Vehicle charging demand image table construction method and device, computer device, and medium

Abstract

The present application relates to the field of new energy vehicle charging technology, and discloses a vehicle charging demand portrait table construction method and device, computer equipment and medium, the construction method comprises: obtaining vehicle operation detection data; simulating the maximum charging power data table of the vehicle based on the vehicle operation detection data; fitting the power consumption overlap duration of the vehicle and the grid scheduling area based on the maximum charging power data table; and generating a vehicle charging demand feature portrait table based on the power consumption overlap duration. The present application solves the problem that vehicle charging characteristics parameters required to effectively support electric vehicle charging and discharging behavior cannot be obtained.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle charging technology, specifically to a method, apparatus, computer equipment, and medium for constructing a vehicle charging demand profile. Background Technology

[0002] With the large-scale growth of electric vehicles, utilizing the power grid to charge these vehicles will inevitably impact the operational stability of urban power grids, potentially leading to a series of problems. When too many electric vehicles are charging simultaneously at charging stations, the excessive electricity demand significantly increases the grid's dispatching pressure during peak hours. This results in issues such as limited driving range, excessive grid load, and long charging queues, potentially leading to insufficient battery power. The root cause of these problems lies in the fact that current electric vehicle profiles primarily focus on automotive product development and marketing, offering very little detail on the specific charging and discharging characteristics of vehicles, thus failing to effectively support the vehicle charging characteristic parameters required for electric vehicle charging and discharging behavior. Summary of the Invention

[0003] In view of this, the present invention provides a method, apparatus, computer equipment and medium for constructing a vehicle charging demand profile table, in order to solve the problem of the inability to effectively support the vehicle charging characteristic parameters required for the charging and discharging behavior of electric vehicles.

[0004] In a first aspect, the present invention provides a method for constructing a vehicle charging demand profile, the method comprising:

[0005] Obtain vehicle operation and monitoring data;

[0006] A table of maximum charging power data for simulated vehicles based on vehicle operation detection data;

[0007] Based on the maximum charging power data table, the overlap time of power consumption between vehicles and the power grid in the areas requiring dispatch is fitted.

[0008] A profile table of vehicle charging demand characteristics is generated based on the overlapping duration of electricity consumption.

[0009] The vehicle charging demand profile construction method provided in this invention, through the interaction between the vehicle and the grid scheduling area, fits the power consumption overlap time of the vehicle and the grid scheduling area, and generates a vehicle charging demand feature profile based on the power consumption overlap time. This achieves the purpose of obtaining the vehicle charging characteristic parameters required for the charging and discharging behavior of electric vehicles, and solves the problem of not being able to effectively support the vehicle charging characteristic parameters required for the charging and discharging behavior of electric vehicles.

[0010] In one optional implementation, the vehicle operation detection data includes:

[0011] Behavioral data for a single charging trip; behavioral data for a single charging trip includes parking time, charging time, battery temperature, power consumption, and charging amount.

[0012] The vehicle charging demand profile construction method provided in this embodiment of the invention obtains the vehicle's parking time, charging time, battery temperature, power consumption, and charging amount through behavioral data of a single charging trip, providing a data foundation for the subsequent construction of the vehicle charging demand profile.

[0013] In one optional implementation, the maximum charging power data table of the simulated vehicle based on vehicle operation detection data includes:

[0014] The battery temperature is divided into different temperature ranges according to the preset step size;

[0015] The table shows the maximum charging power data of the vehicle under preset charging conditions and based on different temperature ranges.

[0016] The vehicle charging demand profile construction method provided in this invention divides the battery temperature of a single charging trip into different temperature ranges according to a preset step size; and simulates the maximum charging power data table of the vehicle based on different temperature ranges under a preset state of charge. This can obtain the maximum allowable charging power of the vehicle battery under different states of charge and different battery temperatures, providing a data foundation for subsequent fitting of the overlap time of power consumption between the vehicle and the external power grid in the dispatchable area based on the maximum charging power data table.

[0017] In one optional implementation, fitting the overlap time between the vehicle's power consumption and the external power grid's dispatchable area based on the maximum charging power data table includes:

[0018] Based on the maximum charging power data table, extract the parking time and charging time corresponding to the maximum charging power from the behavior data of a single charging trip;

[0019] Obtain the time of sufficient grid load and the time of insufficient grid load in the area requiring grid dispatch;

[0020] Based on the parking time and charging time corresponding to the maximum charging power, the parking time and charging time of vehicles in the area to be dispatched need to be matched.

[0021] The matching parking time and charging time are fitted with the grid load time and grid load insufficiency time to obtain the overlap time of power consumption between the vehicle and the grid in the area requiring dispatch.

[0022] The vehicle charging demand profile table construction method provided in this embodiment of the invention realizes the scheduling interaction between vehicles and the power grid, and the resulting electricity consumption overlap time provides a data foundation for generating the vehicle charging demand feature profile table.

[0023] In an optional implementation, the vehicle operation detection data also includes the vehicle's corresponding model static information and driving data. Before generating the vehicle charging demand characteristic profile table based on the overlapping electricity consumption time, the method for constructing the vehicle charging demand profile table further includes:

[0024] Determine the vehicle's permanent location based on driving data;

[0025] A static profile table of the vehicle is generated based on the vehicle model's static information and its permanent location.

[0026] A dynamic vehicle profile table is generated by summarizing and analyzing parking time, charging time, power consumption, and charging amount.

[0027] The vehicle charging demand profile table construction method provided in this embodiment of the invention can depict all vehicle attributes in the static vehicle profile table and accurately locate the vehicle, and can depict the vehicle's parking time, charging time, power consumption and charging amount summary data during driving and charging, comprehensively reflecting the vehicle's static and dynamic attributes.

[0028] In one optional implementation, generating a vehicle charging demand profile based on overlapping electricity usage duration includes:

[0029] Link the vehicle static profile table and extract the corresponding location coordinates of the vehicle;

[0030] Filter the power grid areas that require dispatching, including vehicle location coordinates;

[0031] Based on the overlap of electricity usage time and the vehicle dynamic profile table, the historical average charging amount of vehicles in the power grid dispatch area is calculated and filtered to generate a vehicle charging demand characteristic profile table.

[0032] In one optional implementation, based on the overlap of electricity usage time, the historical average charging amount of vehicles in the area requiring dispatch from the external power grid is calculated by associating with the vehicle dynamic profile data table, and a vehicle charging demand characteristic profile table is generated, including:

[0033] Based on the overlapping duration of electricity consumption, the historical average charging amount of vehicles in the grid dispatch area is calculated according to the power consumption and charging amount of vehicles in the vehicle dynamic profile data table, and a vehicle charging demand characteristic profile table is generated to show the interaction between vehicles and the grid dispatch area.

[0034] The vehicle charging demand profile table construction method provided in this embodiment of the invention enables the vehicle to interact with the power grid scheduling area containing the vehicle's location coordinates, and generates a vehicle charging demand feature profile table that comprehensively describes the vehicle charging characteristic parameters required for the charging and discharging behavior of electric vehicles.

[0035] Secondly, the present invention provides a vehicle charging demand profile construction device, the device comprising:

[0036] The acquisition module is used to acquire vehicle operation and detection data;

[0037] The module is used to simulate the maximum charging power data table of a vehicle based on vehicle operation detection data;

[0038] The fitting module is used to fit the overlap time of power consumption between vehicles and the power grid in the areas requiring dispatch based on the maximum charging power data table;

[0039] The generation module is used to generate a profile table of vehicle charging demand characteristics based on the duration of overlapping electricity consumption.

[0040] Thirdly, the present invention provides a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the vehicle charging demand profile table construction method of the first aspect or any corresponding embodiment described above.

[0041] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the vehicle charging demand profile construction method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0042] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating the method for constructing a vehicle charging demand profile table according to an embodiment of the present invention.

[0044] Figure 2 This is a flowchart illustrating another method for constructing a vehicle charging demand profile according to an embodiment of the present invention.

[0045] Figure 3 This is a flowchart illustrating another method for constructing a vehicle charging demand profile according to an embodiment of the present invention.

[0046] Figure 4 This is a maximum charging power curve of the first vehicle model according to an embodiment of the present invention;

[0047] Figure 5 This is a maximum charging power curve of the second vehicle model according to an embodiment of the present invention;

[0048] Figure 6 This is a maximum charging power curve of the third vehicle model according to an embodiment of the present invention;

[0049] Figure 7 This is a structural block diagram of a vehicle charging demand profile construction device according to an embodiment of the present invention;

[0050] Figure 8 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] According to an embodiment of the present invention, a method for constructing a vehicle charging demand profile table is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0053] This embodiment provides a method for constructing a vehicle charging demand profile table. Figure 1 This is a flowchart of a method for constructing a vehicle charging demand profile table according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0054] Step S101: Obtain vehicle operation detection data. Specifically, the vehicle operation detection data is used to characterize the inherent characteristics of the vehicle and its operating attributes. For example, the inherent characteristics of the vehicle may include the vehicle model, and the operating attributes may include behavioral data of a single charging trip.

[0055] Step S102: Simulate the maximum charging power data table of the vehicle based on vehicle operation detection data. Specifically, the maximum charging power of the vehicle refers to the maximum allowable charging power of the vehicle. To address the maximum usable charging power limits of different vehicle models under different states of charge and battery temperatures, the maximum charging power of the vehicle is simulated based on vehicle operation detection data to obtain the maximum charging power data table.

[0056] Step S103: Fit the overlap time of power consumption between the vehicle and the grid dispatchable area based on the maximum charging power data table. Specifically, the maximum charging power data table contains the vehicle's maximum charging power. The vehicle needs to consume a certain amount of power to reach its maximum charging power. By comparing this power consumption time with the power consumption time of the grid dispatchable area, the overlap time of power consumption between the vehicle and the grid dispatchable area can be obtained.

[0057] Step S104: Generate a vehicle charging demand feature profile table based on the overlap of electricity consumption duration. Specifically, the vehicle charging demand features can be obtained based on the overlap of electricity consumption duration between the vehicle and the grid dispatch area, i.e., the electricity consumption duration required for the vehicle to reach its maximum charging power and the electricity consumption duration of the grid dispatch area, and finally, a vehicle charging demand feature profile table is generated.

[0058] The vehicle charging demand profile construction method provided in this invention, through the interaction between the vehicle and the grid scheduling area, fits the power consumption overlap time of the vehicle and the grid scheduling area, and generates a vehicle charging demand feature profile based on the power consumption overlap time. This achieves the purpose of obtaining the vehicle charging characteristic parameters required for the charging and discharging behavior of electric vehicles, and solves the problem of not being able to effectively support the vehicle charging characteristic parameters required for the charging and discharging behavior of electric vehicles.

[0059] This embodiment provides a method for constructing a vehicle charging demand profile table. Figure 2 This is a flowchart of a method for constructing a vehicle charging demand profile table according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0060] Step S201: Obtain vehicle operation detection data. Vehicle operation detection data includes: behavioral data for a single charging trip; the behavioral data for a single charging trip includes parking time, charging time, battery temperature, power consumption, and charging amount.

[0061] Step S202: Simulate the maximum charging power data table of the vehicle based on the vehicle operation detection data.

[0062] Specifically, step S202 includes:

[0063] Step S2021: Divide the battery temperature into different temperature ranges according to a preset step size. Specifically, the charging management strategies of the Battery Management System (BMS) of electric vehicles produced by different vehicle manufacturers are significantly different, and the BMS charging management strategies also vary significantly when different State of Charge (SOC) ranges are combined with different battery operating temperature ranges for a single model. Therefore, based on the full life cycle charging process data resources of all electric vehicles, the battery temperature of different models is first divided into different temperature ranges. For example, the battery temperature can be divided into 5°C (degrees Celsius) temperature ranges. This can be set as 0≤T<5, 5≤T<10, etc.

[0064] Step S2022: Simulate the maximum charging power data table of the vehicle under preset state of charge based on different temperature ranges. Specifically, analyze and extract the maximum charging power of the vehicle under different state of charge and different temperature ranges. Based on single-vehicle detection data for a single vehicle model, acquire a large amount of fast charging behavior data, mine the charging characteristics of the vehicle model, and calculate the maximum charging power of the model under different temperatures and different SOCs. The specific processing procedure is as follows: Extract vehicles corresponding to a single model, extract all fast charging process records for each vehicle, divide the battery temperature in each charging trip into several temperature ranges, acquire single-frame data of voltage and current under different state of charge dimensions, remove impurities, and aggregate according to vehicle model to form the maximum charging power under different temperature ranges and different SOCs. State of charge (SOC) is a percentage, and the unit of maximum charging power is watts (W). The indicators included in the maximum charging power data table of the vehicle under preset state of charge based on different battery temperature ranges are shown in Table 1 below:

[0065] Table 1

[0066]

[0067]

[0068] The maximum charging power data for three different vehicle models under different states of charge and based on different battery temperature ranges are shown in Tables 2, 3, and 4 below:

[0069] Table 2 Maximum Charging Power Data for the First Vehicle Model

[0070]

[0071]

[0072]

[0073] Table 3: Maximum Charging Power Data for the Second Model

[0074]

[0075]

[0076]

[0077] Table 4. Maximum Charging Power Data for the Third Vehicle Model

[0078]

[0079]

[0080]

[0081] The maximum charging power curve of the first model is shown below. Figure 4 As shown, the maximum charging power curve of the second model is as follows: Figure 5 As shown, the maximum charging power curve of the third model is as follows: Figure 6 As shown, Figures 4-6 In the diagram, the vertical axis represents different states of charge (SOC), and the horizontal axis represents the corresponding charging power.

[0082] Step S203: Fit the overlap time of power consumption between the vehicle and the grid in the area requiring dispatch based on the maximum charging power data table.

[0083] Specifically, step S203 includes:

[0084] Step S2031: Extract the parking time and charging time corresponding to the maximum charging power from the behavior data of a single charging trip based on the maximum charging power data table. Specifically,

[0085] When charging the vehicle, the parking time and charging time at which the vehicle reaches its maximum charging power are extracted from the vehicle's single-trip behavior data based on the maximum charging power data table. The charging start time is taken as the parking start time, and the charging end time is taken as the parking end time when the maximum charging power is reached. The vehicle indicators included during parking and charging are shown in Table 4 below:

[0086] Table 4

[0087]

[0088]

[0089] Step S2032: Obtain the time of sufficient grid load and the time of insufficient grid load in the area to be dispatched by the power grid; specifically, in conjunction with the vehicle-grid interaction business, associate the power grid dispatch time parameter dimension table to obtain the time of sufficient grid load and the time of insufficient grid load in the area to be dispatched by the power grid.

[0090] Step S2033: Match the parking time and charging time of vehicles within the required dispatch area based on the parking time and charging time corresponding to the maximum charging power. Specifically, in conjunction with the vehicle-grid interaction service, the interaction service provides the grid dispatch time, the city requiring dispatch, the district of the city, and the location coordinates of available vehicle-grid interaction charging facilities within the district. By matching the vehicle parking time with the grid dispatch time, the parking time and charging time of vehicles within the required dispatch area are matched based on the parking time and charging time corresponding to the maximum charging power.

[0091] Step S2034 involves fitting the matched parking and charging times with the periods of sufficient grid load and insufficient grid load, respectively, to obtain the overlap duration of power consumption between the vehicle and the grid in the area requiring grid dispatch. Specifically, based on the matched parking and charging times, and considering vehicle operation segmentation data and vehicle operation characteristics, the same parking segment is summed up across different days to calculate the overlap duration of power consumption between the vehicle and the grid in the area requiring grid dispatch. The amount of electricity charged by the vehicle is the amount of electricity that the grid can dispatch. The overlap duration of power consumption is shown in Table 5 below:

[0092] Table 5

[0093]

[0094]

[0095] Step S204: Generate a vehicle charging demand characteristic profile table based on the overlapping electricity consumption duration. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0096] The vehicle charging demand profile construction method provided in this invention divides the battery temperature into different temperature ranges according to a preset step size; it simulates the maximum charging power data table of the vehicle based on different temperature ranges under a preset state of charge, which can obtain the maximum allowable charging power of the vehicle battery under different states of charge and different battery temperatures. This provides a data foundation for subsequent fitting of the power overlap time between the vehicle and the external power grid in the area requiring scheduling based on the maximum charging power data table, realizing the scheduling interaction between the vehicle and the power grid. The final power overlap time provides a data foundation for generating the vehicle charging demand feature profile table.

[0097] This embodiment provides a method for constructing a vehicle charging demand profile table. The vehicle operation detection data also includes the vehicle's corresponding model static information and driving data. Figure 3 This is a flowchart of a method for constructing a vehicle charging demand profile table according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0098] Step S301: Obtain vehicle operation detection data. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0099] Step S302: Simulate the maximum charging power data table of the vehicle based on vehicle operation detection data. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0100] Step S303: Fit the overlap time between vehicle and grid dispatch areas based on the maximum charging power data table. For details, please refer to [link to relevant documentation]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0101] Step S304: Determine the vehicle's permanent location based on driving data; generate a static vehicle profile table based on vehicle model static information and permanent location; and generate a dynamic vehicle profile table by summarizing and analyzing parking time, charging time, power consumption, and charging amount.

[0102] Specifically, vehicle driving data records the vehicle's driving trajectory, driving time, mileage, etc. Based on the driving trajectory, the vehicle's permanent residence and the city where the trajectory is located can be obtained. Both the permanent residence and the city where the trajectory is located can be used as access cities for the power grid's dispatching area. Vehicle static information includes vehicle model name, vehicle identification number (VIN), nominal battery capacity, vehicle major category information, vehicle minor category information, and vehicle coordinate system. When the permanent residence and the city where the trajectory is located are used as access cities, the static profile table includes the access date. The vehicle static profile table generated based on the vehicle model static information and permanent residence is shown in Table 6 below:

[0103] Table 6

[0104] name type meaning vin string VIN veh_category string Major Categories veh_category_2 string Subcategories city string City where the trajectory is located veh_model_name string Model Name coordinate_system string coordinate system power_amount double nominal power entry_date string Access Date entry_city string Access to cities

[0105] For the vehicle identification number (VIN) in the static profile table, calculate the dynamic profile table indicators for electric vehicles based on their parking time, charging time, power consumption, and charging amount. The dynamic profile table includes the following indicators:

[0106] 1) Parking time.

[0107] 2) Charging time.

[0108] 3) Average daily energy consumption (avg_run_energy_day): Takes the vehicle's driving data, calculates the total daily energy consumption, and takes the average of multiple days.

[0109] 4) Average monthly energy consumption: Takes vehicle driving data, calculates the total monthly energy consumption, and takes the average of multiple months.

[0110] 5) Average energy consumption per kilometer (avg_energy_per_mileage): Take the vehicle's driving data, calculate its total mileage and total power consumption, and calculate the energy consumption per kilometer by dividing the total power consumption by the total mileage.

[0111] 6) Average charging power avg_charge_power_month: Takes the vehicle's charging data, calculates the average power of a single charge using the average total current × average total voltage, and takes the average power of multiple charges.

[0112] 7) Average monthly charge amount avg_charge_energy_month: Takes vehicle charging data, calculates the total monthly charge amount, and takes the average value for multiple months.

[0113] 8) Average monthly charging time avg_charge_time_month: Takes vehicle charging data, calculates monthly charging time, and takes the average value over multiple months.

[0114] 9) Current maximum mileage dashboard_mileage: retrieves the vehicle's operating data, specifically the last mileage reached.

[0115] 10) Average Start SOC (avg_start_soc): Takes the SOC at the start of each trip and calculates the average value over multiple trips.

[0116] 11) Average SOC consumed per run avg_run_soc: Take the difference between the start SOC and end SOC of each run, calculate the SOC difference, and take the average of multiple runs.

[0117] 12) Average SOC at the start of each charge: avg_charge_start_soc: Takes the starting SOC of each charge and the average of multiple charges.

[0118] After summarizing and analyzing the above indicators, a vehicle dynamic profile table is obtained, as shown in Table 7 below:

[0119] Table 7

[0120]

[0121]

[0122] Step S305: Generate a vehicle charging demand feature profile table based on the overlapping duration of electricity consumption.

[0123] Specifically, step S305 includes:

[0124] Step S3051: Associate the vehicle static image table and extract the corresponding location coordinates of the vehicle. Specifically, extract the location coordinates of the current vehicle location based on the vehicle coordinate system in the vehicle static image table.

[0125] Step S3052: Filter the power grid dispatchable areas that include vehicle location coordinates. Specifically, based on the vehicle-power grid interaction service, obtain the power grid dispatchable areas, and filter the areas that include the coordinate information of the current vehicle location.

[0126] Step S3053: Based on the overlapping electricity usage time and the vehicle dynamic profile table, calculate the historical average charging amount of vehicles in the selected grid dispatch area, and generate a vehicle charging demand characteristic profile table. Specifically, based on the overlapping electricity usage time, and according to the power consumption and charging amount indicators of vehicles in the vehicle dynamic profile data table, calculate the historical average charging amount of vehicles in the grid dispatch area, and generate a vehicle charging demand characteristic profile table of vehicle interaction with the grid dispatch area. The vehicle charging demand characteristic profile table is shown in Table 8 below:

[0127] Table 8

[0128]

[0129]

[0130] The vehicle charging demand profile table construction method provided in this embodiment of the invention enables the vehicle to interact with the power grid scheduling area containing the vehicle's location coordinates, and generates a vehicle charging demand feature profile table that comprehensively describes the vehicle charging characteristic parameters required for the charging and discharging behavior of electric vehicles.

[0131] This embodiment also provides a vehicle charging demand profile construction device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0132] This embodiment provides a device for constructing a vehicle charging demand profile, such as... Figure 7 As shown, it includes:

[0133] The acquisition module 701 is used to acquire vehicle operation detection data.

[0134] Module 702 is used to simulate the maximum charging power data table of a vehicle based on vehicle operation detection data.

[0135] The fitting module 703 is used to fit the overlap time of power consumption between the vehicle and the power grid in the area requiring dispatch based on the maximum charging power data table.

[0136] The generation module 704 is used to generate a vehicle charging demand feature profile table based on the overlapping duration of electricity consumption.

[0137] In some alternative implementations, the construction module 702 includes:

[0138] The battery temperature is divided into different temperature ranges according to a preset step size.

[0139] The simulation unit is used to simulate the maximum charging power data table of a vehicle under a preset charging state and based on different temperature ranges.

[0140] In some alternative implementations, the fitting module 703 includes:

[0141] The first extraction unit is used to extract the parking time and charging time corresponding to the maximum charging power from the behavior data of a single charging trip based on the maximum charging power data table.

[0142] The acquisition unit is used to acquire the time of sufficient grid load and the time of insufficient grid load in the area of ​​the power grid that needs to be dispatched.

[0143] The matching unit is used to match the parking time and charging time of vehicles in the area to be dispatched based on the parking time and charging time corresponding to the maximum charging power.

[0144] The fitting unit is used to fit the matched parking time and charging time with the grid load sufficient time and grid load insufficient time to obtain the overlap time of power consumption between the vehicle and the grid in the area requiring dispatch.

[0145] In some alternative implementations, the generation module 704 includes:

[0146] The second extraction unit is used to associate with the vehicle static image table and extract the location coordinates of the vehicle.

[0147] The filtering unit is used to filter the power grid areas that require dispatch, which include vehicle location coordinates;

[0148] The first generation unit is used to calculate the historical average charging amount of vehicles in the grid dispatch area based on the electricity overlap duration and the vehicle dynamic profile table, and generate a vehicle charging demand characteristic profile table.

[0149] The device for constructing a vehicle charging demand profile also includes:

[0150] The second generation unit is used to determine the vehicle's permanent location based on driving data, and to generate a static profile table of the vehicle based on the vehicle model static information and the permanent location.

[0151] The third generation unit is used to generate a vehicle dynamic profile table after summarizing and analyzing parking time, charging time, power consumption and charging amount.

[0152] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0153] In this embodiment, the vehicle charging demand profile construction device is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0154] This invention also provides a computer device having the above-described features. Figure 7 The device shown is for constructing a profile of vehicle charging needs.

[0155] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 8 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 8 Take a processor 10 as an example.

[0156] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0157] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0158] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0159] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0160] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 8 Taking the example of a connection between China and Israel via a bus.

[0161] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0162] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0163] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for constructing a vehicle charging demand profile table, characterized in that, The method includes: Obtain vehicle operation and monitoring data; Based on the vehicle operation detection data, a simulation table of the vehicle's maximum charging power data is provided. Based on the maximum charging power data table, the overlap time between vehicle and grid dispatch areas is fitted, including: Based on the maximum charging power data table, extract the parking time and charging time corresponding to the maximum charging power from the behavior data of a single charging trip; Obtain the time of sufficient grid load and the time of insufficient grid load in the area requiring grid dispatch; Based on the parking time and charging time corresponding to the maximum charging power, the parking time and charging time of vehicles in the area to be dispatched need to be matched. The matching parking time and charging time are fitted with the grid load time and grid load insufficiency time respectively to obtain the power consumption overlap time between the vehicle and the grid in the area requiring dispatch. A vehicle charging demand characteristic profile table is generated based on the aforementioned overlapping electricity consumption duration, including: Link the vehicle static profile table and extract the corresponding location coordinates of the vehicle; Filter the power grid areas that require dispatching and contain the vehicle's location coordinates; Based on the electricity overlap duration and vehicle dynamic profile table, the historical average charging amount of vehicles in the power grid dispatch area is calculated and filtered to generate a vehicle charging demand characteristic profile table.

2. The method according to claim 1, characterized in that, The vehicle operation monitoring data includes: Behavioral data for a single charging trip; the behavioral data for a single charging trip includes parking time, charging time, battery temperature, power consumption, and charging amount.

3. The method according to claim 2, characterized in that, The table of maximum charging power data for the simulated vehicle based on the vehicle operation detection data includes: The battery temperature is divided into different temperature ranges according to a preset step size; The maximum charging power data table of the vehicle is simulated based on the different temperature ranges under the preset charging state.

4. The method according to claim 2, characterized in that, The vehicle operation detection data also includes the vehicle's corresponding model static information and driving data. Before generating the vehicle charging demand characteristic profile table based on the overlapping electricity consumption time, the method further includes: Determine the vehicle's permanent location based on driving data; A vehicle static profile table is generated based on the vehicle model static information and the vehicle's permanent location; The vehicle dynamic profile table is generated by summarizing and analyzing the parking time, charging time, power consumption, and charging amount.

5. The method according to claim 4, characterized in that, Based on the aforementioned overlapping electricity consumption duration, the historical average charging amount of vehicles in the area requiring external power grid dispatch is calculated using the associated vehicle dynamic profile data table, generating a vehicle charging demand characteristic profile table, including: Based on the electricity consumption overlap duration, the historical average charging amount of vehicles in the grid dispatch area is calculated according to the vehicle power consumption and charging amount in the vehicle dynamic profile data table, and a vehicle charging demand characteristic profile table of vehicle interaction with the grid dispatch area is generated.

6. A device for constructing a vehicle charging demand profile, characterized in that, The apparatus used in the vehicle charging demand profile construction method according to any one of claims 1 to 5 includes: The acquisition module is used to acquire vehicle operation and detection data; The module is used to simulate the maximum charging power data table of the vehicle based on the vehicle operation detection data; The fitting module is used to fit the overlap time of power consumption between the vehicle and the power grid in the area requiring dispatch based on the maximum charging power data table; The generation module is used to generate a vehicle charging demand feature profile table based on the overlapping electricity consumption duration.

7. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the vehicle charging demand profile construction method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the vehicle charging demand profile construction method according to any one of claims 1 to 5.

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