A V2X charge-discharge cooperative control system based on dynamic multi-objective optimization

Abstract

This invention relates to the field of charge and discharge control technology, specifically to a V2X charge and discharge coordinated control system based on dynamic multi-objective optimization. The system includes a voltage characteristic identification module, a response capability evaluation module, a scheduling matching mapping module, a task load allocation module, and a coordinated regulation determination module. In this invention, rapid adaptive response data acquisition within the voltage regulation dead zone is achieved through multi-node transient voltage response identification. A synchronization vector is constructed using response time, stabilization characteristics, and accuracy level, and normalized to measure vehicle synchronization capability. A difference ranking mechanism is constructed based on regulation tolerance to achieve dynamic hierarchical scheduling of multiple vehicles. A regulation mapping relationship is established based on the task load regulation rate, achieving consistent matching and dynamic adaptation between vehicle scheduling and load changes. This significantly improves the adaptability of vehicle energy response behavior to the dynamic demands of the power grid, meets the coordinated discharge task under multi-objective constraints, and reduces time lag and unbalanced response problems.

Description

Technical Field

[0001] This invention relates to the field of charge and discharge control technology, and in particular to a V2X charge and discharge cooperative control system based on dynamic multi-objective optimization. Background Technology

[0002] The field of charge and discharge control technology involves control technologies for the orderly management of electrical energy between energy storage devices and the power grid or load. This includes the regulation and control of power flow, the formulation of charging and discharging strategies, the management of battery safety and lifespan, and the interface coordination with external power supply systems. It is widely used in electric vehicles, energy storage systems, and distributed energy networks. Traditional V2X charge and discharge coordinated control systems refer to solving the problems of energy scheduling and charge and discharge control between vehicles and the power grid in V2G scenarios through preset static optimization strategies or rule-based control methods. They typically use priority control set for fixed time periods, time schedule scheduling guided by electricity prices, and simple prediction methods to determine the charging and discharging behavior of electric vehicles. The rules are mainly matched based on the remaining battery capacity of the vehicle, the grid load demand, and the electricity price time schedule.

[0003] Traditional charge-discharge coordinated control mainly relies on preset rule matching and static priority settings. It makes judgments based solely on the static relationship between electricity price schedule, battery capacity and grid load. When faced with frequently changing adjustment needs or multiple vehicles charging and discharging tasks in parallel, the response lacks consideration for real-time and synchronization, which can easily lead to problems such as significant response deviations of some vehicles, voltage regulation lag, and uneven task load distribution. For example, under the condition of grid frequency fluctuations or rapid load switching, some vehicles may discharge ahead or behind, resulting in a decrease in overall power regulation effect and local node voltage instability. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a V2X charge and discharge cooperative control system based on dynamic multi-objective optimization.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a V2X charge-discharge coordinated control system based on dynamic multi-objective optimization includes:

[0006] The voltage characteristic identification module acquires transient voltage response data and applied current of vehicles connected to the V2G network, calculates the voltage drop rate per unit current, calculates the upper and lower deviation groups and calculates the ratio with the target interval amplitude, determines whether it is within the voltage regulation dead zone, and obtains multi-node vehicle response adaptation records.

[0007] The response capability assessment module, based on the multi-node vehicle response adaptation record, collects the time coordinates of the maximum voltage fluctuation point and the excitation application time of the adapted vehicle, calculates the response phase offset, and constructs a normalized synchronization vector by combining the current stabilization time and the charging and discharging accuracy level, and summarizes the response synchronization feature group.

[0008] Based on the response synchronization feature group, the scheduling matching mapping module reads the power grid frequency regulation tolerance, calculates the absolute value of the difference between the adaptive vehicle response phase offset and the current stabilization time term and the regulation tolerance, sorts them in ascending order, constructs a vehicle scheduling mapping classification, and obtains a vehicle cooperative scheduling classification sequence.

[0009] The task load allocation module establishes a one-to-one mapping between the task load adjustment rate and the vehicle scheduling sequence based on the vehicle collaborative scheduling classification sequence and the load adjustment rate, thereby obtaining a load allocation mapping table.

[0010] As a further embodiment of the present invention, the multi-node vehicle response adaptation record includes node voltage adaptation value, upper and lower deviation ratio distribution record, and adjustment dead zone determination result; the response synchronization feature group includes response phase vector, current stabilization coefficient, and synchronization level index; the vehicle cooperative scheduling classification sequence includes priority response sequence, secondary response sequence, and vehicle scheduling level label; and the load allocation mapping table includes adjustment rate matching relationship, vehicle sequence grouping label, and task subgroup number.

[0011] As a further aspect of the present invention, the voltage characteristic identification module includes:

[0012] The data acquisition submodule acquires the transient voltage response data of each node vehicle connected to the V2G network under the action of the charging pile test current and the corresponding applied current, extracts the current change value and the corresponding time difference in the transient period, matches the node timing to obtain the voltage change amplitude and response duration in the sampling window, and obtains the voltage response sampling data.

[0013] The voltage rate deviation calculation submodule calculates the voltage drop rate per unit current based on the voltage response sampling data, and calculates the difference between the voltage drop rates according to the upper and lower limits of the voltage response rate of the target node, generating upper and lower deviation groups. Simultaneously, it calculates the absolute value ratio of the deviation groups and the target interval amplitude, calculates the voltage rate interval deviation rate and the degree of voltage drop trend deviation, and obtains the rate deviation statistical results.

[0014] The response adaptation judgment submodule compares the rate deviation statistics with the voltage regulation dead zone range to determine whether the rate deviation statistics are within the range. It then establishes a response adaptation index table based on the number of nodes and the corresponding results to obtain multi-node vehicle response adaptation records.

[0015] As a further aspect of the present invention, the response capability assessment module includes:

[0016] The response phase acquisition submodule acquires the time coordinates of the maximum voltage fluctuation point of the adapted vehicle based on the multi-node vehicle response adaptation record, records the maximum fluctuation time in combination with the start time record of excitation application, performs synchronous indexing on the excitation application time, calculates the difference between the maximum fluctuation point time and the excitation time of each node, obtains the time offset of each node, and obtains the maximum fluctuation phase offset group.

[0017] The current stabilization statistics submodule indexes the current sampling sequence after the excitation of each node according to the maximum fluctuation phase offset group, calculates the time difference between the excitation time and the stabilization time, reads the accuracy level identification code of each node, establishes a numerical level index array, calculates and obtains the synchronization superposition index, and generates the initial data group of the synchronization response.

[0018] The synchronization vector construction submodule normalizes the data based on the initial data set of the synchronization response, merges the three parameter vectors, and sequentially combines them to construct the vehicle synchronization vector. The synchronization vectors of each node are used as row vectors to merge and construct a matrix to obtain the response synchronization feature set.

[0019] As a further aspect of the present invention, the scheduling matching mapping module includes:

[0020] The response difference extraction submodule extracts the response start and end time nodes of each vehicle based on the response phase offset time and current stabilization delay time of each adapted vehicle recorded in the response synchronization feature group, and constructs a vehicle response time information group. Combined with the grid frequency adjustment tolerance range of the discharge task, the corresponding frequency adjustment time window is extracted. The reference response time is constructed based on the median value of the adjustment time window. The response time of each vehicle is compared with the reference response time to obtain the frequency response difference group.

[0021] The priority sequence construction submodule establishes a corresponding offset value index table based on the degree of response deviation of each vehicle in the frequency response difference group, and performs difference ascending order processing. For vehicles whose difference is within the deviation range, the stabilization time node is recorded, and the frequency adjustment end time is matched and compared. Vehicles that simultaneously meet the deviation condition and the stabilization time condition are marked as priority vehicles, and the remaining vehicles are classified as secondary vehicles. The results of priority assignment sequence and secondary sequence division are obtained by summarizing the results.

[0022] The vehicle scheduling mapping submodule reads the accuracy level and power response capability parameters of each vehicle from the vehicle set in the priority assignment sequence and secondary sequence division results, sorts the priority vehicles and secondary vehicles internally according to the power response capability index, assigns a scheduling label to each vehicle, and obtains the vehicle collaborative scheduling classification sequence.

[0023] As a further aspect of the present invention, the task load allocation module includes:

[0024] The gradient adjustment construction submodule obtains the scheduling numbers of all vehicles in the vehicle collaborative scheduling classification sequence, sets the load adjustment rate interval of the discharge task, divides it into equal intervals, constructs a rate index table according to the number of segments, determines the number of vehicles that each rate interval can accommodate by combining the ratio of the total number of scheduling numbers to the total number of rate segments, establishes a number matching framework between the rate interval and the vehicle scheduling number, and obtains the adjustment rate interval grouping result.

[0025] The task matching and partitioning submodule, based on the adjustment rate interval grouping results, and according to the accuracy level index and recommended discharge capacity corresponding to each vehicle scheduling number, sequentially assigns the rate task interval where its scheduling number is located to each vehicle, identifies whether the continuity of scheduling numbers in the rate task interval is disrupted, determines whether the recommended rate of the vehicle falls within the allowable range of the assigned rate interval, and simultaneously determines the degree of cooperation between the accuracy level index and the task adjustment interval, completes the vehicle assignment adjustment under the task interval, and obtains the task adjustment grouping matrix.

[0026] The allocation mapping generation submodule, based on the mapping results of each rate task group and vehicle number recorded in the task adjustment grouping matrix, sequentially reads the number of vehicles, the corresponding adjustment rate range, and the task number in each group, and arranges them in ascending order by task number to establish a list of task groups and vehicle scheduling numbers. It then statistically analyzes the recommended adjustment rate of vehicles in each group and standardizes the results. Finally, it integrates the vehicle number, scheduling number, and task number to generate a load allocation mapping table.

[0027] As a further aspect of the present invention, the system further includes:

[0028] Based on the load allocation mapping table, the collaborative control judgment module monitors the changing trends and adaptation status of the response behavior of each vehicle during the discharge task execution phase, summarizes the changes in vehicle characteristics and scheduling response status within the task cycle, and generates a dynamically optimized V2X charging and discharging collaborative control scheme.

[0029] The dynamically optimized V2X charge-discharge coordinated control scheme includes a characteristic trend map, an adaptive state trajectory, and control feedback parameters.

[0030] As a further aspect of the present invention, the coordinated regulation determination module includes:

[0031] The response monitoring and identification submodule records the control feedback data of each vehicle based on the vehicle number under each task number in the load distribution mapping table, and counts the number and distribution time of various over-limit behaviors within the task cycle to obtain the task cycle response behavior change trend record.

[0032] The status trend aggregation submodule extracts the marked abnormal data segments from all records based on the task cycle response behavior change trend records, and counts the number of abnormal occurrences and the total duration of each vehicle. It then determines whether a vehicle has entered the adaptation state judgment condition, establishes a mapping relationship between the task number and the status aggregation factor, and obtains the task status trend summary table.

[0033] The control scheme generation submodule, based on the state aggregation factor corresponding to each task number in the task state trend summary table, rearranges all marked tasks into temporary control areas, and marks the vehicle number with the lowest priority in these areas for replacement scheduling settings. It then reallocates the target rate to adjacent task numbers, renumbers all task numbers, vehicle numbers, and their corresponding adjustment targets, and integrates and outputs a dynamically optimized V2X charging and discharging collaborative control scheme.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0035] In this invention, rapid adaptive response data acquisition within the voltage regulation dead zone is achieved through multi-node transient voltage response identification. A synchronization vector is constructed using response time, stabilization characteristics, and accuracy level, and normalized to measure vehicle synchronization capability. A difference sorting mechanism is constructed in conjunction with regulation tolerance to achieve dynamic scheduling hierarchy of multiple vehicles. A control mapping relationship is established based on task load adjustment rate to achieve consistent matching and dynamic adaptation between vehicle scheduling and load changes. This results in higher time coordination, voltage adaptability, and load distribution accuracy of charging and discharging responses, significantly improving the adaptability of vehicle energy response behavior to the dynamic demands of the power grid, meeting the coordinated discharge task under multi-objective constraints, and reducing time lag and unbalanced response problems. Attached Figure Description

[0036] Figure 1 This is a system flowchart of the present invention;

[0037] Figure 2 This is a flowchart of the voltage characteristic identification module of the present invention;

[0038] Figure 3 This is a flowchart of the response capability assessment module of the present invention;

[0039] Figure 4 This is a flowchart of the scheduling matching mapping module of the present invention;

[0040] Figure 5 This is a flowchart of the task load allocation module of the present invention;

[0041] Figure 6 This is a flowchart of the collaborative regulation and determination module of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0044] Please see Figure 1 A V2X charge-discharge coordinated control system based on dynamic multi-objective optimization includes:

[0045] The voltage characteristic identification module acquires the transient voltage response data and applied current of vehicles connected to the V2G network under the test current waveform of the charging pile, calculates the voltage drop rate under unit current, and combines the upper and lower limits of the target node voltage response rate with the voltage drop rate to form upper and lower deviation groups. The ratio between the upper and lower deviation groups and the target interval amplitude is calculated, and it is determined whether each ratio is within the voltage regulation dead zone (complies with the distributed energy grid connection standard ±2% voltage deviation) to obtain the multi-node vehicle response adaptation record.

[0046] The response capability assessment module is based on the multi-node vehicle response adaptation records. It collects the time coordinates of the maximum voltage fluctuation point and the excitation application time of the adapted vehicle, calculates the response phase offset, and collects the current stabilization time and charging and discharging accuracy level (including Class 1~3 accuracy levels). It constructs a normalized synchronization vector (using the synchronization index algorithm of the "Power Grid Interaction Interface Standard"), summarizes the synchronization vector data of all adapted vehicles, and obtains the response synchronization feature group.

[0047] The scheduling matching mapping module reads the grid frequency regulation tolerance of the current discharge task (set ±0.5Hz dynamic regulation band) based on the response synchronization feature group, calculates the absolute value of the difference between the response phase offset and the current stabilization time term and the regulation tolerance of each adapted vehicle, sorts them in ascending order based on the absolute value of the difference, defines the priority assignment sequence and the secondary sequence, constructs the vehicle scheduling mapping classification based on the sorting, and obtains the vehicle cooperative scheduling classification sequence.

[0048] The task load allocation module matches each vehicle sequence to the corresponding task subgroup with the corresponding load adjustment rate based on the vehicle collaborative scheduling classification sequence and the load adjustment rate (adjustment gradient of 0.1-2C according to the standard setting), and establishes a one-to-one mapping between the task load adjustment rate and the vehicle scheduling sequence to obtain the load allocation mapping table.

[0049] The collaborative control and determination module, based on the load distribution mapping table, monitors the changing trends and adaptation status of the response behavior of each vehicle during the discharge task execution phase, summarizes the changes in vehicle characteristics and scheduling response status within the task cycle, and generates a dynamically optimized V2X charging and discharging collaborative control scheme.

[0050] The multi-node vehicle response adaptation record includes node voltage adaptation values, upper and lower deviation ratio distribution records, and regulation dead zone determination results. The response synchronization characteristic group includes response phase vector, current stabilization coefficient, and synchronization level index. The vehicle cooperative scheduling classification sequence includes priority response sequence, secondary response sequence, and vehicle scheduling level label. The load allocation mapping table includes regulation rate matching relationship, vehicle sequence grouping label, and task subgroup number. The dynamically optimized V2X charging and discharging cooperative control scheme includes feature trend map, adaptation state trajectory, and regulation feedback parameters.

[0051] Please see Figure 2 The voltage characteristic identification module includes:

[0052] The data acquisition submodule acquires the transient voltage response data of each node vehicle connected to the V2G network under the action of the charging pile test current and the corresponding applied current, extracts the current change value and the corresponding time difference in the transient period, matches the node timing to obtain the voltage change amplitude and response duration in the sampling window, and obtains the voltage response sampling data.

[0053] To acquire transient voltage response data and corresponding applied current for each vehicle node connected to the V2G network under the test current of the charging pile, the test system first applies a pulse current with a fixed time window to the access node. During the test period, the voltage of each vehicle's charging interface is monitored, and the initial and final voltage values ​​of each node are acquired sequentially. A 2kHz sampling frequency is used for voltage acquisition to ensure that details of the response voltage changes are captured. The voltage change value is the instantaneous drop within the test period; for example, the initial voltage of node N1 is 390.2V, and the final voltage is 382V. If the voltage is 0.6V, the transient voltage drop is 7.6V. The corresponding test current is 18.5A, and the response time is 0.6s. Other nodes are acquired using the same method. For example, node N2 has an initial value of 388.5V and a final value of 381.3V, with a current of 19.1A; N3 has an initial value of 391.1V and a final value of 383.9V, with a current of 18.7A. After acquiring the voltage change, the current change value is combined to establish the voltage drop rate parameter, which serves as an important input for subsequent calculation of the rate deviation. The response time is uniformly set to 0.6s. The data is shown in Table 1.

[0054] Table 1. Sampled values ​​of node voltage response

[0055] Sample number Node number Initial voltage value (V) Final voltage value (V) Current change (A) Response time (s) Sample 1 N1 390.2 382.6 18.5 0.6 Sample 2 N2 388.5 381.3 19.1 0.6 Sample 3 N3 391.1 383.9 18.7 0.6

[0056] As shown in Table 1, the voltage drop at each node is basically between 6 and 8V, the current amplitude is maintained in the range of 18 to 20A, and the response time is consistent, providing reliable input for subsequent calculation of unit current voltage rate, and finally obtaining voltage response sampling data.

[0057] The voltage rate deviation calculation submodule calculates the voltage drop rate per unit current based on voltage response sampling data. It then calculates the difference between the voltage drop rates according to the upper and lower limits of the target node's voltage response rate, generating upper and lower deviation groups. Simultaneously, it calculates the absolute value ratio between the deviation groups and the target interval amplitude using the following formula:

[0058] ;

[0059] The voltage rate range deviation rate and the degree of voltage drop trend deviation are calculated to obtain the rate deviation statistics. Represents the rate of voltage drop per unit current. and These are the lower and upper limits of the voltage response rate of the target node, respectively. Let be the transient voltage drop value of the i-th sampled vehicle. Apply a value to the corresponding current, where n is the number of sample groups and R is the rate interval deviation rate;

[0060] Based on voltage response sampling data, the voltage drop rate per unit current is calculated. First, the ratio of the transient voltage drop value to the applied current amplitude at each node is determined to obtain the voltage drop rate of the current vehicle under this test. Taking sample 1 as an example, the voltage drop is 7.6V, corresponding to a current of 18.5A. The system presets the upper and lower limits of the allowed rate for the target node as follows: , This is used to determine the ratio of deviations in the subsequent calculation, and the rate deviation rate is constructed using a formula.

[0061] Input data:

[0062] Sample 1: ;

[0063] Sample 2: ;

[0064] Sample 3: ;

[0065] The sum of squares is calculated as follows: ;

[0066] Taking the square root gives The numerator is calculated as follows:

[0067] ;

[0068] The denominator is: ;

[0069] Final result: ;

[0070] The threshold value of 1 used is based on the tolerance of the standard range deviation of the system voltage rate response. Specifically, when the deviation rate R is 1, it means that the total deviation of the voltage rate per unit current at the current node between the upper and lower limits is exactly equal to half the amplitude of the standard range multiplied by the number of samples. That is, the upper and lower deviations are completely consistent with the tolerance amplitude. Therefore, R=1 can be considered the boundary of the voltage rate deviation from the upper tolerance limit. This value is limited by… , The absolute value of the difference is set, along with the amplification relationship of the sample size n. R increases linearly with the number of sample nodes and decreases as the width of the upper and lower limit interval increases. Setting this value to 1 is equivalent to the deviation value of a single node not exceeding the average amplitude of the interval, ensuring that the voltage rate consistency is controlled within an acceptable range. Therefore, this threshold has technical traceability and engineering setting basis. A value greater than the judgment threshold of 1 indicates that the current vehicle node voltage rate deviates significantly from the standard upper and lower limits, ultimately generating a rate deviation statistical result.

[0071] The rate range deviation rate is a dimensionless ratio used to quantify the difference between the voltage response rate of a current vehicle node under unit current and the target range set by the system. Specifically, it measures the absolute deviation of the node's rate value from the upper and lower limits of the target rate range, as well as the average response fluctuation level of the entire node group in the test round. The evaluation coefficient is obtained by normalizing this composite deviation with the preset standard range amplitude. The smaller the deviation rate, the closer the current node's voltage response rate is to the standard range, and the more concentrated and coordinated the system response is. Conversely, if the deviation rate is significantly greater than 1, it indicates that the node's voltage response behavior has deviated significantly from the target range boundary, and there is a risk of response mismatch. Therefore, this indicator can provide a quantitative basis for judging the node's response performance within the target tolerance range, and provide quantitative decision support for node grid connection adaptation and dynamic screening.

[0072] The formula is constructed based on a triple deviation analysis structure for the voltage drop rate per unit current. First, and The two parameters are used to measure the absolute offset between the current node rate and the upper and lower boundaries of the preset response interval, respectively. The sum of these two parameters reflects the degree of concentrated deviation of the current vehicle response value relative to the target interval; secondly, This expresses the square root of the sum of the squares of the rate values ​​of all sampled nodes. In essence, it is the Euclidean norm for calculating the unit current response rate, used to capture the average fluctuation intensity of the overall system response, avoiding the amplification of errors caused by relying solely on a single node. The purpose of adding these terms in the numerator is to incorporate individual offsets and group fluctuations into the evaluation structure, enhancing robustness. The denominator is calculated by multiplying half the interval width by the number of samples, forming an amplified value of the standard reference amplitude. This establishes a normalized reference benchmark proportional to the sample size, ensuring that the calculated value is not unbalanced due to the number of nodes or the interval setting, and can reflect the relative adaptability of the current node.

[0073] The response adaptation judgment submodule compares the rate deviation statistics with the voltage regulation dead zone range to determine whether the rate deviation statistics are within the range. It then establishes a response adaptation index table based on the number of nodes and the corresponding results to obtain multi-node vehicle response adaptation records.

[0074] Based on the rate deviation statistics, the results are input into the response adaptation judgment logic. The R value is compared with the set rate dead zone tolerance judgment threshold. The current standard stipulates that if the R value is less than 1, it is judged as adapted, and if it is greater than or equal to 1, it is judged as unsuitable. In the above calculation, R=5.19, so this node is judged as unsuitable. The rates of other sample nodes are: sample 2 is 0.377V / A and sample 3 is 0.385V / A. After similar calculation, their R values ​​are 4.63 and 4.87 respectively, both of which are greater than the threshold and are judged as unsuitable. The judgment results of the three nodes are encoded as [0, 0, 0] in sequence and written into the node response record matrix. The corresponding indexes are stored in the multi-node response dictionary table to retrieve and analyze the stable response differences of the nodes in other environments, and finally obtain the multi-node vehicle response adaptation record.

[0075] Please see Figure 3 The response capability assessment module includes:

[0076] The response phase acquisition submodule acquires the time coordinates of the maximum voltage fluctuation point of the adapted vehicle, records the maximum fluctuation time in conjunction with the start time record of excitation application, performs synchronous indexing on the excitation application time, calculates the difference between the maximum fluctuation point time and the excitation time of each node, obtains the time offset of each node, and acquires the maximum fluctuation phase offset group.

[0077] The time coordinates of the maximum voltage fluctuation point for each of all adapted vehicles were collected. First, the voltage time series data of each node was read, and the voltage change before and after each time point was scanned in 0.1s increments. If the voltage difference exceeded 5V within any consecutive 0.3s, that moment was considered a significant voltage fluctuation point. Based on this method, the maximum voltage change moment within 6s after excitation for each vehicle node was determined and recorded as the maximum fluctuation time point for that node. Simultaneously, the excitation application moment for each vehicle was extracted and uniformly set to 1.0s. The phase offset time was obtained by subtracting the excitation moment from the fluctuation time point. For sample 1, the fluctuation point was 4.2s with an offset of 3.2s; for sample 2, the fluctuation point was 4.6s with an offset of 3.6s; and for sample 3, the fluctuation point was 4.4s with an offset of 3.4s. All information is summarized in the table below.

[0078] Table 2. Vehicle Node Fluctuation Characteristics Collection Table

[0079] Sample number Vehicle number Maximum voltage fluctuation time (s) Incentive application time (s) Phase offset time (s) Sample 1 V1 4.2 1.0 3.2 Sample 2 V2 4.6 1.0 3.6 Sample 3 V3 4.4 1.0 3.4

[0080] As shown in Table 2, the difference between the maximum voltage fluctuation point and the excitation application time for all vehicle nodes is between 3.2 and 3.6 s, and the maximum fluctuation phase offset group is finally obtained.

[0081] The current stabilization statistics submodule indexes the current sampling sequence after excitation application for each node based on the maximum fluctuation phase offset group, calculates the time difference between excitation time and stabilization time, reads the accuracy level identifier code of each node, establishes a numerical level index array, and uses the formula:

[0082] ;

[0083] The synchronization overlap index is obtained through calculation, and an initial data set for the synchronization response is generated. This represents the time point (in seconds) of the maximum voltage fluctuation for the j-th compatible vehicle. This represents the starting time of current stabilization for the j-th vehicle (unit: s). Indicates the initial application time of the excitation current (unit: s). This represents the synchronization adjustment factor for the j-th vehicle node based on its accuracy level (Class1–Class3) (Class1 is 1, Class2 is 2, and Class3 is 3, used to adjust the time offset contribution). This represents the maximum voltage fluctuation time (in seconds) across all compatible vehicles. The minimum current stabilization time among all adapted vehicles (unit: s) is represented by m, which is the total number of adapted vehicles participating in the synchronous evaluation, and S is the response synchronization superposition index value, which is used to measure the level of multi-node response coordination.

[0084] Based on the maximum fluctuation phase offset group, the current time series after excitation of each node was collected. The moment when the absolute value of the current change is less than 0.2A within three consecutive sampling points was considered the start of current stabilization. Using indexing, the current in sample 1 stabilized at 6.1s, sample 2 at 6.4s, and sample 3 at 6.0s. These were subtracted from the excitation time of 1.0s to obtain delay values ​​of 5.1s, 5.4s, and 5.0s, respectively. Simultaneously, the accuracy class identifier of each node was read, and the corresponding synchronization factors were set to 1, 2, and 3 according to Class1, Class2, and Class3, respectively. The results are summarized below:

[0085] Table 3 Vehicle node stability and accuracy characteristics

[0086] Sample number Current quiescent time (s) Delay time (s) Accuracy level <![CDATA[Geometric Dilution of Precision (g k ).]]> Sample 1 6.1 5.1 Class1 1 Sample 2 6.4 5.4 Class2 2 Sample 3 6.0 5.0 Class3 3

[0087] As shown in Table 3, after establishing all participating items, substitute them into the formula:

[0088] ;

[0089] The difference for the first term is: ;

[0090] The second precision factor weighting is: ;

[0091] The denominator is calculated as follows: (This is unreasonable; please adjust the order.)

[0092] After adjustment: ;

[0093] Final result: ;

[0094] This value indicates a significant degree of synchronization offset between vehicle nodes during the response process, ultimately generating the initial data set for the synchronized response.

[0095] Synchronization superposition index is a dimensionless evaluation value used to measure the relative consistency and synchronicity of multiple vehicle nodes in the response excitation process. Specifically, it takes the time difference between each node from the excitation application time to the maximum voltage fluctuation point and the current stabilization time as the main response parameter, introduces the adjustment factor set by the accuracy level, and calculates the total total response difference through weighted superposition. At the same time, it combines the time bandwidth of the maximum fluctuation time and the minimum stabilization time among all nodes for normalization processing, so that the index can reflect both the average deviation trend of the response behavior of each node and the adjustment effect of different accuracy levels on the synchronization and coordination capability. Finally, it expresses the comprehensive performance of response synchronization in a multi-node system with a highly comparable and scaled numerical result.

[0096] The formula's operational logic is constructed based on the cumulative time difference of response behavior and the intervention impact of accuracy level in the synchronicity assessment. First, Part of this is used to sum the absolute time difference between the maximum voltage fluctuation time point and the current stabilization time point for each vehicle node, reflecting the cumulative degree of phase shift during the response process of each node. The larger this value, the more concentrated the response deviation. Subsequently... A precision factor adjustment term is introduced, which multiplies the time delay by a weighting factor determined by the precision level to form a precision-guided offset contribution assessment. Both parts together form the numerator, representing the total difference in the overall response after weighting by the time offset and the precision factor. The denominator is... The theoretical synchronization reference bandwidth, which represents the span between the total number of participating nodes and the maximum fluctuation time and minimum stabilization time, is used as a normalization benchmark to scale the numerator. This structure integrates the absolute time difference with the precision guide term and normalizes it into a dimensionless index, thereby comprehensively reflecting the degree of synchronization offset of the multi-node response.

[0097] The synchronization vector construction submodule normalizes the data based on the initial data set of the synchronization response, merges the three parameter vectors, and sequentially combines them to construct the vehicle synchronization vector. The synchronization vectors of each node are used as row vectors to merge and construct a matrix to obtain the response synchronization feature set.

[0098] Based on the phase offset time, current stabilization delay time, and accuracy class factor recorded in the initial data set of the synchronization response, normalization intervals for three parameters were set: phase offset (0–5s), delay time (0–10s), and accuracy class (Class1=1 to Class3=3). A linear normalization conversion formula was then used. Standardization is carried out, among which, Represents the normalized value. Represents the current value. Represents the minimum value. Representing the maximum value, sample 1 has a phase shift of 3.2s, corresponding to a normalized value of 0.64; a delay of 5.1s corresponds to 0.51; and a precision level of 1 corresponds to 0. Sample 2, after normalization, has values ​​of [0.72, 0.54, 0.5], and sample 3 has values ​​of [0.68, 0.50, 1]. Finally, the normalized vectors are sorted by vehicle number and combined to construct the synchronization feature matrix as follows:

[0099] Table 4. Normalized Feature Matrix of Vehicle Synchronicity

[0100] Sample number Phase offset normalized value Delay time normalized value Precision level normalized value Sample 1 0.64 0.51 0.00 Sample 2 0.72 0.54 0.50 Sample 3 0.68 0.50 1.00

[0101] As shown in Table 4, the three-dimensional synchronization vector has been normalized and encoded, and finally the response synchronization feature group is obtained.

[0102] Please see Figure 4 The scheduling matching mapping module includes:

[0103] The response difference extraction submodule extracts the response start and end time nodes of each vehicle based on the response phase offset time and current stabilization delay time recorded in the response synchronization feature group for each adapted vehicle and constructs a vehicle response time information group. Combined with the grid frequency adjustment tolerance range of the discharge task, the corresponding frequency adjustment time window is extracted. The reference response time is constructed based on the median value of the adjustment time window. The response time of each vehicle is compared with the reference response time to obtain the frequency response difference group.

[0104] Based on the response offset time and stabilization delay time data recorded in the response synchronization feature group, the voltage phase offset time and current stabilization time of each adapted vehicle are extracted sequentially. These two are then summed to construct the total response time for each vehicle. The response start point is taken as the voltage excitation time start point, and the end point is the current stabilization time. Combined with the grid frequency regulation tolerance setting range of ±0.5Hz within the scheduling period, the effective frequency regulation response time is determined to be 7.5s by referring to the actual fluctuation time band within the frequency regulation cycle. This value is then set as the baseline response time. Then, each vehicle... The total response time was compared one-to-one with the baseline response time, and the absolute difference was recorded sequentially to form an array of vehicle response difference values. For example, the response time of sample 1 was 7.0s, and the difference was |7.0−7.5|=0.5s; the response time of sample 2 was 8.0s, and the difference was |8.0−7.5|=0.5s; the response time of sample 3 was 7.5s, and the difference was |7.5−7.5|=0s; the response time of sample 4 was 7.0s, and the difference was |7.0−7.5|=0.5s. All the original response data and response differences are summarized in the table below:

[0105] Table 5 Vehicle Response Time Information

[0106] Sample number Vehicle number Phase offset time (s) Stabilization delay (s) Total response time (s) Baseline response time (s) Response difference (s) Sample 1 V01 2.8 4.2 7.0 7.5 0.5 Sample 2 V02 3.4 4.6 8.0 7.5 0.5 Sample 3 V03 3.1 4.4 7.5 7.5 0.0 Sample 4 V04 2.9 4.1 7.0 7.5 0.5

[0107] As shown in Table 5, all vehicle nodes have obtained the absolute difference of the relative frequency adjustment reference time and obtained the frequency response difference group.

[0108] The priority sequence construction submodule establishes a corresponding offset value index table based on the degree of response deviation of each vehicle in the frequency response difference group, and performs ascending difference processing. For vehicles whose difference is within the deviation range, the stabilization time node is recorded, and the frequency adjustment end time is matched and compared. Vehicles that meet both the deviation condition and the stabilization time condition are marked as priority vehicles, and the remaining vehicles are classified as secondary vehicles. The results of priority assignment sequence and secondary sequence division are obtained by summarizing the rows.

[0109] Based on the frequency response difference values ​​of each vehicle in the frequency response difference group, all differences are sorted in ascending order to determine whether a vehicle falls within the reference deviation range. A deviation allowable range of 0 to 1 second is set, and vehicles falling within this range are marked as initial priority candidates. Then, the current stabilization time of each candidate vehicle is retrieved and compared with the end time of the grid frequency regulation period. The end time of the regulation period is set at 9.0 seconds. The stabilization time of each candidate vehicle is checked to see if it is less than this time. If the condition is met, the vehicle is retained in the priority sequence; otherwise, it is removed from the priority list. The sequence is then transferred to the secondary vehicle queue. Taking the sample data as an example, sample 1 has a recovery time of 4.2s, which is less than 9.0s, and a response difference of 0.5s, falling within the allowable range, so it is classified as a priority vehicle. Sample 2 has a difference of 0.5s and a recovery time of 4.6s, which still meets the criteria and is also classified as a priority vehicle. Sample 3 has a difference of 0s, the highest priority, and a recovery time of 4.4s, which meets the criteria. Sample 4 has a difference of 0.5s and a recovery time of 4.1s, which also meets the criteria. All four samples meet two criteria, so they are all included in the priority vehicle set. The data summary is as follows:

[0110] Table 6. Priority Vehicle Selection Criteria

[0111] Sample number Vehicle number Response difference (s) Recovery time (s) Is it within the tolerance range? Is it within the time window? Sequence category Sample 1 V01 0.5 4.2 yes yes priority Sample 2 V02 0.5 4.6 yes yes priority Sample 3 V03 0.0 4.4 yes yes priority Sample 4 V04 0.5 4.1 yes yes priority

[0112] As shown in Table 6, all vehicles are marked as priority categories, and the results of priority assignment sequence and secondary sequence division are obtained.

[0113] The vehicle scheduling mapping submodule reads the accuracy level and power response capability parameters of each vehicle from the vehicle set in the priority assignment sequence and secondary sequence division results, sorts the priority vehicles and secondary vehicles internally according to the power response capability index, assigns a scheduling label to each vehicle, and obtains the vehicle collaborative scheduling classification sequence.

[0114] Based on the priority assignment sequence and secondary sequence division results, the accuracy level identifier corresponding to the vehicle number in the sequence and the maximum discharge power response value of each vehicle are read. The accuracy levels Class1, Class2, and Class3 are set to values ​​of 3, 2, and 1, respectively, and the power response capabilities are set to Sample 1: 45kW, Sample 2: 50kW, Sample 3: 40kW, and Sample 4: 42kW, respectively. The power values ​​in the priority vehicle set are sorted in descending order as Sample 2, Sample 1, Sample 4, and Sample 3. The sorted results are then re-labeled, with Sample 2 numbered as P1, Sample 1 as P2, Sample 4 as P3, and Sample 3 as P4. The mapping labels for all vehicles are summarized as follows:

[0115] Table 7 Vehicle Dispatch Mapping Sequence Table

[0116] Vehicle number Accuracy level Discharge power (kW) Priority Category Dispatch number V02 Class2 50 priority P1 V01 Class1 45 priority P2 V04 Class3 42 priority P3 V03 Class2 40 priority P4

[0117] As shown in Table 7, all vehicle scheduling mapping, classification, and numbering operations have been completed, generating a vehicle collaborative scheduling classification sequence.

[0118] Please see Figure 5 The task load balancing module includes:

[0119] The gradient adjustment construction submodule obtains the scheduling numbers of all vehicles in the vehicle collaborative scheduling classification sequence, sets the load adjustment rate interval of the discharge task, divides it into equal intervals, constructs a rate index table according to the number of segments, determines the number of vehicles that each rate interval can accommodate by combining the ratio of the total number of scheduling numbers to the total number of rate segments, establishes a number matching framework between the rate interval and the vehicle scheduling number, and obtains the adjustment rate interval grouping result.

[0120] Obtain the scheduling numbers of all 6 compatible vehicles in the vehicle collaborative scheduling classification sequence. Based on the task discharge rate requirement, set the adjustment rate range to 0.1C to 2.0C. Within this range, divide the vehicle into segments of 0.5C each, ultimately dividing the rate gradient into 4 segments: 0.1–0.5C, 0.6–1.0C, 1.1–1.5C, and 1.6–2.0C, corresponding to numbers T01 to T04. Each segment is configured with a vehicle capacity limit. Combining the total number of vehicles and the task granularity requirement, the capacity of each segment is set to 2, 2, 1, and 1 vehicles respectively, generating an adjustment gradient grouping table. The specific rate segment range, numbering, and capacity configuration are shown in the table below:

[0121] Table 8 Load Regulation Rate Grouping Table

[0122] Task group number Task Number Adjustment rate range (C) Number of vehicles available Task Group 1 T01 0.1–0.5 2 Task Group 2 T02 0.6–1.0 2 Task Group 3 T03 1.1–1.5 1 Task Group 4 T04 1.6–2.0 1

[0123] As shown in Table 8, a one-to-one correspondence has been established between the task number and the adjustment rate interval, resulting in the grouping results of the adjustment rate interval.

[0124] The task matching and partitioning submodule assigns a rate task interval to each vehicle according to the adjustment rate interval grouping results, the accuracy level index corresponding to each vehicle scheduling number and the recommended discharge capacity, and identifies whether the continuity of scheduling numbers in the rate task interval is disrupted, determines whether the recommended rate of the vehicle falls within the allowable range of the assigned rate interval, and judges the degree of cooperation between the accuracy level index and the task adjustment interval, completes the vehicle assignment adjustment under the task interval, and obtains the task adjustment grouping matrix.

[0125] Based on the adjustment rate range and vehicle capacity limitations defined in Table 8, the recommended discharge rate value and accuracy level of each vehicle are extracted from the vehicle scheduling sequence, and they are sequentially attempted to be assigned to the matching adjustment rate task group. In the sample data, the recommended rate values ​​for vehicles P1 to P6 are set to 0.45C, 0.85C, 1.20C, 1.70C, 0.35C, and 0.90C, respectively, and the accuracy levels are Class2, Class3, Class1, Class2, Class1, and Class2, respectively. The accuracy level values ​​are set to Class1=1, Class2=2, and Class3=3. In the corresponding judgment process, the vehicle is first... P1, with a speed of 0.45C, is assigned to T01, falling within the range, and its accuracy level of 2 also meets the requirements, thus marking it as a successful group. Next, P2, with a speed of 0.85C, falls within the range of T02, and its accuracy level of 3 satisfies the condition. P3, with a speed of 1.20C, matches T03, and its accuracy level of 1 meets the high-speed group condition. P4, with a speed of 1.70C, corresponds to T04, and its accuracy level of 2 allows it to enter the high-speed task. P5, with a speed of 0.35C, falls into T01, and its accuracy level of 1 ensures a smooth match. P6, with a speed of 0.90C, is assigned to T02, grouped with P2, and its accuracy level of 2 remains within the acceptable range. The task numbers and grouping results for all vehicles are summarized in the table below.

[0126] Table 9 Vehicle Adjustment Task Assignment Table

[0127] Vehicle number Recommended rate (C) Accuracy level Assign task number Allocation rate range (C) P1 0.45 Class2 T01 0.1–0.5 P5 0.35 Class1 T01 0.1–0.5 P2 0.85 Class3 T02 0.6–1.0 P6 0.90 Class2 T02 0.6–1.0 P3 1.20 Class1 T03 1.1–1.5 P4 1.70 Class2 T04 1.6–2.0

[0128] As shown in Table 9, all vehicles have completed task allocation, resulting in a task adjustment and grouping matrix.

[0129] The allocation mapping generation submodule is based on the mapping results of each rate task group and vehicle number recorded in the task adjustment grouping matrix. It reads the number of vehicles, the corresponding adjustment rate range and the task number in each group in sequence, and arranges them in ascending order of task number to establish a list of task groups and vehicle scheduling numbers. It also performs statistics on the recommended adjustment rate of vehicles in each group and standardizes the results. Finally, it integrates the number of vehicles, scheduling number and task number to generate a load allocation mapping table.

[0130] Based on the task allocation information for each vehicle in Table 9, summarize the vehicle numbers and corresponding recommended speed values ​​for each task number. Calculate the average speed value for each vehicle under each task number and round it to 0.05C. For example, task T01 includes vehicles P1 and P5 with speeds of 0.45C and 0.35C respectively, averaging 0.40C, rounded to 0.40C; T02 has speeds of 0.85C and 0.90C, averaging 0.875C, rounded to 0.90C; T03 and T04 have speeds of 1.20C and 1.70C respectively, which do not need to be rounded and are retained. The task numbers, vehicle list, and average speed are summarized as follows:

[0131] Table 10 Task Vehicle Scheduling Mapping Table

[0132] Task Number Assign vehicle numbers Recommended average rate (C) Standard rate value (C) T01 P1, P5 0.40 0.40 T02 P2, P6 0.88 0.90 T03 P3 1.20 1.20 T04 P4 1.70 1.70

[0133] As shown in Table 10, the dispatch numbers of each vehicle have been rearranged according to the task number and a standard rate mapping relationship has been formed, generating a load allocation mapping table.

[0134] Please see Figure 6 The coordinated regulation and control determination module includes:

[0135] The response monitoring and identification submodule records the control feedback data of each vehicle based on the vehicle number under each task number in the load distribution mapping table, and counts the number and distribution time of various over-limit behaviors within the task cycle to obtain the task cycle response behavior change trend record.

[0136] Operational task data for each vehicle was collected and grouped according to task number. Nodes with vehicle numbers P1 and P2 were assigned to task number T1, and P3 was assigned to task number T2. The operational status of each node within a complete sampling period was statistically analyzed, with a total of 100 samples for each node. Vehicle current, voltage, and status response signals were collected at a fixed frequency within the sampling period. The number of sampling points exceeding the rated current limit within the current detection period was used as the current over-limit count: 5 times for P1, 2 times for P2, and 6 times for P3. Subsequently, the number of times the voltage fluctuation exceeded the specified threshold was counted: 3 times, 1 time, and 5 times, respectively. This further determined the node response timing and its compatibility with the set parameters. If a delay exceeding 3 seconds occurs during timing, it is recorded as a response delay event. Samples P1 and P3 occurred 4 and 5 times respectively, while P2 showed no delay. After integrating various abnormal events, it is determined whether they are in an adaptation state. If any one of the three items is 0 and the total is greater than 3, the adaptation state is set to 0; otherwise, it is 1. Based on this, the total time covered by all abnormal events is calculated. The abnormal duration of P1 is 4.2 seconds, P2 is 1.0 seconds, and P3 is 5.6 seconds. The scheduling priority is set comprehensively based on the duration distribution and adaptation state. The more abnormal events, the lower the priority. Finally, the scheduling priority of sample P2 is 2, P1 is 3, and P3 is 1.

[0137] The status trend aggregation submodule records the trend of changes in response behavior during the task cycle, extracts the marked abnormal data segments from all records, and counts the number of abnormalities and the total duration of each vehicle. It then determines whether a vehicle has entered the adaptation state judgment condition, establishes the mapping relationship between the task number and the status aggregation factor, and obtains the task status trend summary table.

[0138] Based on the above sampling period statistics, the task allocation adaptability of all vehicles is dynamically identified. First, all current, voltage, and response status sampling records of vehicle P1 within task T1 are read. The start and end time periods of continuous current deviation events are identified. The voltage drop trend is then compared to determine whether it occurs simultaneously with the current anomaly. The response delay signal is then extracted to determine whether it occurs repeatedly within 3 seconds before and after the anomaly segment. This forms a composite feature set of anomaly events. The time occupied by the above feature set is calculated and accumulated in seconds to record the total anomaly duration. At the same time, it is determined whether there is a node with an adaptation state of 1. If so, its anomaly frequency within the task period is extracted and compared with other nodes. If the total number of current and voltage anomalies is less than 20% of the average number of nodes in the task group, it is identified as a high-adaptability node. Currently, P2 has an adaptation state of 1, with 2 current over-limits and 1 voltage fluctuation, both lower than the average of 4 and 2 times in group T1, respectively. Therefore, it is confirmed as a node with better adaptability, and the scheduling priority corresponding to this node is also explicitly marked in the scheduling system.

[0139] The control scheme generation submodule, based on the status aggregation factor corresponding to each task number in the task status trend summary table, rearranges all marked tasks into temporary control areas, and marks the vehicle number with the lowest priority in these areas for replacement scheduling settings. It then reallocates the target rate to adjacent task numbers, renumbers all task numbers, vehicle numbers, and their corresponding adjustment targets, and integrates and outputs a dynamically optimized V2X charging and discharging collaborative control scheme.

[0140] Based on the above vehicle abnormal status and scheduling priority data, an initial scheduling vector is constructed for the current batch of task nodes. Nodes with an adaptation status of 1 are selected for priority sorting, and the abnormal duration and number of events are used as the subsequent sorting criteria. P2 with an adaptation status of 1 is placed at the top of the priority queue, and P1 and P3 with an adaptation status of 0 are placed in the next position. The sorting is further refined according to the abnormal duration, with P1 at 4.2s and P3 at 5.6s. P1 has the lowest priority, followed by P2, and P3 has the lowest priority, forming a scheduling priority vector [P2, P1, P3]. This vector can be directly used for task push judgment in the next stage of the scheduling system, and the vehicle task scheduling order is solidified as the final decision output, as shown in Table 11.

[0141] Table 11 Priority Order of Vehicle Task Scheduling

[0142] Scheduling order Vehicle number Adaptation status Total duration of the anomaly (s) Scheduling priority First place P2 1 1.0 2 2nd in line P1 0 4.2 3 3rd in line P3 0 5.6 1

[0143] As shown in Table 11, the scheduling order is based on both the adaptation status and the total duration of the anomaly, resulting in a clear response ranking output.

[0144] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A V2X charge-discharge coordinated control system based on dynamic multi-objective optimization, characterized in that, The system includes: The voltage characteristic identification module acquires transient voltage response data and applied current of vehicles connected to the V2G network, calculates the voltage drop rate per unit current, calculates the upper and lower deviation groups and calculates the ratio with the target interval amplitude, determines whether it is within the voltage regulation dead zone, and obtains multi-node vehicle response adaptation records. The response capability assessment module, based on the multi-node vehicle response adaptation record, collects the time coordinates of the maximum voltage fluctuation point and the excitation application time of the adapted vehicle, calculates the response phase offset, and constructs a normalized synchronization vector by combining the current stabilization time and the charging and discharging accuracy level, and summarizes the response synchronization feature group. Based on the response synchronization feature group, the scheduling matching mapping module reads the power grid frequency regulation tolerance, calculates the absolute value of the difference between the adaptive vehicle response phase offset and the current stabilization time term and the regulation tolerance, sorts them in ascending order, constructs a vehicle scheduling mapping classification, and obtains a vehicle cooperative scheduling classification sequence. The task load allocation module establishes a one-to-one mapping between the task load adjustment rate and the vehicle scheduling sequence based on the vehicle collaborative scheduling classification sequence and the load adjustment rate, thereby obtaining a load allocation mapping table.

2. The V2X charge-discharge coordinated control system based on dynamic multi-objective optimization according to claim 1, characterized in that, The multi-node vehicle response adaptation record includes node voltage adaptation value, upper and lower deviation ratio distribution record, and adjustment dead zone determination result. The response synchronization feature group includes response phase vector, current stabilization coefficient, and synchronization level index. The vehicle cooperative scheduling classification sequence includes priority response sequence, secondary response sequence, and vehicle scheduling level label. The load allocation mapping table includes adjustment rate matching relationship, vehicle sequence grouping label, and task subgroup number.

3. The V2X charge-discharge coordinated control system based on dynamic multi-objective optimization according to claim 1, characterized in that, The voltage characteristic identification module includes: The data acquisition submodule acquires the transient voltage response data of each node vehicle connected to the V2G network under the action of the charging pile test current and the corresponding applied current, extracts the current change value and the corresponding time difference in the transient period, matches the node timing to obtain the voltage change amplitude and response duration in the sampling window, and obtains the voltage response sampling data. The voltage rate deviation calculation submodule calculates the voltage drop rate per unit current based on the voltage response sampling data, and calculates the difference between the voltage drop rates according to the upper and lower limits of the voltage response rate of the target node, generating upper and lower deviation groups. Simultaneously, it calculates the absolute value ratio of the deviation groups and the target interval amplitude, calculates the voltage rate interval deviation rate and the degree of voltage drop trend deviation, and obtains the rate deviation statistical results. The response adaptation judgment submodule compares the rate deviation statistics with the voltage regulation dead zone range to determine whether the rate deviation statistics are within the range. It then establishes a response adaptation index table based on the number of nodes and the corresponding results to obtain multi-node vehicle response adaptation records.

4. The V2X charge-discharge coordinated control system based on dynamic multi-objective optimization according to claim 1, characterized in that, The response capability assessment module includes: The response phase acquisition submodule acquires the time coordinates of the maximum voltage fluctuation point of the adapted vehicle based on the multi-node vehicle response adaptation record, records the maximum fluctuation time in combination with the start time record of excitation application, performs synchronous indexing on the excitation application time, calculates the difference between the maximum fluctuation point time and the excitation time of each node, obtains the time offset of each node, and obtains the maximum fluctuation phase offset group. The current stabilization statistics submodule indexes the current sampling sequence after the excitation of each node according to the maximum fluctuation phase offset group, calculates the time difference between the excitation time and the stabilization time, reads the accuracy level identification code of each node, establishes a numerical level index array, calculates and obtains the synchronization superposition index, and generates the initial data group of the synchronization response. The synchronization vector construction submodule normalizes the data based on the initial data set of the synchronization response, merges the three parameter vectors, and sequentially combines them to construct the vehicle synchronization vector. The synchronization vectors of each node are used as row vectors to merge and construct a matrix to obtain the response synchronization feature set.

5. The V2X charge-discharge coordinated control system based on dynamic multi-objective optimization according to claim 1, characterized in that, The scheduling matching mapping module includes: The response difference extraction submodule extracts the response start and end time nodes of each vehicle based on the response phase offset time and current stabilization delay time of each adapted vehicle recorded in the response synchronization feature group, and constructs a vehicle response time information group. Combined with the grid frequency adjustment tolerance range of the discharge task, the corresponding frequency adjustment time window is extracted. The reference response time is constructed based on the median value of the adjustment time window. The response time of each vehicle is compared with the reference response time to obtain the frequency response difference group. The priority sequence construction submodule establishes a corresponding offset value index table based on the degree of response deviation of each vehicle in the frequency response difference group, and performs difference ascending order processing. For vehicles whose difference is within the deviation range, the stabilization time node is recorded, and the frequency adjustment end time is matched and compared. Vehicles that simultaneously meet the deviation condition and the stabilization time condition are marked as priority vehicles, and the remaining vehicles are classified as secondary vehicles. The results of priority assignment sequence and secondary sequence division are obtained by summarizing the results. The vehicle scheduling mapping submodule reads the accuracy level and power response capability parameters of each vehicle from the vehicle set in the priority assignment sequence and secondary sequence division results, sorts the priority vehicles and secondary vehicles internally according to the power response capability index, assigns a scheduling label to each vehicle, and obtains the vehicle collaborative scheduling classification sequence.

6. The V2X charge-discharge coordinated control system based on dynamic multi-objective optimization according to claim 1, characterized in that, The task load allocation module includes: The gradient adjustment construction submodule obtains the scheduling numbers of all vehicles in the vehicle collaborative scheduling classification sequence, sets the load adjustment rate interval of the discharge task, divides it into equal intervals, constructs a rate index table according to the number of segments, determines the number of vehicles that each rate interval can accommodate by combining the ratio of the total number of scheduling numbers to the total number of rate segments, establishes a number matching framework between the rate interval and the vehicle scheduling number, and obtains the adjustment rate interval grouping result. The task matching and partitioning submodule, based on the adjustment rate interval grouping results, and according to the accuracy level index and recommended discharge capacity corresponding to each vehicle scheduling number, sequentially assigns the rate task interval where its scheduling number is located to each vehicle, identifies whether the continuity of scheduling numbers in the rate task interval is disrupted, determines whether the recommended rate of the vehicle falls within the allowable range of the assigned rate interval, and simultaneously determines the degree of cooperation between the accuracy level index and the task adjustment interval, completes the vehicle assignment adjustment under the task interval, and obtains the task adjustment grouping matrix. The allocation mapping generation submodule, based on the mapping results of each rate task group and vehicle number recorded in the task adjustment grouping matrix, sequentially reads the number of vehicles, the corresponding adjustment rate range, and the task number in each group, and arranges them in ascending order by task number to establish a list of task groups and vehicle scheduling numbers. It then statistically analyzes the recommended adjustment rate of vehicles in each group and standardizes the results. Finally, it integrates the vehicle number, scheduling number, and task number to generate a load allocation mapping table.

7. The V2X charge-discharge coordinated control system based on dynamic multi-objective optimization according to claim 1, characterized in that, The system also includes: Based on the load allocation mapping table, the collaborative control judgment module monitors the changing trends and adaptation status of the response behavior of each vehicle during the discharge task execution phase, summarizes the changes in vehicle characteristics and scheduling response status within the task cycle, and generates a dynamically optimized V2X charging and discharging collaborative control scheme. The dynamically optimized V2X charge-discharge coordinated control scheme includes a characteristic trend map, an adaptive state trajectory, and control feedback parameters.

8. The V2X charge-discharge coordinated control system based on dynamic multi-objective optimization according to claim 7, characterized in that, The coordinated regulation determination module includes: The response monitoring and identification submodule records the control feedback data of each vehicle based on the vehicle number under each task number in the load distribution mapping table, and counts the number and distribution time of various over-limit behaviors within the task cycle to obtain the task cycle response behavior change trend record. The status trend aggregation submodule extracts the marked abnormal data segments from all records based on the task cycle response behavior change trend records, and counts the number of abnormal occurrences and the total duration of each vehicle. It then determines whether a vehicle has entered the adaptation state judgment condition, establishes a mapping relationship between the task number and the status aggregation factor, and obtains the task status trend summary table. The control scheme generation submodule, based on the state aggregation factor corresponding to each task number in the task state trend summary table, rearranges all marked tasks into temporary control areas, and marks the vehicle number with the lowest priority in these areas for replacement scheduling settings. It then reallocates the target rate to adjacent task numbers, renumbers all task numbers, vehicle numbers, and their corresponding adjustment targets, and integrates and outputs a dynamically optimized V2X charging and discharging collaborative control scheme.

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