Calculation Method, Device and Equipment for Interactive Capacity between Electric Vehicle and Energy Efficiency Power Plant

Abstract

The present application relates to a method, device, computer device, storage medium, and computer program product for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant. The method includes: obtaining the charging demand data of the electric vehicle; calculating based on the charging start time and the charging end time to obtain the residence time of the electric vehicle; determining the charging time based on the remaining power before charging and the battery capacity; determining the first schedulable capacity during the charging of the electric vehicle based on the residence time and the charging time; determining the second schedulable capacity during the discharging of the electric vehicle based on the remaining power before charging and the battery capacity; calculating based on the first schedulable capacity and the second schedulable capacity to obtain the interactive capacity of the electric vehicle. The entire solution is based on multi-dimensional electric vehicle charging-related data to accurately calculate the schedulable capacity during charging and the schedulable capacity during discharging, and to achieve the calculation of the interactive capacity between the electric vehicle and the energy efficiency power plant during charging and discharging.

Description

Technical Field

[0001] The present application relates to the field of intelligent vehicle technology, and in particular to a method, device, computer equipment, storage medium and computer program product for calculating the interactive capacity of electric vehicles and energy-efficient power plants. Background Art

[0002] Energy shortages and increasing environmental pressures highlight the importance and urgency of developing energy-saving and electric vehicles. The rapid development of the smart car industry can not only meet people's travel needs, but also reduce the resulting environmental problems.

[0003] In the development of smart cars, the charging problem of electric vehicles has become a major problem that bothers people. Electric vehicles can only be charged through charging piles. However, since people charge most of the time at night, it is easy to cause charging queues. Since electric vehicles can also feedback dispatchable capacity to the power grid when discharging, electric vehicles can participate in the interaction of virtual power plants, thereby reducing the charging peak of electric vehicles.

[0004] Therefore, there is an urgent need for a method that can calculate the capacity of electric vehicles interacting with energy-efficient batteries during the charging and discharging process. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for calculating the interactive capacity of electric vehicles and energy-efficient power plants in response to the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for calculating the interactive capacity of electric vehicles and energy-efficient power plants.

[0007] The method includes:

[0008] Obtaining charging demand data of electric vehicles; charging demand data includes battery capacity, charging start time, charging end time, and remaining power before charging;

[0009] The dwell time of the electric vehicle is calculated based on the charging start time and the charging end time;

[0010] Determine the charging time based on the remaining power before charging and the battery capacity;

[0011] Based on the residence time and the charging time, a first dispatchable capacity of the electric vehicle when charging is determined; based on the remaining power before charging and the battery capacity, a second dispatchable capacity of the electric vehicle when discharging is determined;

[0012] The interactive capacity of the electric vehicle is obtained by calculating according to the first dispatchable capacity and the second dispatchable capacity.

[0013] In one embodiment, obtaining the charging demand data of an electric vehicle includes: obtaining the battery type and usage type of the electric vehicle; determining the battery capacity and unit energy consumption of the electric vehicle according to the battery type; determining the daily driving distance of the electric vehicle according to the usage type; calculating based on the battery capacity, unit energy consumption, and daily driving distance to obtain the remaining power before charging of multiple electric vehicles; generating the charging start time and charging end time based on the random simulation method.

[0014] In one embodiment, determining the charging time based on the remaining power before charging and the battery capacity includes: determining the charging power corresponding to the residence time; obtaining the remaining power at the end of charging of the electric vehicle; calculating based on the remaining power at the end of charging, the remaining power before charging, the charging power, and the battery capacity of the electric vehicle to obtain the charging time.

[0015] In one embodiment, determining the first schedulable capacity during the charging of an electric vehicle based on the residence time and the charging time includes: if the residence time is greater than the charging time, calculating based on the charging time and the residence time to obtain the schedulable time; determining the first schedulable capacity during the charging of the electric vehicle based on the schedulable time.

[0016] In one embodiment, determining the first schedulable capacity during the charging of an electric vehicle based on the schedulable time includes: determining the charging power corresponding to the residence time; calculating based on the schedulable time and the charging power to obtain the first schedulable capacity during the charging of the electric vehicle.

[0017] In one embodiment, the above method further includes: if the residence time is less than or equal to the charging time, calculating based on the remaining power at the end of charging, the remaining power before charging, and the battery capacity to obtain the necessary charging capacity.

[0018] In one embodiment, determining the second schedulable capacity during the discharging of an electric vehicle based on the remaining power before charging and the battery capacity includes: determining the multiple relationship between the remaining power before charging and the battery capacity of the electric vehicle; if the multiple relationship is greater than a preset multiple threshold, calculating based on the remaining power before charging, the multiple relationship, and the battery capacity to obtain the second schedulable capacity during the discharging of the electric vehicle.

[0019] In a second aspect, the present application also provides a calculation device for the interactive capacity between an electric vehicle and an energy efficiency power plant. The device includes:

[0020] An acquisition module, configured to acquire the charging demand data of the electric vehicle; the charging demand data includes the battery capacity, the charging start time, the charging end time, and the remaining power before charging;

[0021] A residence time calculation module, configured to calculate based on the charging start time and the charging end time to obtain the residence time of the electric vehicle;

[0022] A charging time determination module, configured to determine the charging time based on the remaining power before charging and the battery capacity;

[0023] A schedulable capacity calculation module, configured to determine a first schedulable capacity during the charging of the electric vehicle based on the residence time and the charging time; and determine a second schedulable capacity during the discharging of the electric vehicle based on the remaining power before charging and the battery capacity;

[0024] An interactive capacity calculation module, configured to calculate based on the first schedulable capacity and the second schedulable capacity to obtain the interactive capacity of the electric vehicle.

[0025] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0026] Obtain the charging demand data of the electric vehicle; the charging demand data includes the battery capacity, the charging start time, the charging end time, and the remaining power before charging;

[0027] Calculate based on the charging start time and the charging end time to obtain the residence time of the electric vehicle;

[0028] Determine the charging time based on the remaining power before charging and the battery capacity;

[0029] Determine a first schedulable capacity during the charging of the electric vehicle based on the residence time and the charging time; determine a second schedulable capacity during the discharging of the electric vehicle based on the remaining power before charging and the battery capacity;

[0030] Calculate based on the first schedulable capacity and the second schedulable capacity to obtain the interactive capacity of the electric vehicle.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0032] Obtain the charging demand data of the electric vehicle; the charging demand data includes the battery capacity, the charging start time, the charging end time, and the remaining power before charging;

[0033] Calculate based on the charging start time and the charging end time to obtain the residence time of the electric vehicle;

[0034] Determine the charging time based on the remaining power before charging and the battery capacity;

[0035] Determine the first dispatchable capacity of the electric vehicle during charging based on the residence time and the charging time; determine the second dispatchable capacity of the electric vehicle during discharging based on the remaining power before charging and the battery capacity;

[0036] Calculate according to the first dispatchable capacity and the second dispatchable capacity to obtain the interactive capacity of the electric vehicle.

[0037] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0038] Obtain the charging demand data of the electric vehicle; the charging demand data includes the battery capacity, the charging start time, the charging end time, and the remaining power before charging;

[0039] Calculate according to the charging start time and the charging end time to obtain the residence time of the electric vehicle;

[0040] Determine the charging time based on the remaining power before charging and the battery capacity;

[0041] Determine the first dispatchable capacity of the electric vehicle during charging based on the residence time and the charging time; determine the second dispatchable capacity of the electric vehicle during discharging based on the remaining power before charging and the battery capacity;

[0042] Calculate according to the first dispatchable capacity and the second dispatchable capacity to obtain the interactive capacity of the electric vehicle.

[0043] The above calculation method, device, computer device, storage medium, and computer program product for the interactive capacity between the electric vehicle and the energy efficiency power plant obtain the charging demand data of the electric vehicle; the charging demand data includes the battery capacity, the charging start time, the charging end time, and the remaining power before charging; calculate according to the charging start time and the charging end time to obtain the residence time of the electric vehicle; determine the charging time based on the remaining power before charging and the battery capacity; determine the first dispatchable capacity of the electric vehicle during charging based on the residence time and the charging time; determine the second dispatchable capacity of the electric vehicle during discharging based on the remaining power before charging and the battery capacity; calculate according to the first dispatchable capacity and the second dispatchable capacity to obtain the interactive capacity of the electric vehicle. The entire solution calculates the residence time and the charging time of the electric vehicle based on multi-dimensional electric vehicle charging-related data, and then accurately calculates the dispatchable capacity during charging according to the residence time and the charging time, and calculates the dispatchable capacity during discharging according to the remaining power before charging and the battery capacity. Finally, the interactive capacity is calculated based on the charging dispatchable capacity and the discharging dispatchable capacity, realizing the calculation of the interactive capacity between the electric vehicle and the energy efficiency power plant during charging and discharging. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 It is an application environment diagram of the method for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant in an embodiment;

[0046] Figure 2 It is a schematic flowchart of the method for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant in an embodiment;

[0047] Figure 3 It is a schematic flowchart of the method for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant in another embodiment;

[0048] Figure 4 It is a schematic flowchart of the method for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant in yet another embodiment;

[0049] Figure 5 It is a structural block diagram of the device for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant in an embodiment;

[0050] Figure 6 It is an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the following further details the present application in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] With the development of power electronics technology, modern control and communication technology, an electric vehicle can be regarded as a virtual power energy storage system under V2G (Vehicle to Grid) control. Stimulated by incentives such as electricity prices, an electric vehicle can change its charging mode (such as uncontrolled charging, orderly charging, and intelligent charging, etc.) to achieve the transformation of charging power on the time scale (such as the peak shaving and valley filling effect, etc.); or in an emergency, feedback electric energy to the system according to the system demand to assist the system operation. Under V2G control, an electric vehicle can not only be a load of the system, but also an energy storage device and a distributed power source, becoming an active participant in assisting the operation of the power system.

[0053] Therefore, calculate the necessary charging capacity, schedulable charging capacity of electric vehicles, and, as mobile energy storage, the schedulable capacity of electric vehicles to discharge and feed back to the power grid. This is the basis for optimizing the interaction of electric vehicles participating in an energy efficiency power plant and making full use of the charging demand of electric vehicles to reduce the charging peak of electric vehicles.

[0054] The method for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant provided by the embodiments of this application can be applied to an application environment such as Figure 1 shown. Among them, user 102 operates on terminal 104. User 102 performs an operation for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant on the display interface of terminal 104. Terminal 104 listens for and responds to the operation for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant, and obtains the charging demand data of the electric vehicle; the charging demand data includes the battery capacity, charging start time, charging end time, and remaining power before charging; calculate based on the charging start time and the charging end time to obtain the residence time of the electric vehicle; based on the remaining power before charging and the battery capacity, determine the charging time; based on the residence time and the charging time, determine the first schedulable capacity when the electric vehicle is charging; based on the remaining power before charging and the battery capacity, determine the second schedulable capacity when the electric vehicle is discharging; calculate according to the first schedulable capacity and the second schedulable capacity to obtain the interactive capacity of the electric vehicle. Among them, terminal 104 can be but is not limited to various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and the Internet of Things devices can be smart TVs, in-vehicle smart devices, etc.

[0055] In one embodiment, as Figure 2 shown, a method for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant is provided. Taking the terminal 104 in Figure 1 as an example, the method includes the following steps:

[0056] Step 202, obtain the charging demand data of the electric vehicle.

[0057] Among them, the charging demand data includes the battery capacity, charging start time, charging end time, and remaining power before charging. The battery capacity refers to the rated maximum capacity of the electric vehicle battery.

[0058] Specifically, the terminal listens for and responds to the operation for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant, obtains a set of electric vehicles, and for each electric vehicle in the set of electric vehicles, randomly samples to generate the battery capacity, charging start time, charging end time, and remaining power before charging of the electric vehicle. The remaining power before charging is the remaining capacity SOC of the electric vehicle battery before charging.

[0059] Step 204, calculate based on the charging start time and the charging end time to obtain the residence time of the electric vehicle.

[0060] Specifically, the terminal subtracts the charging cut-off time from the charging start time to obtain the residence time of the electric vehicle, in minutes, i.e., min. Calculate the residence time t of vehicle i iw , and its calculation process is shown in formula (1).

[0061] t iw = t ie - t is (1)

[0062] where t is represents the charging start time of the i-th vehicle, and t ie represents the charging cut-off time of the i-th vehicle.

[0063] Step 206, determine the charging time based on the remaining power before charging and the battery capacity.

[0064] Among them, the charging time refers to the charging time when the remaining power of the electric vehicle is charged to be greater than or equal to the preset power threshold.

[0065] Specifically, subtract the preset power threshold of the electric vehicle from the remaining power before charging to obtain the required charging power, and divide the required charging power by the charging power of the electric vehicle to obtain the charging time.

[0066] Step 208, determine the first schedulable capacity when the electric vehicle is charging based on the residence time and the charging time; determine the second schedulable capacity when the electric vehicle is discharging based on the remaining power before charging and the battery capacity.

[0067] Among them, the first schedulable capacity refers to the schedulable capacity when the electric vehicle is charging, specifically referring to the energy provided by the energy efficiency power plant or the charging pile to the electric vehicle for charging, in degrees. The second schedulable capacity refers to the schedulable capacity when the electric vehicle is discharging, specifically referring to the energy that the electric vehicle can provide to the energy efficiency power plant or the charging pile through discharging.

[0068] Specifically, when calculating the schedulable capacity when the electric vehicle is charging, the terminal determines the magnitude relationship between the residence time and the charging time, selects the parameters for calculating the schedulable charging capacity according to the magnitude relationship between the residence time and the charging time, and then calculates the schedulable charging capacity when the electric vehicle is charging.

[0069] When calculating the schedulable capacity when the electric vehicle is charging, the terminal determines the magnitude relationship between the remaining power before charging and the battery capacity, and calculates the second schedulable capacity during discharging according to the magnitude relationship between the remaining power before charging and the battery capacity.

[0070] The first schedulable capacity and the second schedulable capacity can be calculated in parallel or serially by the terminal. That is, the terminal first calculates the first schedulable capacity and then the second schedulable capacity, or the terminal can also first calculate the second schedulable capacity and then the first schedulable capacity.

[0071] Step 210: Calculate based on the first schedulable capacity and the second schedulable capacity to obtain the interactive capacity of the electric vehicle.

[0072] Specifically, the terminal adds the first schedulable capacity and the second schedulable capacity of the electric battery to obtain the schedulable capacity of the electric vehicle during charging and discharging, thereby obtaining the interactive capacity of the electric vehicle. The schedulable capacity of each electric vehicle in the electric vehicle set is calculated repeatedly, and the accumulative sum is used to obtain the schedulable capacity of the energy-efficient power plant corresponding to the electric vehicle set.

[0073] In the above method for calculating the interactive capacity between the electric vehicle and the energy-efficient power plant, the charging demand data of the electric vehicle is obtained; the charging demand data includes the battery capacity, the start time of charging, the end time of charging, and the remaining power before charging; based on the start time of charging and the end time of charging, the residence time of the electric vehicle is calculated; based on the remaining power before charging and the battery capacity, the charging time is determined; based on the residence time and the charging time, the first schedulable capacity during charging of the electric vehicle is determined; based on the remaining power before charging and the battery capacity, the second schedulable capacity during discharging of the electric vehicle is determined; based on the first schedulable capacity and the second schedulable capacity, the interactive capacity of the electric vehicle is calculated. The entire solution is based on multi-dimensional data related to electric vehicle charging, calculates the residence time and charging time of the electric vehicle, and then accurately calculates the schedulable capacity during charging based on the residence time and the charging time, and calculates the schedulable capacity during discharging based on the remaining power before charging and the battery capacity. Finally, the interactive capacity is calculated based on the charging schedulable capacity and the discharging schedulable capacity, realizing the calculation of the interactive capacity between the electric vehicle and the energy-efficient power plant during charging and discharging.

[0074] In an alternative embodiment, as Figure 3 shown, obtaining the charging demand data of the electric vehicle includes:

[0075] Step 302: Obtain the battery type and usage type of the electric vehicle.

[0076] Among them, the battery type refers to the battery types of electric vehicles with different load types, including four types: L7e (Heavy Quadricycles), M1, N1, and N2. An L7e vehicle is a small truck with a maximum load of 550 kg, an M1 vehicle is a passenger car with a maximum of 8 seats, an N1 vehicle is a truck with a maximum load of 3500 kg, and an N2 vehicle is a truck with a load of 3500 - 12000 kg. The proportions of the above four different types of electric vehicles in the market are 1.5%, 87.5%, 10%, and 1% respectively. The battery capacity and per-kilometer battery energy consumption of electric vehicles can be determined according to the battery type.

[0077] The usage type refers to the travel usage type of electric vehicles, including three types: HBW (Home - Based - Work), HBO (Home - Based - Other), and NHB (Non - Home - Based). An HBW vehicle is a private vehicle used for home - to - work, an HBO vehicle is a private vehicle used for non - work - related travel, and an NHB vehicle is a company vehicle. The proportions of the above three different types of electric vehicles in the market are 61%, 30%, and 90% respectively.

[0078] The per - unit energy consumption refers to the energy consumption per kilometer of an electric vehicle. The battery capacity refers to the maximum capacity of the electric vehicle's battery.

[0079] Based on the proportion of the battery type of the electric vehicle, the terminal randomly samples to obtain the battery type, and based on the proportion of the usage type, randomly samples to obtain the usage type.

[0080] Step 304: Determine the battery capacity and per - unit energy consumption of the electric vehicle according to the battery type.

[0081] Specifically, the terminal determines the probability distribution characteristics of the battery capacity and per - unit energy consumption according to the battery type of the electric vehicle, randomly samples based on the probability distribution characteristics of the battery capacity to obtain the battery capacity of the electric vehicle, and then randomly samples based on the probability distribution characteristics of the per - unit energy consumption to obtain the per - unit energy consumption of the electric vehicle.

[0082] Step 306: Determine the daily driving distance of the electric vehicle according to the usage type.

[0083] Specifically, the terminal obtains the probability distribution function of the preset single-day driving distance. The average value of the single-day driving distance and the parameter value of the distribution standard deviation in the probability distribution function of the preset single-day driving distance are unknown. Based on the usage type, the average value of the single-day driving distance and the distribution standard deviation corresponding to this usage type are obtained. Furthermore, the average value of the single-day driving distance and the distribution standard deviation are used to determine the probability distribution function of the single-day driving distance of the electric vehicle. Then, based on the probability distribution function of the single-day driving distance of the electric vehicle and the Monte Carlo sampling algorithm, sampling is performed to obtain the single-day driving distance of the electric vehicle.

[0084] Step 308: Calculate based on the battery capacity, unit energy consumption, and single-day driving distance to obtain the remaining power before charging of multiple electric vehicles.

[0085] Specifically, multiply the unit energy consumption by the single-day driving distance to obtain the electricity consumption. Subtract the electricity consumption from the battery capacity to obtain the remaining power of the electric vehicle before charging. The above operations are performed for each electric vehicle to obtain the remaining power before charging of each electric vehicle.

[0086] Step 310: Generate the charging start time and the charging end time based on the random simulation method.

[0087] Specifically, the terminal obtains the set of charging start times and the set of charging end times. Based on the random simulation method and the set of charging start times, the charging start time is generated. Based on the random simulation method and the set of charging end times, the charging end time is generated.

[0088] Furthermore, different battery types have corresponding battery capacity distribution types and unit energy consumption distribution types. For each electric vehicle, based on the correspondence between the battery type and the battery capacity distribution type, the battery capacity distribution type of this electric vehicle is determined. Based on the correspondence between the battery type and the unit energy consumption distribution type and the unit energy consumption distribution type, the unit energy consumption distribution type of this electric vehicle is determined. Then, based on the battery capacity distribution type of the electric vehicle and the Monte Carlo sampling algorithm, sampling is performed to obtain the battery capacity of this electric vehicle, that is, the battery capacity. Based on the unit energy consumption distribution type of the electric vehicle and the Monte Carlo sampling algorithm, sampling is performed to obtain the unit energy consumption of this electric vehicle.

[0089] The correspondence between different types of battery types and battery capacity distribution types is shown in Table 1:

[0090] Table 1 Probability distribution of the battery capacity (i.e., the battery capacity) of electric vehicles

[0091]

[0092] For electric vehicles with battery types L7e and M1, the battery capacity D follows the gamma distribution shown in Equation (2). The gamma distribution function is as follows:

[0093]

[0094] Wherein, D is the battery capacity of the electric vehicle; α D is the shape parameter; β D is the scale parameter; 「.

[0095] The battery capacity D of the N1 and N2 electric vehicle batteries follows a normal distribution as shown in Equation (3):

[0096]

[0097] Wherein, μ D is the mean battery capacity; σ D is the standard deviation.

[0098] The terminal first determines the battery capacity distribution type of each electric vehicle based on the correspondence between the battery type and the battery capacity distribution type, determines the corresponding battery capacity under each parameter according to the battery capacity distribution type, obtains the preset battery capacity data set, and then samples the preset battery capacity data set based on the battery capacity distribution type and the Monte Carlo sampling algorithm to obtain the battery capacity.

[0099] The correspondence between different types of battery types and the unit energy consumption distribution types is shown in Table 2:

[0100] Table 2 Probability distribution of energy consumption per kilometer of electric vehicles

[0101]

[0102]

[0103] Table 2 gives the probability distribution of energy consumption per kilometer (C e ) of different types of electric vehicles. The unit energy consumption C of electric vehicles of battery types L7e and M1 follows a gamma distribution as shown in Equation (2), and the unit energy consumption C of electric vehicles of battery types N1 and N2 follows a normal distribution as shown in Equation (3).

[0104] The terminal first determines the unit energy consumption distribution type of each electric vehicle based on the correspondence between the battery type and the unit energy consumption distribution type, determines the corresponding unit energy consumption under each parameter according to the unit energy consumption distribution type, obtains the preset unit energy consumption set, and then samples the preset unit energy consumption set based on the unit energy consumption distribution type and the Monte Carlo sampling algorithm to obtain the unit energy consumption.

[0105] According to different traffic uses (HBW, HBO, NHB), the daily driving distance (d) of electric vehicles follows a normal distribution as shown in Equation (4).

[0106]

[0107] where μ d is the average daily driving distance; σ d is the standard deviation of this distribution. For HBW and HBO types of electric vehicles, μ d is 35.9 km and σ d is 19.6 km; while for NHB type of electric vehicles, μ d is 87.1 km and σ d is 24.5 km.

[0108] In this embodiment, the battery capacity, unit energy consumption, and single-day driving distance are accurately determined based on the battery type and usage type, and the remaining power before charging is accurately calculated based on multi-dimensional parameters related to the vehicle battery.

[0109] In an alternative embodiment, determining the charging time based on the remaining power before charging and the battery capacity includes: determining the charging power corresponding to the stay time; obtaining the remaining power at the end of charging of the electric vehicle; calculating based on the remaining power at the end of charging, the remaining power before charging, the charging power, and the battery capacity of the electric vehicle to obtain the charging time.

[0110] Among them, the remaining power at the end of charging is equal to the preset power threshold. According to the length of the stay time, the stay time is divided into a first level, a second level, and a third level, and different levels of stay time correspond to different charging powers.

[0111] Specifically, the terminal determines the stay time level to which it belongs according to the magnitude of the stay time, and then determines the charging power according to the corresponding relationship between the stay time level and the charging power. Finally, subtract the remaining power before charging from the preset power threshold of the electric vehicle to obtain the required charging power, and divide the required charging power by the charging power of the electric vehicle to obtain the charging time.

[0112] Further, the stay interval of the first level is t iw > 180, the stay interval of the second level is 60 <t iw ≤ 180, and the stay interval of the third level is t iw ≤ 60. The charging power corresponding to the stay interval of the first level is 3 kw, the charging power corresponding to the stay interval of the second level is 7 kw, and the charging power corresponding to the stay interval of the third level is 25 kw.

[0113] The relationship between the charging power and the stay time level is shown in formula (5).

[0114]

[0115] When the residence time t iw > 180 minutes, the slow charging mode with a charging power of 3 kW is selected for the electric vehicle; when the residence time is 60 < t iw ,, 180 minutes, the conventional charging mode with a charging power of 7 kW is selected for the electric vehicle; when the residence time t iw ,, 60 minutes, the fast charging mode with a charging power of 25 kW is selected for the electric vehicle.

[0116] The charging time t iEE The calculation expression is as shown in formula (6).

[0117]

[0118] Wherein, C N represents the battery capacity of the electric vehicle, SOC ie represents the remaining power at the end of charging of the i-th vehicle, SOC is represents the remaining power before charging of the i-th vehicle, P ic represents the charging power of the i-th vehicle.

[0119] In this embodiment, the corresponding charging power is obtained based on the residence time level, and then the charging time is accurately calculated.

[0120] In an alternative embodiment, determining the first schedulable capacity when the electric vehicle is charging based on the residence time and the charging time includes: if the residence time is greater than the charging time, calculating according to the charging time and the residence time to obtain the schedulable time; based on the schedulable time, determining the first schedulable capacity when the electric vehicle is charging.

[0121] Specifically, the terminal compares the residence time with the charging time to obtain a first comparison result. If the first comparison result indicates that the residence time is greater than the charging time, the residence time is subtracted from the charging time to obtain the schedulable time, and then the schedulable time is calculated with the charging power of the electric vehicle to obtain the first schedulable capacity.

[0122] In an alternative embodiment, determining the first schedulable capacity when the electric vehicle is charging based on the schedulable time includes: determining the charging power corresponding to the residence time; calculating based on the schedulable time and the charging power to obtain the first schedulable capacity when the electric vehicle is charging.

[0123] Specifically, the terminal determines the residence time level to which it belongs according to the magnitude of the residence time, and then determines the charging power according to the corresponding relationship between the residence time level and the charging power. Then, the schedulable time is multiplied by the charging power of the electric vehicle, and the multiplication result is divided by 60 min to unify the unit of the calculation result, and the first schedulable capacity is obtained.

[0124] The first schedulable capacity C iAFC The calculation expression is as shown in formula (7):

[0125]

[0126] In an optional embodiment, the above method further includes: if the residence time is less than or equal to the charging time, then calculate based on the remaining power at the end of charging, the remaining power before charging, and the battery capacity to obtain the necessary charging capacity.

[0127] Specifically, the terminal compares the residence time with the charging time to obtain a first comparison result. If the first comparison result indicates that the residence time is less than or equal to the charging time, then subtract the remaining power before charging from the remaining power at the end of charging to obtain the required power, and then multiply the required power by the battery power to obtain the necessary charging capacity.

[0128] The necessary charging capacity C iEE The calculation expression is as shown in formula (8):

[0129] C iEE =(SOC ie -SOC is )·C N (8)

[0130] In order to extend the cycle life of the power lithium-ion battery, set the remaining power at the end of charging to 0.8, that is, SOC ie =0.8.

[0131] Accumulate the necessary charging capacities of all electric vehicles that need to be charged in the electric vehicle set to obtain the minimum power that the energy-efficient battery needs to provide, reduce resource consumption, and improve the charging efficiency between the electric vehicle and the energy-efficient power plant.

[0132] In an optional embodiment, determining the second schedulable capacity when the electric vehicle discharges based on the remaining power before charging and the battery capacity includes: determining the multiple relationship between the remaining power before charging and the battery capacity of the electric vehicle; if the multiple relationship is greater than a preset multiple threshold, then calculate based on the remaining power before charging, the multiple relationship, and the battery capacity to obtain the second schedulable capacity when the electric vehicle discharges.

[0133] Among them, the remaining power before charging has a corresponding power level, which is divided into a first power level, a second power level, a third power level, and a fourth power level according to the magnitude of the remaining power before charging. Further, if the remaining power before charging is in the SOC is ≥80% interval, it is the first power level. If the remaining power before charging is in 50%≤SOC isIf the remaining power before charging is in the range of < 80%, it is the second power level. If the remaining power before charging is in the range of 30% ≤ SOC is < 50%, it is the third power level. If the remaining power before charging is in the range of SOC is < 30%, it is the fourth power level.

[0134] Specifically, the terminal divides the remaining power before charging by the battery capacity to obtain the multiple relationship between the remaining power before charging and the battery capacity. If the multiple relationship is greater than the preset multiple threshold, it determines the power level of the remaining power before charging, determines the corresponding schedulable capacity according to the power level, and obtains the second schedulable capacity. Further, according to the power level, it determines the calculation expression of the corresponding schedulable capacity to obtain the second schedulable capacity. If the remaining power before charging is at the first power level, the calculation expression of the schedulable capacity is (SOC is - 0.6)·C N , subtracts the first power threshold from the remaining power before charging. The second power threshold can be 0.6, and multiplies the subtraction result by the battery capacity to obtain the second schedulable capacity. If the remaining power before charging is at the second power level, the calculation expression of the schedulable capacity is 0.2·C N , multiplies the remaining power before charging by the second power threshold. The second power threshold can be 0.2 to obtain the second schedulable capacity. If the remaining power before charging is at the first power level, the calculation expression of the schedulable capacity is (SOC is - 0.3)·C N , subtracts the third power threshold from the remaining power before charging. The third power threshold can be 0.3, and multiplies the subtraction result by the battery capacity to obtain the second schedulable capacity. If the remaining power before charging is at the first power level, the second schedulable capacity is zero. The second schedulable capacity C iV2G , that is, the calculation expression of the V2G capacity of the i-th vehicle is as shown in formula (9):

[0135]

[0136] In this embodiment, according to the level of the remaining power before charging, the calculation expression of the discharge schedulable capacity is accurately determined, and then the accurate discharge schedulable capacity is calculated to reduce the consumption of electric power resources.

[0137] To facilitate understanding of the technical solution provided by the embodiments of the present application, as Figure 4 shown, the calculation method of the interactive capacity between the electric vehicle and the energy efficiency power plant provided by the embodiments of the present application is briefly described by taking the complete calculation process of the interactive capacity between the electric vehicle and the energy efficiency power plant:

[0138] (1) Obtain the battery type and usage type of the electric vehicle.

[0139] (2) Determine the battery capacity and unit energy consumption of the electric vehicle according to the battery type.

[0140] (3) Determine the daily driving distance of the electric vehicle according to the usage type.

[0141] (4) Calculate based on the battery capacity, unit energy consumption, and daily driving distance to obtain the remaining power before charging for multiple electric vehicles.

[0142] (5) Generate the start charging time and end charging time based on the random simulation method.

[0143] (6) Calculate according to the start charging time and end charging time to obtain the residence time of the electric vehicle.

[0144] (7) Determine the charging power corresponding to the residence time; obtain the remaining power at the end of charging of the electric vehicle.

[0145] (8) Calculate according to the remaining power at the end of charging, the remaining power before charging, the charging power, and the battery capacity of the electric vehicle to obtain the charging time.

[0146] (9) If the residence time is greater than the charging time, calculate according to the charging time and the residence time to obtain the schedulable time.

[0147] (10) Determine the charging power corresponding to the residence time; calculate based on the schedulable time and the charging power to obtain the first schedulable capacity during the charging of the electric vehicle.

[0148] (11) If the residence time is less than or equal to the charging time, calculate based on the remaining power at the end of charging, the remaining power before charging, and the battery capacity to obtain the necessary charging capacity.

[0149] (12) Determine the multiple relationship between the remaining power before charging and the battery capacity of the electric vehicle; if the multiple relationship is greater than the preset multiple threshold, calculate based on the remaining power before charging, the multiple relationship, and the battery capacity to obtain the second schedulable capacity during the discharging of the electric vehicle.

[0150] (13) Add the first schedulable capacity and the second schedulable capacity, and subtract the necessary charging capacity from the added result to obtain the interactive capacity of the electric vehicle.

[0151] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are displayed in sequence according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0152] Based on the same inventive concept, an embodiment of the present application further provides an interactive capacity calculation device for an electric vehicle and an energy efficiency power plant for implementing the above-mentioned interactive capacity calculation method for an electric vehicle and an energy efficiency power plant. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the following interactive capacity calculation device for an electric vehicle and an energy efficiency power plant can refer to the limitations on the interactive capacity calculation method for an electric vehicle and an energy efficiency power plant in the above text, and will not be repeated here.

[0153] In one embodiment, as Figure 5 shown, an interactive capacity calculation device for an electric vehicle and an energy efficiency power plant is provided, including: an acquisition module 502, a residence time calculation module 504, a charging time determination module 506, a schedulable capacity calculation module 508, and an interactive capacity calculation module 510, where:

[0154] The acquisition module 502 is used to acquire the charging demand data of the electric vehicle; the charging demand data includes the battery capacity, the charging start time, the charging end time, and the remaining power before charging;

[0155] The residence time calculation module 504 is used to calculate based on the charging start time and the charging end time to obtain the residence time of the electric vehicle;

[0156] The charging time determination module 506 is used to determine the charging time based on the remaining power before charging and the battery capacity;

[0157] The schedulable capacity calculation module 508 is used to determine the first schedulable capacity when the electric vehicle is charging based on the residence time and the charging time; determine the second schedulable capacity when the electric vehicle is discharging based on the remaining power before charging and the battery capacity;

[0158] The interactive capacity calculation module 510 is configured to calculate based on the first schedulable capacity and the second schedulable capacity to obtain the interactive capacity of the electric vehicle.

[0159] In one embodiment, the acquisition module 502 is further configured to acquire the battery type and the usage type of the electric vehicle; determine the battery capacity and the unit energy consumption of the electric vehicle according to the battery type; determine the daily driving distance of the electric vehicle according to the usage type; calculate based on the battery capacity, the unit energy consumption, and the daily driving distance to obtain the remaining power before charging of multiple electric vehicles; generate the charging start time and the charging end time based on the stochastic simulation method.

[0160] In one embodiment, the charging time determination module 506 is further configured to determine the charging power corresponding to the staying time; acquire the remaining power at the end of charging of the electric vehicle; calculate based on the remaining power at the end of charging, the remaining power before charging, the charging power, and the battery capacity of the electric vehicle to obtain the charging time.

[0161] In one embodiment, the schedulable capacity calculation module 508 is further configured to, if the staying time is greater than the charging time, calculate based on the charging time and the staying time to obtain the schedulable time; determine the first schedulable capacity when the electric vehicle is charging based on the schedulable time.

[0162] In one embodiment, the schedulable capacity calculation module 508 is further configured to determine the charging power corresponding to the staying time; calculate based on the schedulable time and the charging power to obtain the first schedulable capacity when the electric vehicle is charging.

[0163] In one embodiment, the schedulable capacity calculation module 508 is further configured to, if the staying time is less than or equal to the charging time, calculate based on the remaining power at the end of charging, the remaining power before charging, and the battery capacity to obtain the necessary charging capacity.

[0164] In one embodiment, the schedulable capacity calculation module 508 is further configured to determine the multiple relationship between the remaining power before charging and the battery capacity of the electric vehicle; if the multiple relationship is greater than a preset multiple threshold, calculate based on the remaining power before charging, the multiple relationship, and the battery capacity to obtain the second schedulable capacity when the electric vehicle discharges.

[0165] Each module in the above-mentioned interactive capacity calculation device of the electric vehicle and the energy efficiency power plant can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in the form of hardware or be independent of it, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0166] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in Figure 6 . The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for calculating the interactive capacity between an electric vehicle and an energy efficiency power plant. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0167] Those skilled in the art can understand that Figure 6 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0168] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0169] Obtain the charging demand data of the electric vehicle; the charging demand data includes the battery capacity, the charging start time, the charging end time, and the remaining power before charging;

[0170] Calculate based on the charging start time and the charging end time to obtain the residence time of the electric vehicle;

[0171] Based on the remaining power before charging and the battery capacity, determine the charging time;

[0172] Based on the residence time and the charging time, determine the first schedulable capacity when the electric vehicle is charging; based on the remaining power before charging and the battery capacity, determine the second schedulable capacity when the electric vehicle is discharging;

[0173] Calculate according to the first schedulable capacity and the second schedulable capacity to obtain the interactive capacity of the electric vehicle.

[0174] In one embodiment, when the processor executes the computer program, the following steps are further implemented: obtaining the charging demand data of the electric vehicle, including: obtaining the battery type and usage type of the electric vehicle; determining the battery capacity and unit energy consumption of the electric vehicle according to the battery type; determining the daily driving distance of the electric vehicle according to the usage type; calculating based on the battery capacity, unit energy consumption and daily driving distance to obtain the remaining power before charging of multiple electric vehicles; generating the charging start time and charging end time based on the random simulation method.

[0175] In one embodiment, when the processor executes the computer program, the following steps are further implemented: determining the charging time based on the remaining power before charging and the battery capacity, including: determining the charging power corresponding to the stay time; obtaining the remaining power of the electric vehicle at the end of charging; calculating based on the remaining power at the end of charging, the remaining power before charging, the charging power and the battery capacity of the electric vehicle to obtain the charging time.

[0176] In one embodiment, when the processor executes the computer program, the following steps are further implemented: determining the first schedulable capacity during the charging of the electric vehicle based on the stay time and the charging time, including: if the stay time is greater than the charging time, calculating based on the charging time and the stay time to obtain the schedulable time; determining the first schedulable capacity during the charging of the electric vehicle based on the schedulable time.

[0177] In one embodiment, when the processor executes the computer program, the following steps are further implemented: determining the first schedulable capacity during the charging of the electric vehicle based on the schedulable time, including: determining the charging power corresponding to the stay time; calculating based on the schedulable time and the charging power to obtain the first schedulable capacity during the charging of the electric vehicle.

[0178] In one embodiment, when the processor executes the computer program, the following steps are further implemented: if the stay time is less than or equal to the charging time, calculating based on the remaining power at the end of charging, the remaining power before charging and the battery capacity to obtain the necessary charging capacity.

[0179] In one embodiment, when the processor executes the computer program, the following steps are further implemented: determining the second schedulable capacity during the discharging of the electric vehicle based on the remaining power before charging and the battery capacity, including: determining the multiple relationship between the remaining power before charging and the battery capacity of the electric vehicle; if the multiple relationship is greater than the preset multiple threshold, calculating based on the remaining power before charging, the multiple relationship and the battery capacity to obtain the second schedulable capacity during the discharging of the electric vehicle.

[0180] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0181] Obtain the charging demand data of the electric vehicle; the charging demand data includes battery capacity, charging start time, charging end time, and remaining power before charging;

[0182] Calculate based on the charging start time and the charging end time to obtain the residence time of the electric vehicle;

[0183] Determine the charging time based on the remaining power before charging and the battery capacity;

[0184] Determine the first schedulable capacity when the electric vehicle is charging based on the residence time and the charging time; determine the second schedulable capacity when the electric vehicle is discharging based on the remaining power before charging and the battery capacity;

[0185] Calculate according to the first schedulable capacity and the second schedulable capacity to obtain the interactive capacity of the electric vehicle.

[0186] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: Obtaining the charging demand data of the electric vehicle includes: obtaining the battery type and usage type of the electric vehicle; determining the battery capacity and unit energy consumption of the electric vehicle according to the battery type; determining the single-day driving distance of the electric vehicle according to the usage type; calculating based on the battery capacity, unit energy consumption, and single-day driving distance to obtain the remaining power before charging of multiple electric vehicles; generating the charging start time and the charging end time based on the random simulation method.

[0187] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: Determining the charging time based on the remaining power before charging and the battery capacity includes: determining the charging power corresponding to the residence time; obtaining the remaining power at the end of charging of the electric vehicle; calculating according to the remaining power at the end of charging, the remaining power before charging, the charging power, and the battery capacity of the electric vehicle to obtain the charging time.

[0188] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: Determining the first schedulable capacity when the electric vehicle is charging based on the residence time and the charging time includes: if the residence time is greater than the charging time, calculate according to the charging time and the residence time to obtain the schedulable time; determine the first schedulable capacity when the electric vehicle is charging based on the schedulable time.

[0189] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: Determining the first schedulable capacity when the electric vehicle is charging based on the schedulable time includes: determining the charging power corresponding to the residence time; calculating based on the schedulable time and the charging power to obtain the first schedulable capacity when the electric vehicle is charging.

[0190] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: If the residence time is less than or equal to the charging time, calculate based on the remaining power at the end of charging, the remaining power before charging, and the battery capacity to obtain the necessary charging capacity.

[0191] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: Determining the second schedulable capacity when the electric vehicle discharges based on the remaining power before charging and the battery capacity includes: determining the multiple relationship between the remaining power before charging and the battery capacity of the electric vehicle; if the multiple relationship is greater than a preset multiple threshold, calculate based on the remaining power before charging, the multiple relationship, and the battery capacity to obtain the second schedulable capacity when the electric vehicle discharges.

[0192] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the following steps:

[0193] Obtain the charging demand data of the electric vehicle; the charging demand data includes the battery capacity, the charging start time, the charging end time, and the remaining power before charging;

[0194] Calculate based on the charging start time and the charging end time to obtain the residence time of the electric vehicle;

[0195] Determine the charging time based on the remaining power before charging and the battery capacity;

[0196] Determine the first schedulable capacity when the electric vehicle charges based on the residence time and the charging time; determine the second schedulable capacity when the electric vehicle discharges based on the remaining power before charging and the battery capacity;

[0197] Calculate based on the first schedulable capacity and the second schedulable capacity to obtain the interactive capacity of the electric vehicle.

[0198] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: Obtaining the charging demand data of the electric vehicle includes: obtaining the battery type and the usage type of the electric vehicle; determining the battery capacity and the unit energy consumption of the electric vehicle according to the battery type; determining the single-day driving distance of the electric vehicle according to the usage type; calculating based on the battery capacity, the unit energy consumption, and the single-day driving distance to obtain the remaining power before charging of multiple electric vehicles; generating the charging start time and the charging end time based on the random simulation method.

[0199] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the remaining power before charging and the battery capacity, determining the charging time includes: determining the charging power corresponding to the dwell time; obtaining the remaining power of the electric vehicle when charging is cut off; and calculating according to the remaining power at the end of charging, the remaining power before charging, the charging power and the battery capacity of the electric vehicle to obtain the charging time.

[0200] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the residence time and the charging time, determining the first dispatchable capacity when the electric vehicle is charging includes: if the residence time is greater than the charging time, calculating according to the charging time and the residence time to obtain the dispatchable time; based on the dispatchable time, determining the first dispatchable capacity when the electric vehicle is charging.

[0201] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the schedulable time, determining the first schedulable capacity when the electric vehicle is charging includes: determining the charging power corresponding to the dwell time; calculating based on the schedulable time and the charging power to obtain the first schedulable capacity when the electric vehicle is charging.

[0202] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the dwell time is less than or equal to the charging time, the necessary charging capacity is obtained by calculation based on the remaining power at the end of charging, the remaining power before charging, and the battery capacity.

[0203] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the remaining power before charging and the battery capacity, determining the second dispatchable capacity of the electric vehicle when discharging includes: determining the multiple relationship between the remaining power before charging and the battery capacity of the electric vehicle; if the multiple relationship is greater than a preset multiple threshold, then calculating based on the remaining power before charging, the multiple relationship and the battery capacity to obtain the second dispatchable capacity of the electric vehicle when discharging.

[0204] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0205] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0206] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A calculation method for the interactive capacity between an electric vehicle and an energy efficiency power plant, characterized in that, The method includes: Obtaining the charging demand data of the electric vehicle; the charging demand data includes battery capacity, charging start time, charging end time, and remaining power before charging; Calculating according to the charging start time and the charging end time to obtain the residence time of the electric vehicle; Determining the charging time based on the remaining power before charging and the battery capacity; If the residence time is greater than the charging time, calculating according to the charging time and the residence time to obtain the schedulable time; determining the charging power corresponding to the residence time; calculating based on the schedulable time and the charging power to obtain the first schedulable capacity when the electric vehicle is charging; determining the second schedulable capacity when the electric vehicle is discharging based on the remaining power before charging and the battery capacity; Calculating according to the first schedulable capacity and the second schedulable capacity to obtain the interactive capacity of the electric vehicle.

2. The method according to claim 1, characterized in that, The obtaining the charging demand data of the electric vehicle includes: Obtaining the battery type and usage type of the electric vehicle; Determining the battery capacity and unit energy consumption of the electric vehicle according to the battery type; Determining the daily driving distance of the electric vehicle according to the usage type; Calculating according to the battery capacity, the unit energy consumption, and the daily driving distance to obtain the remaining power before charging of multiple electric vehicles; Generating the charging start time and the charging end time based on the random simulation method.

3. The method according to claim 1, characterized in that The determining the charging time based on the remaining power before charging and the battery capacity includes: Determining the charging power corresponding to the residence time; Obtaining the remaining power at the end of charging of the electric vehicle; Calculating according to the remaining power at the end of charging, the remaining power before charging, the charging power, and the battery capacity of the electric vehicle to obtain the charging time.

4. The method according to claim 1, wherein It further includes: If the residence time is less than or equal to the charging time, calculating based on the remaining power at the end of charging, the remaining power before charging, and the battery capacity to obtain the necessary charging capacity.

5. The method according to claim 1, wherein The determining the second schedulable capacity when the electric vehicle is discharging based on the remaining power before charging and the battery capacity includes: Determining the multiple relationship between the remaining power before charging and the battery capacity of the electric vehicle; If the multiple relationship is greater than a preset multiple threshold, calculating based on the remaining power before charging, the multiple relationship, and the battery capacity to obtain the second schedulable capacity when the electric vehicle is discharging.

6. An interactive capacity calculation device for an electric vehicle and an energy efficiency power plant, characterized in that, The device includes: An obtaining module, configured to obtain the charging demand data of the electric vehicle; the charging demand data includes battery capacity, charging start time, charging end time, and remaining power before charging; A residence time calculation module, configured to calculate according to the charging start time and the charging end time to obtain the residence time of the electric vehicle; A charging time determination module, configured to determine the charging time based on the remaining power before charging and the battery capacity; A schedulable capacity calculation module, configured to, if the residence time is greater than the charging time, calculate according to the charging time and the residence time to obtain a schedulable time; determine a charging power corresponding to the residence time; calculate based on the schedulable time and the charging power to obtain a first schedulable capacity when the electric vehicle is charging; determine a second schedulable capacity when the electric vehicle is discharging based on the remaining power before charging and the battery capacity. An interactive capacity calculation module, configured to calculate according to the first schedulable capacity and the second schedulable capacity to obtain the interactive capacity of the electric vehicle.

7. The device according to claim 6, characterized in that, The acquisition module is further configured to acquire the battery type and the usage type of the electric vehicle; determine the battery capacity and the unit energy consumption of the electric vehicle according to the battery type; determine the single-day driving distance of the electric vehicle according to the usage type; calculate according to the battery capacity, the unit energy consumption and the single-day driving distance to obtain the remaining power before charging of multiple electric vehicles; generate a charging start time and a charging end time based on the random simulation method.

8. The device according to claim 6, characterized in that, The charging time determination module is further configured to determine a charging power corresponding to the residence time; acquire the remaining power at the end of charging of the electric vehicle; calculate according to the remaining power at the end of charging, the remaining power before charging, the charging power and the battery capacity of the electric vehicle to obtain the charging time.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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