235 results about "Road congestion" patented technology

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

The invention discloses a smart city public safety management system based on video monitoring, and relates to the technical field of public safety management, original video data streams and a real-time road data set D are collected in real time through an API interface, a time sequence feature matrix TSM and a road congestion coefficient Ck are generated, and the data integration efficiency is improved. The behavior feature extraction module identifies group behavior features through target detection and an optical flow algorithm, and encryption transmission guarantees privacy. The risk prediction module calculates a cross-regional risk index RRI based on federated learning, triggers a response mechanism and accurately early warns risks, and the dynamic decision module combines real-time data to generate a resource scheduling scheme and selects a proper path and callable resources. Through the combination of the execution feedback module and the driving iterative optimization module, the algorithm is dynamically adjusted, closed-loop management is formed, the problems of data splitting, response lag and model stiffness of a traditional system are solved, and the real-time performance, the accuracy and the scene adaptability of public safety management are remarkably improved.

The invention belongs to the technical field of data processing, and particularly relates to a vehicle brake reminding method and system based on big data, and the method comprises the steps: collecting environment data around a vehicle through a sensor in the vehicle, and collecting historical driving data, including driving habits of a driver, a vehicle driving track and a road congestion condition; formats of data from different sources are unified, subsequent processing and analysis are facilitated, and data from different sensors and external data sources are fused; based on the driving state of the current vehicle and the surrounding environment data, the road congestion condition and the accident occurrence probability are analyzed in real time, a machine learning or deep learning model is utilized, historical data and real-time data are combined, and the necessity and the emergency degree of braking under the current road condition are evaluated; according to the real-time analysis result and the brake risk assessment, whether the driver needs to be reminded to brake is determined, and the effects of predicting possible brake risks in the future, reminding the driver to pay attention to the front road condition and taking necessary precautionary measures are achieved.

The invention relates to an intelligent bus combined scheduling method and device and a storage medium, and relates to the field of scheduling optimization. Bus card swiping records are called to establish a card swiping station feature set, navigation station triggering is established to carry out navigation record matching analysis, and a space-time popularity prediction map is established; constructing a multi-source traffic flow data set to perform road congestion state prediction and construct a region-level road congestion state diagram; establishing a station passenger flow demand, route travel time and a congestion state according to the space-time popularity prediction map and the region-level road congestion state map; a multi-target scheduling channel is configured to take station passenger flow demands, route travel time and congestion states as input data, and route bus scheduling optimization is carried out through a passenger waiting time target, a scheduling cost target, a vehicle utilization rate target and a congestion influence target. The technical problems of insufficient data utilization, limited prediction precision and lack of flexibility of a scheduling strategy in bus scheduling are solved.

The invention discloses an intelligent logistics scheduling method based on Beidou positioning, and the method comprises the steps: obtaining the real-time position information of a vehicle, and constructing a traffic state matrix in combination with road congestion data; judging a weather influence area based on the weather change trend, and obtaining a weather risk area in combination with the traffic state matrix; obtaining inventory state data, and generating a resource demand priority list in combination with the weather risk area; adopting a multi-objective optimization algorithm, taking the resource demand priority list, the traffic state matrix and the weather risk area as input, obtaining an optimal path set, and generating a preliminary scheduling scheme; if a dynamic task insertion request is received, the task insertion feasibility is judged, the resource demand priority list is adjusted, the multi-objective optimization algorithm is operated again, and a corrected scheduling scheme is generated. Through multi-dimensional data fusion and intelligent algorithm optimization, the real-time performance, flexibility and reliability of logistics scheduling are improved, and an effective solution is provided for resource allocation in a complex environment.

This invention discloses a method, system, and storage medium for electric vehicle charging scheduling based on dynamic data fusion, relating to the field of data processing technology. The method includes the following steps: acquiring the status monitoring parameters of all standby charging interfaces and filtering out available charging piles that meet the requirements; classifying vehicles into charging-priority vehicles and cost-priority vehicles based on the real-time status data and environmental parameters of the electric vehicles requesting charging; for charging-priority vehicles, adjusting the charging pile search range, introducing output power to determine the target charging pile and performing a locking operation; for cost-priority vehicles, calculating the cost optimization coefficient based on arrival time prediction and electricity price fluctuations within the time period, and generating a preferred charging pile sequence. The user finally selects and locks the charging pile, while dynamically adjusting the locking time according to road congestion. This method achieves efficient allocation of charging pile resources, optimizing user experience and economic costs.

The invention provides a method, a device and equipment for predicting road congestion, and a computer storage medium, relates to the technical field of traffic, and is used for improving the speed of discovering congestion of a sensing blind area road section. The method comprises the steps that vehicle driving information corresponding to an upstream area and vehicle driving information corresponding to a downstream area of a target road section are continuously obtained, the upstream area is a vehicle driving-in area relative to the target road section, and the downstream area is a vehicle driving-in area relative to the target road section; wherein the following operations are executed every time the vehicle driving information of the downstream area at a first historical moment and the vehicle driving information of the upstream area at a second historical moment meet a preset flow condition, and it is determined that the target road section is congested, when the vehicle driving information of the downstream area at the first historical moment and the vehicle driving information of the upstream area at the second historical moment meet the preset flow condition; wherein the difference value between the first historical moment and the second historical moment is not greater than a preset time range.

The invention belongs to the field of urban traffic management, and provides a hierarchical dynamic routing system and method for a computing power-energy fusion network, and the method comprises the steps: dividing a fusion network graph into a plurality of region sub-graphs, and dividing a path table and a connection point path table based on each region sub-graph; monitoring the running states of the road, the edge computing node and the charging station in real time, and predicting the road congestion intensity, the edge computing node queuing estimation information and the charging constraint; and determining a candidate path set in the partition path table and the connection point path table according to an online request of the vehicle, screening each candidate path in the candidate path set based on the road congestion intensity, the edge computing node queuing estimation information and the charging constraint, and determining an optimal path. According to the method, online retrieval is converted into rapid combination and evaluation of small candidate sets from full-graph traversal; traffic, energy and computing power are incorporated into the same decision framework, and systematicness deviation caused by evaluation of the future at present is avoided.

The invention relates to the technical field of traffic management, in particular to a school entrance and exit pedestrian traffic management system and method based on the Internet of Things, and the method comprises the steps: obtaining the road traffic monitoring data of a school entrance and exit in a school leaving period; the road congestion degree is evaluated by extracting vehicle distribution data; evaluating the traffic interference degree by identifying the vehicle driving direction and the mixed driving behavior of the vehicle and the pedestrian; and evaluating the school leaving speed of the students according to the road congestion degree and the traffic interference degree, and adjusting the school leaving strategy according to the school leaving speed of the students. The school leaving speed of the students is evaluated by quantifying the road congestion degree and the traffic interference degree of the school entrances and exits, the school leaving efficiency of the students is improved, and meanwhile the road traffic safety risk of the school entrances and exits is reduced.

The invention relates to the technical field of traffic planning, in particular to a road congestion shunting system suitable for urban design, which comprises a node congestion identification module, a signal time sequence adjustment module, a path interference screening module, a signal priority ordering module and a linkage time period correction module. According to the method, a multi-direction traffic pressure concentration area is formed through path overlapping structure identification, an association boundary of a path group and a node state is opened, a green light starting time sequence is adjusted in combination with a column head static continuous behavior, a vehicle response and period synchronization relation is reconstructed, and structural proximity judgment is introduced in path conflict screening. High-interference paragraphs are identified by utilizing the correlation degree of guidance, speed and lane parameters, a node sorting mechanism dynamically generates priorities through the consistency of a frequent path and a queuing trend, real-time refreshing of a signal response sequence is realized, linkage rhythm correction is bidirectionally verified according to release delay and an adjacent node blocking state, a starting segment is dynamically adjusted, and the linkage rhythm is corrected. Chained dislocation is avoided.

The invention relates to a real-time reminding method and system based on vehicle driving behaviors and electronic equipment, and belongs to the field of intelligent driving. Comprising the steps of collecting monitoring data of vehicle driving in real time, inputting a pre-established violation behavior recognition model, and pre-judging violation behaviors in vehicle driving; if it is judged that the violation behavior exists or the non-standard driving action causes the safety risk, scanning a manifest file of an application program based on the vehicle-mounted voice service to verify the application capability; after verification is completed, a first reminding message is sent to the driving side so as to correct the driving behavior; and responding to the road congestion message displayed by the navigation equipment, generating a navigation plan in combination with the vehicle position and the environmental road information around the vehicle position, and sending a second reminding message to the driving side to re-plan the route. According to the scheme, the problems of lagging and unsafety caused when violation behaviors in driving cannot be judged in time or safety risks exist are solved, the optimal route can be planned again after correction, and the driving experience of a user is further improved.

The invention belongs to the technical field of logistics distribution, and particularly relates to an unmanned aerial vehicle and truck collaborative distribution method based on multi-target reinforcement learning, and the method comprises the steps: order clustering: a logistics service provider carries out the clustering of orders according to a K-means method, enables the orders with the similar distribution time and address to be divided into one order cluster, and carries out the clustering of the orders according to a K-means method; the unmanned aerial vehicles are delivered by the truck and the unmanned aerial vehicles carried by the truck; according to the NSGA-II algorithm, multiple targets such as path cost, distribution time and carbon emission are considered, and optimal paths of multiple schemes are provided; and planning an optimal path by adopting a reinforcement learning method. According to the cooperative logistics distribution method based on the unmanned aerial vehicle and the truck, the three targets of logistics distribution time, cost and carbon emission are considered, the dynamic conditions of low-altitude traffic restriction of the unmanned aerial vehicle, the road congestion condition of the truck, load constraint and the like are also considered, the optimal path is optimized by adopting a reinforcement learning mode, and the logistics distribution efficiency is improved. And parameter setting of reinforcement learning is automatically updated by using a non-dominated sorting algorithm, so that multi-dimensional optimal path selection can be provided for logistics service providers.

The invention discloses an intelligent highway abnormal event identification method based on multi-mode perception, and relates to the field of congestion identification, and the method comprises the steps: collecting the dynamic data of a highway in real time through a sensor network; feature extraction is carried out based on historical and real-time traffic flow data of each road section of the expressway, the road congestion condition is judged through feature fusion, and a congestion index is generated; based on the congestion event, triggering traceability analysis, and generating a feature vector of the source area; carrying out model training through a deep reinforcement learning model, obtaining a trained congestion reason identification and classification model, and obtaining a congestion reason label; and generating early warning information based on an identification result, and updating a navigation route through the linkage navigation platform. The method has the advantages that abnormal events on the expressway are accurately recognized by fusing video monitoring, radar, weather and toll station data, real-time traffic early warning is provided through the deep learning and prediction model, and road safety and traffic efficiency are improved.

The invention provides an urban road network traffic congestion state prediction method and system based on deep learning, and relates to the technical field of intelligent traffic control, and the method comprises the steps: obtaining congestion state image data of an urban road network, and carrying out the preprocessing to extract structured road congestion information; constructing a road network topological graph based on the road congestion information, and performing structure sensing state coding on the road network topological graph to generate a structure sensing state vector fusing local congestion features and global topological position information; and inputting the structure perception state vector into an improved deep learning prediction model, performing feature deepening and enhancement through a graph neural network which dynamically adjusts a graph structure and aggregates node information, and finally outputting road network congestion state prediction results of a plurality of time scales in the future. According to the method, accurate prediction of the urban road network congestion state can be realized, and reliable technical support is provided for intelligent traffic management, resource scheduling optimization, travel path planning and the like.

The invention provides a precision perception and decision-making integrated traffic control method, system and device, and relates to the technical field of traffic control, and the method comprises the steps: collecting high-precision traffic flow information and pedestrian and non-motor vehicle information of a preset lane range, and constructing a target traffic signal lamp preset range scene model; collecting a high-precision lane image set; vehicle type identification and vehicle distance identification are carried out to generate a road congestion coefficient; positioning the type and the position of the sudden traffic event; performing influence range prediction to obtain a traffic event prediction influence range; and performing traffic signal lamp control decision analysis to obtain a target control parameter, and performing a control decision of a target traffic signal lamp. The technical problems that an existing traffic control system is usually set as timing period signal lamp control, traffic demand differences in different time periods and different road conditions are neglected, flexible adjustment cannot be carried out when sudden traffic accidents occur, and congestion or traffic accidents are caused are solved.

The invention provides a vehicle cruise speed control method and device, a vehicle-mounted electronic control unit and a vehicle, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring the current speed and navigation map information of a vehicle in an intelligent driving mode; the navigation map information comprises road congestion information and vehicle positions of vehicles; determining a first variable speed of the vehicle according to the road congestion information and the vehicle position; and determining a first variable speed position of the vehicle based on the first variable speed and the current vehicle speed, and controlling the vehicle to perform variable speed cruise driving at the first variable speed position according to the first variable speed. The device is used for executing the method. The variable speed of the vehicle is determined according to the navigation map information of the vehicle, and the variable speed position is determined according to the variable speed and the current vehicle speed of the vehicle, so that the vehicle is controlled to perform variable-speed cruise driving at the variable speed position according to the variable speed. By adjusting the vehicle speed in advance, the probability of occurrence of accidents that the front vehicle collides with the rear vehicle and the rear vehicle collides with the rear vehicle is reduced, and therefore the road passing efficiency and safety are improved.

The invention discloses a traffic signal scheduling system and method based on road section congestion information, and relates to the technical field of traffic management and control. Comprising the following steps: S1, data acquisition and preprocessing: acquiring original traffic data of a road section in real time through a multi-source data acquisition unit, the original traffic data comprising traffic flow, vehicle speed, queuing length, occupancy and travel time; meanwhile, external environment data are obtained, wherein the external environment data comprise weather information, holiday and festival information and traffic event information; traffic data and external environment data are acquired in real time through the multi-source data acquisition unit, data quality is guaranteed through cleaning, denoising and standardized preprocessing, and the problem that data fusion of an existing system is insufficient is solved; by means of an adaptive weight learning model, historical traffic data, real-time data and environmental data are combined to dynamically adjust a congestion evaluation index weight, and the defect that a fixed or empirical weight is difficult to adapt to traffic dynamic changes is overcome.

The invention belongs to the technical field of material allocation intellectualization, and particularly relates to a medical emergency material management allocation method, system, equipment and medium based on a large model, and the method comprises the steps: receiving the related information of an emergency, and generating the type and number of recommended medical materials through calling a medical large model and a general large model; the recommended medical supplies are audited and adjusted, and a finally allocated medical supply list is determined; calculating warehouses meeting reserve conditions according to the medical material demand information and the reserve of the warehouses, and screening out a recommended warehouse list; the optimal path of the unmanned aerial vehicle from the warehouse to the event occurrence site and the optimal path of the emergency vehicle from the warehouse to the event occurrence site are planned, and the paths of the emergency vehicle are calibrated in combination with the real-time road congestion data; and sending the material allocation information and the path planning to a warehouse administrator, an emergency vehicle driver and an unmanned aerial vehicle driving system. And the emergency response speed and the rescue efficiency are improved.

The invention discloses a vehicle guiding method and device, equipment, a storage medium and a product. The method comprises the following steps: acquiring main road vehicle information and ramp vehicle information of a target road section; determining a main road congestion condition according to the main road vehicle information, and determining a ramp congestion condition according to the ramp vehicle information; according to the main road congestion condition and the ramp congestion condition, whether a rightmost lane of the main road is reserved or not is judged; and sending corresponding guide information to the main road vehicle of the target road section according to the judgment result. According to the method, the ramp congestion condition is reduced, the traffic efficiency is improved, and the risk of accidents is reduced.

The invention relates to the technical field of image data processing, in particular to an intelligent management system for smart city monitoring big data, and the system obtains a road monitoring image at each moment, screens out a judgment point from pixel points in the road monitoring image, determines the attribution degree of the judgment point, and obtains the road monitoring image at each moment. And the processor is used for screening out target pixel points from the judgment points, forming a target connected domain by using adjacent target pixel points, determining an updated gradient amplitude of the pixel points in the target connected domain, obtaining a road monitoring gradient enhanced image at each moment, and determining a road congestion value at each moment so as to judge whether the road is currently congested or not. According to the method, by amplifying the gradient features of the pixel points in the target connected domain, the adverse effect on road congestion judgment caused by poor edge detection effect due to image blurring is eliminated.

The invention provides a road congestion detection method, device and equipment and a storage medium, which can realize accurate and real-time detection of highway congestion conditions and effectively avoid one-sidedness of single parameter detection. The method comprises the following steps: acquiring a lane image of each lane in a target road section through a data acquisition module; analyzing the lane images to obtain vehicle features of vehicles corresponding to the lanes; determining the traffic flow and the lane occupancy of each lane in the target road section according to the vehicle characteristics; determining a comprehensive congestion index of the target road section according to the traffic flow and the lane occupancy; and if the comprehensive congestion index exceeds a congestion judgment threshold, determining that the target road section is in a congestion state.

The invention provides a vehicle early warning method and system based on low-speed vehicle group detection, and the method comprises the steps: classifying vehicle driving roads based on vehicle driving road data and climate data obtained in real time, and dynamically setting a corresponding target vehicle speed threshold value; constructing a plurality of dynamic windows, calculating the total number of vehicles in the dynamic windows and the number of vehicles with the vehicle speeds meeting a target vehicle speed threshold value through the dynamic windows, and obtaining the number of low-speed vehicles; calculating the distance between adjacent vehicles and the speed gradient of the adjacent vehicles based on the number of the low-speed vehicles and the plurality of dynamic windows; and based on the distance between the adjacent vehicles and the speed gradient of the adjacent vehicles, identifying a low-speed vehicle group and sending an early warning to the vehicles. According to the invention, the problem of road congestion caused by difficulty in timely early warning of vehicles running on a road due to incapability of identifying local low-speed vehicle groups in the prior art is solved. According to the invention, road low-speed vehicle groups are accurately identified, and early warning is timely sent to remind vehicles, so that road congestion is dealt with, and the traffic efficiency is improved.

The invention belongs to the technical field of fingerprint identification, and provides an intelligent pure electric vehicle braking energy recovery control method, which comprises the following steps of: acquiring data such as front images, vehicle distance speed, traffic flow and road congestion by virtue of a camera, a millimeter wave radar and an Internet of Vehicles module; identifying the road condition type by using a convolutional neural network and a logic judgment rule through a vehicle-mounted processor; driver operation and vehicle state data are recorded through an ECU, and aggressive, mild, economical and other driving habits are identified by using a clustering analysis algorithm and a K-Means algorithm; the method comprises the following steps: constructing an MLP neural network prediction model by taking vehicle speed, road condition type and driving habit type as an input layer and taking future vehicle speed change and braking demand as an output layer, and gradually reducing kinetic energy recovery strength in advance according to a specific time interval and amplitude when it is predicted that a vehicle is about to enter a congested road section and a driver is aggressive habit; and adjusting the recovery force according to a preset strategy curve when the road condition changes suddenly during driving.

The invention provides a route recommendation method and device based on traffic trip, electronic equipment and a storage medium, and relates to the technical field of urban traffic planning, and the method comprises the steps: determining a first number of alternative traffic routes in road network topological data according to a trip starting position and a trip terminal position; extracting traffic flow information matched with the first number of alternative traffic paths from the real-time traffic demand data; according to the real-time traffic flow information, carrying out traffic cost measurement and calculation on each alternative traffic path to obtain target cost measurement and calculation data corresponding to each alternative traffic path; and screening the first number of alternative traffic paths based on the target cost measurement data to obtain a target recommended path. According to the method, the optimal traffic travel path is screened based on the real-time traffic demand data, the defect that a traditional static distribution model cannot reflect the condition of road congestion is overcome, and the traffic travel path recommendation effect is improved.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

The embodiment of the invention discloses a vehicle driving monitoring method and device, electronic equipment and a medium. The method comprises the following steps: monitoring vehicle data and road data on a target road through a congestion monitoring camera, and calculating a road traffic jam index of the target road; if the road traffic jam index of the target section on the target road exceeds the preset traffic jam index, scheduling a target number of violation monitoring cameras to move to a target position range on the moving track; wherein the laying direction of the moving track is consistent with the direction of the target road; the violation monitoring camera moves on the moving track; the target position range corresponds to the actual position of the target interval; and monitoring whether the vehicle in the target interval runs illegally or not through the violation monitoring camera. According to the scheme, under the condition of road congestion, the traffic violation monitoring camera on the mobile track is dispatched to monitor the congested road section in a targeted manner, the vehicle traffic violation behavior is determined in time, and the road traffic safety is improved.

The invention relates to a road congestion detection method and device, a vehicle and a medium. The method comprises the following steps: acquiring a current frame image of a road scene around the vehicle and inertial data of the vehicle; and obtaining congestion degree information of the road according to the current frame image and the inertial data.

The invention relates to the technical field of traffic signal control, and provides a traffic accident surrounding traffic signal response method and system, and the method comprises the steps: obtaining main road traffic flow parameters and secondary road exit vehicle existence information; judging whether the trunk road is in a trunk road congestion state or not; judging whether the secondary road is in a secondary road exit abnormal retention state or not; identifying a key exit passive blocking event; a control instruction is sent to an upstream intersection of the target intersection corresponding to the key exit passive blocking event, so that the upstream intersection of the target intersection controls a main road direction signal lamp to be red, and a traffic gap is created; turning the signal lamp in the main road direction of the target intersection into green light, and emptying vehicles in the main road area in front of the key exit of the secondary road; after it is confirmed that the vehicle is emptied, the vehicle at the key exit is released; and after the release is completed, judging that the emergency condition is relieved and recovering traffic signal control. The method has the advantage of avoiding traffic delay and potential public safety risks.

The present application provides a range extender control method and an electronic device. The method obtains road condition data of the advanced driver assistance system in the vehicle and navigation data of the navigation system, and then, based on the road condition data and the navigation data, detects long-term congestion events and short-term congestion events in the road ahead of the vehicle, and determines the congestion state of the road ahead. According to the congestion state and the event detection results, the operation of the range extender in the vehicle is controlled to achieve range extension control based on road congestion conditions. This method can combine the advanced driver assistance system and the navigation system to accurately determine road congestion conditions, and combine the congestion state and the event detection results of long-term and short-term events to determine the specific adjustment strategy of the range extender, which can avoid frequent start-stop and high-load operation of the range extender in a congested environment, extend the service life of the range extender, and reduce the noise and emissions of the range extender.