A dispatching system and method for V2G vehicle-grid interactive charging

Abstract

The present invention discloses a scheduling system and method for V2G vehicle-grid interactive charging, which relates to the field of charging scheduling. The scheduling system comprises: a central control module, which is used to edit the start and stop instructions and control the start and stop commands of various functional modules and functional units, allocate charging station and electric vehicle identity IDs, and store all battery swap data; a verification module, which is used to receive the identity ID and status information of the battery swap point and the vehicle, perform identity authentication and feedback the results; a trend generation module, which is used to collect GPS and travel records of several vehicles that have passed identity authentication, analyze the historical driving habit data of the vehicles, predict the future driving routes and time distribution, and provide more targeted suggestions and risk warnings through the combination of big data analysis and real-time traffic data, so as to help users make better decisions in the battery swap process, monitor and evaluate the execution of the battery swap plan by the battery swap target in real time, and dynamically adjust the priority of each battery swap vehicle according to the feedback.

Description

Technical Field

[0001] The present invention relates to the technical field of charging scheduling, and in particular to a scheduling system and method for V2G vehicle-to-grid interactive charging. Background Art

[0002] V2G vehicle-grid mutual charging can realize the interaction between electric vehicles and the power grid, so that electric vehicles can absorb electricity when the power grid load is low, and release electricity when the power grid load is high, earning price difference income, so that the parked electric vehicles are regarded as movable energy storage devices, which can absorb electricity from the power grid for charging, and can also feed back the stored electricity to the power grid when the power grid needs it, providing emergency power support, thereby enhancing the stability of the power grid. Charging scheduling of charging stations is a type of optimization problem that is difficult to solve. Most electric vehicle charging strategies provide functional services such as "peak shaving and valley filling" for the power grid, optimizing the voltage quality of the power grid, and saving charging costs;

[0003] Due to the uneven distribution of charging piles and the shortage of charging resources in some areas, users have to wait for a long time, and the demand and supply of charging and battery replacement are not matched. Users are faced with information islands and cannot obtain real-time charging information and changes. The traditional system cannot flexibly adjust the charging time, resulting in waste of resources, lack of dynamic adjustment capabilities, and slow response to changes in user demand. Electric vehicles with traditional charging modes fail to effectively exchange energy with the power grid, resulting in waste, and usually lack effective feedback channels, resulting in poor user experience. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the above-mentioned shortcomings of the prior art, the present invention provides a scheduling system and method for V2G vehicle-grid interactive charging, which can effectively solve the problems of the prior art.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] The present invention discloses a scheduling system for V2G vehicle-grid interactive charging, comprising:

[0009] The central control module is used to edit and control the start and stop instructions of each functional module and functional unit, allocate charging station and electric vehicle identity IDs, and store all battery replacement data;

[0010] The verification module is used to receive the identity ID and status information of the battery swap point and the vehicle, perform identity verification and feedback the results;

[0011] The trend generation module is used to collect GPS and travel records of several authenticated vehicles, analyze the historical driving habit data of the vehicles, predict the future driving routes and time distribution, and form a trajectory trend report;

[0012] The acquisition module is used to collect charging power, power and charging time data from battery swapping points and vehicles and convert them into machine-readable data;

[0013] The correlation analysis module is used to evaluate the correlation between the target and the battery swap stations related to the driving path based on the vehicle's historical charging records and driving habits and trajectory trend reports;

[0014] A plan generation module is used to calculate a battery swap plan including charging time and location based on the battery swap vehicle's power, destination, and energy storage status of the associated battery swap point;

[0015] A priority sorting module is used to prioritize the battery swapping vehicles participating in the same battery swapping scheme according to preset distance, power and time;

[0016] The interactive unit is used for the battery swapping vehicle to receive the sorted battery swapping plans and provide feedback.

[0017] Furthermore, the acquisition module uses a clustering algorithm to cluster historical driving data, identify several driving modes, obtain common routes and driving habits, build a prediction model through a deep learning algorithm, input pre-processed historical trajectory data, user and environmental characteristics, and use real-time traffic data as a correction reference to predict the target battery replacement vehicle coordinates, expected route and participation time of the next cycle, integrate and analyze the results, and output a trajectory trend report containing estimated departure time, arrival time, route suggestions and potential risk warnings.

[0018] Furthermore, the specific working process of the association analysis module is: obtaining historical charging time, historical charging location, historical charging status, current vehicle location, planned destination, charging station location, charging station status and charging station capacity data, cleaning and standardizing input data, extracting the visit frequency, usage time and charging amount characteristics of each charging station, calculating the distance from the vehicle to each charging station and the destination, and referring to the trajectory trend, defining several indicators for each charging station, and quantifying the output of the association between the target vehicle and the charging station.

[0019] Furthermore, the interaction unit includes a login module, a receiving module and a feedback module. The login module is interactively connected to the receiving module and the feedback module through a wireless network. The login module is used to verify the owner's identity ID and provide interaction permissions. The receiving module is used to receive the battery replacement plan transmitted by the priority sorting module, display it and choose whether to apply it. After choosing to apply, a reminder will be given within a preset time period. The feedback module is used to regularly request the power usage status and feedback on the battery replacement plan after the battery replacement vehicle applies the battery replacement plan, record the feedback data and submit it to the execution analysis module, and submit the driving data to the trend generation module.

[0020] Furthermore, the interactive unit is interactively connected to an execution analysis module via a wireless network. The execution analysis module evaluates the execution of the battery swapping plan by the battery swapping target based on feedback information, calculates the execution index, compares it with the preset standard, and obtains the execution level.

[0021] Furthermore, an adjustment module is provided at the lower level of the execution analysis module, and the adjustment module is interactively connected to the priority sorting module through a wireless network. The adjustment module is used to dynamically adjust the priority of each battery swapping vehicle in the same battery swapping scheme. When the execution degree of the target is higher than the current attribute threshold, the priority of the battery swapping scheme is increased. Conversely, if the execution degree is low, the priority is lowered. The priority sorting module re-sorts and sends the results based on the adjustment results.

[0022] Furthermore, the central control module is interactively connected to the verification module, the trend generation module and the acquisition module through a wireless network, the acquisition module is interactively connected to the correlation analysis module through a wireless network, the correlation analysis module is interactively connected to the solution generation module through a wireless network, the solution generation module is interactively connected to the priority sorting module through a wireless network, and the priority sorting module is interactively connected to the interaction unit through a wireless network.

[0023] A scheduling method for V2G vehicle-grid interactive charging, comprising the following steps:

[0024] Step 1: Obtain the location, power and utilization information of each charging station in the power grid, and identify the vehicle type, current power, expected mileage and destination of the connected charging vehicle;

[0025] Step 2: Update the vehicle's power and charging requirements in real time, collect user driving data and charging preferences, analyze historical driving data and charging data, and evaluate the vehicle's charging behavior and driving patterns;

[0026] Step 3: Based on the current route and destination, calculate the correlation between different charging stations, combine traffic data, vehicle charging behavior and driving mode to evaluate the accessibility and time cost of a charging station, and output a candidate list of charging stations;

[0027] Step 4: Use historical driving data and navigation algorithms to build a model to predict the driver's future travel, display the user's driving behavior in different time periods, and provide feedback on the prediction results;

[0028] Step 5: Calculate the effective battery swap time window based on the current power of the battery swap target and the energy storage status of the associated location, evaluate the economic feasibility of different charging stations, generate a battery swap plan, and sort the different plans according to priority;

[0029] Step 6: Push the battery swap plan to the user end, provide a battery swap plan including the estimated charging and battery swap time, location and cost information, and guide the user to confirm and provide feedback on the battery swap plan;

[0030] Step 7: During the process of the battery swapping vehicle executing the battery swapping plan, dynamically collect the user's actual operation data, compare the planned battery swapping plan with the user's actual execution, calculate the execution index, and analyze the reasons. If the execution degree is higher than the comparison standard, combined with the user's needs and the status of the battery swapping location, increase the recommendation priority, otherwise, lower it.

[0031] Furthermore, the calculation formula for the correlation between the user's frequently used routes and charging stations in step 3 is:

[0032] ;

[0033] In the formula, Represents the correlation between the user's frequently used routes and charging stations, represents the attribute vector of the i-th charging station, n represents the number of locations in the user's common route, Represents the user's frequently used routes, expressed as a sequence of locations. Represents the position on the path The importance weight of Represents the point on the common path from charging station i to the user The distance Represents the comprehensive value score of charging station i, represents the attribute vector of the jth destination, represents the distance from charging station i to destination j, and M represents the maximum distance value.

[0034] Furthermore, the calculation logic of the correlation calculation formula is:

[0035] If the distance between the charging station and the destination increases, the correlation decreases accordingly, and vice versa;

[0036] If the number of times a user uses a charging station reaches a preset threshold, the relevance of the charging station to the user will be increased accordingly, otherwise, it will be decreased;

[0037] If the comprehensive value score of the charging station increases, the correlation will increase accordingly, otherwise, it will decrease.

[0038] (III) Beneficial effects

[0039] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0040] By combining big data analysis with real-time traffic data, more targeted suggestions and risk warnings are provided to help users make better decisions during the battery replacement process. Deep learning algorithms are used to analyze the vehicle's historical driving data to build an accurate prediction model. The vehicle's future driving route and time distribution can be predicted in real time, and a detailed trajectory trend report can be formed. The decision-making ability of the dispatching system is improved, enabling it to adapt to complex traffic environments, and effective management and control of functional modules, including the allocation and change of identity IDs, to achieve comprehensive monitoring of charging stations and electric vehicles, thereby optimizing resource allocation, improving dispatch efficiency, and increasing the utilization rate of charging stations.

[0041] By aggregating and analyzing large amounts of historical data, as well as quantitatively analyzing the characteristics of charging stations, we scientifically evaluate the correlation between target vehicles and charging stations, so as to efficiently recommend the optimal battery replacement solution and enhance the accuracy of charging services. By combining big data analysis with real-time traffic data, we provide more targeted suggestions and risk warnings to help users make better decisions during the battery replacement process, effectively reducing the impact of human factors on scheduling efficiency. The implementation of the solution does not rely on a single data source, but integrates historical driving data, current charging station status and real-time traffic information, thereby improving the transparency and rationality of decision-making.

[0042] By real-time monitoring and evaluating the execution of the battery swap plan by the battery swap target, and dynamically adjusting the priority of each battery swap vehicle based on feedback, the scheduling plan can more flexibly respond to changes in actual operation, adapt to the needs in different situations, and form a closed-loop feedback mechanism, so that the scheduling system can be continuously optimized according to the actual implementation situation, thereby improving the battery swap efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 It is a schematic diagram of the framework of the present invention.

[0045] The numbers in the figure represent, respectively, 1. Central control module; 2. Verification module; 3. Trend generation module; 4. Collection module; 5. Correlation analysis module; 6. Solution generation module; 7. Priority sorting module; 8. Interaction unit; 81. Login module; 82. Receiving module; 83. Feedback module; 9. Execution analysis module; 10. Adjustment module. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments 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 making creative work are within the scope of protection of the present invention.

[0047] The present invention will be further described below in conjunction with the embodiments.

[0048] Embodiment 1: A dispatching system for V2G vehicle-grid interactive charging in this embodiment, such as Figure 1 As shown, including:

[0049] Central control module 1 is used to edit and control the start and stop commands of each functional module and unit, allocate the charging station and electric vehicle identity ID, and store all battery swap data. All battery swap data is effectively stored to ensure the traceability and integrity of the information;

[0050] Verification module 2 is used to receive the identity ID and status information of the battery swap point and the vehicle, perform identity authentication and feedback the results, increase the security of the system, and effectively prevent fraud;

[0051] Trend generation module 3, used to collect GPS and travel records of several authenticated vehicles, analyze the historical driving habit data of the vehicles, predict the future driving routes and time distribution, and form a trajectory trend report;

[0052] The acquisition module 4 is used to collect charging power, power and charging time data from battery swapping points and vehicles and convert them into machine-readable data; the acquisition module 4 uses a clustering algorithm to cluster historical driving data, identify several driving modes, obtain common routes and driving habits, and build a prediction model through a deep learning algorithm. The pre-processed historical trajectory data, user and environmental characteristics are input, and the real-time traffic data is used as a correction reference to predict the coordinates of the target battery swapping vehicle in the next cycle, the expected path and the time of participation. The analysis results are integrated and outputted. The trajectory trend report containing the estimated departure time, arrival time, route suggestions and potential risk prompts is formed by collecting charging power, power and charging time data into a machine-readable format for subsequent analysis;

[0053] The correlation analysis module 5 is used to evaluate the correlation between the target and the battery swap stations related to the driving path based on the vehicle's historical charging records and driving habits and based on the trajectory trend report; the specific working process of the correlation analysis module 5 is: obtaining historical charging time, historical charging location, historical charging status, current vehicle location, planned destination, charging station location, charging station status and charging station capacity data, cleaning and standardizing input data, extracting the visit frequency, usage time and charging amount characteristics of each charging station, calculating the distance from the vehicle to each charging station and the destination, and referring to the trajectory trend, defining several indicators for each charging station, quantifying and outputting the correlation between the target vehicle and the charging station, accurately evaluating the correlation between the target vehicle and the battery swap station, and effectively improving the battery swap efficiency;

[0054] The solution generation module 6 is used to calculate the battery replacement solution including the charging time and location based on the battery replacement vehicle's power, destination and energy storage status of the associated battery replacement point to ensure the timeliness and effectiveness of charging;

[0055] Priority sorting module 7, used to prioritize the battery swapping vehicles participating in the same battery swapping scheme according to preset distance, power and time, optimize resource allocation during the battery swapping process, and improve overall efficiency;

[0056] The interactive unit 8 is used for the battery-swapping vehicle to receive the sorted battery-swapping plans and provide feedback. The interactive unit 8 includes a login module 81, a receiving module 82 and a feedback module 83. The login module 81 is interactively connected with the receiving module 82 and the feedback module 83 through a wireless network. The login module 81 is used to verify the vehicle owner's identity ID and provide interactive authority. The receiving module 82 is used to receive the battery-swapping plan transmitted by the priority sorting module 7, display it and choose whether to apply it. After choosing to apply, a reminder will be given within a preset time period. The feedback module 83 is used to regularly request the power usage status and feedback on the battery-swapping vehicle after the battery-swapping vehicle applies the battery-swapping plan, record the feedback data and submit it to the execution analysis module 9, and submit the driving data to the trend generation module 3 to ensure that users can obtain information and provide feedback in a timely manner, thereby promoting the optimization of system performance.

[0057] As a preferred implementation in this embodiment, Figure 1 As shown, the central control module 1 is interactively connected with the verification module 2, the trend generation module 3 and the acquisition module 4 through a wireless network, the acquisition module 4 is interactively connected with the correlation analysis module 5 through a wireless network, the correlation analysis module 5 is interactively connected with the solution generation module 6 through a wireless network, the solution generation module 6 is interactively connected with the priority sorting module 7 through a wireless network, and the priority sorting module 7 is interactively connected with the interaction unit 8 through a wireless network.

[0058] In the specific implementation process of this embodiment, the central control module 1 performs overall control over each module, the verification module 2 verifies the identity of the battery swap point and the vehicle, the collection module 4 collects data from both, the correlation analysis module 5 analyzes the correlation between the vehicle and the battery swap point, the solution generation module 6 generates a recommended battery swap solution, the priority sorting module 7 sorts the vehicles, the owner's identity is logged in through the login module, the battery swap solution is received through the receiving module 82, and the completion of the battery swap solution is fed back through the feedback module 83;

[0059] An efficient identity authentication process ensures security while improving user experience. In-depth driving habit analysis effectively predicts vehicle behavior and improves the accuracy of battery swap scheduling. Intelligent battery swap solution calculation and optimization improves charging efficiency and convenience through real-time data collection and analysis. Continuous monitoring and improvement of system performance ensures that user needs can be reflected and processed in a timely manner.

[0060] Embodiment 2: In other aspects, this embodiment also provides a scheduling method for V2G vehicle-grid interactive charging, comprising the following steps:

[0061] Step 1: Obtain the location, power and utilization information of each charging station in the power grid, and identify the vehicle type, current power, expected mileage and destination of the connected charging vehicle;

[0062] Step 2: Update the vehicle's power and charging requirements in real time, collect user driving data and charging preferences, analyze historical driving data and charging data, and evaluate the vehicle's charging behavior and driving patterns;

[0063] Step 3: Based on the current route and destination, calculate the correlation between different charging stations, combine traffic data, vehicle charging behavior and driving mode to evaluate the accessibility and time cost of a charging station, and output a candidate list of charging stations;

[0064] Step 4: Use historical driving data and navigation algorithms to build a model to predict the driver's future travel, display the user's driving behavior in different time periods, and provide feedback on the prediction results;

[0065] Step 5: Calculate the effective battery swap time window based on the current power of the battery swap target and the energy storage status of the associated location, evaluate the economic feasibility of different charging stations, generate a battery swap plan, and sort the different plans according to priority;

[0066] Step 6: Push the battery swap plan to the user end, provide a battery swap plan including the estimated charging and battery swap time, location and cost information, and guide the user to confirm and provide feedback on the battery swap plan;

[0067] Step 7: During the process of the battery swapping vehicle executing the battery swapping plan, dynamically collect the user's actual operation data, compare the planned battery swapping plan with the user's actual execution, calculate the execution index, and analyze the reasons. If the execution degree is higher than the comparison standard, combined with the user's needs and the status of the battery swapping location, increase the recommendation priority, otherwise, lower it.

[0068] As a preferred implementation of this embodiment, in step 3, the correlation between the user's frequently used routes and the charging stations is calculated, and the calculation logic of the correlation calculation formula is:

[0069] If the distance between the charging station and the destination increases, the correlation decreases accordingly, and vice versa;

[0070] If the number of times a user uses a charging station reaches a preset threshold, the relevance of the charging station to the user will be increased accordingly, otherwise, it will be decreased;

[0071] If the comprehensive value score of the charging station increases, the relevance will increase accordingly, otherwise, it will decrease;

[0072] The specific formula for calculating the correlation is:

[0073] ;

[0074] In the formula, Represents the correlation between the user's frequently used routes and charging stations. The higher the value, the stronger the correlation between the charging station and the user's route, and the more attractive it is to the user. represents the attribute vector of the i-th charging station, indicating the attributes location, power and cost, n represents the number of locations in the user's common route, Represents the user's frequently used routes, expressed as a sequence of locations. Represents the position on the path The importance weighted value is based on the user's historical stay time or visit frequency. Represents the point on the common path from charging station i to the user The distance Represents the comprehensive value score of charging station i, including power, availability and cost, providing a quantitative basis for the attractiveness of the charging station. represents the attribute vector of the jth destination, including location, type, and user preferences, represents the distance from charging station i to destination j, M represents the maximum distance value, which provides an upper limit for the distance score and ensures that all distance ratios are between 0 and 1 for normalization.

[0075] Compared with existing technologies, real-time analysis of the identity and charging information of charging stations and electric vehicles makes scheduling decisions more scientific and accurate, and can respond to market demand in a timely manner. The introduction of future driving behavior prediction and periodic feedback mechanisms allows scheduling strategies to be dynamically adjusted as user behavior changes, thereby improving the level of intelligence.

[0076] Through battery swap solutions based on cost-effective charging time and location, users can be provided with more economical options, charging costs can be reduced, priority can be given to charging stations that are highly relevant to the user's itinerary, the user's charging waiting time can be reduced, and utilization efficiency can be improved. Charging solutions can be tailored for users based on their preferences and historical data, allowing users to experience greater convenience and satisfaction when using the system. Through real-time monitoring and analysis of execution, it can respond quickly to different demand changes, realize dynamic adjustment of priorities, and further optimize resource utilization efficiency.

[0077] Embodiment 3: In this embodiment, as Figure 1 As shown, the interactive unit 8 is interactively connected to the execution analysis module 9 via a wireless network. The execution analysis module 9 evaluates the execution of the battery swap target on the battery swap plan based on the feedback information, calculates the execution index, compares it with the preset standard, and obtains the execution level. The execution analysis module 9 is provided with an adjustment module 10 at the lower level. The adjustment module 10 is interactively connected to the priority sorting module 7 via a wireless network. The adjustment module 10 is used to dynamically adjust the priority of each battery swap vehicle in the same battery swap plan. When the execution degree of the target is higher than the current attribute threshold, the priority of the battery swap plan is increased. On the contrary, if the execution degree is low, the priority is lowered. The priority sorting module 7 re-sorts and sends the results based on the adjustment result.

[0078] Compared with the existing technology, the priority of the battery swap plan is adjusted in real time according to the execution degree of each vehicle, making the scheduling more flexible and efficient, ensuring the best effect of resource allocation. Different vehicles can have different priorities in the same battery swap plan to meet individual needs and improve overall operational efficiency. The execution of the battery swap goals can be evaluated in real time through feedback information, which promotes system self-correction and ensures that the scheduling strategy is consistent with actual operations. By dynamically adjusting the priority of each vehicle in the battery swap plan, charging and battery swap resources can be used more effectively to reduce idleness and waste.

[0079] In summary, the present invention can integrate the information of charging stations and vehicles in real time, including charging demand, power, location, etc., and provide a more accurate scheduling solution. By analyzing user behavior and charging station status, the method has the ability to dynamically adjust the scheduling strategy to ensure better adaptation to user real-time demand and power grid conditions, comprehensively consider charging cost, charging time and power grid load, generate a cost-effective power replacement solution, optimize user experience and resource utilization, and based on the user feedback mechanism, the system can continuously optimize the power replacement solution through interaction, so that users have a stronger sense of participation and enhance the flexibility of user decision-making;

[0080] Through historical data and trajectory trend analysis, it is possible to predict users' future travel and improve the accuracy of predictions, thereby optimizing charging plans. This not only takes into account users' charging needs, but also the load on the power grid and the status of charging facilities. It improves the resource utilization of the entire system from a global optimization perspective, sets up an execution analysis mechanism, and timely adjusts the priority of the battery swap plan to ensure the effective execution of the scheduling plan. It can also be adjusted according to different charging station characteristics and user needs. It has good scalability and can adapt to different types of electric vehicles and charging facilities.

[0081] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dispatching system for V2G vehicle-grid interactive charging, characterized in that: include: The central control module (1) is used to edit and control the start and stop instructions of each functional module and functional unit, allocate charging station and electric vehicle identity IDs, and store all battery replacement data; A verification module (2) is used to receive the identity ID and status information of the battery swap point and the vehicle, perform identity verification and feedback the result; A trend generation module (3) is used to collect GPS and travel records of several authenticated vehicles, analyze the historical driving habit data of the vehicles, predict the future driving routes and time distribution, and form a trajectory trend report; A collection module (4) is used to collect charging power, power and charging time data from battery swapping points and vehicles and convert them into machine-readable data; A correlation analysis module (5) is used to evaluate the correlation between the target and the battery swap stations related to the driving path based on the vehicle's historical charging records and driving habits and based on the trajectory trend report; A plan generation module (6) is used to calculate a battery swap plan including charging time and location based on the battery capacity of the battery swap vehicle, the destination and the energy storage status of the associated battery swap point; A priority sorting module (7), used to prioritize battery swapping vehicles participating in the same battery swapping scheme according to preset distance, power and time; An interaction unit (8), configured for the battery swapping vehicle to receive the sorted battery swapping solutions and provide feedback; The specific working process of the correlation analysis module (5) is as follows: obtaining historical charging time, historical charging location, historical charging status, current vehicle location, planned destination, charging station location, charging station status and charging station capacity data, cleaning and standardizing input data, extracting the visit frequency, usage time and charging amount characteristics of each charging station, calculating the distance from the vehicle to each charging station and the destination, and referring to the trajectory trend, defining a number of indicators for each charging station, and quantifying and outputting the correlation between the target vehicle and the charging station; The interaction unit (8) comprises a login module (81), a receiving module (82) and a feedback module (83), wherein the login module (81) is interactively connected with the receiving module (82) and the feedback module (83) via a wireless network, wherein the login module (81) is used to verify the identity ID of the vehicle owner and provide interaction authority, wherein the receiving module (82) is used to receive the battery replacement scheme transmitted by the priority sorting module (7), display it and select whether to apply it, and after selecting to apply it, a reminder is issued at a preset time period, wherein the feedback module (83) is used to periodically request the power consumption status and feedback on the scheme of the battery replacement vehicle after the battery replacement scheme is applied, record the feedback data and submit it to the execution analysis module (9), and submit the driving data to the trend generation module (3); The interactive unit (8) is interactively connected to an execution analysis module (9) via a wireless network. The execution analysis module (9) evaluates the execution of the battery swap target on the battery swap plan based on feedback information, calculates the execution index, compares it with a preset standard, and obtains the execution level.

2. A dispatching system for V2G vehicle-grid interactive charging according to claim 1, characterized in that: The acquisition module (4) uses a clustering algorithm to cluster historical driving data, identify several driving modes, obtain common routes and driving habits, build a prediction model through a deep learning algorithm, input pre-processed historical trajectory data, user and environmental characteristics, use real-time traffic data as a correction reference, predict the coordinates of the target battery replacement vehicle in the next cycle, the expected route and the time of participation, integrate the analysis results, and output a trajectory trend report containing the expected departure time, arrival time, route suggestions and potential risk warnings.

3. A dispatching system for V2G vehicle-grid interactive charging according to claim 1, characterized in that: The execution degree analysis module (9) is provided with an adjustment module (10) at the lower level. The adjustment module (10) is interactively connected with the priority sorting module (7) via a wireless network. The adjustment module (10) is used to dynamically adjust the priority of each battery swapping vehicle in the same battery swapping scheme. When the execution degree of the target is higher than the current attribute threshold, the priority of the battery swapping scheme is increased. On the contrary, if the execution degree is low, the priority is decreased. According to the adjustment result, the priority sorting module (7) re-sorts and sends the result.

4. A dispatching system for V2G vehicle-grid interactive charging according to claim 1, characterized in that: The central control module (1) is interactively connected to the verification module (2), the trend generation module (3) and the acquisition module (4) via a wireless network; the acquisition module (4) is interactively connected to the correlation analysis module (5) via a wireless network; the correlation analysis module (5) is interactively connected to the solution generation module (6) via a wireless network; the solution generation module (6) is interactively connected to the priority sorting module (7) via a wireless network; and the priority sorting module (7) is interactively connected to the interaction unit (8) via a wireless network.

5. A scheduling method for V2G vehicle-grid interactive charging, the method is an implementation method of a scheduling system for V2G vehicle-grid interactive charging based on any one of claims 1-4, characterized in that: The following steps are involved: Step 1: Obtain the location, power and utilization information of each charging station in the power grid, and identify the vehicle type, current power, expected mileage and destination of the connected charging vehicle; Step 2: Update the vehicle's power and charging requirements in real time, collect user driving data and charging preferences, analyze historical driving data and charging data, and evaluate the vehicle's charging behavior and driving patterns; Step 3: Based on the current route and destination, calculate the correlation between different charging stations, combine traffic data, vehicle charging behavior and driving mode to evaluate the accessibility and time cost of a charging station, and output a candidate list of charging stations; Step 4: Use historical driving data and navigation algorithms to build a model to predict the driver's future travel, display the user's driving behavior in different time periods, and provide feedback on the prediction results; Step 5: Calculate the effective battery swap time window based on the current power of the battery swap target and the energy storage status of the associated location, evaluate the economic feasibility of different charging stations, generate a battery swap plan, and sort the different plans according to priority; Step 6: Push the battery swap plan to the user end, provide a battery swap plan including the estimated charging and battery swap time, location and cost information, and guide the user to confirm and provide feedback on the battery swap plan; Step 7: During the process of the battery swapping vehicle executing the battery swapping plan, dynamically collect the user's actual operation data, compare the planned battery swapping plan with the user's actual execution, calculate the execution index, and analyze the reasons. If the execution degree is higher than the comparison standard, combined with the user's needs and the status of the battery swapping location, increase the recommendation priority, otherwise, lower it.

6. A scheduling method for V2G vehicle-grid interactive charging according to claim 5, characterized in that: The calculation formula for the correlation between the user's frequently used routes and charging stations in step 3 is: ； In the formula, Represents the correlation between the user's frequently used routes and charging stations, represents the attribute vector of the i-th charging station, n represents the number of locations in the user's common route, Represents the user's frequently used routes, expressed as a sequence of locations. Represents the position on the path The importance weight of Represents the point on the common path from charging station i to the user The distance Represents the comprehensive value score of charging station i, represents the attribute vector of the jth destination, represents the distance from charging station i to destination j, and M represents the maximum distance value.

7. A scheduling method for V2G vehicle-grid interactive charging according to claim 5, characterized in that: The calculation logic of the correlation calculation formula is: If the distance between the charging station and the destination increases, the correlation decreases accordingly, and vice versa; If the number of times a user uses a charging station reaches a preset threshold, the relevance of the charging station to the user will be increased accordingly, otherwise, it will be decreased; If the comprehensive value score of the charging station increases, the correlation will increase accordingly, otherwise, it will decrease.

Images