New energy automobile energy efficiency remote management method and device

Abstract

The invention discloses a new energy automobile energy efficiency remote management method and device, and relates to the related field of data processing, and the method comprises the steps: reading an execution task of a new energy automobile through a central management system, and obtaining a real-time data set through intelligent communication equipment installed on the new energy automobile; activating an analysis network, executing driving behavior analysis of the real-time data set, establishing a driving behavior analysis result, and generating a driving energy efficiency suggestion; historical data of the new energy automobile are called, electric quantity consumption fitting prediction is carried out according to the historical data, the driving energy efficiency suggestion and the driving behavior analysis result, and a fitting prediction result is established; adjusting power consumption data, fitting a prediction result according to the power consumption data, and executing a task to perform energy efficiency charging matching of the path charging station; and performing energy efficiency management according to the energy efficiency charging matching result and the driving energy efficiency suggestion. The technical problem that existing new energy automobile energy efficiency management is inaccurate is solved, and the technical effect of improving the accuracy of energy efficiency management is achieved.

Description

Technical Field

[0001] The present application relates to data processing related fields, and in particular to a method and device for remotely managing energy efficiency of new energy vehicles. Background Art

[0002] New energy vehicles, with their low-carbon, environmentally friendly and energy-saving characteristics, have become an important development direction of the future automotive industry. However, new energy vehicles face many challenges in the process of promotion and use, especially in terms of energy efficiency management and endurance. Energy efficiency management of new energy vehicles is of great significance to improving vehicle endurance, reducing operating costs and reducing environmental pollution. Effective energy efficiency management can help drivers plan driving routes reasonably and optimize driving behavior, thereby reducing unnecessary power consumption. In terms of energy efficiency management of new energy vehicles, existing methods mainly rely on the vehicle's own energy management system, which performs preliminary analysis and control of the vehicle's energy consumption by monitoring parameters such as battery status, driving speed, and acceleration. However, this method mainly relies on the vehicle's own sensor data, lacks comprehensiveness and real-time performance, resulting in a lack of pertinence and accuracy in energy efficiency management, making it difficult to achieve the optimal energy efficiency management strategy.

[0003] Among the current relevant technologies, the energy efficiency management of new energy vehicles has the technical problem of inaccurate energy efficiency management. Summary of the invention

[0004] The present application provides a method and device for remote management of energy efficiency of new energy vehicles, uses a central management system to read the execution tasks of new energy vehicles, obtains real-time data sets collected by on-board sensors through intelligent communication equipment, activates the analysis network of the central management system, performs driving behavior analysis on the real-time data sets, establishes driving behavior analysis results, and generates driving energy efficiency suggestions, calls historical data of new energy vehicles, combines driving energy efficiency suggestions and driving behavior analysis results, performs power consumption fitting prediction, establishes fitting prediction results, performs energy efficiency charging matching of path charging stations based on power data, fitting prediction results and execution tasks in the real-time data sets, and performs energy efficiency management based on energy efficiency charging matching results and driving energy efficiency suggestions, thereby achieving precise control and optimization of the energy efficiency of new energy vehicles and achieving the technical effect of improving the accuracy of energy efficiency management.

[0005] This application provides a method for remote management of energy efficiency of new energy vehicles, including: The execution tasks of the new energy vehicle are read by the central management system, and the real-time data set is obtained through the intelligent communication device installed in the new energy vehicle, and the real-time data set is collected by the on-board sensor installed in the new energy vehicle; the analysis network of the central management system is activated, the driving behavior analysis of the real-time data set is performed, the driving behavior analysis results are established, and driving energy efficiency suggestions are generated; the historical data of the new energy vehicle is called, and the power consumption fitting prediction is performed according to the historical data, the driving energy efficiency suggestions, and the driving behavior analysis results, and the fitting prediction results are established; the power data in the real-time data set is called, and the energy efficiency charging matching of the path charging station is performed according to the power data, the fitting prediction results, and the execution tasks; energy efficiency management is performed according to the energy efficiency charging matching results and the driving energy efficiency suggestions.

[0006] In a possible implementation, the activating the analysis network of the central management system, performing driving behavior analysis of the real-time data set, establishing the driving behavior analysis result, and performing the following processing: After preprocessing the real-time data set, the data is classified into vehicle driving data, vehicle control data, and environmental data; the average speed is calculated based on the vehicle driving data to establish the average speed feature; the acceleration and braking features are extracted based on the vehicle driving data to establish the sudden acceleration feature and the sudden braking feature; the vehicle control mode is obtained based on the vehicle control data, and the first behavior compensation is generated based on the control mode; the road characteristics and traffic characteristics in the environmental data are extracted, and the second behavior compensation is established according to the road characteristics and traffic characteristics; the driving behavior analysis results are established using the average speed characteristics, sudden acceleration characteristics, sudden braking characteristics, first behavior compensation, and second behavior compensation.

[0007] In a possible implementation, the driving behavior analysis result is established by using the average speed feature, the sudden acceleration feature, the sudden braking feature, the first behavior compensation, and the second behavior compensation, and the following processing is performed: Get the compensation coefficient for the first line: ; Use the compensation coefficient of the first behavior to compensate for the sudden acceleration characteristics and sudden braking characteristics, and establish the first compensation result; establish the compensation coefficient of the second behavior compensation: ;in, Characterizes road slope compensation, Characterize road congestion compensation, is the slope angle, is the slope influence coefficient, is the traffic impact coefficient, is the traffic density; the first compensation result is recompensed by the compensation coefficient of the second behavior compensation to establish the final compensation result; the driving behavior analysis result is established using the average speed characteristics and the final compensation result.

[0008] In a possible implementation, the energy efficiency charging matching of the path charging station is performed based on the power data, the fitting prediction result, and the execution task, and the following processing is performed: The driving route of the new energy vehicle is established by executing the task, and the charging stations associated with the driving route are obtained, wherein the charging stations include general charging stations and special charging stations; the power consumption end point is identified according to the power data and the fitting prediction results, and the charging stations are screened according to the power consumption end point identification results to generate the screening results; the arrival time of the charging stations in the screening results is predicted for the new energy vehicle, and the time period electricity price is determined according to the arrival time prediction results, and the first energy efficiency penalty coefficient is generated based on the time period electricity price; the charging adaptation analysis is performed using the arrival time prediction results, the general charging stations and the special charging stations, and the second energy efficiency penalty coefficient is established based on the charging adaptation analysis results; the path adaptation degree of the charging stations in the screening results is evaluated, and a third energy efficiency penalty coefficient is established; the charging stations in the screening results are screened using the first energy efficiency penalty coefficient, the second energy efficiency penalty coefficient and the third energy efficiency penalty coefficient, and the energy efficiency charging matching is completed according to the screening results.

[0009] In a possible implementation, the charging adaptation analysis is performed using the arrival time prediction result, the general charging station, and the dedicated charging station, and the following processing is performed: The charging station queues in the screening results are predicted based on the arrival time prediction results, and the waiting adaptation analysis results are established based on the queue prediction results. The charging interfaces of the charging stations in the screening results are read according to the universal charging station and special charging station identifiers, and the charging speed adaptation evaluation is performed based on the charging interface to establish the charging speed adaptation analysis results. The waiting adaptation analysis results and the charging speed adaptation analysis results are used as the charging adaptation analysis results.

[0010] In a possible implementation, the historical data of new energy vehicles is called, and a power consumption fitting prediction is performed according to the historical data, driving energy efficiency suggestions, and driving behavior analysis results, and a fitting prediction result is established, and the following processing is performed: The driving behavior analysis results are used to fit the power consumption under non-optimization and establish a baseline power consumption; historical data is used to evaluate the execution of driving energy efficiency recommendations, and the execution evaluation results are used to generate power compensation; and power consumption fitting prediction is completed based on power compensation and baseline power consumption.

[0011] In a possible implementation, the following processing is performed: Determine whether the execution task has a subtask level; if a subtask level exists, establish a level influence coefficient of the subtask; perform a level influence penalty on the energy efficiency charging matching result through the level influence coefficient, and reconstruct the energy efficiency charging matching result according to the level influence penalty result.

[0012] This application also provides a new energy vehicle energy efficiency remote management device, including: A real-time data set acquisition module, the real-time data set acquisition module is used to use the central management system to read the execution tasks of the new energy vehicle, and obtain the real-time data set through the intelligent communication device installed in the new energy vehicle, and the real-time data set is collected by the on-board sensor installed in the new energy vehicle; a driving behavior analysis module, the driving behavior analysis module is used to activate the analysis network of the central management system, perform driving behavior analysis of the real-time data set, establish driving behavior analysis results, and generate driving energy efficiency suggestions; a power consumption fitting prediction module, the power consumption fitting prediction module is used to call the historical data of the new energy vehicle, perform power consumption fitting prediction according to the historical data, driving energy efficiency suggestions, and driving behavior analysis results, and establish fitting prediction results; an energy efficiency charging matching module, the energy efficiency charging matching module is used to call the power data in the real-time data set, and perform energy efficiency charging matching of the path charging station according to the power data, fitting prediction results, and execution tasks; an energy efficiency management module, the energy efficiency management module is used to perform energy efficiency management according to the energy efficiency charging matching results and driving energy efficiency suggestions.

[0013] The method and device for remote management of energy efficiency of new energy vehicles proposed in this application first use the central management system to read the execution tasks of the new energy vehicles, and obtain the real-time data set through the intelligent communication equipment installed in the new energy vehicles. The real-time data set is collected by the on-board sensors installed in the new energy vehicles, and then the analysis network of the central management system is activated to perform driving behavior analysis of the real-time data set, establish driving behavior analysis results, and generate driving energy efficiency suggestions. Then, the historical data of the new energy vehicle is called, and the power consumption fitting prediction is performed based on the historical data, driving energy efficiency suggestions, and driving behavior analysis results. The fitting prediction results are established, and then the power data in the real-time data set is called, and the energy efficiency charging matching of the path charging station is performed based on the power data, fitting prediction results, and execution tasks. Finally, energy efficiency management is performed based on the energy efficiency charging matching results and driving energy efficiency suggestions, thereby achieving the technical effect of improving the accuracy of energy efficiency management. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A flow chart of a method for remotely managing energy efficiency of new energy vehicles provided in an embodiment of the present application.

[0016] Figure 2A schematic diagram of the structure of a new energy vehicle energy efficiency remote management device provided in an embodiment of the present application.

[0017] Explanation of reference numerals: real-time data set acquisition module 10 , driving behavior analysis module 20 , power consumption fitting prediction module 30 , energy efficiency charging matching module 40 , energy efficiency management module 50 . DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides a method for remotely managing the energy efficiency of new energy vehicles, such as Figure 1 As shown, the method includes: Step S100, use the central management system to read the execution tasks of the new energy vehicle, and obtain the real-time data set through the intelligent communication device installed in the new energy vehicle, and the real-time data set is obtained through the vehicle-mounted sensors installed in the new energy vehicle. Specifically, the central management system is a remote system responsible for managing and monitoring the energy efficiency of new energy vehicles, which is deployed in the data center. The central management system reads the task information that the new energy vehicle currently needs to perform, including the starting point, destination, and estimated completion time of the task. Through the intelligent communication devices installed on the new energy vehicle (such as vehicle-mounted Wi-Fi, 4G / 5G modules, etc.), data is collected in real time from the vehicle-mounted sensors (such as speed sensors, acceleration sensors, GPS locators, etc.) to form a real-time data set.

[0022] Step S200, activate the analysis network of the central management system, perform driving behavior analysis of the real-time data set, establish driving behavior analysis results, and generate driving energy efficiency recommendations. Specifically, the analysis network of the central management system preprocesses the real-time data set to remove noise and invalid data, and then extracts features such as speed, acceleration, and braking. Using the extracted features, the driver's driving behavior is evaluated and analyzed to establish driving behavior analysis results. Based on the driving behavior analysis results, the central management system generates recommendations for improving energy efficiency, such as smooth driving, reducing sudden acceleration and braking, etc.

[0023] In a possible implementation, the analysis network of the central management system is activated, the driving behavior analysis of the real-time data set is performed, and the driving behavior analysis result is established. Step S200 further includes step S210, after preprocessing the real-time data set, the data is classified into vehicle driving data, vehicle control data, and environmental data. Specifically, the real-time data set uploaded from the new energy vehicle is preprocessed, including data cleaning (removing invalid or abnormal data), data smoothing (reducing data fluctuations), data normalization (converting data to the same scale), etc. The preprocessed data is divided into three categories: vehicle driving data (such as speed, driving distance, driving time, etc.), vehicle control data (such as throttle opening, braking force, steering angle, etc.), and environmental data (such as road type, weather conditions, traffic flow, etc.). Step S220, average speed calculation is performed based on vehicle driving data to establish average speed features. Specifically, based on the speed information in the vehicle driving data, the average speed of the new energy vehicle over a period of time is calculated, and the average speed is used as a feature of driving behavior analysis. Step S230, acceleration and braking feature extraction is performed based on vehicle driving data, and sudden acceleration features and sudden braking features are established. Specifically, based on the acceleration and braking information in the vehicle driving data, the sudden acceleration and sudden braking features are extracted. Among them, the sudden acceleration feature is obtained by calculating the number of times or the duration that the absolute value of the acceleration exceeds a certain threshold. The sudden braking feature is obtained by calculating the number of times or the duration that the absolute value of the braking force exceeds a certain threshold. Step S240, based on the vehicle control data, the control mode of the vehicle is obtained, and the first behavior compensation is generated based on the control mode. Specifically, based on the vehicle control data, the control mode of the new energy vehicle (such as economic mode, sports mode, etc.) is obtained. According to the control mode, the first behavior compensation is generated. For example, in the economic mode, the weights of sudden acceleration and sudden braking will be reduced to encourage smooth driving.

[0024] Step S250, extract the road characteristics and traffic characteristics in the environmental data, and establish the second behavior compensation according to the road characteristics and traffic characteristics. Specifically, extract the road characteristics (such as road type, slope, etc.) and traffic characteristics (such as traffic flow, traffic density, etc.) in the environmental data. According to the road characteristics and traffic characteristics, establish the second behavior compensation. For example, when driving on a slope, it is necessary to increase energy consumption, so the weight of the driving behavior analysis result is adjusted accordingly. Step S260, use the average speed characteristics, rapid acceleration characteristics, sudden braking characteristics, first behavior compensation, and second behavior compensation to establish the driving behavior analysis result. Specifically, the average speed characteristics, rapid acceleration characteristics, sudden braking characteristics, first behavior compensation and second behavior compensation are combined to establish the driving behavior analysis result. The driving behavior analysis result is a series of characteristic values ​​used to evaluate the driving energy efficiency of new energy vehicles. This implementation method comprehensively considers the impact of multiple factors on driving behavior, including vehicle driving data, vehicle control data and environmental data. By preprocessing, classifying and extracting features from these data, accurate and comprehensive driving behavior analysis results are established. At the same time, by introducing a behavior compensation mechanism, the driving behavior analysis results are adjusted according to different conditions to make them more in line with actual conditions, thereby improving the accuracy of driving energy efficiency assessment.

[0025] In a possible implementation, the driving behavior analysis result is established by using the average speed feature, the sudden acceleration feature, the sudden braking feature, the first behavior compensation, and the second behavior compensation. Step S260 further includes step S261, obtaining a compensation coefficient of the first behavior compensation: , the sudden acceleration feature and the sudden braking feature are score compensated by the compensation coefficient of the first behavior compensation, and the first compensation result is established. Specifically, the current control mode of the new energy vehicle, such as the economic mode or the sports mode, is determined according to the vehicle control data, and the corresponding first behavior compensation coefficient is obtained according to the control mode, wherein the compensation coefficient of the economic mode is 1.0, and the compensation coefficient of the sports mode is 1.1. The first behavior compensation coefficient is used to score compensate the sudden acceleration feature and the sudden braking feature, that is, in the economic mode, the scores of these features will not change; in the sports mode, the scores of these features will increase proportionally to reflect the changes in driving behavior brought about by the sports mode. After the score compensation, the first compensation result is established, which includes the adjusted scores of the sudden acceleration feature and the sudden braking feature.

[0026] Step S262, establishing a compensation coefficient for the second behavior compensation: ;in, Characterizes road slope compensation, Characterize road congestion compensation, is the slope angle, is the slope influence coefficient, is the traffic impact coefficient, The first compensation result is recompensed by the compensation coefficient of the second behavior compensation to establish the final compensation result. Specifically, the environmental data includes road characteristics and traffic characteristics. According to the road characteristics and traffic characteristics, the compensation coefficient of the second behavior compensation is calculated, including road slope compensation and road congestion compensation. Among them, the road slope compensation is calculated based on the slope angle and the slope influence coefficient, and the road congestion compensation is calculated based on the traffic density and the traffic influence coefficient. The first compensation result is recompensed using the compensation coefficient of the second behavior compensation, that is, the influence of road and traffic conditions on driving behavior is considered, and the score of the driving behavior analysis result is further adjusted. After recompensation, the final compensation result is established, which includes the driving behavior feature score after two compensations. Step S263, the driving behavior analysis result is established using the average speed feature and the final compensation result. Specifically, the average speed feature is combined with the final compensation result (adjusted sudden acceleration feature and sudden braking feature) to establish the driving behavior analysis result, which reflects the driving behavior energy efficiency of new energy vehicles under the current control mode and environmental conditions. This implementation method introduces the first behavior compensation and second behavior compensation mechanisms to adjust the driving behavior analysis results according to different control modes and environmental conditions, making them more in line with actual conditions and improving the accuracy and reliability of driving behavior analysis.

[0027] Step S300, call the historical data of the new energy vehicle, perform a power consumption fitting prediction based on the historical data, driving energy efficiency suggestions, and driving behavior analysis results, and establish a fitting prediction result. Specifically, call the historical data of the new energy vehicle from the database, including historical driving data, energy consumption data, etc. Combined with the historical data, driving energy efficiency suggestions, and driving behavior analysis results, perform a power consumption fitting prediction, and establish a fitting prediction result, that is, predict the future power consumption.

[0028] In a possible implementation, the historical data of the new energy vehicle is called, and the power consumption fitting prediction is performed according to the historical data, the driving energy efficiency suggestions, and the driving behavior analysis results, and the fitting prediction results are established. Step S300 further includes step S310, using the driving behavior analysis results to fit the power consumption under non-optimization and establish a benchmark power consumption. Specifically, using the driving behavior analysis results, combined with the basic parameters of the new energy vehicle (such as battery capacity, motor efficiency, etc.), a power consumption model under a non-optimized state is established. This model simulates the power that the new energy vehicle will consume according to the current driving behavior without any energy efficiency optimization measures. By running the benchmark model, the power that the new energy vehicle is expected to consume under a given driving behavior is calculated, and this power is called the benchmark power consumption. Step S320, using historical data to evaluate the execution of driving energy efficiency suggestions, and using the execution evaluation results to generate power compensation. Specifically, from the historical data of the new energy vehicle, find driving records similar to the current driving behavior, these records include the driving behavior data, power consumption data, and whether the driving energy efficiency suggestions were executed at that time. For each matched driving record, the execution degree of the driving energy efficiency suggestion is calculated by comparing the difference between the actual driving behavior and the recommended driving behavior. The smaller the difference, the higher the execution degree. According to the execution degree, a power compensation value is calculated, which reflects the amount of power that can be saved if the driving energy efficiency suggestion is followed compared with the current driving behavior. Step S330, complete the power consumption fitting prediction based on the power compensation and the reference power consumption. Specifically, the reference power consumption calculated in step S310 is combined with the power compensation calculated in step S320, and the power compensation is subtracted from the reference power consumption to obtain a new power consumption prediction value, which reflects the amount of power that the new energy vehicle is expected to consume in consideration of the execution degree of the driving energy efficiency suggestion. This new power consumption prediction value is used as the fitting prediction result. This implementation method comprehensively considers the impact of driving behavior on power consumption by combining the power consumption fitting under non-optimization and the execution degree evaluation of driving energy efficiency suggestions, improves the accuracy of power consumption fitting prediction, and helps to formulate a more reasonable energy efficiency management strategy.

[0029] Step S400, call the power data in the real-time data set, and perform energy efficiency charging matching of the path charging station based on the power data, fitting prediction results, and execution tasks. Specifically, establish the driving path of the new energy vehicle according to the execution task, and obtain the charging station information associated with the driving path. According to the power data and fitting prediction results, identify the power consumption end point and select the appropriate charging station.

[0030] In a possible implementation, the energy efficiency charging matching of the path charging station is performed according to the power data, the fitting prediction results, and the execution task. Step S400 further includes step S410, using the execution task to establish the driving path of the new energy vehicle, and obtaining the charging station associated with the driving path, and the charging station includes a general charging station and a special charging station. Specifically, according to the current location of the new energy vehicle and the execution task (such as the delivery location of the goods), the optimal driving path is generated using the map service. All charging stations on the driving path are queried through the database or API interface, including general charging stations (open to all brands of vehicles) and special charging stations (open only to specific brands or models). Step S420, according to the power data and the fitting prediction results, the power consumption end point is identified, and the charging station is screened according to the power consumption end point identification result to generate a screening result. Specifically, the current power, expected driving distance and fitting prediction results of the new energy vehicle (power consumption prediction based on historical data and driving behavior analysis) are analyzed to predict the power consumption end point of the vehicle before completing the current task (i.e., the location where the power may be exhausted), and the charging station within its range is screened according to the location. Step S430, predict the arrival time of the charging station in the screening results for the new energy vehicle, determine the time period electricity price based on the arrival time prediction result, and generate the first energy efficiency penalty coefficient based on the time period electricity price. Specifically, predict the arrival time of each screened charging station according to the driving path and the current traffic conditions. According to the time period electricity price of the charging station (different time periods have different electricity prices, which are divided into peak, flat and valley electricity prices), calculate the electricity price at each predicted arrival time point, and generate the first energy efficiency penalty coefficient based on this (the higher the electricity price, the greater the penalty coefficient), which is used to evaluate the charging cost. Step S440, perform charging adaptation analysis using the arrival time prediction results, general charging stations and special charging stations, and establish the second energy efficiency penalty coefficient based on the charging adaptation analysis results. Specifically, analyze the charging speed, queuing situation (if any) and charging efficiency of general charging stations and special charging stations, calculate the charging time at different charging stations according to the predicted arrival time and the charging capacity of the charging station, and generate the second energy efficiency penalty coefficient based on this (the longer the charging time, the greater the penalty coefficient; the longer the queuing time, the greater the penalty coefficient), which is used to evaluate the charging efficiency and time cost.

[0031] Step S450, evaluate the path fitness of the charging stations in the screening results and establish a third energy efficiency penalty coefficient. Specifically, evaluate the fitness of the screened charging stations with the driving path, including the distance, degree of deviation and impact on the overall driving time. According to these evaluation results, generate a third energy efficiency penalty coefficient (the farther the path deviates or the more the driving time is increased, the larger the penalty coefficient), which is used to evaluate the path deviation and driving time cost. Step S460, use the first energy efficiency penalty coefficient, the second energy efficiency penalty coefficient, and the third energy efficiency penalty coefficient to screen the charging stations in the screening results, and complete energy efficiency charging matching according to the screening results. Specifically, the first, second and third energy efficiency penalty coefficients are comprehensively considered to sort or score the screened charging stations. Select the optimal charging station for charging matching according to the sorting or scoring results. This implementation method ensures that new energy vehicles are charged in time before completing the task by accurately predicting power consumption, arrival time and charging capacity of the charging station, while reducing charging costs and time costs, and optimizing the energy efficiency management of new energy vehicles.

[0032] In a possible implementation, the charging adaptation analysis is performed using the arrival time prediction results, the general charging stations and the dedicated charging stations, and step S440 further includes step S441, which predicts the charging station queue in the screening results according to the arrival time prediction results, and establishes the waiting adaptation analysis results based on the queue prediction results. Specifically, based on the current position, speed, remaining power of the new energy vehicle and the geographical location of the target charging station, the estimated time for the new energy vehicle to arrive at each charging station in the screening results is predicted. Based on historical data and real-time information (such as the number of vehicles in the current charging station, the average charging time, etc.), the queue situation of the charging station when the new energy vehicle arrives is predicted, including the estimated number of vehicles waiting for charging, the estimated waiting time, etc. Based on the queue prediction results, the waiting time of the new energy vehicle at each charging station is evaluated, and the impact of the waiting time on energy efficiency (such as power consumption during the waiting period, time cost, etc.) is considered to establish the waiting adaptation analysis results, which reflects the energy efficiency loss caused by waiting when charging at different charging stations. Step S442, read the charging interface of the charging station in the screening result according to the identification of the general charging station and the special charging station, perform charging speed adaptation evaluation based on the charging interface, and establish the charging speed adaptation analysis result. Specifically, read the type (such as fast charging, slow charging, etc.) and quantity of the charging interface of each charging station in the screening result according to the identification of the general charging station and the special charging station. According to the charging demand of the new energy vehicle and the type of the charging interface of the charging station, evaluate the charging speed of the new energy vehicle at each charging station, including considering factors such as the compatibility of the charging interface, the charging power, and the battery capacity and charging strategy of the new energy vehicle, and establish the charging speed adaptation analysis result, which is an evaluation of the charging speed of the new energy vehicle at each charging station. Step S443, take the waiting adaptation analysis result and the charging speed adaptation analysis result as the charging adaptation analysis result. Specifically, the waiting adaptation analysis result and the charging speed adaptation analysis result are integrated to form the charging adaptation analysis result, which comprehensively considers the impact of waiting time and charging speed on energy efficiency, and is a comprehensive evaluation of the energy efficiency impact of charging of new energy vehicles at each charging station. This implementation method improves the comprehensiveness and accuracy of charging adaptation analysis by comprehensively considering waiting time and charging speed factors, thereby optimizing the charging strategy of new energy vehicles and improving the level of energy efficiency management.

[0033] In a possible implementation, step S400 further includes step S470, determining whether there is a subtask level for the execution task. Specifically, the task information currently being executed by the new energy vehicle is read from the central management system. Analyze whether the task information contains subtasks, and determine the level of each subtask, wherein the subtask level is divided based on factors such as the urgency, importance, and time requirements of the task. Step S480, if there is a subtask level, establish the level influence coefficient of the subtask. Specifically, determine the division criteria of the subtask level, such as urgent, important, general, etc., and assign an influence coefficient to each subtask level. This coefficient is a numerical value used to indicate the degree of influence of the subtask of the level on the energy efficiency charging matching result, wherein urgent or important subtasks will be assigned a higher influence coefficient. Step S490, perform a level influence penalty on the energy efficiency charging matching result through the level influence coefficient, and reconstruct the energy efficiency charging matching result according to the level influence penalty result. Specifically, according to the level influence coefficient of the subtask, the energy efficiency charging matching result is punished by the level influence, including adjusting the priority of the charging station. According to the result of the level impact penalty, the energy efficiency charging matching result is reconstructed to ensure that the energy efficiency is maximized while meeting the subtask level requirements. This implementation method considers the level of the subtask and gives priority to the charging needs of urgent or important tasks, ensuring that the energy efficiency of new energy vehicles is maximized while meeting the execution requirements.

[0034] Step S500, energy efficiency management is performed according to the energy efficiency charging matching results and driving energy efficiency suggestions. Specifically, according to the energy efficiency charging matching results and driving energy efficiency suggestions, energy efficiency management is performed on new energy vehicles, such as adjusting driving behavior, selecting the best charging station, etc., to improve energy utilization efficiency and reduce energy consumption. The embodiment of the present application uses a central management system to read the execution tasks of new energy vehicles, obtains real-time data sets collected by vehicle-mounted sensors through intelligent communication devices, activates the analysis network of the central management system, performs driving behavior analysis on the real-time data sets, establishes driving behavior analysis results, and generates driving energy efficiency suggestions, calls historical data of new energy vehicles, combines driving energy efficiency suggestions and driving behavior analysis results, performs power consumption fitting prediction, establishes fitting prediction results, performs energy efficiency charging matching of path charging stations according to power data, fitting prediction results and execution tasks in the real-time data sets, and performs energy efficiency management according to energy efficiency charging matching results and driving energy efficiency suggestions. Technical means such as these realize accurate control and optimization of the energy efficiency of new energy vehicles and achieve the technical effect of improving the accuracy of energy efficiency management.

[0035] In the above, refer to Figure 1 The remote management method of energy efficiency of new energy vehicles according to an embodiment of the present invention is described in detail. Figure 2 A new energy vehicle energy efficiency remote management device according to an embodiment of the present invention is described.

[0036] The new energy vehicle energy efficiency remote management device according to the embodiment of the present invention is used to solve the technical problem of inaccurate management of existing new energy vehicle energy efficiency management, and achieve the technical effect of improving the accuracy of energy efficiency management. The new energy vehicle energy efficiency remote management device includes: a real-time data set acquisition module 10, a driving behavior analysis module 20, a power consumption fitting prediction module 30, an energy efficiency charging matching module 40, and an energy efficiency management module 50.

[0037] The real-time data set acquisition module 10 is used to use the central management system to read the execution tasks of the new energy vehicle, and to obtain the real-time data set through the intelligent communication device installed in the new energy vehicle, and the real-time data set is collected by the vehicle-mounted sensors installed in the new energy vehicle; the driving behavior analysis module 20 is used to activate the analysis network of the central management system, perform driving behavior analysis of the real-time data set, establish driving behavior analysis results, and generate driving energy efficiency suggestions; the power consumption fitting prediction module 30 is used to call the historical data of the new energy vehicle, perform power consumption fitting prediction based on the historical data, driving energy efficiency suggestions, and driving behavior analysis results, and establish fitting prediction results; the energy efficiency charging matching module 40 is used to call the power data in the real-time data set, and perform energy efficiency charging matching of the path charging station based on the power data, fitting prediction results, and execution tasks; the energy efficiency management module 50 is used to perform energy efficiency management based on the energy efficiency charging matching results and driving energy efficiency suggestions.

[0038] The specific configuration of the driving behavior analysis module 20 will be described in detail below. As described above, the analysis network of the central management system is activated, the driving behavior analysis of the real-time data set is performed, and the driving behavior analysis result is established. The driving behavior analysis module 20 may further include: a data classification unit is used to classify the data into vehicle driving data, vehicle control data, and environmental data after preprocessing the real-time data set; an average speed calculation unit is used to calculate the average speed based on the vehicle driving data and establish an average speed feature; a feature extraction unit is used to extract acceleration and braking features based on the vehicle driving data and establish a sudden acceleration feature and a sudden braking feature; a first behavior compensation generation unit is used to obtain the control mode of the vehicle based on the vehicle control data and generate a first behavior compensation based on the control mode; a second behavior compensation establishment unit is used to extract road features and traffic features in the environmental data and establish a second behavior compensation according to the road features and traffic features; a driving behavior analysis result establishment unit is used to establish a driving behavior analysis result using the average speed feature, the sudden acceleration feature, the sudden braking feature, the first behavior compensation, and the second behavior compensation.

[0039] The driving behavior analysis result is established by using the average speed feature, the sudden acceleration feature, the sudden braking feature, the first behavior compensation, and the second behavior compensation. The driving behavior analysis result establishment unit may further include: a first behavior compensation coefficient acquisition subunit for acquiring a compensation coefficient for the first behavior compensation: ; The first compensation result establishment subunit is used to score compensate the sudden acceleration feature and the sudden braking feature through the compensation coefficient of the first behavior compensation, and establish the first compensation result; the second behavior compensation coefficient establishment subunit is used to establish the compensation coefficient of the second behavior compensation: ;in, Characterizes road slope compensation, Characterize road congestion compensation, is the slope angle, is the slope influence coefficient, is the traffic impact coefficient, is the traffic density; the final compensation result establishing subunit is used to re-compensate the first compensation result through the compensation coefficient of the second behavior compensation to establish the final compensation result; the driving behavior analysis result establishing subunit is used to establish the driving behavior analysis result using the average speed characteristics and the final compensation result.

[0040] The specific configuration of the energy efficiency charging matching module 40 will be described in detail below. As described above, the energy efficiency charging matching of the path charging station is performed based on the power data, the fitting prediction results, and the execution task. The energy efficiency charging matching module 40 may further include: a charging station acquisition unit is used to establish the driving path of the new energy vehicle by using the execution task, and obtain the charging stations associated with the driving path, and the charging stations include general charging stations and special charging stations; a charging station screening unit is used to identify the power consumption end point according to the power data and the fitting prediction results, and screen the charging stations based on the power consumption end point identification results to generate a screening result; a first energy efficiency penalty coefficient generation unit is used to screen the arrival time of the new energy vehicle at the charging station in the screening result Prediction, determine the time period electricity price with the arrival time prediction result, and generate a first energy efficiency penalty coefficient based on the time period electricity price; the second energy efficiency penalty coefficient establishment unit is used to use the arrival time prediction result, the general charging station and the special charging station to perform charging adaptation analysis, and establish the second energy efficiency penalty coefficient based on the charging adaptation analysis result; the third energy efficiency penalty coefficient establishment unit is used to evaluate the path fitness of the charging stations in the screening results and establish the third energy efficiency penalty coefficient; the energy efficiency charging matching unit is used to use the first energy efficiency penalty coefficient, the second energy efficiency penalty coefficient, and the third energy efficiency penalty coefficient to screen the charging stations in the screening results, and complete energy efficiency charging matching according to the screening results.

[0041] Among them, charging adaptation analysis is performed using the arrival time prediction results, general charging stations and special charging stations, and the second energy efficiency penalty coefficient establishing unit may further include: a waiting adaptation analysis result establishing subunit is used to predict the charging station queue in the screening results according to the arrival time prediction results, and establish the waiting adaptation analysis results with the queue prediction results; a charging speed adaptation evaluation subunit is used to read the charging interface of the charging station in the screening results according to the general charging station and special charging station identifiers, perform charging speed adaptation evaluation based on the charging interface, and establish the charging speed adaptation analysis results; the charging adaptation analysis result establishing subunit is used to use the waiting adaptation analysis results and the charging speed adaptation analysis results as the charging adaptation analysis results.

[0042] The specific configuration of the power consumption fitting prediction module 30 will be described in detail below. As described above, the historical data of the new energy vehicle is called, and the power consumption fitting prediction is performed according to the historical data, the driving energy efficiency suggestions, and the driving behavior analysis results, and the fitting prediction results are established. The power consumption fitting prediction module 30 may further include: a reference power consumption establishment unit for performing non-optimized power consumption fitting using the driving behavior analysis results to establish a reference power consumption; a power compensation generation unit for evaluating the execution of the driving energy efficiency suggestions using the historical data, and generating power compensation using the execution evaluation results; and a power consumption fitting prediction unit for completing the power consumption fitting prediction based on the power compensation and the reference power consumption.

[0043] Among them, the energy efficiency charging matching module 40 may further include: a judgment unit for judging whether there is a subtask level for the execution task; a level influence coefficient establishment unit for establishing the level influence coefficient of the subtask if there is a subtask level; a level influence penalty unit for performing a level influence penalty on the energy efficiency charging matching result through the level influence coefficient, and reconstructing the energy efficiency charging matching result according to the level influence penalty result.

[0044] The new energy vehicle energy efficiency remote management device provided in the embodiment of the present invention can execute the new energy vehicle energy efficiency remote management method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0045] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0046] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A remote management method for energy efficiency of new energy vehicles, characterized in that: The method comprises: Using the central management system to read the execution tasks of the new energy vehicle, and obtaining the real-time data set through the intelligent communication device installed in the new energy vehicle, the real-time data set is collected by the on-board sensor installed in the new energy vehicle; Activate the central management system's analysis network to perform driving behavior analysis of real-time data sets, build driving behavior analysis results, and generate driving energy efficiency recommendations; Call the historical data of new energy vehicles, make a fitting prediction of power consumption based on the historical data, driving energy efficiency suggestions, and driving behavior analysis results, and establish a fitting prediction result; Call the power data in the real-time data set, and perform energy efficiency charging matching of the path charging station based on the power data, fitting prediction results, and executing tasks; Perform energy efficiency management based on energy efficiency charging matching results and driving energy efficiency recommendations.

2. The remote management method for energy efficiency of new energy vehicles according to claim 1, characterized in that: The activating the analysis network of the central management system, performing driving behavior analysis of the real-time data set, and establishing driving behavior analysis results also includes: After preprocessing the real-time data set, the data is classified into vehicle driving data, vehicle control data, and environmental data; Calculate the average speed based on the vehicle driving data and establish the average speed feature; Extract acceleration and braking features based on vehicle driving data, and establish sudden acceleration features and sudden braking features; Acquire a control mode of the vehicle based on the vehicle control data, and generate a first behavior compensation based on the control mode; Extract road features and traffic features from environmental data, and establish second behavior compensation based on the road features and traffic features; The driving behavior analysis results are established using average speed characteristics, sudden acceleration characteristics, sudden braking characteristics, first behavior compensation, and second behavior compensation.

3. The remote management method for energy efficiency of new energy vehicles according to claim 2, characterized in that: The method of establishing the driving behavior analysis result by using the average speed feature, the sudden acceleration feature, the sudden braking feature, the first behavior compensation, and the second behavior compensation further includes: Get the compensation coefficient for the first line: ; The sudden acceleration feature and the sudden braking feature are scored and compensated by using the compensation coefficient of the first behavior to establish a first compensation result; Establish the compensation coefficient for the second line of compensation: ; in, Characterizes road slope compensation, Characterize road congestion compensation, is the slope angle, is the slope influence coefficient, is the traffic impact coefficient, is the traffic density; Re-compensate the first compensation result by using the compensation coefficient of the second line to establish a final compensation result; The driving behavior analysis results are established using the average speed characteristics and the final compensation results.

4. The remote management method for energy efficiency of new energy vehicles according to claim 1, characterized in that: The energy efficiency charging matching of the path charging station according to the power data, the fitting prediction results, and the execution of the task also includes: Establishing a driving route of the new energy vehicle by executing the task, and obtaining charging stations associated with the driving route, wherein the charging stations include general charging stations and special charging stations; The power consumption endpoint is identified according to the power data and the fitting prediction results, and the charging stations are screened according to the power consumption endpoint identification results to generate screening results; Predicting the arrival time of the new energy vehicles at the charging stations in the screening results, determining the time period electricity price based on the arrival time prediction result, and generating a first energy efficiency penalty coefficient based on the time period electricity price; Using the arrival time prediction results, general charging stations and dedicated charging stations to conduct charging adaptation analysis, and establishing a second energy efficiency penalty coefficient based on the charging adaptation analysis results; Evaluate the path suitability of the charging stations in the screening results and establish a third energy efficiency penalty coefficient; The charging stations in the screening results are screened using the first energy efficiency penalty coefficient, the second energy efficiency penalty coefficient, and the third energy efficiency penalty coefficient, and energy efficiency charging matching is completed according to the screening results.

5. The remote management method for energy efficiency of new energy vehicles according to claim 4, characterized in that: The charging adaptation analysis using the arrival time prediction result, the general charging station and the dedicated charging station also includes: According to the arrival time prediction results, the charging station queue prediction within the screening results is performed, and the waiting adaptation analysis results are established based on the queue prediction results; Read the charging interface of the charging station in the screening result according to the universal charging station and the dedicated charging station identification, perform charging speed adaptation evaluation based on the charging interface, and establish a charging speed adaptation analysis result; The waiting adaptation analysis result and the charging speed adaptation analysis result are taken as the charging adaptation analysis result.

6. The remote management method for energy efficiency of new energy vehicles according to claim 1, characterized in that: The calling of historical data of new energy vehicles, performing power consumption fitting prediction based on historical data, driving energy efficiency suggestions, and driving behavior analysis results, and establishing fitting prediction results also includes: Use the driving behavior analysis results to fit the power consumption under non-optimization and establish the baseline power consumption; Use historical data to evaluate the implementation of driving energy efficiency recommendations, and use the implementation evaluation results to generate power compensation; Complete the power consumption fitting prediction based on power compensation and benchmark power consumption.

7. The remote management method for energy efficiency of new energy vehicles according to claim 1, characterized in that: The method further comprises: Determine whether the execution task has a subtask level; If there is a subtask level, then establish the subtask level influence coefficient; The level impact penalty of the energy-efficient charging matching result is performed through the level impact coefficient, and the energy-efficient charging matching result is reconstructed according to the level impact penalty result.

8. A remote management device for energy efficiency of new energy vehicles, characterized in that: The device is used to implement the new energy vehicle energy efficiency remote management method according to any one of claims 1 to 7, and the device comprises: A real-time data set acquisition module, which is used to read the execution tasks of the new energy vehicle using the central management system and acquire the real-time data set through the intelligent communication device installed in the new energy vehicle. The real-time data set is acquired through the vehicle-mounted sensors installed in the new energy vehicle; A driving behavior analysis module, the driving behavior analysis module is used to activate the analysis network of the central management system, perform driving behavior analysis of the real-time data set, establish driving behavior analysis results, and generate driving energy efficiency recommendations; A power consumption fitting prediction module, which is used to call historical data of new energy vehicles, perform power consumption fitting prediction based on historical data, driving energy efficiency suggestions, and driving behavior analysis results, and establish a fitting prediction result; An energy efficiency charging matching module, which is used to call the power data in the real-time data set, and perform energy efficiency charging matching of the path charging station based on the power data, fitting prediction results, and executing tasks; An energy efficiency management module is used to perform energy efficiency management based on energy efficiency charging matching results and driving energy efficiency suggestions.

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