Charging leakage monitoring method and system based on fusion terminal

Abstract

The present invention discloses a charging leakage monitoring method and system based on a fusion terminal, which is applied to the field of intelligent electrical technology. The method includes: when it is detected that the leakage current is greater than a preset value, obtaining the status data of the target vehicle; calculating the SOC characteristic data based on the charging SOC data, and inputting the SOC characteristic data, the total mileage data and the charging number data into a preset line loss prediction model to obtain the line loss data output by the line loss prediction model; if the line loss data is greater than or equal to the preset value, judging that the leakage is a leakage of the target vehicle; if the line loss data is less than the preset value, judging that the leakage is a leakage of the target charging pile. The present invention can predict the possible aging of the vehicle cable when only the SOC data of the vehicle charging is obtained, and then judge whether the current leakage event is caused by the vehicle or the charging pile, which effectively protects the data privacy of the vehicle and ensures charging safety.

Description

Technical Field

[0001] The present invention relates to the field of intelligent electrical technology, and in particular to a charging leakage monitoring method and system based on a fusion terminal. Background Art

[0002] A substation fusion terminal, also known as a substation smart fusion terminal, is an intelligent terminal device that integrates functions such as power supply information collection in the distribution substation, equipment status monitoring and communication networking, local analysis and decision-making, master station communication and collaborative interaction, and edge computing. With the promotion and use of fusion terminals in various substations, higher computing power is provided to the substations, and more complex calculations are completed through smart charging piles. These smart charging piles can specify a dedicated charging plan for connected new energy vehicles based on the battery status and fast charging capabilities of the new energy vehicles. This not only enables high-voltage fast charging but also effectively reduces battery damage during charging. However, when smart charging piles are widely used, leakage events may occur during the charging process. Although some new energy vehicles currently have their own leakage detection function, this information may be related to the vehicle's privacy information and cannot be shared with the charging pile. This makes it difficult to determine whether a leakage event occurs during charging, whether it is a vehicle leakage or a charging pile leakage. In existing technologies, personnel are often dispatched to the charging pile to perform leakage detection on site when a leakage event occurs, which is very wasteful. Summary of the Invention

[0003] In order to at least overcome the above-mentioned deficiencies in the prior art, the purpose of the present application is to provide a charging leakage monitoring method and system based on a fusion terminal.

[0004] In a first aspect, an embodiment of the present application provides a charging leakage monitoring method based on a fusion terminal, comprising:

[0005] When a target vehicle is charging at a target charging pile and a leakage current greater than a preset value is detected, status data of the target vehicle is obtained; the status data includes charging SOC data, total mileage data, and charging times data;

[0006] Calculating charging efficiency data and SOC change rate variance of the target vehicle as SOC characteristic data based on the charging SOC data, and inputting the SOC characteristic data, total mileage data, and charging times data into a preset line loss prediction model to obtain line loss data output by the line loss prediction model;

[0007] If the line loss data is greater than or equal to a preset value, the leakage is determined to be a target vehicle leakage; if the line loss data is less than a preset value, the leakage is determined to be a target charging pile leakage;

[0008] If it is determined that the number of times the target vehicle has leaked electricity exceeds a threshold, a leakage warning of the target vehicle is issued to a superior platform of the target vehicle.

[0009] In one possible implementation, the charging efficiency data is calculated according to the following formula:

[0010]

[0011] Where Z1 is the charging efficiency data, ΔSOC a The actual SOC increase in the charging SOC data, ΔSOC t is the theoretical SOC increase, calculated according to the following formula:

[0012]

[0013] Where T is the charging time, I is the rated charging current, and Q is the rated capacity of the battery.

[0014] In one possible implementation, the SOC change rate variance is calculated according to the following formula:

[0015]

[0016]

[0017] Where Z2 is the variance of SOC change rate, n is the number of samples in the platform phase of the charging SOC data, and SOC i The SOC data sampled at the i-th moment in the charging SOC data, SOC i+1 is the SOC data sampled at the i+1th moment in the charging SOC data, t i is the i-th moment, t i+1 is the i+1th moment, For r i The average value of .

[0018] In a possible implementation, the line loss prediction model adopts the following formula:

[0019]

[0020] Where ΔP is the line loss data, Z1 is the charging efficiency data, Z2 is the variance of the SOC change rate, M is the value normalized according to the position of the total mileage data in the interval [0, 120000], and is 1 when the total mileage data is greater than 120000, N is the number of charging times data, α1, α2, α3, α4, α5 and α6 are fitting parameters, and к is the acceleration effect coefficient, which is 0.001.

[0021] In a possible implementation, the fitting and generation of the line loss prediction model includes:

[0022] Performing charge and discharge simulations on the power battery and the corresponding cable in a laboratory environment to obtain first test data; the first test data includes a sample SOC curve and a sample temperature curve of the cable during each charge and discharge cycle;

[0023] Calculating charging efficiency data and SOC change rate variance for each charge and discharge cycle based on the sample SOC curve, calculating the total mileage corresponding to each charge and discharge cycle based on the total discharge amount in the sample SOC curve, and using the charging efficiency data, SOC change rate variance, total mileage, and charge and discharge times as first sample data;

[0024] Performing a variable temperature accelerated test on the cable according to a sample temperature curve of the cable, and obtaining line loss data corresponding to different charge and discharge cycles of the sample temperature curve as second sample data; the line loss data is the ratio of the ground resistance threshold to the currently detected cable ground resistance; the ground resistance threshold is the minimum resistance value of the cable that can be safely used;

[0025] The fitting parameters of the line loss prediction model are fitted according to the corresponding first sample data and second sample data.

[0026] In a second aspect, the present application also provides a charging leakage monitoring system based on a fusion terminal, including:

[0027] a leakage indicator, configured at a target charging pile and monitoring a leakage current of the target charging pile;

[0028] A station terminal is configured to receive signals sent by the leakage indicator and the target charging pile;

[0029] The station terminal is also configured as:

[0030] When the target vehicle is charging at the target charging pile and the leakage current detected by the leakage indicator is greater than a preset value, the status data of the target vehicle is obtained through the target charging pile; the status data includes charging SOC data, total mileage data and charging times data;

[0031] Calculating charging efficiency data and SOC change rate variance of the target vehicle as SOC characteristic data based on the charging SOC data, and inputting the SOC characteristic data, total mileage data, and charging times data into a preset line loss prediction model to obtain line loss data output by the line loss prediction model;

[0032] If the line loss data is greater than or equal to a preset value, the leakage is determined to be a target vehicle leakage; if the line loss data is less than a preset value, the leakage is determined to be a target charging pile leakage;

[0033] If it is determined that the number of times the target vehicle has leaked electricity exceeds a threshold, a leakage warning of the target vehicle is issued to a superior platform of the target vehicle.

[0034] In a possible implementation, the station terminal is further configured to:

[0035] The charging efficiency data is calculated according to the following formula:

[0036]

[0037] Where Z1 is the charging efficiency data, ΔSOC a The actual SOC increase in the charging SOC data, ΔSOC t is the theoretical SOC increase, calculated according to the following formula:

[0038]

[0039] Where T is the charging time, I is the rated charging current, and Q is the rated capacity of the battery.

[0040] In a possible implementation, the station terminal is further configured to:

[0041] The SOC change rate variance is calculated according to the following formula:

[0042]

[0043]

[0044] Where Z2 is the variance of SOC change rate, n is the number of samples in the platform phase of the charging SOC data, and SOC i The SOC data sampled at the i-th moment in the charging SOC data, SOC i+1 is the SOC data sampled at the i+1th moment in the charging SOC data, t i is the i-th moment, t i+1 is the i+1th moment, For r i The average value of .

[0045] In a possible implementation, the line loss prediction model adopts the following formula:

[0046]

[0047] Where ΔP is the line loss data, Z1 is the charging efficiency data, Z2 is the variance of the SOC change rate, M is the value normalized according to the position of the total mileage data in the interval [0, 120000], and is 1 when the total mileage data is greater than 120000, N is the number of charging times data, α1, α2, α3, α4, α5 and α6 are fitting parameters, and к is the acceleration effect coefficient, which is 0.001.

[0048] In a possible implementation, the fitting and generation of the line loss prediction model includes:

[0049] Performing charge and discharge simulations on the power battery and the corresponding cable in a laboratory environment to obtain first test data; the first test data includes a sample SOC curve and a sample temperature curve of the cable during each charge and discharge cycle;

[0050] Calculating charging efficiency data and SOC change rate variance for each charge and discharge cycle based on the sample SOC curve, calculating the total mileage corresponding to each charge and discharge cycle based on the total discharge amount in the sample SOC curve, and using the charging efficiency data, SOC change rate variance, total mileage, and charge and discharge times as first sample data;

[0051] Performing a variable temperature accelerated test on the cable according to a sample temperature curve of the cable, and obtaining line loss data corresponding to different charge and discharge cycles of the sample temperature curve as second sample data; the line loss data is the ratio of the ground resistance threshold to the currently detected cable ground resistance; the ground resistance threshold is the minimum resistance value of the cable that can be safely used;

[0052] The fitting parameters of the line loss prediction model are fitted according to the corresponding first sample data and second sample data.

[0053] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0054] The charging leakage monitoring method and system based on the fusion terminal of the present invention can predict the possible aging of the vehicle cables when only the SOC data of the vehicle charging is obtained, and then determine whether the current leakage event is caused by the vehicle or the charging pile, which effectively protects the vehicle data privacy and ensures charging safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0056] Figure 1 This is a schematic diagram of the steps of the method according to the embodiment of the present application;

[0057] Figure 2This is a schematic diagram of the system architecture of an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of illustration and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of the embodiments of the present application. It should be understood that the operations of the flowcharts can be implemented out of sequence, and steps without logical context can be reversed or implemented simultaneously. In addition, those skilled in the art, under the guidance of the contents of this application, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0059] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0060] Please refer to Figure 1 , which is a flow chart of a charging leakage monitoring method based on a fusion terminal provided in an embodiment of the present invention. Furthermore, the charging leakage monitoring method based on a fusion terminal may specifically include the contents described in the following steps S1 to S3.

[0061] S1: When a target vehicle is charging at a target charging pile and a leakage current is detected to be greater than a preset value, status data of the target vehicle is obtained; the status data includes charging SOC data, total mileage data, and charging times data;

[0062] S2: Calculating charging efficiency data and SOC change rate variance of the target vehicle based on the charging SOC data as SOC characteristic data, and inputting the SOC characteristic data, total mileage data, and charging times data into a preset line loss prediction model to obtain line loss data output by the line loss prediction model;

[0063] S3: If the line loss data is greater than or equal to a preset value, the leakage is determined to be a target vehicle leakage; if the line loss data is less than the preset value, the leakage is determined to be a target charging pile leakage;

[0064] S4: If it is determined that the number of times the target vehicle has experienced electrical leakage exceeds a threshold, a electrical leakage warning of the target vehicle is issued to a higher-level platform of the target vehicle.

[0065] When the embodiment of the present application is implemented, when the target vehicle is charging at the smart charging pile, it is generally accompanied by a leakage event. In the prior art, leakage detection is generally achieved by detecting the current difference between the live wire and the neutral wire, so the detection process can only reveal the leakage situation at the back end of the leakage detector. The leakage detector is generally at the front end of the charging pile, so the leakage event it detects may be generated by the charging pile or by the vehicle. In the embodiment of the present application, the trigger condition for turning on the leakage current detection is that the leakage current is greater than the preset value. It should be understood that the preset value is generally a small value that will not trigger a power outage and an alarm. For example, if the warning value for the leakage current to trigger an alarm is 30ma, 20ma can be used as the preset value.

[0066] In the embodiment of the present application, the data that is easier to obtain from the target vehicle mainly includes the SOC data during the charging process, the total mileage data and the charging times data, which will show the status of the battery and the vehicle's power cables to a certain extent. At this time, the charging SOC data needs to be processed to form a feature for expressing the line loss of the vehicle's power cables. Among them, the charging efficiency data refers to the difference between the actual charging capacity and the theoretical charging capacity, which can characterize the aging of the battery and thus reflect the line loss of part of the power cable; and the SOC change rate variance refers to the variance of the change rate of the SOC curve in the plateau period, which characterizes the smoothness of the SOC curve and can express the change in the SOC curve caused by the change in the battery's internal resistance. The increase in the battery's internal resistance will increase the battery's heat generation, thereby aggravating the aging of the power cable attached to the battery. After integrating the mileage data, the line loss aging of the power cable can be well characterized. At the same time, as the battery ages, new energy vehicles will charge more frequently, so taking into account the impact of the charging times data can reflect the aging of the cable to a certain extent.

[0067] In an embodiment of the present application, based on the acquired SOC characteristic data, total mileage data, and charging number data, a preset prediction model can be used to predict the degree of cable aging of the target vehicle. This model can take into account the influence of cable heating during battery use and can also characterize the natural aging of the cable to a certain extent through the number of charging times. If the prediction result is cable aging and leakage, it can be determined that the target vehicle has leakage, and a warning notification can be issued to the vehicle. If the prediction result is not cable aging and leakage, it can be determined that the charging pile has leakage, and relevant personnel can be dispatched to inspect and repair the charging pile.

[0068] In an embodiment of the present application, for the same target vehicle, as long as it is charged at a smart charging pile in a substation equipped with a fusion terminal, the fusion terminal can obtain the leakage status of the vehicle. If the leakage occurs frequently, even if the leakage current does not reach the warning value, the fusion terminal can also issue a corresponding warning to the superior platform of the target vehicle. The superior platform can inform the owner of the need for maintenance of the vehicle based on the warning, thereby effectively improving driving safety. It should be understood that the fusion terminal's judgment on the number of leakage events of the vehicle may not be limited to the same substation, but the superior platform of the fusion terminal can realize the aggregation of detection data of multiple substations, thereby better judging the condition of the target vehicle.

[0069] In one possible implementation, the charging efficiency data is calculated according to the following formula:

[0070]

[0071] Where Z1 is the charging efficiency data, ΔSOC a The actual SOC increase in the charging SOC data, ΔSOC t is the theoretical SOC increase, calculated according to the following formula:

[0072]

[0073] Where T is the charging time, I is the rated charging current, and Q is the rated capacity of the battery.

[0074] When the embodiment of the present application is implemented, the above formula is used to express the charging efficiency data, wherein the charging efficiency data can show the loss of electricity during the charging process, which includes both leakage electricity and electricity loss caused by circuit and battery aging.

[0075] In one possible implementation, the SOC change rate variance is calculated according to the following formula:

[0076]

[0077]

[0078] Where Z2 is the variance of SOC change rate, n is the number of samples in the platform phase of the charging SOC data, and SOC i The SOC data sampled at the i-th moment in the charging SOC data, SOC i+1 is the SOC data sampled at the i+1th moment in the charging SOC data, t i is the i-th moment, t i+1 is the i+1th moment, For r i The average value of .

[0079] When the embodiment of the present application is implemented, the variance of the SOC change rate is the variance of the platform phase of the charging SOC data. The charging SOC curve is generally steeper in the initial and final stages, and many features are difficult to sample accurately. Therefore, sampling in the platform phase can effectively improve the accuracy of feature detection.

[0080] In a possible implementation, the line loss prediction model adopts the following formula:

[0081]

[0082] Where ΔP is the line loss data, Z1 is the charging efficiency data, Z2 is the variance of the SOC change rate, M is the value normalized according to the position of the total mileage data in the interval [0, 120000], and is 1 when the total mileage data is greater than 120000, N is the number of charging times data, α1, α2, α3, α4, α5 and α6 are fitting parameters, and к is the acceleration effect coefficient, which is 0.001.

[0083] When implementing the embodiment of the present application, the above-mentioned fitting function is used as the line loss prediction model, wherein the charging efficiency data shows a strong nonlinear expression of the line loss, so the process is characterized by fitting the quadratic term and the linear term; the SOC change rate variance has a strong correlation with the total mileage, so while the SOC change rate variance and the total mileage are separately made into linear terms, the interaction term of the SOC change rate variance and the total mileage is constructed to express the impact of the heating of the entire power system on the cable; it should be understood that the total mileage here needs to be normalized according to the position in the interval [0,120000] to increase the accuracy of the fitting process. When the total mileage of the example vehicle is 100,000 kilometers, it is normalized to the interval [0,120,000] and is 0.833. When the total mileage of the vehicle exceeds 120,000 kilometers, it is normalized to the interval [0,120,000] and is 1. This can more accurately characterize the impact of the vehicle mileage. When comprehensively considering the impact of the charging number data, the embodiment of the present application uses an accelerated natural index in the fitting function, and the accelerated natural index can effectively express the characteristics of the increase in the number of charging times over time, and thus express the natural aging of the cable to a certain extent.

[0084] In a possible implementation, the fitting and generation of the line loss prediction model includes:

[0085] Performing charge and discharge simulations on the power battery and the corresponding cable in a laboratory environment to obtain first test data; the first test data includes a sample SOC curve and a sample temperature curve of the cable during each charge and discharge cycle;

[0086] Calculating charging efficiency data and SOC change rate variance for each charge and discharge cycle based on the sample SOC curve, calculating the total mileage corresponding to each charge and discharge cycle based on the total discharge amount in the sample SOC curve, and using the charging efficiency data, SOC change rate variance, total mileage, and charge and discharge times as first sample data;

[0087] Performing a variable temperature accelerated test on the cable according to a sample temperature curve of the cable, and obtaining line loss data corresponding to different charge and discharge cycles of the sample temperature curve as second sample data; the line loss data is the ratio of the ground resistance threshold to the currently detected cable ground resistance; the ground resistance threshold is the minimum resistance value of the cable that can be safely used;

[0088] The fitting parameters of the line loss prediction model are fitted according to the corresponding first sample data and second sample data.

[0089] The implementation of the embodiment of the present application requires fitting the fitting parameters of the line loss prediction model. During the fitting process, the corresponding charging efficiency data and SOC change rate variance, as well as the total mileage data and the number of charging times need to be established with the relationship between the cable damage. Therefore, in the embodiment of the present application, it is necessary to first perform a charge and discharge cycle on the battery and obtain the corresponding charging efficiency data and SOC change rate variance, and then convert the discharged power into the total mileage, thereby forming a data set of the independent variables of the fitting function. As for the evaluation of cable line loss, the embodiment of the present application obtains line loss data by performing an accelerated test on the temperature state of the cable obtained during the battery charging and discharging process. The line loss data needs to be normalized first. The normalization process of the embodiment of the present application is to normalize the line loss to the interval [0,1], where 0 represents complete losslessness and 1 represents complete damage; based on this, the embodiment of the present application uses the ground resistance threshold as the numerator for calculation. It should be understood that performing variable temperature accelerated testing on cables under a known temperature curve belongs to the prior art, which is often used for the analysis of material aging and other conditions. After obtaining the above data set, the fitting parameters in the above formula can be fitted. For example, the fitting parameters after fitting are α1=0.152, α2=0.098, α3=0.123, α4=0.082, α5=0.105, α6=0.047, and the fitting function is:

[0090]

[0091] Based on the same inventive concept, please refer to Figure 2 , this application also provides a charging leakage monitoring system based on a fusion terminal, including:

[0092] a leakage indicator, configured at a target charging pile and monitoring a leakage current of the target charging pile;

[0093] A station terminal is configured to receive signals sent by the leakage indicator and the target charging pile;

[0094] The station terminal is also configured as:

[0095] When the target vehicle is charging at the target charging pile and the leakage current detected by the leakage indicator is greater than a preset value, the status data of the target vehicle is obtained through the target charging pile; the status data includes charging SOC data, total mileage data and charging times data;

[0096] Calculating charging efficiency data and SOC change rate variance of the target vehicle as SOC characteristic data based on the charging SOC data, and inputting the SOC characteristic data, total mileage data, and charging times data into a preset line loss prediction model to obtain line loss data output by the line loss prediction model;

[0097] If the line loss data is greater than or equal to a preset value, the leakage is determined to be a target vehicle leakage; if the line loss data is less than a preset value, the leakage is determined to be a target charging pile leakage;

[0098] If it is determined that the number of times the target vehicle has leaked electricity exceeds a threshold, a leakage warning of the target vehicle is issued to the upper level platform of the target vehicle.

[0099] In a possible implementation, the station terminal is further configured to:

[0100] The charging efficiency data is calculated according to the following formula:

[0101]

[0102] Where Z1 is the charging efficiency data, ΔSOC a The actual SOC increase in the charging SOC data, ΔSOC t is the theoretical SOC increase, calculated according to the following formula:

[0103]

[0104] Where T is the charging time, I is the rated charging current, and Q is the rated capacity of the battery.

[0105] In a possible implementation, the station terminal is further configured to:

[0106] The SOC change rate variance is calculated according to the following formula:

[0107]

[0108]

[0109] Where Z2 is the variance of SOC change rate, n is the number of samples in the platform phase of the charging SOC data, and SOC i The SOC data sampled at the i-th moment in the charging SOC data, SOC i+1 is the SOC data sampled at the i+1th moment in the charging SOC data, t i is the i-th moment, t i+1 is the i+1th moment, For r i The average value of .

[0110] In a possible implementation, the line loss prediction model adopts the following formula:

[0111]

[0112] Where ΔP is the line loss data, Z1 is the charging efficiency data, Z2 is the variance of the SOC change rate, M is the value normalized according to the position of the total mileage data in the interval [0, 120000], and is 1 when the total mileage data is greater than 120000, N is the number of charging times data, α1, α2, α3, α4, α5 and α6 are fitting parameters, and к is the acceleration effect coefficient, which is 0.001.

[0113] In a possible implementation, the fitting and generation of the line loss prediction model includes:

[0114] Performing charge and discharge simulations on the power battery and the corresponding cable in a laboratory environment to obtain first test data; the first test data includes a sample SOC curve and a sample temperature curve of the cable during each charge and discharge cycle;

[0115] Calculating charging efficiency data and SOC change rate variance for each charge and discharge cycle based on the sample SOC curve, calculating the total mileage corresponding to each charge and discharge cycle based on the total discharge amount in the sample SOC curve, and using the charging efficiency data, SOC change rate variance, total mileage, and charge and discharge times as first sample data;

[0116] Performing a variable temperature accelerated test on the cable according to a sample temperature curve of the cable, and obtaining line loss data corresponding to different charge and discharge cycles of the sample temperature curve as second sample data; the line loss data is the ratio of the ground resistance threshold to the currently detected cable ground resistance; the ground resistance threshold is the minimum resistance value of the cable that can be safely used;

[0117] The fitting parameters of the line loss prediction model are fitted according to the corresponding first sample data and second sample data.

[0118] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or can be electrical, mechanical or other forms of connection.

[0120] The units described as separate components may or may not be physically separated. As units, it is obvious that a person of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0121] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0122] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or grid device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0123] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A charging leakage monitoring method based on a fusion terminal is characterized by: include: When a target vehicle is charging at a target charging pile and a leakage current greater than a preset value is detected, status data of the target vehicle is obtained; the status data includes charging SOC data, total mileage data, and charging times data; Calculating charging efficiency data and SOC change rate variance of the target vehicle as SOC characteristic data based on the charging SOC data, and inputting the SOC characteristic data, total mileage data, and charging times data into a preset line loss prediction model to obtain line loss data output by the line loss prediction model; If the line loss data is greater than or equal to a preset value, the leakage is determined to be a target vehicle leakage; if the line loss data is less than a preset value, the leakage is determined to be a target charging pile leakage; If it is determined that the number of times the target vehicle has leaked electricity exceeds a threshold, a leakage warning of the target vehicle is issued to a higher-level platform of the target vehicle; The charging efficiency data is calculated according to the following formula: Where Z1 is the charging efficiency data, ΔSOC a The actual SOC increase in the charging SOC data, ΔSOC t is the theoretical SOC increase, calculated according to the following formula: Where T is the charging time, I is the rated charging current, and Q is the rated capacity of the battery; The line loss prediction model adopts the following formula: Where ΔP is the line loss data, Z1 is the charging efficiency data, Z2 is the variance of the SOC change rate, M is the value normalized according to the position of the total mileage data in the interval [0, 120000], and is 1 when the total mileage data is greater than 120000, N is the number of charging times data, α1, α2, α3, α4, α5 and α6 are fitting parameters, and к is the acceleration effect coefficient, which is 0.

001.

2. The charging leakage monitoring method based on the fusion terminal according to claim 1 is characterized in that: The SOC change rate variance is calculated according to the following formula: Where Z2 is the variance of SOC change rate, n is the number of samples in the platform phase of the charging SOC data, and SOC i The SOC data sampled at the i-th moment in the charging SOC data, SOC i+1 is the SOC data sampled at the i+1th moment in the charging SOC data, t i is the i-th moment, t i+1 is the i+1th moment, For r i The average value of .

3. The charging leakage monitoring method based on the fusion terminal according to claim 1 is characterized in that: The fitting and generation of the line loss prediction model includes: Performing charge and discharge simulations on the power battery and the corresponding cable in a laboratory environment to obtain first test data; the first test data includes a sample SOC curve and a sample temperature curve of the cable during each charge and discharge cycle; Calculating charging efficiency data and SOC change rate variance for each charge and discharge cycle based on the sample SOC curve, calculating the total mileage corresponding to each charge and discharge cycle based on the total discharge amount in the sample SOC curve, and using the charging efficiency data, SOC change rate variance, total mileage, and charge and discharge times as first sample data; Performing a variable temperature accelerated test on the cable according to a sample temperature curve of the cable, and obtaining line loss data corresponding to different charge and discharge cycles of the sample temperature curve as second sample data; the line loss data is the ratio of the ground resistance threshold to the currently detected cable ground resistance; the ground resistance threshold is the minimum resistance value of the cable that can be safely used; The fitting parameters of the line loss prediction model are fitted according to the corresponding first sample data and second sample data.

4. The charging leakage monitoring system based on the fusion terminal is characterized by: include: a leakage indicator, configured at a target charging pile and monitoring a leakage current of the target charging pile; a fusion terminal configured to receive signals sent by the leakage indicator and the target charging pile; The fusion terminal is further configured as: When the target vehicle is charging at the target charging pile and the leakage current detected by the leakage indicator is greater than a preset value, the status data of the target vehicle is obtained through the target charging pile; the status data includes charging SOC data, total mileage data and charging times data; Calculating charging efficiency data and SOC change rate variance of the target vehicle as SOC characteristic data based on the charging SOC data, and inputting the SOC characteristic data, total mileage data, and charging times data into a preset line loss prediction model to obtain line loss data output by the line loss prediction model; If the line loss data is greater than or equal to a preset value, the leakage is determined to be a target vehicle leakage; if the line loss data is less than a preset value, the leakage is determined to be a target charging pile leakage; If it is determined that the number of times the target vehicle has leaked electricity exceeds a threshold, a leakage warning of the target vehicle is issued to a higher-level platform of the target vehicle; The converged terminal is further configured to: The charging efficiency data is calculated according to the following formula: Where Z1 is the charging efficiency data, ΔSOC a The actual SOC increase in the charging SOC data, ΔSOC t is the theoretical SOC increase, calculated according to the following formula: Where T is the charging time, I is the rated charging current, and Q is the rated capacity of the battery; The line loss prediction model adopts the following formula: Where ΔP is the line loss data, Z1 is the charging efficiency data, Z2 is the variance of the SOC change rate, M is the value normalized according to the position of the total mileage data in the interval [0, 120000], and is 1 when the total mileage data is greater than 120000, N is the number of charging times data, α1, α2, α3, α4, α5 and α6 are fitting parameters, and к is the acceleration effect coefficient, which is 0.

001.

5. The charging leakage monitoring system based on the fusion terminal according to claim 4 is characterized in that: The converged terminal is further configured to: The SOC change rate variance is calculated according to the following formula: Where Z2 is the variance of SOC change rate, n is the number of samples in the platform phase of the charging SOC data, and SOC i The SOC data sampled at the i-th moment in the charging SOC data, SOC i+1 is the SOC data sampled at the i+1th moment in the charging SOC data, t i is the i-th moment, t i+1 is the i+1th moment, For r i The average value of .

6. The charging leakage monitoring system based on the fusion terminal according to claim 4 is characterized in that: The fitting and generation of the line loss prediction model includes: Performing charge and discharge simulations on the power battery and the corresponding cable in a laboratory environment to obtain first test data; the first test data includes a sample SOC curve and a sample temperature curve of the cable during each charge and discharge cycle; Calculating charging efficiency data and SOC change rate variance for each charge and discharge cycle based on the sample SOC curve, calculating the total mileage corresponding to each charge and discharge cycle based on the total discharge amount in the sample SOC curve, and using the charging efficiency data, SOC change rate variance, total mileage, and charge and discharge times as first sample data; Performing a variable temperature accelerated test on the cable according to a sample temperature curve of the cable, and obtaining line loss data corresponding to different charge and discharge cycles of the sample temperature curve as second sample data; the line loss data is the ratio of the ground resistance threshold to the currently detected cable ground resistance; the ground resistance threshold is the minimum resistance value of the cable that can be safely used; The fitting parameters of the line loss prediction model are fitted according to the corresponding first sample data and second sample data.

Images