vehicle Routing Problem with Non-Full Loadof Less Freight Quantity

Freight quantity is usually less than the half of vehicle capacity,and the effectiveness of transportation utilizing a single vehicle is not satisfactory enough. In this paper,a sequential heuristic algorithm is presented, whichconsists of clustering and chaining. Lastly, the method is applied to a casestudy.

Joint position optimization,user association,and resource allocation for load balancing in UAV-assisted wireless networks

Unbalanced traffic distribution in cellular networks results in congestion and degrades spectrum efficiency.To tackle this problem,we propose an Unmanned Aerial Vehicle(UAV)-assisted wireless network in which the UAV acts as an aerial relay to divert some traffic from the overloaded cell to its adjacent underloaded cell.To fully exploit its potential,we jointly optimize the UAV position,user association,spectrum allocation,and power allocation to maximize the sum-log-rate of all users in two adjacent cells.To tackle the complicated joint optimization problem,we first design a genetic-based algorithm to optimize the UAV position.Then,we simplify the problem by theoretical analysis and devise a low-complexity algorithm according to the branch-and-bound method,so as to obtain the optimal user association and spectrum allocation schemes.We further propose an iterative power allocation algorithm based on the sequential convex approximation theory.The simulation results indicate that the proposed UAV-assisted wireless network is superior to the terrestrial network in both utility and throughput,and the proposed algorithms can substantially improve the network performance in comparison with the other schemes.

Dynamic characteristics of hydraulic power steering system with accumulator in load-haul-dump vehicle

Using hydraulic power steering system of model EIMCO 922 load-haul-dump vehicle as a simulation example, the dynamic characteristics of hydraulic power steering system in load-haul-dump vehicle were simulated and discussed with SIMULINK software and hydraulic control theory. The results show that the dynamic characteristics of hydraulic power steering system are improved obviously by using bladder accumulator, the hydraulic power steering system of model EIMCO 922 load-haul-dump vehicle generates vibration at the initial stage under the normal steering condition of pulse input, and its static response time is 0.25 s shorter than that without bladder accumulator. Under the normal steering working condition, the capacity of steering accumulator for absorbing pulse is directly proportional to the cross section area of connecting pipeline, and inversely proportional to the length of connecting pipeline. At the same time, the precharge pressure of nitrogen in steering accumulator should be 60%80% of the rated minimum working pressure of hydraulic power steering system. Under the abnormal steering working condition, the steering cylinder piston may obtain higher motion velocity, and the dynamic response velocity of hydraulic power steering system can be increased by reducing the pressure drop of hydraulic pipelines between the accumulator and steering cylinder and by increasing the rated pressure of hydraulic power steering system, but the dynamic characteristics of hydraulic power steering system in load-haul-dump vehicle have nothing to do with the precharge pressure of nitrogen in steering accumulator.

Impact of Electric Vehicle Charging on Power Load Based on TOU Price

Large-scale electric vehicle charging has a significant impact on power grid load, disorderly charging will increase power grid peak load. This article proposes an orderly charging mechanism based on TOU price. To build an orderly charging model by researching TOU price and user price reaction model. This article research the impact of electric vehicle charging on grid load by orderly charging model. With this model the grid’s peak and valley characteristics, the utilization of charging equipment, the economics of grid operation can all be improved.

Optimal Operation of Electric Vehicles and Distributed Generation Resources in Smart Grid Considering Load Management

Technology advancement and the global tendency to use renewable energy in distributed generation units in the distribution network have been proposed as sources of energy supply.Despite the complexity of their protection,as well as the operation of distributed generation resources in the distribution network,factors such as improving reliability,increasing production capacity of the distribution network,stabilizing the voltage of the distribution network,reducing peak clipping losses,as well as economic and environmental considerations,have expanded the influence of distributed generation(DG)resources in the distribution network.The location of DG sources and their capacity are the key factors in the effectiveness of distributed generation in the voltage stability of distribution systems.Nowadays,along with the scattered production sources of electric vehicles with the ability to connect to the network,due to having an energy storage system,they are known as valuable resources that can provide various services to the power system.These vehicles can empower the grid or be used as a storage supply source when parked and connected to the grid.This paper introduces and studies a two-stage planning framework for the concurrent management of many electric vehicles and distributed generation resources with private ownership.In the first stage,the aim is to increase the profit of electric vehicles and distributed generation sources;finally,the purpose is to reduce operating costs.The proposed scheduling framework is tested on a distribution network connected to bus 5 of the RBTS sample network.Besides distributed generation sources and electric vehicles,we integrate time-consistent load management into the system.Due to distributed generation sources such as photovoltaic systems and wind turbines and the studied design in the modeling,we use the Taguchi TOAT algorithm to generate and reduce the scenario to ensure the uncertainty in renewable energy.MATLAB software is used to solve the problem and select the optimal answer.

Management of Charging Load of Electric Vehicles for Optimal Capacity Utilisation of Distribution Transformers

A de-centralised load management technique exploiting the flexibility in the charging of Electric Vehicles (EVs) is presented. Two charging regimes are assumed. The Controlled Charging Regime (CCR) between 16:30 hours and 06:00 hours of the next day and the Uncontrolled Charging Regime (UCR) between 06:00 hours and 16:30 hours of the same day. During the CCR, the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers (EVSCCs) at EV owners’ premises and the EV Load Controller (EVLC) at the local LV distribution substation. The EVLC sorts the EVs batteries in ascending order of their states of charge (SoC) and sends command signals for charging to as many EVs as the transformer could allow at that interval based on the condition of the transformer as analysed by the Distribution Transformer Monitor (DTM). A real and typical urban LV area distribution network in Great Britain (GB) is used as the case study. The technique is applied on the LV area when its transformer is carrying the future load demand of the area on a typical winter weekday in the year 2050. To achieve the load management, load demand of the LV area network is decomposed into Non-EV load and EV load. The load on the transformer is managed by varying the EV load in an optimisation objective function which maximises the capacity utilisation of the transformer subject to operational constraints and non-disruption of daily trips of EV owners. Results show that with the proposed load management technique, LV distribution networks could accommodate high uptake of EVs without compromising the useful normal life expectancy of distribution transformers before the need for capacity reinforcement.

Power Quality Issues Concerning Photovoltaic Generation and Electrical Vehicle Loads in Distribution Grids

The high utilization level of renewable generation including residential photovoltaic (PV) systems together with the uncontrolled charging of electric vehicles (EVs) can have a significant impact on load characteristics in distribution networks. Harmonic content of PV generation, EV charging loads, and their influence on power quality indicators in residential distribution networks are discussed in this paper. For investigating likely power quality scenarios, PV generation and EV charging measurement results including current harmonic amplitude and phase angle values are used and compared with present load characteristics. Different modelling scenarios are analysed and a simplified model of harmonics in PVs and EVs is offered. The results of the study show moderate additional harmonic distortion in residential load current and voltage distortion at the substation’s busbar when PV generation and EV loading are added. The scenarios presented in this paper can be further used for modelling the actual harmonic loads of the PVs and EVs in distribution networks.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

Study on the Fuzzy Reliability of the Bearing in Planetary Transmission System for Heavy-Loaded Vehicles

Based on the fuzzy mathematics theory and reliability design methods, the fuzzy reliability of the rolling bearing in the planetary transmission system for heavy-loaded vehicles is analysed. The reliability and the fuzzy life expectancy of bearings in a practical planetary gear box are calculated in detail. The results show that the methods give a reference to the correct assessment of such type of bearing. Some measures to improve the reliability are also

Anti-slip security assessment between main cable and saddle of the middle pylon under extreme vehicle loads

The traffic condition of Taizhou Yangtze River Bridge is obtained to a certain extent, according to the traffic investigation nearby Taizhou Bridge. The statistical characteristics of the traffic conditions are acquired by statistical analysis. Simulation of the extreme vehicle loads and the sensitive analysis of load parameters are carried out based on these data complemented, which would guide the determination of the frictional coefficient between the main cables and the saddle.