Method and Device for Remote Management of Energy Efficiency of New Energy Vehicles

Abstract

The present invention discloses a method and device for remote management of the energy efficiency of new energy vehicles, which relates to the field of data processing. The method includes: using a central management system to read the execution tasks of new energy vehicles, and obtaining a real-time data set through intelligent communication devices installed in new energy vehicles; activating an analysis network, performing driving behavior analysis on the real-time data set, establishing driving behavior analysis results, and generating driving energy efficiency suggestions; calling the historical data of new energy vehicles, and performing power consumption fitting prediction according to the historical data, driving energy efficiency suggestions, and driving behavior analysis results to establish fitting prediction results; calling power consumption data, and performing energy efficiency charging matching for path charging stations according to the power consumption data, fitting prediction results, and execution tasks; and performing energy efficiency management according to the energy efficiency charging matching results and driving energy efficiency suggestions. It solves the technical problem of inaccurate management existing in the existing energy efficiency management of new energy vehicles, and achieves the technical effect of improving the accuracy of energy efficiency management.

Description

Technical Field

[0001] This application relates to the field of data processing, and particularly to a method and device for remote management of the energy efficiency of new energy vehicles. Background Art

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

[0003] In the related technologies at the present stage, there is a technical problem of inaccurate energy efficiency management in the energy efficiency management of new energy vehicles. Summary of the Invention

[0004] This application provides a method and device for remote management of the energy efficiency of new energy vehicles. By using the central management system to read the execution tasks of new energy vehicles, obtaining a real-time data set collected by on-vehicle sensors through intelligent communication devices, activating the analysis network of the central management system, performing driving behavior analysis on the real-time data set, establishing the results of driving behavior analysis, and generating driving energy efficiency suggestions, calling the historical data of new energy vehicles, combining the driving energy efficiency suggestions and the results of driving behavior analysis, performing power consumption fitting prediction, establishing the results of fitting prediction, performing energy efficiency charging matching for path charging stations according to the power data, fitting prediction results, and execution tasks in the real-time data set, and performing energy efficiency management according to the results of energy efficiency charging matching and driving energy efficiency suggestions, etc., precise control and optimization of the energy efficiency of new energy vehicles are achieved, and the technical effect of improving the accuracy of energy efficiency management is achieved.

[0005] This application provides a method for remote management of the energy efficiency of new energy vehicles, including:

[0006] The central management system reads the execution tasks of new energy vehicles and obtains real-time data sets through intelligent communication devices installed in new energy vehicles. The real-time data sets are collected by in-vehicle sensors installed in new energy vehicles; activate the analysis network of the central management system, perform driving behavior analysis on the real-time data sets, establish driving behavior analysis results, and generate driving energy efficiency suggestions; call the historical data of new energy vehicles, and perform power consumption fitting prediction based on the historical data, driving energy efficiency suggestions, and driving behavior analysis results to establish fitting prediction results; call the power data in the real-time data sets, and perform energy efficiency charging matching for path charging stations based on the power data, fitting prediction results, and execution tasks; perform energy efficiency management based on the energy efficiency charging matching results and driving energy efficiency suggestions.

[0007] In a possible implementation, when activating the analysis network of the central management system, performing driving behavior analysis on the real-time data sets, and establishing driving behavior analysis results, the following processing is performed:

[0008] After preprocessing the real-time data sets, classify the data into vehicle driving data, vehicle control data, and environmental data; calculate the average speed based on the vehicle driving data to establish an average speed feature; extract acceleration and braking features based on the vehicle driving data to establish a sharp acceleration feature and a sharp braking feature; obtain the 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 the environmental data, and establish a second behavior compensation based on the road features and traffic features; establish driving behavior analysis results using the average speed feature, sharp acceleration feature, sharp braking feature, first behavior compensation, and second behavior compensation.

[0009] In a possible implementation, when establishing driving behavior analysis results using the average speed feature, sharp acceleration feature, sharp braking feature, first behavior compensation, and second behavior compensation, the following processing is performed:

[0010] Obtain the compensation coefficient of the first behavior compensation: [[ID=1^4]]; perform score compensation on the sharp acceleration feature and the sharp braking feature through the compensation coefficient of the first behavior compensation to establish a first compensation result; establish the compensation coefficient of the second behavior compensation: ; where represents road slope compensation, represents road congestion compensation, is the slope angle, is the slope influence coefficient, is the traffic influence coefficient, is the traffic density; perform re-compensation on the first compensation result through the compensation coefficient of the second behavior compensation to establish a final compensation result; establish driving behavior analysis results using the average speed feature and the final compensation result.

[0011] In a possible implementation, the energy-efficient charging matching of the path charging station is performed based on the power consumption data, the fitting prediction result, and the execution task, and the following processing is performed:

[0012] Establish the driving path of the new energy vehicle by using the execution task, and obtain the charging stations associated with the driving path. The charging stations include general charging stations and special charging stations; identify the power consumption end point according to the power consumption data and the fitting prediction result, screen the charging stations based on the power consumption end point identification result to generate a screening result; predict the arrival time of the new energy vehicle at the charging stations within the screening result, determine the time-of-use electricity price based on the arrival time prediction result, and generate a first energy efficiency penalty coefficient based on the time-of-use electricity price; perform charging adaptation analysis by using the arrival time prediction result, the general charging stations, and the special charging stations, and establish a second energy efficiency penalty coefficient based on the charging adaptation analysis result; evaluate the path adaptability of the charging stations within the screening result to establish a third energy efficiency penalty coefficient; screen the charging stations within the screening result by using the first energy efficiency penalty coefficient, the second energy efficiency penalty coefficient, and the third energy efficiency penalty coefficient, and complete the energy-efficient charging matching according to the screening result.

[0013] In a possible implementation, the charging adaptation analysis is performed by using the arrival time prediction result, the general charging stations, and the special charging stations, and the following processing is performed:

[0014] Predict the charging station queue within the screening result according to the arrival time prediction result, and establish a waiting adaptation analysis result based on the queue prediction result; read the charging interfaces of the charging stations within the screening result according to the general charging station and special charging station identifiers, and perform a charging speed adaptation evaluation based on the charging interfaces to establish a charging speed adaptation analysis result; use the waiting adaptation analysis result and the charging speed adaptation analysis result as the charging adaptation analysis result.

[0015] 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 suggestion, and the driving behavior analysis result to establish a fitting prediction result, and the following processing is performed:

[0016] Perform power consumption fitting under non-optimization by using the driving behavior analysis result to establish a baseline power consumption; evaluate the execution degree of the driving energy efficiency suggestion by using the historical data, and generate a power compensation by using the execution degree evaluation result; complete the power consumption fitting prediction based on the power compensation and the baseline power consumption.

[0017] In a possible implementation, the following processing is performed:

[0018] Determine whether there is a sub-task level for the execution task; if there is a sub-task level, establish a level influence coefficient for the sub-task; perform level influence punishment 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 punishment result.

[0019] The present application also provides an energy efficiency remote management device for a new energy vehicle, including:

[0020] A real-time data set acquisition module, which is used to read the execution task of the new energy vehicle by using the central management system, 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 in-vehicle sensors installed in the new energy vehicle; a driving behavior analysis module, which is used to activate the analysis network of the central management system, perform driving behavior analysis on the real-time data set, establish a driving behavior analysis result, and generate a driving energy efficiency suggestion; a power consumption fitting prediction module, which is used to call the historical data of the new energy vehicle, and perform power consumption fitting prediction according to the historical data, driving energy efficiency suggestion, and driving behavior analysis result, 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 for the path charging stations according to the power data, fitting prediction result, and execution task; an energy efficiency management module, which is used to perform energy efficiency management according to the energy efficiency charging matching result and the driving energy efficiency suggestion.

[0021] It is intended to propose an energy efficiency remote management method and device for a new energy vehicle through the present application. First, use the central management system to read the execution task of the new energy vehicle, and obtain 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 in-vehicle sensors installed in the new energy vehicle. Then, activate the analysis network of the central management system, perform driving behavior analysis on the real-time data set, establish a driving behavior analysis result, and generate a driving energy efficiency suggestion. Next, call the historical data of the new energy vehicle, perform power consumption fitting prediction according to the historical data, driving energy efficiency suggestion, and driving behavior analysis result, and establish a fitting prediction result. Then, call the power data in the real-time data set, perform energy efficiency charging matching for the path charging stations according to the power data, fitting prediction result, and execution task. Finally, perform energy efficiency management according to the energy efficiency charging matching result and the driving energy efficiency suggestion, achieving the technical effect of improving the accuracy of energy efficiency management. Description of the Drawings

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the devices according to the embodiments of the present application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in sequence. Instead, 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 steps can be removed from these processes.

[0023] Figure 1 It is a schematic flow chart of the method for remote management of the energy efficiency of new energy vehicles provided by the embodiments of the present application.

[0024] Figure 2 It is a schematic structural diagram of the device for remote management of the energy efficiency of new energy vehicles provided by the embodiments of the present application.

[0025] 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 implementation manners

[0026] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0027] 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 with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0028] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, 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. The terms "first" and "second" are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product or server comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules 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 commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0029] The embodiments of this application provide a method for remote management of the energy efficiency of new energy vehicles, as Figure 1 shown, the method includes:

[0030] Step S100, using a central management system to read the execution tasks of a new energy vehicle, and obtaining a real-time data set through an intelligent communication device installed in the new energy vehicle, where the real-time data set is collected by in-vehicle 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 and is deployed in a data center. The central management system reads the task information that the new energy vehicle currently needs to execute, including the starting point, destination, estimated completion time, etc. of the task. Through an intelligent communication device (such as an in-vehicle Wi-Fi, 4G / 5G module, etc.) installed in the new energy vehicle, data is collected in real time from in-vehicle sensors (such as a speed sensor, an acceleration sensor, a GPS locator, etc.) to form a real-time data set.

[0031] Step S200, activating the analysis network of the central management system, performing driving behavior analysis on the real-time data set, establishing a driving behavior analysis result, and generating a driving energy efficiency recommendation. Specifically, the analysis network of the central management system preprocesses the real-time data set to remove noise and invalid data, then extracts features such as speed, acceleration, and braking, and uses the extracted features to evaluate and analyze the driving behavior of the driver to establish a driving behavior analysis result. Based on the driving behavior analysis result, the central management system generates recommendations for improving energy efficiency, such as driving smoothly, reducing sudden acceleration and sudden braking, etc.

[0032] In a possible implementation, the analysis network of the activation central management system performs driving behavior analysis on the real-time data set and establishes the driving behavior analysis result. 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, preprocessing the real-time data set uploaded from new energy vehicles includes 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 condition, traffic flow, etc.). Step S220, based on the vehicle driving data, calculates the average speed and establishes the average speed feature. Specifically, based on the speed information in the vehicle driving data, calculates the average speed of the new energy vehicle over a period of time and takes the average speed as a feature of driving behavior analysis. Step S230, based on the vehicle driving data, extracts the acceleration and braking features and establishes the sharp acceleration feature and the sharp braking feature. Specifically, based on the acceleration and braking information in the vehicle driving data, extracts the sharp acceleration and sharp braking features. Among them, the sharp acceleration feature is obtained by calculating the number of times or the duration of the absolute value of the acceleration exceeding a certain threshold. The sharp braking feature is obtained by calculating the number of times or the duration of the absolute value of the braking force exceeding a certain threshold. Step S240, based on the vehicle control data, obtains the control mode of the vehicle and generates the first behavior compensation based on the control mode. Specifically, based on the vehicle control data, obtains the control mode of the new energy vehicle (such as economy mode, sport mode, etc.). According to the control mode, generates the first behavior compensation. For example, in the economy mode, the weights of sharp acceleration and sharp braking are reduced to encourage smooth driving.

[0033] Step S250: Extract road features and traffic features from the environmental data, and establish a second behavior compensation based on the road features and traffic features. Specifically, extract road features (such as road type, slope, etc.) and traffic features (such as traffic flow, traffic density, etc.) from the environmental data. Establish a second behavior compensation based on the road features and traffic features. For example, when driving on a slope, the energy consumption needs to be increased, so the weight of the driving behavior analysis result is adjusted accordingly. Step S260: Establish a driving behavior analysis result by using the average speed feature, sudden acceleration feature, sudden braking feature, first behavior compensation, and second behavior compensation. Specifically, comprehensively consider the average speed feature, sudden acceleration feature, sudden braking feature, first behavior compensation, and second behavior compensation to establish a driving behavior analysis result. The driving behavior analysis result is a series of eigenvalue used to evaluate the driving energy efficiency of new energy vehicles. This implementation method comprehensively considers the influence of multiple factors on driving behavior, including vehicle driving data, vehicle control data, and environmental data. By preprocessing, classifying, and feature extracting these data, an accurate and comprehensive driving behavior analysis result is established. At the same time, by introducing a behavior compensation mechanism, the driving behavior analysis result is adjusted according to different conditions to make it more in line with the actual situation and improve the evaluation accuracy of driving energy efficiency.

[0034] In a possible implementation manner, for the step S260 of establishing a driving behavior analysis result by using the average speed feature, sudden acceleration feature, sudden braking feature, first behavior compensation, and second behavior compensation, it further includes step S261: Obtain the compensation coefficient of the first behavior compensation: , perform score compensation on the sudden acceleration feature and sudden braking feature through the compensation coefficient of the first behavior compensation, and establish a first compensation result. Specifically, determine the current control mode of the new energy vehicle according to the vehicle control data, such as the economic mode or the sport mode. According to the control mode, obtain the corresponding first behavior compensation coefficient. Among them, the compensation coefficient of the economic mode is 1.0, and the compensation coefficient of the sport mode is 1.1. Use the first behavior compensation coefficient to perform score compensation on the sudden acceleration feature and sudden braking feature, that is, in the economic mode, the scores of these features will not change; in the sport mode, the scores of these features will increase proportionally to reflect the driving behavior changes brought by the sport mode. After score compensation, establish a first compensation result, which contains the scores of the sudden acceleration feature and sudden braking feature after adjustment.

[0035] Step S262: Establish the compensation coefficient of the second behavior compensation: ; where represents the road slope compensation, represents the road congestion compensation, is the slope angle, is the slope influence coefficient, is the traffic influence coefficient, Let the traffic density be used to perform re - compensation on the first compensation result through the compensation coefficient with the second row being compensation, thereby establishing 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 with the second row being compensation is calculated, which includes two parts: 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 compensation coefficient with the second row being compensation is used to perform re - compensation on the first compensation result, that is, considering the influence of road and traffic conditions on driving behavior, the score of the driving behavior analysis result is further adjusted. After re - compensation, the final compensation result is established, and this result includes the scores of driving behavior characteristics after two compensations. In step S263, the driving behavior analysis result is established by using the average speed characteristic and the final compensation result. Specifically, the average speed characteristic is combined with the final compensation result (the adjusted rapid acceleration characteristic and rapid braking characteristic) to establish the driving behavior analysis result, which reflects the driving behavior energy efficiency of the new - energy vehicle under the current control mode and environmental conditions. This implementation method improves the accuracy and reliability of driving behavior analysis by introducing the first - row compensation and second - row compensation mechanisms to adjust the driving behavior analysis result according to different control modes and environmental conditions, making it more in line with the actual situation.

[0036] In step S300, the historical data of the new - energy vehicle is called, and the power consumption fitting prediction is performed according to the historical data, driving energy efficiency suggestions, and driving behavior analysis result to establish the fitting prediction result. Specifically, the historical data of the new - energy vehicle, including historical driving data, energy consumption data, etc., is called from the database. Combining the historical data, driving energy efficiency suggestions, and driving behavior analysis result, the power consumption fitting prediction is performed to establish the fitting prediction result, that is, to predict the future power consumption situation.

[0037] In a possible implementation, the historical data of the new energy vehicle is called, and the power consumption fitting prediction is carried out according to the historical data, driving energy efficiency suggestions, and driving behavior analysis results to establish a fitting prediction result. Step S300 further includes step S310 of using the driving behavior analysis result to perform power consumption fitting under non-optimization to establish a baseline power consumption. Specifically, using the driving behavior analysis result and combining the basic parameters of the new energy vehicle (such as battery capacity, motor efficiency, etc.), a power consumption model in a non-optimized state is established. This model simulates the power consumption of the new energy vehicle according to the current driving behavior without any energy efficiency optimization measures. By running the baseline model, the power consumption that the new energy vehicle is expected to consume under a given driving behavior is calculated, and this power consumption is called the baseline power consumption. Step S320 is to evaluate the execution degree of the driving energy efficiency suggestion by using the historical data, and generate a power compensation by using the execution degree evaluation result. Specifically, from the historical data of the new energy vehicle, driving records similar to the current driving behavior are found. These records include the driving behavior data, power consumption data at that time, and whether the driving energy efficiency suggestion was executed. For each matching 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 power that can be saved compared with the current driving behavior if the driving energy efficiency suggestion is executed. Step S330 is to complete the power consumption fitting prediction based on the power compensation and the baseline power consumption. Specifically, the baseline power consumption calculated in step S310 is combined with the power compensation calculated in step S320, and the power compensation is subtracted from the baseline power consumption to obtain a new power consumption prediction value. This value reflects the power consumption that the new energy vehicle is expected to consume considering the execution degree of the driving energy efficiency suggestion. This new power consumption prediction value is used as the fitting prediction result. This implementation comprehensively considers the impact of driving behavior on power consumption by combining power consumption fitting under non-optimization and the evaluation of the execution degree of driving energy efficiency suggestions, improves the accuracy of power consumption fitting prediction, and helps to formulate a more reasonable energy efficiency management strategy.

[0038] Step S400 is to call the power data in the real-time dataset and perform energy-efficient charging matching for the path charging stations based on the power data, fitting prediction result, and execution task. Specifically, the driving path of the new energy vehicle is established according to the execution task, and the charging station information associated with the driving path is obtained. The power consumption end point is identified based on the power data and the fitting prediction result, and suitable charging stations are screened.

[0039] In a possible implementation manner, for the energy-efficient charging matching of the path charging stations based on the power consumption data, fitting prediction results, and execution tasks, step S400 further includes step S410 of establishing the driving path of the new energy vehicle by using the execution task and obtaining the charging stations associated with the driving path, where the charging stations include general charging stations and dedicated charging stations. Specifically, according to the current position of the new energy vehicle and the execution task (such as the goods delivery location), the optimal driving path is generated by using the map service. All charging stations on this driving path are queried through the database or API interface, including general charging stations (open to all vehicle brands) and dedicated charging stations (only open to specific brands or models). Step S420 is to identify the power consumption end point according to the power consumption data and fitting prediction results, and screen the charging stations based on the power consumption end point identification result to generate a screening result. Specifically, analyze the current power of the new energy vehicle, the estimated driving distance, and the fitting prediction result (the power consumption prediction based on historical data and driving behavior analysis), predict the power consumption end point of the vehicle before completing the current task (i.e., the possible location where the power runs out), and screen out the charging stations within its range according to this location. Step S430 is to predict the arrival time of the new energy vehicle at the charging stations within the screening result, determine the time-of-use electricity price based on the arrival time prediction result, and generate the first energy efficiency penalty coefficient based on the time-of-use electricity price. Specifically, according to the driving path and the current traffic conditions, predict the time to reach each screened charging station. According to the time-of-use electricity price of the charging station (the electricity price is different at different times, divided into peak, flat peak, and off-peak 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 is to perform charging adaptation analysis by using the arrival time prediction result, general charging stations, and dedicated charging stations, and establish the second energy efficiency penalty coefficient based on the charging adaptation analysis result. Specifically, analyze the charging speed, queuing situation (if any), and charging efficiency of the general charging stations and dedicated charging stations, calculate the charging time at different charging stations according to the predicted arrival time and the charging capacity of the charging stations, 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.

[0040] Step S450: Evaluate the path adaptability of the charging stations in the screening results and establish the third energy efficiency penalty coefficient. Specifically, evaluate the adaptability of the screened charging stations to the driving path, including distance, deviation degree, and impact on the overall driving time. Generate the third energy efficiency penalty coefficient based on these evaluation results (the farther the path deviation or the more the driving time increases, the greater the penalty coefficient), which is used to evaluate the path deviation and the cost of driving time. Step S460: Screen the charging stations in the screening results using the first energy efficiency penalty coefficient, the second energy efficiency penalty coefficient, and the third energy efficiency penalty coefficient, and complete the energy efficiency charging matching according to the screening results. Specifically, comprehensively consider the first, second, and third energy efficiency penalty coefficients, and 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 the power consumption, arrival time, and charging capacity of the charging stations, while reducing the charging cost and time cost, and optimizing the energy efficiency management of new energy vehicles.

[0041] In a possible implementation, charging adaptation analysis is performed using the arrival time prediction result, general charging stations, and dedicated charging stations. Step S440 further includes step S441, where a charging station queue prediction within the screening result is performed according to the arrival time prediction result, and a waiting adaptation analysis result is established based on the queue prediction result. 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 reach each charging station within the screening result is predicted. According to historical data and real-time information (such as the number of vehicles in the current charging station, 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 to be charged, the estimated waiting time, etc. Based on the queue prediction result, 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 waiting, time cost, etc.) is considered to establish a waiting adaptation analysis result, which reflects the energy efficiency loss caused by waiting when charging at different charging stations. Step S442, read the charging interfaces of the charging stations within the screening result according to the general charging station and dedicated charging station identifiers, and perform a charging speed adaptation evaluation based on the charging interfaces to establish a charging speed adaptation analysis result. Specifically, according to the identifiers of the general charging station and dedicated charging station, read the charging interface types (such as fast charging, slow charging, etc.) and quantities of each charging station within the screening result. According to the charging requirements of the new energy vehicle and the charging interface types of the charging stations, evaluate the charging speed of the new energy vehicle at each charging station, including considering factors such as the compatibility of the charging interfaces, charging power, etc., as well as the battery capacity and charging strategy of the new energy vehicle, to establish a charging speed adaptation analysis result, which is an evaluation of the charging speed of the new energy vehicle at each charging station. Step S443, use 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 a 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 the new energy vehicle when charging at each charging station. This implementation method improves the comprehensiveness and accuracy of the charging adaptation analysis by comprehensively considering the factors of waiting time and charging speed, thereby optimizing the charging strategy of the new energy vehicle and improving the energy efficiency management level.

[0042] In a possible implementation, step S400 further includes step S470 of determining whether there is a sub-task level for the task being executed. Specifically, the task information of the new energy vehicle currently being executed is read from the central management system. Analyze whether the task information contains sub-tasks and determine the level of each sub-task. Among them, the sub-task level is divided based on factors such as the urgency, importance, and time requirements of the task. Step S480, if there is a sub-task level, establish the level influence coefficient of the sub-task. Specifically, determine the division criteria for the sub-task level, such as urgent, important, general, etc., and assign an influence coefficient to each sub-task level. This coefficient is a numerical value used to represent the influence degree of the sub-task of this level on the energy efficiency charging matching result. Among them, sub-tasks that are urgent or important will be assigned a higher influence coefficient. Step S490, perform 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 sub-task, perform level influence penalty on the energy efficiency charging matching result, including adjusting the priority of the charging station, etc. According to the result of the level influence penalty, reconstruct the energy efficiency charging matching result to ensure that while meeting the requirements of the sub-task level, the energy efficiency is maximized. This implementation method gives priority to ensuring the charging requirements of urgent or important tasks by considering the level of sub-tasks, and ensures that while meeting the requirements of the task being executed, the energy efficiency of the new energy vehicle is maximized.

[0043] Step S500, perform energy efficiency management according to the energy efficiency charging matching result and the driving energy efficiency suggestion. Specifically, according to the energy efficiency charging matching result and the driving energy efficiency suggestion, perform energy efficiency management on the new energy vehicle, such as adjusting the driving behavior, selecting the best charging station, etc., to improve the energy utilization efficiency and reduce energy consumption. In the embodiment of the present application, the central management system is used to read the tasks being executed by the new energy vehicle, the real-time data set collected by the vehicle-mounted sensor is obtained through the intelligent communication device, the analysis network of the central management system is activated, the driving behavior analysis is performed on the real-time data set, the driving behavior analysis result is established, and the driving energy efficiency suggestion is generated. The historical data of the new energy vehicle is called, and combined with the driving energy efficiency suggestion and the driving behavior analysis result, the power consumption fitting prediction is performed, the fitting prediction result is established, and according to the power data, the fitting prediction result and the tasks being executed in the real-time data set, the energy efficiency charging matching of the path charging station is performed. According to the energy efficiency charging matching result and the driving energy efficiency suggestion, technical means such as energy efficiency management are used to achieve precise control and optimization of the energy efficiency of the new energy vehicle, and the technical effect of improving the accuracy of energy efficiency management is achieved.

[0044] In the above text, reference is made to Figure 1 The method for remotely managing the energy efficiency of a new energy vehicle according to an embodiment of the present invention is described in detail. Next, reference will be made to Figure 2 Describe the device for remotely managing the energy efficiency of a new energy vehicle according to an embodiment of the present invention.

[0045] The new energy vehicle energy efficiency remote management device according to an embodiment of the present invention is used to solve the technical problem of inaccurate management existing in the 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.

[0046] The real-time data set acquisition module 10 is used to read the execution tasks of the new energy vehicle by using the central management system, 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 in-vehicle 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 the driving behavior analysis of the real-time data set, establish the driving behavior analysis result, and generate the driving energy efficiency suggestion; the power consumption fitting prediction module 30 is used to call the historical data of the new energy vehicle, and perform the power consumption fitting prediction according to the historical data, the driving energy efficiency suggestion, and the driving behavior analysis result, and establish the fitting prediction result; the energy efficiency charging matching module 40 is used to call the power data in the real-time data set, and perform the energy efficiency charging matching of the path charging stations according to the power data, the fitting prediction result, and the execution tasks; the energy efficiency management module 50 is used to perform the energy efficiency management according to the energy efficiency charging matching result and the driving energy efficiency suggestion.

[0047] Next, the specific configuration of the driving behavior analysis module 20 will be described in detail. As described above, by activating the analysis network of the central management system, performing the driving behavior analysis of the real-time data set, and establishing the driving behavior analysis result, the driving behavior analysis module 20 may further include: a data classification unit for preprocessing the real-time data set and classifying the data into vehicle driving data, vehicle control data, and environmental data; an average speed calculation unit for calculating the average speed based on the vehicle driving data and establishing the average speed feature; a feature extraction unit for extracting the acceleration and braking features based on the vehicle driving data and establishing the sharp acceleration feature and the sharp braking feature; a first behavior compensation generation unit for obtaining the control mode of the vehicle based on the vehicle control data and generating the first behavior compensation based on the control mode; a second behavior compensation establishment unit for extracting the road features and traffic features in the environmental data and establishing the second behavior compensation according to the road features and traffic features; a driving behavior analysis result establishment unit for establishing the driving behavior analysis result by using the average speed feature, the sharp acceleration feature, the sharp braking feature, the first behavior compensation, and the second behavior compensation.

[0048] Among them, the driving behavior analysis result is established by using the average speed feature, the sharp acceleration feature, the hard 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 the compensation coefficient of the first behavior compensation: ; a first compensation result establishment subunit for performing score compensation on the sharp acceleration feature and the hard braking feature through the compensation coefficient of the first behavior compensation to establish a first compensation result; a second behavior compensation coefficient establishment subunit for establishing the compensation coefficient of the second behavior compensation: ; where represents road slope compensation, represents road congestion compensation, is the slope angle, is the slope influence coefficient, is the traffic influence coefficient, is the traffic density; the final compensation result establishment subunit is used to perform re-compensation on the first compensation result through the compensation coefficient of the second behavior compensation to establish a final compensation result; the driving behavior analysis result establishment subunit is used to establish the driving behavior analysis result by using the average speed feature and the final compensation result.

[0049] Next, the specific configuration of the energy-efficient charging matching module 40 will be described in detail. As described above, based on the battery power data, the fitting prediction result, and the execution task, the energy-efficient charging matching for the path charging stations is performed. The energy-efficient charging matching module 40 may further include: a charging station acquisition unit for establishing the driving path of the new energy vehicle by using the execution task and acquiring the charging stations associated with the driving path, where the charging stations include general charging stations and special charging stations; a charging station screening unit for identifying the battery power consumption end point according to the battery power data and the fitting prediction result, screening the charging stations based on the battery power consumption end point identification result to generate a screening result; a first energy efficiency penalty coefficient generation unit for predicting the arrival time of the new energy vehicle at the charging stations within the screening result, determining the time-of-use electricity price based on the arrival time prediction result, and generating a first energy efficiency penalty coefficient based on the time-of-use electricity price; a second energy efficiency penalty coefficient establishment unit for performing charging adaptation analysis by using the arrival time prediction result, the general charging stations, and the special charging stations, and establishing a second energy efficiency penalty coefficient based on the charging adaptation analysis result; a third energy efficiency penalty coefficient establishment unit for evaluating the path adaptability of the charging stations within the screening result to establish a third energy efficiency penalty coefficient; an energy-efficient charging matching unit for screening the charging stations within the screening result by using the first energy efficiency penalty coefficient, the second energy efficiency penalty coefficient, and the third energy efficiency penalty coefficient, and completing the energy-efficient charging matching according to the screening result.

[0050] Among them, using the arrival time prediction result, general charging stations, and dedicated charging stations for charging adaptation analysis, the second energy efficiency penalty coefficient establishment unit may further include: a waiting adaptation analysis result establishment subunit for predicting the charging station queue within the screening result according to the arrival time prediction result, and establishing a waiting adaptation analysis result based on the queue prediction result; a charging speed adaptation evaluation subunit for reading the charging interfaces of the charging stations within the screening result according to the general charging station and dedicated charging station identifiers, and performing a charging speed adaptation evaluation based on the charging interfaces to establish a charging speed adaptation analysis result; a charging adaptation analysis result establishment subunit for using the waiting adaptation analysis result and the charging speed adaptation analysis result as the charging adaptation analysis result.

[0051] Next, the specific configuration of the power consumption fitting prediction module 30 will be described in detail. As described above, by invoking the historical data of new energy vehicles and performing power consumption fitting prediction based on the historical data, driving energy efficiency suggestions, and driving behavior analysis results to establish a fitting prediction result, the power consumption fitting prediction module 30 may further include: a reference power consumption establishment unit for performing power consumption fitting under non-optimization using the driving behavior analysis result to establish a reference power consumption; a power compensation generation unit for evaluating the execution degree of the driving energy efficiency suggestions using the historical data and generating a power compensation using the execution degree evaluation result; a power consumption fitting prediction unit for completing the power consumption fitting prediction based on the power compensation and the reference power consumption.

[0052] Among them, the energy efficiency charging matching module 40 may further include: a judgment unit for judging whether there is a sub-task level in the execution task; a level influence coefficient establishment unit for establishing a level influence coefficient of the sub-task if there is a sub-task 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.

[0053] The new energy vehicle energy efficiency remote management device provided by the embodiments of the present invention can execute the new energy vehicle energy efficiency remote management method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0054] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The included respective units and modules 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 mutual distinction and do not limit the protection scope of the present invention.

[0055] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand 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 principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recited in the present application can be executed in a sequence different from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for remote management of the energy efficiency of new energy vehicles, characterized in that, The method includes: Using a central management system to read the execution tasks of a new energy vehicle, and obtaining a real-time data set through an intelligent communication device installed in the new energy vehicle, where the real-time data set is collected by on-vehicle sensors installed in the new energy vehicle; Activating the analysis network of the central management system, performing driving behavior analysis on the real-time data set, establishing driving behavior analysis results, and generating driving energy efficiency suggestions; Invoking the historical data of the new energy vehicle, and performing power consumption fitting prediction based on the historical data, driving energy efficiency suggestions, and driving behavior analysis results to establish fitting prediction results; Invoking the power data in the real-time data set, and performing energy efficiency charging matching for path charging stations based on the power data, fitting prediction results, and execution tasks; Performing energy efficiency management according to the energy efficiency charging matching results and driving energy efficiency suggestions; The performing energy efficiency charging matching for path charging stations based on the power data, fitting prediction results, and execution tasks further includes: Using the execution tasks to establish the driving path of the new energy vehicle, and obtaining the charging stations associated with the driving path, where the charging stations include general charging stations and special charging stations; Identifying the power consumption end point based on the power data and fitting prediction results, screening the charging stations according to the power consumption end point identification results to generate screening results; Predicting the arrival time of the new energy vehicle at the charging stations within the screening results, determining the time-of-use electricity price based on the arrival time prediction results, and generating a first energy efficiency penalty coefficient based on the time-of-use electricity price; Performing charging adaptation analysis using the arrival time prediction results, general charging stations, and special charging stations, and establishing a second energy efficiency penalty coefficient based on the charging adaptation analysis results; Evaluating the path adaptability of the charging stations within the screening results to establish a third energy efficiency penalty coefficient; Screening the charging stations within the screening results using the first energy efficiency penalty coefficient, the second energy efficiency penalty coefficient, and the third energy efficiency penalty coefficient, and completing the energy efficiency charging matching according to the screening results.

2. The method for remote management of the energy efficiency of a new energy vehicle according to claim 1, characterized in that, The activating the analysis network of the central management system, performing driving behavior analysis on the real-time data set, and establishing driving behavior analysis results further includes: After preprocessing the real-time data set, classifying the data into vehicle driving data, vehicle control data, and environmental data; Calculating the average speed based on the vehicle driving data to establish an average speed feature; Extracting acceleration and braking features based on the vehicle driving data to establish a sharp acceleration feature and a sharp braking feature; Obtaining the control mode of the vehicle based on the vehicle control data, and generating a first behavior compensation based on the control mode; Extracting road features and traffic features from the environmental data, and establishing a second behavior compensation according to the road features and traffic features; Establishing driving behavior analysis results using the average speed feature, sharp acceleration feature, sharp braking feature, first behavior compensation, and second behavior compensation.

3. The method for remote management of the energy efficiency of a new energy vehicle according to claim 2, wherein, The establishing driving behavior analysis results using the average speed feature, sharp acceleration feature, sharp braking feature, first behavior compensation, and second behavior compensation further includes: Obtain the compensation coefficient with the first row as compensation: ; Performing score compensation on the sharp acceleration feature and the sharp braking feature through the compensation coefficient of the first behavior compensation to establish a first compensation result; Establish a compensation coefficient with the second row as compensation: ; Among them, characterizes road slope compensation, characterizes road congestion compensation, is the slope angle, is the slope influence coefficient, is the traffic influence coefficient, is the traffic density; Performing re-compensation on the first compensation result through the compensation coefficient of the second behavior compensation to establish a final compensation result; Establish the driving behavior analysis result by using the average speed feature and the final compensation result.

4. The method for remote management of the energy efficiency of a new energy vehicle according to claim 1, wherein, The charging adaptation analysis using the arrival time prediction result, the general charging station, and the dedicated charging station further includes: Perform the charging station queue prediction within the screening result according to the arrival time prediction result, and establish the waiting adaptation analysis result based on the queue prediction result; Read the charging interfaces of the charging stations in the screening result according to the general charging station and dedicated charging station identifiers, evaluate the charging speed adaptation based on the charging interfaces, and establish the charging speed adaptation analysis result; Use the waiting adaptation analysis result and the charging speed adaptation analysis result as the charging adaptation analysis result.

5. The method for remote management of the energy efficiency of a new energy vehicle according to claim 1, wherein, The step of calling the historical data of the new energy vehicle and performing the power consumption fitting prediction according to the historical data, the driving energy efficiency suggestion, and the driving behavior analysis result to establish the fitting prediction result further includes: Perform the power consumption fitting under non-optimization by using the driving behavior analysis result to establish the baseline power consumption; Evaluate the execution degree of the driving energy efficiency suggestion by using the historical data, and generate the power compensation by using the execution degree evaluation result; Complete the power consumption fitting prediction based on the power compensation and the baseline power consumption.

6. The method for remote management of the energy efficiency of a new energy vehicle according to claim 1, wherein, The method further includes: Judge whether there is a sub-task level for the execution task; If there is a sub-task level, establish the level influence coefficient of the sub-task; Perform the 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.

7. 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 1-6. The device includes: A real-time data set acquisition module, which is used to read the execution task of the new energy vehicle by using the central management system and obtain 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 in-vehicle sensors installed in the new energy vehicle; A driving behavior analysis module, which is used to activate the analysis network of the central management system, perform the driving behavior analysis of the real-time data set, establish the driving behavior analysis result, and generate the driving energy efficiency suggestion; A power consumption fitting prediction module, which is used to call the historical data of the new energy vehicle and perform the power consumption fitting prediction according to the historical data, the driving energy efficiency suggestion, and the driving behavior analysis result to establish the 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 the energy efficiency charging matching of the path charging stations according to the power data, the fitting prediction result, and the execution task; An energy efficiency management module, which is used to perform the energy efficiency management according to the energy efficiency charging matching result and the driving energy efficiency suggestion.

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