New energy automobile charging and discharging optimization control method and system

Abstract

The invention provides a new energy vehicle charging and discharging optimization control method and system, and relates to the technical field of new energy vehicles, and the method comprises the steps: obtaining the electric energy supply data of a charging station network and the operation data of a target vehicle in the charging and discharging process in real time, and summarizing and outputting first data; preprocessing the first data by using a preset preprocessing method, and outputting to-be-analyzed data; inputting the to-be-analyzed data and the obtained vehicle using behavior data of the user into a preset analysis model for comprehensive analysis in combination with an obtained preset index, and outputting a model analysis result; and on the basis of the model analysis result, an optimization method is selected from a method database in combination with a preset optimization target, and a charging and discharging strategy and a control method of the target vehicle are adjusted and optimized on the basis of the optimization method. Intelligentization and individuation of the charging and discharging process of the new energy automobile can be improved, the energy management strategy of the automobile is optimized, and the performance of the automobile and the user experience are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to a new energy vehicle charging and discharging optimization control method and system. Background Art

[0002] Vehicle charging and discharging control directly affects the energy efficiency, battery life and user experience of new energy vehicles. By finely controlling and optimizing the charging and discharging process of new energy vehicles, energy consumption can be reduced, carbon emissions can be reduced, battery life can be extended, driving comfort can be improved, and the sustainable development of the new energy vehicle industry can be promoted.

[0003] At present, new energy vehicles still have problems such as high system complexity, low charging and discharging efficiency, and insufficient data security in charging and discharging control.

[0004] Therefore, the present invention provides a new energy vehicle charging and discharging optimization control method and system. Summary of the invention

[0005] The present invention provides a new energy vehicle charging and discharging optimization control method and system, which are used to improve the intelligence and personalization of the new energy vehicle charging and discharging process, optimize the vehicle's energy management strategy, and enhance the vehicle's performance and user experience.

[0006] In one aspect, the present invention provides a new energy vehicle charging and discharging optimization control method, comprising: Step 1: Acquire the power supply data of the charging station network and the operation data of the target vehicle during the charging and discharging process in real time, and summarize and output the first data; Step 2: preprocessing the first data using a preset preprocessing method, and outputting data to be analyzed; Step 3: Combine the acquired preset indicators, input the data to be analyzed and the acquired user's car use behavior data into a preset analysis model for comprehensive analysis, and output the model analysis results; Step 4: Based on the model analysis results and in combination with the preset optimization target, an optimization method is selected from the method database, and the charging and discharging strategy and control method of the target vehicle are adjusted and optimized based on the optimization method.

[0007] Preferably, the real-time acquisition of the power supply data of the charging station network includes: Based on the positioning data of the target vehicle and the scanning and identification data of the target vehicle by the charging station, the site information of the charging station where the target vehicle is located and the charging pile information of the corresponding site are determined; Based on the site information and charging pile information, power supply data with preset authority is sent to the target vehicle.

[0008] Preferably, the operating data of the target vehicle during the charging and discharging process includes: The target vehicle's energy storage system includes charging process data during the charging process and discharging process data during the discharging process.

[0009] Preferably, the aggregating and outputting the first data includes: The data features of the power supply data and the operation data are obtained, and the power supply data and the operation data are sorted according to preset data aggregation rules and based on the data features to output first data.

[0010] Preferably, step 2 includes: Identify abnormal data in the first data, and select a preprocessing method matching the identification result from a method database; The abnormal data in the first data is preprocessed based on the preprocessing method, and the data to be analyzed is output.

[0011] Preferably, step 3 includes: Parsing the data to be analyzed to obtain first operating data of the target vehicle when it is DC charged at the charging station, second operating data of the target vehicle when it is AC charged, and third operating data of the vehicle's own power generation equipment during power generation; At the same time, based on the data to be analyzed, fourth operation data corresponding to the discharge process of the target vehicle during use is obtained by parsing; The user's driving behavior data and the usage behavior data of the in-vehicle equipment are captured in real time through a preset sensor group in the target vehicle, and at the same time, the user's privacy requirements are obtained, and based on the privacy requirements, the driving behavior data and the usage behavior data authorized by the user are aggregated and output as vehicle usage behavior data; Obtain the user's indicator selection instruction, select matching evaluation indicators from the indicator database, and construct an indicator set; Obtaining the user's data analysis requirements, and selecting matching data analysis models and prediction models from the model library based on the data analysis requirements; Combining the indicator set, and inputting the first operation data, the second operation data, the third operation data, the fourth operation data and the vehicle use behavior data into the data analysis model for correlation analysis, to obtain a correlation analysis result of the target vehicle charging and discharging process and the user's vehicle use behavior; Based on the correlation analysis result and according to a preset curve construction method, a user behavior curve and a charge-discharge curve are constructed; The correlation analysis results, the behavior curve and the charge-discharge curve are input into the prediction model for prediction, and the user's vehicle use behavior prediction data and the vehicle's charge-discharge prediction data are obtained, and the prediction analysis results are summarized.

[0012] Preferably, step 4 includes: Based on the prediction analysis result, and in combination with the acquired real-time positioning data of the target vehicle, the user's driving route data, and the charging station data within a preset range around the driving route, the first data to be optimized corresponding to the power station charging is summarized; At the same time, the speed limit data, terrain data and distance data of surrounding vehicles in the driving route data are obtained in real time through the preset sensor group of the target vehicle, and the second data to be optimized corresponding to the self-charging is output; Extracting features from the first data to be optimized and the second data to be optimized, and constructing a first feature set based on the extracted features, selecting a charging optimization method that matches the first feature set and a preset charging optimization index from a method database, and optimizing a charging strategy and a control method of a target vehicle based on the charging optimization method, and outputting a first optimization plan; At the same time, based on the preset sensor group, the surrounding environment parameters of the target vehicle during the discharge process and the user's usage data of the discharge device in the target vehicle are obtained in real time, and combined with the prediction analysis result, the third data to be optimized corresponding to the vehicle discharge is output; Extracting features from the third data to be optimized and constructing a second feature set, selecting a matching discharge optimization method from a method database in combination with a preset discharge optimization index, optimizing a discharge strategy and a control method of a target vehicle based on the discharge optimization method, and outputting a second optimization plan; Perform charge-discharge collaborative analysis on the first optimization plan and the second optimization plan, output a charge-discharge optimization plan, and send it to the user through a preset notification method. At the same time, send the charge-discharge optimization plan authorized by the user to the vehicle manufacturer corresponding to the target vehicle.

[0013] On the other hand, the present invention also provides a new energy vehicle charging and discharging optimization control system, comprising: A data acquisition module, used to acquire in real time the power supply data of the charging station network and the operation data of the target vehicle during the charging and discharging process, and summarize and output the first data; A preprocessing module, used to preprocess the first data using a preset preprocessing method and output data to be analyzed; A data analysis module, which is used to combine the acquired preset indicators, input the data to be analyzed and the acquired user's car use behavior data into a preset analysis model for comprehensive analysis, and output the model analysis results; The charging and discharging optimization module is used to select an optimization method from a method database based on the model analysis results and in combination with a preset optimization target, and to adjust and optimize the charging and discharging strategy and control method of the target vehicle based on the optimization method.

[0014] The present invention provides a new energy vehicle charging and discharging optimization control method and system, which realizes refined control and optimization of the new energy vehicle charging and discharging process through real-time data acquisition, comprehensive analysis and intelligent optimization and adjustment, thereby improving energy utilization efficiency and extending battery life, while meeting user needs and achieving sustainable development of energy and the environment. The present invention improves the intelligence and personalization of the new energy vehicle charging and discharging process, optimizes the energy management strategy, and improves the vehicle's performance and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a new energy vehicle charging and discharging optimization control method provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the framework of a new energy vehicle charging and discharging optimization control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, an embodiment of the present invention provides a new energy vehicle charging and discharging optimization control method, comprising: Step 1: Acquire the power supply data of the charging station network and the operation data of the target vehicle during the charging and discharging process in real time, and summarize and output the first data; Step 2: preprocessing the first data using a preset preprocessing method, and outputting the data to be analyzed; Step 3: Combine the obtained preset indicators, input the data to be analyzed and the obtained user's car use behavior data into the preset analysis model for comprehensive analysis, and output the model analysis results; Step 4: Based on the model analysis results and in combination with the preset optimization goals, an optimization method is selected from the method database, and the charging and discharging strategy and control method of the target vehicle are adjusted and optimized based on the optimization method.

[0019] In this embodiment, charging station network: a collection of new energy vehicle charging stations, a facility that provides power supply and charging services, for example, a network of multiple electric vehicle charging stations in a city; In this embodiment, power supply data: power supply conditions provided in the charging station network, including information such as power price, charging pile status, and charging speed. For example, the power price of charging station A is 0.5 yuan / kWh, charging pile B is currently available, and the charging speed is 50 kW; In this embodiment, the target vehicle refers to a specific electric vehicle that needs to be optimized for charging and discharging. For example, the electric vehicle model of a specific user; In this embodiment, operating data: operating status data of the target vehicle during the charging and discharging process, such as battery capacity, charging speed, vehicle location, etc. For example, the current battery capacity of the target vehicle is 60% and is being charged; In this embodiment, the first data is a summary of the power supply data of the charging station network and the operating data of the target vehicle. For example, the electricity price of charging station A is 0.6 yuan / kWh, and the battery capacity of the target vehicle is 70%; In this embodiment, the pre-processing method is preset: a fixed method or process for pre-processing the first data for subsequent analysis, for example, data cleaning and format conversion of the power supply data and the operation data; In this embodiment, data to be analyzed: data to be input into the analysis model for comprehensive analysis after preprocessing, for example, power supply data and operation data after cleaning; In this embodiment, the preset index refers to a measurement standard or index preset in the optimization process, which is used to evaluate the effect of the charging and discharging process, such as charging efficiency, battery health status, user satisfaction, etc.; In this embodiment, vehicle usage behavior data: vehicle-related behavior data generated by the user in daily use, such as charging habits, driving modes, charging time periods, etc., for example, the average number of times the user charges per week, the frequent driving routes, etc.; In this embodiment, the preset analysis model is a mathematical model or algorithm established in advance for comprehensively analyzing the data to be analyzed and the user's vehicle usage behavior data, for example, a charging optimization model based on machine learning; In this embodiment, the model analysis result refers to the result obtained by comprehensively analyzing the data to be analyzed and the vehicle usage behavior data through a preset analysis model, for example, the best charging strategy recommendation obtained based on the model analysis result; In this embodiment, preset optimization target: an optimization target or objective function preset in the optimization process, used to guide the selection and adjustment of the optimization method, for example, maximizing energy efficiency, extending battery life, etc.; In this embodiment, method database: a database storing various optimization methods and strategies, used to select the most suitable optimization method according to the model analysis results, for example, including charging power control strategy, charging period adjustment strategy, etc.; In this embodiment, the optimization method is an optimization method selected from the method database according to the model analysis results and the preset optimization target, and is used to adjust and optimize the charging and discharging strategy and control method of the target vehicle. For example, a dynamic power adjustment algorithm, a charging period intelligent scheduling algorithm, etc.; In this embodiment, the charging and discharging strategy and control method: the specific operating method and control strategy during the charging and discharging process of the target vehicle, aims to adjust and optimize the charging and discharging behavior of the target vehicle according to the model analysis results and preset optimization goals, so as to improve energy utilization efficiency, extend battery life, meet user needs, etc. Specifically, for example, charging power control, charging period adjustment, battery health management, intelligent charging and discharging scheduling, and fast charging control, etc.

[0020] Implementation principle and beneficial effects of this embodiment: The present invention realizes refined control and optimization of the charging and discharging process of new energy vehicles through real-time data acquisition, comprehensive analysis and intelligent optimization and adjustment, thereby improving energy utilization efficiency, extending battery life, and meeting user needs, and realizing sustainable development of energy and environment. The present invention improves the intelligence and personalization of the charging and discharging process of new energy vehicles, optimizes energy management strategies, and improves vehicle performance and user experience.

[0021] An embodiment of the present invention provides a new energy vehicle charging and discharging optimization control method, which obtains power supply data of a charging station network in real time, including: Based on the positioning data of the target vehicle and the scanning and identification data of the target vehicle by the charging station, the site information of the charging station where the target vehicle is located and the charging pile information of the corresponding site are determined; Based on the site information and charging pile information, the power supply data with preset permissions is sent to the target vehicle.

[0022] In this embodiment, positioning data: data used to determine the exact location of the target vehicle, typically including global positioning system (GPS) data or location information obtained by other positioning technologies. For example, longitude and latitude coordinates; In this embodiment, scanning and identification data: vehicle information obtained by scanning and identifying the target vehicle, such as vehicle license plate, entry time, vehicle color, etc.; In this embodiment, site information: describes the relevant information of the charging station, such as the site name, address, number of available charging piles, etc. For example, charging station A is located at XX Street and provides 10 charging piles; In this embodiment, charging pile information: describes relevant information of the charging pile, such as charging power, charging interface type, whether it is occupied, etc. For example, charging pile 1 supports fast charging, and charging pile 2 supports slow charging; In this embodiment, preset permissions: pre-set permissions that allow the target vehicle to use the charging pile for charging, which may include data type authorization of power supply data, charging fee payment authorization, charging pile reservation permission, etc. For example, a user reserves a charging pile and authorizes payment through a mobile phone app.

[0023] The implementation principle and beneficial effects of this embodiment: The present invention determines the site information and charging pile information of the charging station where the target vehicle is located through positioning data and scanning identification data, and then sends the power supply data of preset authority to the target vehicle to realize accurate power supply to the target vehicle. This method can improve the convenience and efficiency of the charging process of new energy vehicles, reduce the time for users to find charging piles, improve user experience, and better meet the charging needs of users through intelligent power supply methods.

[0024] An embodiment of the present invention provides a new energy vehicle charging and discharging optimization control method, wherein the operating data of the target vehicle during the charging and discharging process includes: The target vehicle's energy storage system includes charging process data during the charging process and discharging process data during the discharging process.

[0025] In this embodiment, energy storage system: a system used to store electrical energy in a new energy vehicle, usually a battery pack or other type of energy storage device, responsible for storing electrical energy for use by the vehicle; In this embodiment, charging process data: relevant data of the energy storage system of the target vehicle during the charging process, such as charging power, charging current, charging voltage, charging temperature, etc., used to monitor the status and performance of the charging process. Specifically, for example, during the charging process, data such as charging power of 5kW, charging current of 10A, charging voltage of 400V, and charging temperature of 25 degrees Celsius are recorded; In this embodiment, the discharge process data refers to the relevant data of the energy storage system of the target vehicle during the discharge process, such as discharge power, discharge current, discharge voltage, discharge temperature, etc., which are used to monitor the state and performance of the discharge process. Specifically, for example, during the discharge process, data such as the discharge power of 4kW, the discharge current of 8A, the discharge voltage of 380V, and the discharge temperature of 30 degrees Celsius are recorded.

[0026] The implementation principle and beneficial effects of this embodiment: The present invention monitors and records the operating data of the target vehicle during the charging and discharging process, including the charging and discharging process data of the energy storage system, and combines other information for comprehensive analysis and optimization adjustment to achieve refined control and optimization of the charging and discharging process.

[0027] An embodiment of the present invention provides a new energy vehicle charging and discharging optimization control method, which summarizes and outputs first data, including: The data features of the power supply data and the operation data are obtained, and the power supply data and the operation data are sorted according to the preset data aggregation rules and based on the data features, and the first data is output.

[0028] In this embodiment, data features refer to features or attributes of representative or key information in the power supply data and operation data, including power, current, voltage, etc. of the power supply data, and charging process data, discharging process data, etc. of the operation data. In this embodiment, preset data aggregation rules are methods of arranging and aggregating data features according to preset rules or algorithms, including operations such as data aggregation, data screening, and data processing, so as to generate useful aggregated data.

[0029] Implementation principle and beneficial effects of this embodiment: The present invention generates and outputs first data by extracting data features and collating and processing them according to preset data aggregation rules. The present invention helps to conduct a deeper analysis and optimization of the charging process and power supply data, improve data processing efficiency, and reduce information redundancy, so as to better understand the charging and discharging process of new energy vehicles and provide data support for subsequent optimization of energy management strategies.

[0030] An embodiment of the present invention provides a new energy vehicle charging and discharging optimization control method, in step 2, including: Identify abnormal data in the first data, and select a preprocessing method matching the identification result from a method database; The abnormal data in the first data is preprocessed based on the preprocessing method, and the data to be analyzed is output.

[0031] In this embodiment, abnormal data refers to data points in the first data that do not conform to the normal pattern or the expected pattern, which may be abnormal data caused by sensor failure, data transmission error, system failure, etc. For example, in the first data, abnormal data of a negative charging power appears, or data points of a sudden abnormal fluctuation in the charging current appear; In this embodiment, the preprocessing method is a method for processing abnormal data, which aims to repair or eliminate abnormal data to ensure the accuracy and reliability of subsequent analysis, including data cleaning, data interpolation, outlier processing and other technologies. Different preprocessing methods can be adopted for abnormal data, such as deleting abnormal data or replacing abnormal data with reasonable values, such as setting negative power data to zero; using statistical methods or machine learning algorithms to detect and process outliers, such as using the mean, median or interpolation method to replace outliers.

[0032] The implementation principle and beneficial effects of this embodiment: The present invention identifies abnormal data in the first data and selects a corresponding preprocessing method for processing to ensure the quality and accuracy of the data. The present invention can improve the accuracy and reliability of the data, reduce the impact of abnormal data on the analysis results, and improve the analysis efficiency and accuracy of the charging and discharging process data.

[0033] An embodiment of the present invention provides a new energy vehicle charging and discharging optimization control method, in step 3, including: Parsing the data to be analyzed to obtain first operating data of the target vehicle when it is DC charged at the charging station, second operating data of the target vehicle when it is AC charged, and third operating data of the vehicle's own power generation equipment during power generation; At the same time, based on the data to be analyzed, fourth operation data corresponding to the discharge process of the target vehicle during use is obtained by parsing; The user's driving behavior data and the usage behavior data of the in-vehicle equipment are captured in real time through the preset sensor group in the target vehicle. At the same time, the user's privacy requirements are obtained, and based on the privacy requirements, the driving behavior data and usage behavior data authorized by the user are aggregated and output as vehicle usage behavior data; Obtain the user's indicator selection instruction, select matching evaluation indicators from the indicator database, and construct an indicator set; Obtain the user's data analysis needs, and select matching data analysis models and prediction models from the model library based on the data analysis needs; Combine the indicator set, and input the first operation data, the second operation data, the third operation data, the fourth operation data and the vehicle use behavior data into the data analysis model for correlation analysis, to obtain a correlation analysis result of the target vehicle charging and discharging process and the user's vehicle use behavior; Based on the correlation analysis results and according to a preset curve construction method, a user behavior curve and a charge-discharge curve are constructed; The correlation analysis results, behavior curves, and charge-discharge curves are input into the prediction model for prediction, and the user's vehicle use behavior prediction data and the vehicle's charge-discharge prediction data are obtained, and the prediction analysis results are summarized.

[0034] In this embodiment, the first operating data is the operating data generated when the target vehicle is DC charged at the charging station, including information such as charging power, charging time, and charging current; In this embodiment, the second operating data: namely, the operating data generated when the target vehicle is AC charged at the charging station, including information such as AC charging power, charging efficiency, and charging mode; In this embodiment, the third operating data: operating data generated by the vehicle's own power generation equipment during the power generation process, including information such as power generation power, power generation time, and power generation efficiency; In this embodiment, the fourth operating data: operating data corresponding to the discharge process of the target vehicle during use, including vehicle driving data, battery discharge data and other information; In this embodiment, the preset sensor group: a set of sensors pre-installed inside the target vehicle, used to capture the user's driving behavior data and the usage behavior data of the in-vehicle equipment in real time; In this embodiment, driving behavior data: data recording the user's behavior and habits during driving, such as acceleration, deceleration, turning, and other behavior data; In this embodiment, usage behavior data: records the user's behavior data during the use of the vehicle's internal equipment, such as the frequency of air conditioning use, audio usage, seat adjustment, etc.; In this embodiment, privacy requirements refer to the user's requirements and restrictions on the privacy of his personal information and data. Here, privacy requirements refer to the user's requirements for privacy protection of his driving behavior data and in-vehicle device usage behavior data; In this embodiment, vehicle use behavior data refers to the behavior data generated by the user when driving a car and using in-car devices, including driving habits, device operation records, etc.; In this embodiment, the indicator selection instruction is an instruction given by the user, which is used to select indicators for subsequent correlation analysis in the indicator database; In this embodiment, the indicator database: a database storing various evaluation indicators, used to select a matching indicator set therefrom; In this embodiment, the evaluation index: an index used to evaluate the vehicle charging and discharging process and the user's vehicle use behavior, such as charging efficiency, driving behavior score, device use frequency, etc.; In this embodiment, the indicator set: a set of selected evaluation indicators used for data analysis and association analysis; In this embodiment, data analysis demand: the user's demand and requirements for data analysis, including the content and purpose to be analyzed; In this embodiment, the model library: a library storing various data analysis models and prediction models, used to select a suitable model according to data analysis requirements; In this embodiment, data analysis model: a model used to analyze data and obtain related results, which can be a statistical model, a machine learning model, etc.; In this embodiment, the prediction model: a model for predicting the user's vehicle use behavior and the vehicle's charging and discharging conditions based on the association analysis results and the preset curve construction method, for example, a machine learning model such as a neural network, a decision tree, etc. can be used for prediction; In this embodiment, correlation analysis: by analyzing the correlation between different data, find out the rules and connections between them, for example, analyzing the correlation between the user's driving behavior data and the vehicle charging and discharging data; In this embodiment, the association analysis result: based on the conclusions obtained from the association analysis and the data association results, for example, it is found that the frequent rapid acceleration behavior of the user may affect the charging efficiency of the vehicle; In this embodiment, the preset curve construction method is a method set in advance for constructing a behavior curve and a charge-discharge curve, for example, constructing a curve based on a rule derived from historical data and a model; In this embodiment, the behavior curve: a user behavior change curve constructed based on the user behavior data and the association analysis results, for example, a behavior curve constructed based on the user's driving habits and vehicle charging conditions; In this embodiment, the charging and discharging curve: a vehicle charging and discharging condition change curve constructed based on vehicle charging and discharging data and correlation analysis results, for example, a charging curve constructed based on vehicle charging efficiency and user behavior; In this embodiment, the vehicle use behavior prediction data: the prediction data of the user's future vehicle use behavior obtained according to the prediction model, for example, predicting the user's driving habits and vehicle use tomorrow; In this embodiment, the charge and discharge prediction data: prediction data of the future charge and discharge conditions of the vehicle obtained according to the prediction model, for example, the prediction of the time and charge amount of the next charge of the vehicle; In this embodiment, the prediction analysis result is: a comprehensive prediction result of the user's vehicle usage behavior and the vehicle charging and discharging conditions obtained according to the prediction model, for example, energy-saving suggestions and charging plans obtained according to the user behavior prediction and vehicle charging prediction.

[0035] The implementation principle and beneficial effects of this embodiment: The present invention captures the operation data, driving behavior data and usage behavior data of the target vehicle in real time, combines the user's privacy requirements and indicator selection instructions, and uses the data analysis model and prediction model to perform correlation analysis and prediction on the data, thereby obtaining the user's vehicle usage behavior prediction data and the vehicle's charging and discharging prediction data. The present invention helps to optimize energy management strategies, improve vehicle performance and user experience, while meeting the user's personalized needs and promoting the development and promotion of new energy vehicle technology.

[0036] An embodiment of the present invention provides a new energy vehicle charging and discharging optimization control method, in step 4, including: Based on the prediction analysis results, and in combination with the acquired real-time positioning data of the target vehicle, the user's driving route data, and the charging station data within a preset range around the driving route, the first data to be optimized corresponding to the power station charging is summarized; At the same time, the preset sensor group of the target vehicle obtains the speed limit data, terrain data and distance data of surrounding vehicles in the driving route data in real time, and outputs the second data to be optimized corresponding to the self-charging; Extracting features of the first data to be optimized and the second data to be optimized, and constructing a first feature set based on the extracted features, selecting a charging optimization method that matches the first feature set and a preset charging optimization index from a method database, and optimizing a charging strategy and a control method of a target vehicle based on the charging optimization method, and outputting a first optimization plan; At the same time, based on the preset sensor group, the surrounding environment parameters of the target vehicle during the discharge process and the user's usage data of the discharge device in the target vehicle are obtained in real time, and combined with the prediction analysis results, the third data to be optimized corresponding to the vehicle discharge is output; Extracting features from the third data to be optimized and constructing a second feature set, selecting a matching discharge optimization method from a method database in combination with a preset discharge optimization index, optimizing a discharge strategy and a control method of the target vehicle based on the discharge optimization method, and outputting a second optimization plan; Perform charge-discharge collaborative analysis on the first optimization plan and the second optimization plan, output a charge-discharge optimization plan, and send it to the user through a preset notification method. At the same time, send the charge-discharge optimization plan authorized by the user to the vehicle manufacturer corresponding to the target vehicle.

[0037] In this embodiment, real-time positioning data: the current position data of the target vehicle, used to determine the location of the vehicle, for example, GPS data can provide the latitude and longitude information of the target vehicle; In this embodiment, driving route data: recording the driving route information of the target vehicle, including the starting point, the end point and the passing points, etc. For example, the driving trajectory of the vehicle can be recorded using the vehicle navigation system; In this embodiment, preset range: a pre-set range or area used to obtain information about charging stations around the driving route; In this embodiment, the charging station data includes information such as the location of the charging station, the type of charging pile, and the charging speed. For example, the charging station database records detailed information of each charging station; In this embodiment, the first data to be optimized: the data to be optimized corresponding to charging at the power station, which is used to perform charging optimization operations, for example, the charging demand and charging pile conditions of the target vehicle when it arrives at the charging station; In this embodiment, speed limit data: speed limit information of the target vehicle on the driving route, which is used to optimize the vehicle's driving strategy, for example, recording that the speed limit of a certain section of the road is 60 kilometers per hour; In this embodiment, terrain data: data of terrain features around the target vehicle, such as mountains, plains, etc., used to optimize the energy utilization of the vehicle. For example, when a vehicle travels in a mountainous area, it consumes more energy; In this embodiment, the distance data of surrounding vehicles: records the distance information of other vehicles around the target vehicle, which is used to optimize the driving safety and efficiency of the vehicle, for example, detecting the distance between the surrounding vehicles and the target vehicle to avoid collision; In this embodiment, self-charging refers to the process in which the target vehicle charges its own power generation equipment, for example, a new energy vehicle charges itself through an on-board generator or a kinetic energy recovery system; In this embodiment, the second data to be optimized: the data to be optimized corresponding to self-charging, used for performing a discharge optimization operation, for example, energy utilization and environmental parameters of the target vehicle during self-charging; In this embodiment, the first feature set is a feature set extracted from the first data to be optimized and the second data to be optimized, and is used to describe relevant features of the target vehicle during charging and discharging. For example, the first feature set may include the distance to the charging station, the speed limit section, the terrain features, etc.; In this embodiment, the charging optimization index is preset: an index set in advance for evaluating the optimization effect of the charging process, for example, charging time, charging cost, energy utilization efficiency, etc. can all be used as charging optimization indexes; In this embodiment, the charging optimization method: a method for optimizing the charging strategy and the control method selected from the method database according to the first feature set and the preset charging optimization index, for example, selecting the optimal charging pile for charging according to the distance to the charging station and the charging cost; In this embodiment, the charging strategy and control method: a specific implementation scheme for optimizing the charging process of the target vehicle according to the charging optimization method, for example, adjusting the charging power, charging start and end time, etc. to optimize the charging process; In this embodiment, the first optimization plan: an optimization plan for the charging process obtained after optimization by the charging optimization method, for example, determining the best charging strategy and control method; In this embodiment, ambient environment parameters: parameters related to the ambient environment of the target vehicle during the discharge process, such as temperature, humidity, etc. For example, discharging in a high or low temperature environment may affect battery performance; In this embodiment, usage data: data on the user's usage of the discharge device in the target vehicle, which is used to optimize the discharge process, for example, recording the user's usage of the vehicle's air conditioner, seat heating and other devices; In this embodiment, the third data to be optimized: data to be optimized corresponding to vehicle discharge, used for performing discharge optimization operations, for example, including energy consumption and environmental parameters during the discharge process; In this embodiment, the second feature set is a feature set extracted from the third data to be optimized, and is used to describe relevant features of the target vehicle during the discharge process. For example, the second feature set may include ambient temperature, humidity, discharge power requirement, etc.; In this embodiment, the preset discharge optimization index is an index set in advance for evaluating the optimization effect of the discharge process, for example, the discharge rate, battery health status, discharge cost, etc. can all be used as the discharge optimization index; In this embodiment, the discharge optimization method is: a method for optimizing the discharge strategy and control method selected from the method database according to the second feature set and the preset discharge optimization index, for example, selecting the optimal discharge power according to the ambient temperature and the battery health status. In this embodiment, the discharge strategy and control method: a specific implementation scheme for optimizing the discharge process of the target vehicle according to the discharge optimization method, for example, adjusting the discharge power, the discharge start and end time, etc. to optimize the discharge process; In this embodiment, the second optimization plan is an optimization plan for the discharge process obtained after optimization by the discharge optimization method, for example, the best discharge strategy and control method are determined; In this embodiment, collaborative analysis: a comprehensive analysis is performed on the charging optimization plan and the discharging optimization plan to achieve collaborative optimization of the charging and discharging process. Through collaborative analysis, the charging and discharging processes can be better balanced and the overall energy utilization efficiency can be improved; In this embodiment, the charging and discharging optimization scheme: the charging and discharging optimization scheme obtained through collaborative analysis, which comprehensively considers various indicators and optimization plans of the charging and discharging process, can improve energy management efficiency, reduce energy costs, and optimize user experience; In this embodiment, a preset notification method is a method of sending the optimization plan to the user and the vehicle manufacturer through a preset notification method. For example, the user can be notified by a mobile phone App push message or email, and the optimization plan can be sent to the vehicle manufacturer's server at the same time.

[0038] The implementation principle and beneficial effects of this embodiment: Based on the prediction analysis results and real-time data, the present invention combines feature extraction and optimization methods to optimize the charging and discharging strategies of the target vehicle, and realizes the formulation of charging and discharging collaborative analysis and optimization schemes. The present invention can improve energy utilization efficiency, optimize driving safety and efficiency, enhance user experience, and provide optimization schemes to vehicle manufacturers.

[0039] like Figure 2 As shown, an embodiment of the present invention provides a new energy vehicle charging and discharging optimization control system, including: A data acquisition module, used to acquire in real time the power supply data of the charging station network and the operation data of the target vehicle during the charging and discharging process, and summarize and output the first data; A preprocessing module, used to preprocess the first data using a preset preprocessing method and output the data to be analyzed; A data analysis module is used to combine the acquired preset indicators, input the data to be analyzed and the acquired user's car use behavior data into a preset analysis model for comprehensive analysis, and output the model analysis results; The charging and discharging optimization module is used to select an optimization method from the method database based on the model analysis results and in combination with the preset optimization goals, and to adjust and optimize the charging and discharging strategy and control method of the target vehicle based on the optimization method.

[0040] Implementation principle and beneficial effects of this embodiment: The present invention realizes refined control and optimization of the charging and discharging process of new energy vehicles through real-time data acquisition, comprehensive analysis and intelligent optimization and adjustment, thereby improving energy utilization efficiency, extending battery life, and meeting user needs, and realizing sustainable development of energy and environment. The present invention improves the intelligence and personalization of the charging and discharging process of new energy vehicles, optimizes energy management strategies, and improves vehicle performance and user experience.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A new energy vehicle charging and discharging optimization control method, characterized in that: include: Step 1: Acquire the power supply data of the charging station network and the operation data of the target vehicle during the charging and discharging process in real time, and summarize and output the first data; Step 2: preprocessing the first data using a preset preprocessing method, and outputting data to be analyzed; Step 3: Combine the acquired preset indicators, input the data to be analyzed and the acquired user's car use behavior data into a preset analysis model for comprehensive analysis, and output the model analysis results; Step 4: Based on the model analysis results and in combination with the preset optimization target, an optimization method is selected from the method database, and the charging and discharging strategy and control method of the target vehicle are adjusted and optimized based on the optimization method.

2. A new energy vehicle charging and discharging optimization control method according to claim 1, characterized in that: The real-time acquisition of the power supply data of the charging station network includes: Based on the positioning data of the target vehicle and the scanning and identification data of the target vehicle by the charging station, the site information of the charging station where the target vehicle is located and the charging pile information of the corresponding site are determined; Based on the site information and charging pile information, power supply data with preset authority is sent to the target vehicle.

3. The method for optimizing charging and discharging control of a new energy vehicle according to claim 1, characterized in that: The operating data of the target vehicle during the charging and discharging process includes: The target vehicle's energy storage system includes charging process data during the charging process and discharging process data during the discharging process.

4. The method for optimizing charging and discharging control of a new energy vehicle according to claim 1, characterized in that: The aggregating and outputting the first data includes: The data features of the power supply data and the operation data are obtained, and the power supply data and the operation data are sorted according to preset data aggregation rules and based on the data features to output first data.

5. The method for optimizing charging and discharging control of a new energy vehicle according to claim 1, characterized in that: Step 2 includes: Identify abnormal data in the first data, and select a preprocessing method matching the identification result from a method database; The abnormal data in the first data is preprocessed based on the preprocessing method, and the data to be analyzed is output.

6. The method for optimizing charging and discharging control of a new energy vehicle according to claim 1, characterized in that: Step 3 includes: Parsing the data to be analyzed to obtain first operating data of the target vehicle when it is DC charged at the charging station, second operating data of the target vehicle when it is AC charged, and third operating data of the vehicle's own power generation equipment during power generation; At the same time, based on the data to be analyzed, fourth operation data corresponding to the discharge process of the target vehicle during use is obtained by parsing; The user's driving behavior data and the usage behavior data of the in-vehicle equipment are captured in real time through a preset sensor group in the target vehicle, and at the same time, the user's privacy requirements are obtained, and based on the privacy requirements, the driving behavior data and the usage behavior data authorized by the user are aggregated and output as vehicle usage behavior data; Obtain the user's indicator selection instruction, select matching evaluation indicators from the indicator database, and construct an indicator set; Obtaining the user's data analysis requirements, and selecting matching data analysis models and prediction models from the model library based on the data analysis requirements; Combining the indicator set, and inputting the first operation data, the second operation data, the third operation data, the fourth operation data and the vehicle use behavior data into the data analysis model for correlation analysis, to obtain a correlation analysis result of the target vehicle charging and discharging process and the user's vehicle use behavior; Based on the correlation analysis result and according to a preset curve construction method, a user behavior curve and a charge-discharge curve are constructed; The correlation analysis results, the behavior curve and the charge-discharge curve are input into the prediction model for prediction, and the user's vehicle use behavior prediction data and the vehicle's charge-discharge prediction data are obtained, and the prediction analysis results are summarized.

7. A new energy vehicle charging and discharging optimization control method according to claim 6, characterized in that: Step 4 includes: Based on the prediction analysis result, and in combination with the acquired real-time positioning data of the target vehicle, the user's driving route data, and the charging station data within a preset range around the driving route, the first data to be optimized corresponding to the power station charging is summarized; At the same time, the speed limit data, terrain data and distance data of surrounding vehicles in the driving route data are obtained in real time through the preset sensor group of the target vehicle, and the second data to be optimized corresponding to the self-charging is output; Extracting features from the first data to be optimized and the second data to be optimized, and constructing a first feature set based on the extracted features, selecting a charging optimization method that matches the first feature set and a preset charging optimization index from a method database, and optimizing a charging strategy and a control method of a target vehicle based on the charging optimization method, and outputting a first optimization plan; At the same time, based on the preset sensor group, the surrounding environment parameters of the target vehicle during the discharge process and the user's usage data of the discharge device in the target vehicle are obtained in real time, and combined with the prediction analysis result, the third data to be optimized corresponding to the vehicle discharge is output; Extracting features from the third data to be optimized and constructing a second feature set, selecting a matching discharge optimization method from a method database in combination with a preset discharge optimization index, optimizing a discharge strategy and a control method of a target vehicle based on the discharge optimization method, and outputting a second optimization plan; Perform charge-discharge collaborative analysis on the first optimization plan and the second optimization plan, output a charge-discharge optimization plan, and send it to the user through a preset notification method. At the same time, send the charge-discharge optimization plan authorized by the user to the vehicle manufacturer corresponding to the target vehicle.

8. A new energy vehicle charging and discharging optimization control system, characterized in that: include: A data acquisition module, used to acquire in real time the power supply data of the charging station network and the operation data of the target vehicle during the charging and discharging process, and summarize and output the first data; A preprocessing module, used to preprocess the first data using a preset preprocessing method and output data to be analyzed; A data analysis module, which is used to combine the acquired preset indicators, input the data to be analyzed and the acquired user's car use behavior data into a preset analysis model for comprehensive analysis, and output the model analysis results; The charging and discharging optimization module is used to select an optimization method from a method database based on the model analysis results and in combination with a preset optimization target, and to adjust and optimize the charging and discharging strategy and control method of the target vehicle based on the optimization method.

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