Electric vehicle charging control method and system

Abstract

The invention provides an electric vehicle charging control method and system, and the method comprises the steps: carrying out the charging planning of an electric vehicle through a monitored charging state, and obtaining a charging scheduling domain of the electric vehicle in an estimated charging time period; load information of electric energy input power of the electric vehicle in the charging process is determined through the charging scheduling domain, the charging load bearing capacity of the electric vehicle in the charging process is extracted from the load information, and the electric energy fluctuation margin of the electric vehicle in the charging process is determined by combining the charging load bearing capacity with the charging state of the electric vehicle; determining the electric energy buffering capacity of the electric vehicle through the charging characteristics; predicting an electric energy compensation index when the charging station charges the electric vehicle in the next stage according to the electric energy buffering capacity and the electric energy fluctuation margin; and when the electric energy compensation index is in the preset threshold interval, reducing the charging power of the charging station to the electric vehicle in the next stage. By adopting the scheme of the invention, intelligent dynamic compensation regulation and control can be realized on the electric energy when the electric vehicle is charged, so that charging imbalance is avoided.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle charging, and more specifically, to an electric vehicle charging control method and system. Background Art

[0002] An electric car is a car that is powered by electricity. Compared with traditional internal combustion engine cars, they use electric motors or electric drive systems to achieve vehicle power transmission. Therefore, electric cars have lower exhaust emissions and higher energy utilization than traditional fuel cars. They can further reduce carbon emissions and reduce impact on the environment by using clean energy electricity.

[0003] Electric vehicle charging control is a method of managing and controlling the charging process of electric vehicles through the charging status of electric vehicles. Intelligent charging control can adjust the charging power of electric vehicles according to real-time charging data, balance the load, optimize the charging strategy of electric vehicles, and achieve the goals of grid load balance and energy conservation and emission reduction. With the increasing popularity of new energy vehicles, the number of electric vehicle charging has gradually increased, and the use of charging stations has become complicated. The risk of overload during electric vehicle charging has greatly increased. In order to maintain the safety of the power grid, high-quality control of charging has become the basis for the efficient operation of charging stations. However, in the prior art, the existing electric vehicle charging control methods are of low quality. For example, manual control has no precise parameter basis for control and is completely dependent on experience accumulation. In addition, when there are many electric vehicle charging locations, monitoring the charging of each electric vehicle by manpower will cause a waste of human resources. There are also methods that only adjust by monitoring the electric vehicle itself, which has certain limitations. Therefore, how to realize intelligent dynamic compensation control of the electric energy during electric vehicle charging to avoid charging imbalance has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides an electric vehicle charging control method and system, which can realize intelligent dynamic compensation and regulation of the electric energy when the electric vehicle is charged, thereby avoiding charging imbalance.

[0005] In a first aspect, the present application provides an electric vehicle charging control method, comprising the following steps: Monitor the charging status of electric vehicles in the charging station at the current stage; Based on the monitored charging status, a charging schedule for the electric vehicle is performed to obtain a charging scheduling domain for the electric vehicle within an estimated charging time period, and then the load information of the electric energy input power of the electric vehicle during the charging process is determined by the charging scheduling domain; Extracting the charging load bearing capacity of the electric vehicle during the charging process from the load information, and determining the power fluctuation margin of the electric vehicle during the charging process through the charging load bearing capacity combined with the charging state of the electric vehicle; Acquiring charging characteristics of the electric vehicle, and determining the electric energy buffering amount of the electric vehicle according to the charging characteristics; Predicting an electric energy compensation index when the charging station charges the electric vehicle in the next stage according to the electric energy buffer amount and the electric energy fluctuation margin; When the electric energy compensation index is within a preset threshold range, the charging power of the charging station to the electric vehicle is reduced in the next stage.

[0006] In some embodiments, charging planning is performed for an electric vehicle based on the monitored charging status, and obtaining a charging scheduling domain for the electric vehicle within an estimated charging time period specifically includes: Extracting an estimated charging time interval for the electric vehicle from the monitored charging status; The estimated charging time interval is segmented to obtain a plurality of charging time periods; Determine the battery SOC value range corresponding to each charging period according to the charging state of the electric vehicle; The charging scheduling domain of the electric vehicle is composed of all charging periods and the battery SOC value ranges corresponding to the charging periods.

[0007] In some embodiments, the load information of the electric energy input power of the electric vehicle during the charging process determined by the charging scheduling domain specifically includes: Selecting a plurality of charging data points in a charging scheduling domain of an electric vehicle; The load information of the electric energy input power during the charging process of the electric vehicle is determined based on all charging data points.

[0008] In some embodiments, extracting the charging load bearing capacity of the electric vehicle during the charging process from the load information specifically includes: Determine the load bearing amount corresponding to each charging power in the load information; The charging load bearing capacity of the electric vehicle during the charging process is determined by all load bearing capacities.

[0009] In some embodiments, determining the electric energy buffer amount of the electric vehicle according to the charging characteristics specifically includes: Determining a charging safety factor of the electric vehicle based on the charging characteristics; The electric energy buffer capacity of the electric vehicle is determined according to the charging safety factor.

[0010] In some embodiments, it also includes: When the electric energy compensation index is less than a preset threshold value interval, increasing the charging power of the charging station to the electric vehicle in the next stage; When the electric energy compensation index is greater than a preset threshold value interval, the charging power of the charging station to the electric vehicle in the next stage is kept unchanged.

[0011] In some embodiments, the charging status of the electric vehicles in the charging station at the current stage is monitored by a communication module in the charging pile of the charging station.

[0012] In a second aspect, the present application provides an electric vehicle charging control system, comprising: A monitoring module is used to monitor the charging status of electric vehicles in the charging station at the current stage; A processing module, configured to plan charging of the electric vehicle based on the monitored charging status, obtain a charging scheduling domain of the electric vehicle within an estimated charging time period, and further determine load information of the electric energy input power of the electric vehicle during the charging process from the charging scheduling domain; The processing module is further used to extract the charging load bearing capacity of the electric vehicle during the charging process from the load information, and determine the power fluctuation margin of the electric vehicle during the charging process through the charging load bearing capacity combined with the charging state of the electric vehicle; The processing module is further used to obtain the charging characteristics of the electric vehicle, and determine the electric energy buffering amount of the electric vehicle according to the charging characteristics; The processing module is further used to predict the electric energy compensation index when the charging station charges the electric vehicle in the next stage according to the electric energy buffer amount and the electric energy fluctuation margin; The execution module is used to reduce the charging power of the charging station to the electric vehicle in the next stage when the electric energy compensation index is within a preset threshold range.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned electric vehicle charging control method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned electric vehicle charging control method when executed.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the present application, the charging status of electric vehicles in the charging station in the current stage is monitored; charging planning is performed for the electric vehicles based on the charging status obtained by monitoring, and a charging scheduling domain for the electric vehicles in the estimated charging time period is obtained, and then the load information of the electric energy input power of the electric vehicles during the charging process is determined by the charging scheduling domain; the charging load bearing capacity of the electric vehicle during the charging process is extracted from the load information, and the electric energy fluctuation margin of the electric vehicle during the charging process is determined by the charging load bearing capacity combined with the charging status of the electric vehicle; the charging characteristics of the electric vehicle are obtained, and the electric energy buffering capacity of the electric vehicle is determined by the charging characteristics; the electric energy compensation index of the charging station when charging the electric vehicle in the next stage is predicted according to the electric energy buffering capacity and the electric energy fluctuation margin; when the electric energy compensation index is in a preset threshold range, the charging power of the charging station to the electric vehicle in the next stage is reduced.

[0016] It can be seen that in the present application, the electric energy compensation index when the charging station charges the electric vehicle in the next stage is predicted based on the electric energy buffer amount and the electric energy fluctuation margin, and then when the electric energy compensation index is in the preset threshold range, the charging power of the charging station to the electric vehicle in the next stage is reduced; wherein, since the charging load tolerance reflects the maximum charging load that the battery of the electric vehicle can withstand during the charging process, the charging load tolerance can be combined with the electric state data of the electric vehicle to determine the acceptable power fluctuation range (i.e., the electric energy fluctuation margin) of the electric vehicle within the estimated charging time interval, and the electric energy fluctuation margin can be used to understand the flexibility and feasibility of the electric vehicle in planning the charging time. Adjustability; furthermore, the degree to which the battery of the electric vehicle is kept within the safe operating range during the charging process (i.e., the amount of electric energy buffer) can be determined by the charging safety factor used to ensure the safety of the battery during the charging process of the electric vehicle, and then the electric energy buffer can be used to ensure that the battery of the electric vehicle is charged within a safe, reliable and efficient range; finally, when the electric energy compensation index is in a preset threshold range, the charging power of the charging station to the electric vehicle is reduced in the next stage, and electric energy compensation is implemented for the electric vehicle being charged in the charging station based on the electric energy compensation index and the preset threshold range; in summary, this scheme can realize intelligent dynamic compensation and regulation of the electric energy when the electric vehicle is charging, thereby avoiding charging imbalance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1is an exemplary flow chart of an electric vehicle charging control method according to some embodiments of the present application; Figure 2 is an exemplary flow chart of determining a charging scheduling domain according to some embodiments of the present application; Figure 3 is an exemplary flow chart of determining power fluctuation margin according to some embodiments of the present application; Figure 4 is a schematic diagram of the structure of an electric vehicle charging control system according to some embodiments of the present application; Figure 5 It is a structural schematic diagram of a computer device for implementing an electric vehicle charging control method according to some embodiments of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] refer to Figure 1 , which is an exemplary flow chart of an electric vehicle charging control method according to some embodiments of the present application. The electric vehicle charging control method 100 mainly includes the following steps: In step 101, the charging status of electric vehicles in the charging station at the current stage is monitored.

[0021] In specific implementation, the charging pile in the charging station is equipped with a communication module, which is used to communicate with the electric vehicle and transmit the charging status and data. Therefore, the charging status of the electric vehicle in the charging station at the current stage can be monitored through the communication module in the charging pile corresponding to the electric vehicle. The charging status includes: the battery capacity of the electric vehicle, the estimated charging time interval, the charging time point, the charging current, the charging voltage, the charging power, the battery SOC value (State-of-Charge, referred to as SOC, i.e. the battery state of charge), the charging start time, the charging end time, the battery SOC value at the charging start time, the target battery SOC value at the charging end time, the minimum SOC value and the maximum SOC value set for the battery, etc., wherein each charging time point has a corresponding charging current, charging voltage, charging power and battery SOC value, etc., which can also be determined by other methods in other embodiments, which are not limited here.

[0022] It should be noted that the charging status in this application refers to the charging status of the electric vehicle in the current charging stage, which will not be repeated here.

[0023] In step 102, charging planning is performed on the electric vehicle based on the monitored charging status to obtain a charging scheduling domain for the electric vehicle within an estimated charging time period, and then the load information of the electric energy input power of the electric vehicle during the charging process is determined by the charging scheduling domain.

[0024] In some embodiments, reference Figure 2 As shown, this figure is an exemplary flow chart of determining a charging scheduling domain in some embodiments of the present application. In this embodiment, charging planning is performed on an electric vehicle based on the monitored charging status, and obtaining a charging scheduling domain for the electric vehicle within an estimated charging time period can be achieved by using the following steps: First, in step 1021, the estimated charging time interval of the electric vehicle is extracted from the monitored charging status; Next, in step 1022, the estimated charging time interval is segmented to obtain a plurality of charging time periods; Then, in step 1023, the battery SOC value ranges corresponding to each charging period are determined according to the charging state of the electric vehicle; Finally, in step 1024, the charging scheduling domain of the electric vehicle is formed by all charging time periods and the battery SOC value ranges corresponding to the charging time periods.

[0025] It should be noted that the estimated charging time interval in the present application represents the estimated time range for an electric vehicle to be fully charged. In specific implementation, the estimated charging time interval of the electric vehicle can be extracted from the charging status obtained through monitoring in the following manner, namely: the estimated charging time interval in the charging status of the electric vehicle can be retrieved and extracted through the structured query language (Structured Query Language, SQL). In other embodiments, the extraction can also be performed through other methods, which are not specifically limited here.

[0026] It should be noted that the charging time period in the present application reflects the power state of the electric vehicle in different time periods. In specific implementation, the estimated charging time interval is constrained and segmented to obtain multiple charging time periods, which can be implemented in the following manner, namely: first, an expert formulates a constraint rule based on the charging situation of the electric vehicle, and then the estimated charging time interval of the electric vehicle can be segmented based on the constraint rule through a segmentation algorithm, and finally multiple charging time periods are obtained. The constraint rule, for example, can be: the time period when the electric vehicle is charged at maximum power from the start of charging to the highest SOC value as the first charging time period, the time period when the electric vehicle is at the highest SOC value to the end of charging as the second charging time period, the time period when the electric vehicle is charged to the target battery SOC value as the third charging time period, the time period when the electric vehicle is discharged from the start of charging at maximum power to the lowest SOC value set for the battery as the fourth charging time period, the time period when the electric vehicle is always at the lowest SOC value set for the battery as the fifth charging time period, and the time period when the electric vehicle is charged at maximum power from the lowest SOC value set for the battery to the end of charging as the sixth charging time period. In other embodiments, other methods can also be used for constraint segmentation, which is not limited here.

[0027] In specific implementation, the battery SOC value range corresponding to each charging time period is determined according to the charging status of the electric vehicle, which can be achieved in the following manner, namely: select a charging time period, extract all charging time points in the selected charging time period from the charging status of the electric vehicle, and form the battery SOC value range corresponding to the selected charging time period with the battery SOC values ​​corresponding to all the charging time points extracted in the selected charging time period, repeat the above steps to obtain the battery SOC value range corresponding to the remaining charging time periods. In other embodiments, other methods can also be used for determination, which is not limited here.

[0028] It should be noted that the battery SOC value range in the present application represents a set consisting of all battery SOC values ​​within the corresponding charging period, wherein the battery SOC value reflects the degree to which the battery of the electric vehicle has been charged at the corresponding charging time point, that is, the power state of the electric vehicle battery, which will not be repeated here.

[0029] It should be noted that the charging scheduling domain in the present application represents the maximum operating area of ​​charging and discharging of electric vehicles. The charging process of electric vehicles can be regulated through the charging scheduling domain to formulate charging strategies (for example: intelligent scheduling of charging time periods and charging power of electric vehicles) to meet user needs, load requirements of charging equipment and power supply capacity of the power grid. In specific implementation, the charging scheduling domain of electric vehicles composed of all charging time periods and battery SOC value ranges corresponding to the charging time periods can be implemented in the following way, that is, the area enclosed by each charging time period and the battery SOC value range corresponding to each charging time period can be used as the charging scheduling domain of the electric vehicle. In other embodiments, it can also be determined by other methods, which are not limited here.

[0030] In some embodiments, the charging scheduling domain determines the load information of the electric energy input power of the electric vehicle during the charging process by adopting the following steps: Selecting a plurality of charging data points in a charging scheduling domain of an electric vehicle; The load information of the electric energy input power during the charging process of the electric vehicle is determined based on all charging data points.

[0031] In specific implementation, the charging data points in the present application represent key points selected in the charging scheduling domain of the electric vehicle. The selection of multiple charging data points in the charging scheduling domain of the electric vehicle can be implemented in the following manner, namely: a plurality of points can be selected at intervals from the charging scheduling domain of the electric vehicle based on the SOC change trend of the electric vehicle through a machine learning method, and the selected points are all used as charging data points. For example, points corresponding to the places where the SOC change rate in the charging scheduling domain is larger are selected at intervals, thereby obtaining multiple charging data points in the charging scheduling domain, wherein the point corresponding to the start time of charging of the electric vehicle in the charging scheduling domain is the first charging data point, and the point corresponding to the end time of charging of the electric vehicle in the charging scheduling domain is the last charging data point. In other embodiments, multiple charging data points can also be selected by other methods, which are not specifically limited here.

[0032] In specific implementation, the load information of the electric energy input power of the electric vehicle during the charging process can be determined based on all the charging data points. The following method can be used, namely: first, all the charging data points are arranged in the order of the corresponding charging time points; then, the charging power between every two adjacent charging data points is calculated using the battery capacity of the electric vehicle and the SOC change rate between every two adjacent charging data points, where the charging power between two adjacent charging data points = the battery capacity of the electric vehicle × (the SOC change rate between two adjacent charging data points ÷ the charging time difference between two adjacent charging data points), wherein the SOC change rate between every two adjacent charging data points can be calculated by a numerical differentiation method; finally, the calculated charging powers are arranged in the order of the corresponding charging data points to form the load information of the electric energy input power of the electric vehicle during the charging process. In other embodiments, it can also be determined by other methods, which are not limited here.

[0033] It should be noted that the load information of the electric energy input power in the present application represents the power variation range that the battery can withstand during the charging process of the electric vehicle. Therefore, by determining the load information of the electric energy input power during the charging process of the electric vehicle, the change of the charging power of the electric vehicle over time can be observed, so as to optimize the charging strategy, evaluate the battery health status, improve the charging efficiency, and ensure the safety and reliability of the charging process.

[0034] In step 103, the charging load bearing capacity of the electric vehicle during the charging process is extracted from the load information, and the power fluctuation margin of the electric vehicle during the charging process is determined by combining the charging load bearing capacity with the charging state of the electric vehicle.

[0035] In some embodiments, extracting the charging load bearing capacity of the electric vehicle during the charging process from the load information can be achieved by using the following steps: Determine the load bearing amount corresponding to each charging power in the load information; The charging load bearing capacity of the electric vehicle during the charging process is determined by all load bearing capacities.

[0036] In specific implementation, the load bearing capacity in the present application reflects the intensity of the charging load borne by the battery of the electric vehicle under a specific charging power. The larger the load bearing capacity, the higher the load borne by the battery of the electric vehicle during the charging process. The load bearing capacity corresponding to each charging power in the load information can be determined in the following manner, namely: select a charging power in the load information, and use the ratio of the charging power to the battery capacity of the electric vehicle as the load bearing capacity corresponding to the battery of the electric vehicle at the charging power. Repeat the above steps to obtain the load bearing capacity corresponding to the battery of the electric vehicle at the remaining charging power. In other embodiments, other methods can also be used for determination, which is not limited here. Determining the charging load bearing capacity of the electric vehicle during the charging process through all load bearing capacities can be implemented in the following manner, namely: the maximum load bearing capacity among all load bearing capacities can be used as the charging load bearing capacity of the electric vehicle during the charging process. In other embodiments, it can also be determined by other methods, which is not limited here.

[0037] It should be noted that the charging load tolerance in the present application reflects the maximum charging load that the battery of an electric vehicle can withstand during the charging process, that is, it indicates the limit value of the load tolerance of the battery of an electric vehicle under different charging power conditions. Therefore, the maximum tolerable pressure of the battery during the charging process can be understood through the charging load tolerance. The determination of the charging load tolerance is of great significance for optimizing the charging strategy, improving the charging efficiency and extending the battery life, and will not be elaborated here.

[0038] In some embodiments, reference Figure 3 As shown in FIG. 1 , this figure is an exemplary flow chart of determining the power fluctuation margin in some embodiments of the present application. In this embodiment, the power fluctuation margin of the electric vehicle during the charging process is determined by combining the charging load bearing capacity with the charging state of the electric vehicle, which can be implemented by the following steps: First, in step 1031, the charging end time of the electric vehicle and the target battery SOC value at the charging end time are extracted from the charging state of the electric vehicle; Next, in step 1032, the charging scheduling domain of the electric vehicle is obtained; Then, in step 1033, a charging time margin coefficient of the electric vehicle is determined; Finally, in step 1034, the power fluctuation margin of the electric vehicle during the charging process is determined by the charging end time, the target battery SOC value, the charging scheduling domain, the charging load tolerance and the charging time margin coefficient.

[0039] It should be noted that the charging time margin coefficient in the present application reflects the margin of the electric vehicle to complete the charging task within the available charging time range. In specific implementation, the charging time margin coefficient of the electric vehicle can be determined in the following manner, namely: first, multiply the difference between one percent of the target battery SOC value and one percent of the battery SOC value at the start of charging by the battery capacity of the electric vehicle; then divide the obtained product by the maximum charging power provided by the charging pile of the electric vehicle; finally, the difference between the total time included in the estimated charging time interval of the electric vehicle and the quotient obtained above is used as the charging time margin coefficient of the electric vehicle. In other embodiments, it can also be determined by other methods, which are not limited here.

[0040] In specific implementation, the power fluctuation margin of the electric vehicle during the charging process is determined by the charging end time, the target battery SOC value, the charging scheduling domain, the charging load tolerance and the charging time margin coefficient. The following method can be used to achieve this, namely: first, a charging time point is selected from the charging scheduling domain, and then the battery SOC value corresponding to the charging time point is obtained. Then, the difference between the target battery SOC value and the battery SOC value corresponding to the charging time point is calculated, multiplied by the battery capacity of the electric vehicle, and then divided by the quotient of the charging load tolerance. Then, the time difference between the charging end time and the charging time point is subtracted from the quotient obtained by the above calculation, and then the ratio of the obtained difference to the charging time margin coefficient is used as the schedulable charging amount of the electric vehicle during the charging process. The above steps are repeated, and the remaining charging time points are selected from the charging scheduling domain and the above calculation is repeated. Finally, the schedulable charging amount at each charging time point is obtained to form the power fluctuation margin of the electric vehicle during the charging process. In other embodiments, other methods can also be used for determination, which is not limited here.

[0041] It should be noted that the power fluctuation margin in the present application represents the power fluctuation range that the electric vehicle can accept and adjust within the estimated charging time interval, wherein the dispatchable charging amount in the power fluctuation margin reflects the charging degree that the electric vehicle can adjust at the corresponding charging time point. Therefore, the power fluctuation margin can be used to understand the flexibility and adjustability of electric vehicles in planning charging time, as well as the extent to which electric vehicles can respond to the scheduling needs of the power grid, so as to adjust the charging time to support the stable operation of the power grid without affecting user use, reflecting the coordination ability between electric vehicles and the power grid, and helping the power grid to balance the load in different time periods.

[0042] In step 104, the charging characteristics of the electric vehicle are obtained, the electric energy buffering capacity of the electric vehicle is determined based on the charging characteristics, and the electric energy compensation index when the charging station charges the electric vehicle in the next stage is predicted based on the electric energy buffering capacity and the electric energy fluctuation margin.

[0043] In specific implementation, the charging characteristics of the electric vehicle can be obtained from the vehicle manual of the electric vehicle or the official website of the manufacturer. The charging characteristics may include: the minimum safe charging state of the battery (i.e., the minimum SOC), the maximum safe charging state of the battery (i.e., the maximum SOC), the safe charging voltage and current range of the battery, the safe temperature range of the battery during the charging process, the maximum charging power of the electric vehicle charger and the charging efficiency of the electric vehicle (i.e., the power conversion efficiency during the charging process), etc., which will not be repeated here.

[0044] In some embodiments, determining the electric energy buffer amount of the electric vehicle according to the charging characteristics may be implemented by the following steps: Determining a charging safety factor of the electric vehicle based on the charging characteristics; The electric energy buffer capacity of the electric vehicle is determined according to the charging safety factor.

[0045] It should be noted that the charging safety factor in the present application is used to ensure the battery safety during the charging process of the electric vehicle. In specific implementation, the charging safety factor of the electric vehicle determined by the charging characteristics can be achieved in the following manner, namely: the charging safety factor of the electric vehicle can be set by relevant experts based on the charging characteristics of the electric vehicle and the historical charging data of the electric vehicle (for example: the charging frequency, time and habits of the electric vehicle users). The value of the charging safety factor is usually between 1.1 and 1.2, which depends on the charging characteristics and usage environment of the electric vehicle. In other embodiments, other methods can also be used for determination, which is not limited here.

[0046] In specific implementation, determining the electric energy buffer capacity of the electric vehicle based on the charging safety factor can be achieved in the following manner, namely: first, for each charging time point in the charging scheduling domain of the electric vehicle, calculate the difference between the target battery SOC value and the battery SOC value corresponding to each charging time point, and then multiply the largest difference among all the differences obtained by the charging safety factor, and finally use the obtained product value as the electric energy buffer capacity of the electric vehicle. In other embodiments, other methods can also be used for determination, which is not limited here.

[0047] It should be noted that the electric energy buffer amount in the present application reflects the extent to which the battery of an electric vehicle remains within a safe operating range during the charging process. Therefore, the electric energy buffer amount is an important parameter to ensure that the battery of an electric vehicle is charged within a safe, reliable and efficient range. It limits the maximum SOC of charging, protects the battery from risks such as overcharging and high temperature, optimizes the efficiency and safety of the electric vehicle charging process, and extends the battery life.

[0048] In step 105, the electric energy compensation index when the charging station charges the electric vehicle in the next stage is predicted according to the electric energy buffer amount and the electric energy fluctuation margin.

[0049] In some embodiments, the following steps may be used to predict the power compensation index of the charging station when charging the electric vehicle in the next stage according to the power buffer amount and the power fluctuation margin: Extracting the dispatchable charging amount of the electric vehicle corresponding to the charging time point in the current stage from the electric energy fluctuation margin; The electric energy compensation index when the charging station charges the electric vehicle in the next stage is determined by the dispatchable charging amount and the electric energy buffer amount.

[0050] In specific implementation, extracting the dispatchable charging amount of the electric vehicle at the charging time point corresponding to the current stage from the electric energy fluctuation margin can be implemented in the following manner, namely: obtaining the specific time of the current stage, and then extracting the dispatchable charging amount at the charging time point corresponding to the specific time from the electric energy fluctuation margin, the dispatchable charging amount reflects the charging degree that the electric vehicle can adjust in the current stage, and other methods can also be used to determine it in other embodiments, which are not limited here; determining the electric energy compensation index when the charging station charges the electric vehicle in the next stage by the dispatchable charging amount and the electric energy buffer amount can be implemented in the following manner, namely: the product value of the dispatchable charging amount and the electric energy buffer amount can be used as the electric energy compensation index when the charging station charges the electric vehicle in the next stage, and other methods can also be used to determine it in other embodiments, which are not limited here.

[0051] It should be noted that the electric energy compensation index in the present application is an indicator used to determine whether charging compensation is needed for electric vehicles in the next stage. Therefore, the electric energy compensation index can be used to supervise whether electric vehicles are charged according to the planned power, and then the charging power compensation can be performed on the electric vehicles being charged. It will not be repeated here.

[0052] In step 106, when the electric energy compensation index is within a preset threshold range, the charging power of the charging station to the electric vehicle is reduced in the next stage.

[0053] It should be noted that the threshold interval in the present application is an interval for judging whether the charging power of the electric vehicle is within the normal working range. As a preferred embodiment, the threshold interval can be preset in the following manner, namely: the historical electric energy fluctuation range can be analyzed through a machine learning algorithm based on the historical electric energy compensation indicators of the charging station in different time periods (such as peak period, non-peak period, seasonal changes, etc.) collected from historical experimental data, as well as the charging demand of the electric vehicle, the utilization rate of the charging station and other specific conditions, and then the threshold interval for judging whether the charging power of the electric vehicle is within the normal working range is set according to the obtained electric energy fluctuation range. The threshold interval is usually (0, 1), so that the electric vehicle in the charging process is controlled for electric energy compensation in the next stage based on the threshold interval. Other methods can also be used for setting in other embodiments, which are not limited here.

[0054] In specific implementation, when the electric energy compensation index is in a preset threshold interval, reducing the charging power of the charging station to the electric vehicle in the next stage can be achieved in the following manner, namely: if the preset threshold interval is (0, 1), when the electric energy compensation index is in the preset threshold interval, that is, when the electric energy compensation index is greater than 0 and less than 1, it means that the charging power of the electric vehicle in the next stage is excessive, so that the product value of the charging power of the electric vehicle at the corresponding charging time point in the current stage and the electric energy compensation index is used as the compensation amount of the charging power of the electric vehicle by the charging station in the next stage, then in the next stage, the charging power of the charging station to the electric vehicle is controlled to reduce the compensation amount, wherein the compensation amount reflects the degree of electric energy compensation for the electric vehicle, and other methods can also be used in other embodiments, which are not limited here.

[0055] In addition, in some embodiments, when the electric energy compensation index is less than a preset threshold interval, the charging power of the charging station to the electric vehicle in the next stage is increased; when the electric energy compensation index is greater than the preset threshold interval, the charging power of the charging station to the electric vehicle in the next stage is kept unchanged.

[0056] In specific implementation, if the preset threshold interval is (0, 1), when the electric energy compensation index is less than the preset threshold interval, that is, when the electric energy compensation index is less than or equal to 0, it indicates that the charging power of the electric vehicle in the next stage is insufficient, so that the ratio of the charging power of the electric vehicle at the corresponding charging time point in the current stage to the electric energy buffer amount is used as the compensation amount of the charging power of the charging station to the electric vehicle in the next stage, and then the charging power of the charging station to the electric vehicle is controlled to increase the compensation amount in the next stage, wherein the compensation amount reflects the degree of electric energy compensation for the electric vehicle, and other methods can also be used in other embodiments, which are not limited here; when the electric energy compensation index is greater than the preset threshold interval, that is, when the electric energy compensation index is greater than or equal to 1, it indicates that the charging power of the electric vehicle in the next stage is normal, and then the charging power of the charging station to the electric vehicle is kept unchanged in the next stage; the electric energy compensation control of the electric vehicle when charging in the charging station is realized by the above method, and other methods can also be used in other embodiments, which are not limited here.

[0057] In addition, in another aspect of the present application, in some embodiments, the present application provides an electric vehicle charging control system, referring to Figure 4 , which is a schematic diagram of the structure of an electric vehicle charging control system according to some embodiments of the present application, the electric vehicle charging control system 400 includes: a monitoring module 401, a processing module 402 and an execution module 403, which are respectively described as follows: Monitoring module 401, in this application, the monitoring module 401 is mainly used to monitor the charging status of electric vehicles in the charging station at the current stage; Processing module 402, in the present application, the processing module 402 is mainly used to plan charging of the electric vehicle based on the monitored charging status, and obtain a charging scheduling domain of the electric vehicle within the estimated charging time period; The processing module 402 in the present application is also used to extract the charging load bearing capacity of the electric vehicle during the charging process from the load information, and determine the power fluctuation margin of the electric vehicle during the charging process through the charging load bearing capacity combined with the charging state of the electric vehicle; The processing module 402 in the present application is also used to obtain the charging characteristics of the electric vehicle, determine the electric energy buffer of the electric vehicle through the charging characteristics, and predict the electric energy compensation index when the charging station charges the electric vehicle in the next stage according to the electric energy buffer and the electric energy fluctuation margin; The processing module 402 in the present application is also used to obtain the charging characteristics of the electric vehicle, determine the electric energy buffer of the electric vehicle through the charging characteristics, and predict the electric energy compensation index when the charging station charges the electric vehicle in the next stage according to the electric energy buffer and the electric energy fluctuation margin; The execution module 403 in the present application is mainly used to reduce the charging power of the charging station to the electric vehicle in the next stage when the electric energy compensation index is within a preset threshold range.

[0058] The above describes in detail the examples of the electric vehicle charging control method and system provided by the embodiments of the present application. It can be understood that in order to realize the above functions, the corresponding device includes hardware structures and / or software modules corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware 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 this application.

[0059] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned electric vehicle charging control method.

[0060] In some embodiments, reference Figure 5 , the dotted line in the figure indicates that the unit or the module is optional, and the figure is a schematic diagram of the structure of the computer device implementing the electric vehicle charging control method of the present application. The electric vehicle charging control method in the above embodiment can be Figure 5The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device 500 can be a terminal device, a server or a chip.

[0061] The processor 501 may be a general-purpose processor or a special-purpose processor. For example, the processor 501 may be a central processing unit (CPU), which may be used to control the computer device 500, execute software programs, and process data of the software programs. The computer device 500 may also include a communication unit 505 to implement signal input (reception) and output (transmission).

[0062] For example, the computer device 500 may be a chip, the communication unit 505 may be an input and / or output circuit of the chip, or the communication unit 505 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other devices.

[0063] For another example, the computer device 500 may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0064] The computer device 500 may include one or more memories 502, on which a program 504 is stored. The program 504 can be executed by the processor 501 to generate instructions 503, so that the processor 501 performs the method described in the above method embodiment according to the instructions 503. Optionally, data (such as a target audit model) can also be stored in the memory 502. Optionally, the processor 501 can also read the data stored in the memory 502, and the data can be stored at the same storage address as the program 504, or the data can be stored at a different storage address from the program 504.

[0065] The processor 501 and the memory 502 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of the terminal device.

[0066] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 501. The processor 501 can be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0067] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0068] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned electric vehicle charging control method when executed.

[0069] In summary, in the electric vehicle charging control method and system disclosed in the embodiment of the present application, the charging status of the electric vehicle in the charging station in the current stage is monitored; the charging of the electric vehicle is planned based on the charging status obtained by monitoring, and the charging scheduling domain of the electric vehicle in the estimated charging time period is obtained, and then the load information of the electric energy input power of the electric vehicle during the charging process is determined by the charging scheduling domain; the charging load bearing capacity of the electric vehicle during the charging process is extracted from the load information, and the electric energy fluctuation margin of the electric vehicle during the charging process is determined by the charging load bearing capacity combined with the charging status of the electric vehicle; the charging characteristics of the electric vehicle are obtained, and the electric energy buffering amount of the electric vehicle is determined by the charging characteristics; the electric energy compensation index of the charging station when charging the electric vehicle in the next stage is predicted according to the electric energy buffering amount and the electric energy fluctuation margin; when the electric energy compensation index is in a preset threshold range, the charging power of the charging station to the electric vehicle in the next stage is reduced; the electric energy when the electric vehicle is charged can be intelligently and dynamically compensated and regulated, thereby avoiding charging imbalance.

[0070] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0071] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for controlling charging of an electric vehicle, characterized in that: The steps include: Monitor the charging status of electric vehicles in the charging station at the current stage; Based on the monitored charging status, a charging schedule for the electric vehicle is performed to obtain a charging scheduling domain for the electric vehicle within an estimated charging time period, and then the load information of the electric energy input power of the electric vehicle during the charging process is determined by the charging scheduling domain; Extracting the charging load bearing capacity of the electric vehicle during the charging process from the load information, and determining the power fluctuation margin of the electric vehicle during the charging process through the charging load bearing capacity combined with the charging state of the electric vehicle; Acquiring charging characteristics of the electric vehicle, and determining the electric energy buffering amount of the electric vehicle according to the charging characteristics; Predicting an electric energy compensation index when the charging station charges the electric vehicle in the next stage according to the electric energy buffer amount and the electric energy fluctuation margin; When the electric energy compensation index is within a preset threshold range, the charging power of the charging station to the electric vehicle is reduced in the next stage.

2. The method according to claim 1, characterized in that Based on the monitored charging status, the charging plan for electric vehicles is carried out, and the charging scheduling domain of electric vehicles in the estimated charging time period is obtained, which specifically includes: Extracting an estimated charging time interval for the electric vehicle from the monitored charging status; The estimated charging time interval is segmented to obtain a plurality of charging time periods; Determine the battery SOC value range corresponding to each charging period according to the charging state of the electric vehicle; The charging scheduling domain of the electric vehicle is composed of all charging periods and the battery SOC value ranges corresponding to the charging periods.

3. The method according to claim 1, characterized in that The load information of the electric energy input power of the electric vehicle during the charging process determined by the charging scheduling domain specifically includes: Selecting a plurality of charging data points in a charging scheduling domain of an electric vehicle; The load information of the electric energy input power during the charging process of the electric vehicle is determined based on all charging data points.

4. The method according to claim 1, characterized in that Extracting the charging load bearing capacity of the electric vehicle during the charging process from the load information specifically includes: Determine the load bearing amount corresponding to each charging power in the load information; The charging load bearing capacity of the electric vehicle during the charging process is determined by all load bearing capacities.

5. The method according to claim 1, characterized in that Determining the electric energy buffer amount of the electric vehicle by using the charging characteristics specifically includes: Determining a charging safety factor of the electric vehicle based on the charging characteristics; The electric energy buffer capacity of the electric vehicle is determined according to the charging safety factor.

6. The method according to claim 1, characterized in that Also includes: When the electric energy compensation index is less than a preset threshold value interval, increasing the charging power of the charging station to the electric vehicle in the next stage; When the electric energy compensation index is greater than a preset threshold value interval, the charging power of the charging station to the electric vehicle in the next stage is kept unchanged.

7. The method according to claim 1, characterized in that The charging status of the electric vehicles in the charging station at the current stage is monitored through the communication module in the charging pile of the charging station.

8. An electric vehicle charging control system, characterized in that: include: A monitoring module is used to monitor the charging status of electric vehicles in the charging station at the current stage; A processing module, configured to plan charging of the electric vehicle based on the monitored charging status, obtain a charging scheduling domain of the electric vehicle within an estimated charging time period, and further determine load information of the electric energy input power of the electric vehicle during the charging process from the charging scheduling domain; The processing module is further used to extract the charging load bearing capacity of the electric vehicle during the charging process from the load information, and determine the power fluctuation margin of the electric vehicle during the charging process through the charging load bearing capacity combined with the charging state of the electric vehicle; The processing module is further used to obtain the charging characteristics of the electric vehicle, and determine the electric energy buffering amount of the electric vehicle according to the charging characteristics; The processing module is further used to predict the electric energy compensation index when the charging station charges the electric vehicle in the next stage according to the electric energy buffer amount and the electric energy fluctuation margin; The execution module is used to reduce the charging power of the charging station to the electric vehicle in the next stage when the electric energy compensation index is within a preset threshold range.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the electric vehicle charging control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the electric vehicle charging control method according to any one of claims 1 to 7.

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