An electric vehicle cluster control method and system

Abstract

The application discloses a kind of electric vehicle cluster control method and system, method includes: obtaining electric vehicle travel data, according to electric vehicle travel data, establish the behavior characteristic model of electric vehicle;Based on behavior characteristic model, calculate the operation domain of electric vehicle participating in power grid auxiliary service;According to the operation domain of all electric vehicles, control electric vehicle cluster response power grid change auxiliary service demand.The technical scheme provided by the application determines the operation domain of electric vehicle by determining the behavior characteristic model of electric vehicle, can determine the available capacity of electric vehicle cluster, further based on the operation domain response power grid change auxiliary service demand can improve the output stability of electric vehicle cluster, while unnecessary loss of power battery can be avoided, help to reduce the aging cost of power battery.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of height measurement, and in particular to an electric vehicle cluster control method and system. BACKGROUND

[0002] In recent years, with the gradual depletion of global fossil energy, the development and utilization of new energy has become a global consensus. In this context, electric vehicles, as a new clean energy transportation tool, have received unprecedented attention. The popularity of electric vehicles not only promotes green travel, but also leads to the rapid development of V2G technology. V2G technology allows electric vehicles to provide surplus energy to the grid during charging, providing auxiliary services such as regulating grid frequency and voltage. The emergence of this technology not only improves the flexibility and stability of the grid, but also provides new sources of income for electric vehicle owners. However, it is not easy for electric vehicles to participate in grid auxiliary services, and it is necessary to fully consider the behavior characteristics and charging demand of electric vehicles, as well as the life loss of power batteries. How to fully utilize the adjustable capacity of electric vehicle clusters to output or input stable power to the grid while reducing the life loss of power batteries during the service process has become a key problem for electric vehicles to participate in grid auxiliary services.

[0003] Although electric vehicles participating in grid auxiliary services have broad prospects, existing control methods still face many challenges. Some methods do not consider the impact of power battery aging characteristics on electric vehicles providing primary frequency regulation for the grid, resulting in poor frequency regulation effect. At the same time, some methods do not build a perfect control area for electric vehicles to provide active support for the grid, and set the SOC (state of charge) range too conservatively, causing waste of electric vehicle available capacity. In addition, existing methods also have deficiencies in dealing with the problem of unstable output of electric vehicle clusters providing auxiliary services for the grid, and are unable to effectively control the life loss of power batteries. These problems limit the widespread application of electric vehicles in grid auxiliary services and need to be solved urgently. SUMMARY

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification to avoid obscuring the purpose of this section, abstract and title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0005] In view of the above existing problems, the present application is proposed. Therefore, the present application provides an electric vehicle cluster control method to solve the problems of unstable output, waste of available capacity and high life loss of power batteries when electric vehicle clusters provide auxiliary services for the grid.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a method for controlling a cluster of electric vehicles, comprising:

[0008] Obtaining travel data of the electric vehicles, and establishing a behavior characteristic model of the electric vehicles based on the travel data;

[0009] Calculating an operating domain of the electric vehicles participating in grid ancillary services based on the behavior characteristic model;

[0010] Controlling the cluster of electric vehicles to respond to the demand for grid ancillary services based on the operating domain of all the electric vehicles.

[0011] As a preferred scheme of the method for controlling the cluster of electric vehicles, the method comprises:

[0012] Designing boundary constraint conditions for the electric vehicles participating in the grid ancillary services based on the behavior characteristic model, and calculating the operating domain of the electric vehicles participating in the grid ancillary services based on the behavior characteristic model and the boundary constraint conditions.

[0013] As a preferred scheme of the method for controlling the cluster of electric vehicles, the method comprises:

[0014] The demand for grid ancillary services comprises:

[0015] Obtaining the demand for grid ancillary services;

[0016] Judging whether the electric vehicles can participate in the grid ancillary services based on the operating domain of each electric vehicle.

[0017] As a preferred scheme of the method for controlling the cluster of electric vehicles, the method comprises:

[0018] Controlling the cluster of electric vehicles to respond to the demand for grid ancillary services comprises:

[0019] For each electric vehicle participating in the grid ancillary services, changing the charging and discharging state of the electric vehicle to provide power for the grid in sequence according to a response priority list.

[0020] As a preferred scheme of the method for controlling the cluster of electric vehicles, the method comprises:

[0021] Judging whether the electric vehicles can participate in the grid ancillary services comprises:

[0022] Judging whether the electric vehicle is within a discharging boundary, and if the electric vehicle is within the discharging boundary, the electric vehicle can participate in the grid ancillary services;

[0023] The size of the power provided by the electric vehicles in the cluster is obtained, and the adjustable power of the cluster is calculated, denoted as:

[0024]

[0025] wherein, P cl_total is the adjustable power of the cluster, N represents the number of electric vehicles in the cluster within the discharging boundary, is the size of the power provided by the electric vehicles in the cluster.

[0026] If the power shortage of the power grid is less than the sum of the callable power of the electric vehicle cluster, the power output is allocated according to the proportion of the corresponding callable power of each electric vehicle cluster.

[0027] As a preferred scheme of the electric vehicle cluster control method provided by the application, wherein:

[0028] The proportion of the corresponding callable power of each electric vehicle cluster for allocating the power output is denoted as:

[0029]

[0030] wherein, is the power provided by the electric vehicle cluster a for the power grid, is the adjustable power of the a-th cluster, P cls is the total power provided by all electric vehicle clusters for the power grid, P cls_total is the sum of the callable power of all electric vehicle clusters.

[0031] As a preferred scheme of the electric vehicle cluster control method provided by the application, wherein:

[0032] The response priority list comprises,

[0033] The parameters DIS and the remaining charging duration of the electric vehicles are obtained;

[0034] The DIS distance of each electric vehicle is arranged in order;

[0035] If the DIS distance is equal, the remaining charging duration of each electric vehicle is arranged in order, and the construction of the response priority list is completed.

[0036] In a second aspect, the application provides a system for controlling electric vehicle clusters, comprising,

[0037] An obtaining module is configured to obtain electric vehicle travel data;

[0038] A calculation module is configured to establish a behavior characteristic model of the electric vehicle according to the electric vehicle travel data, and calculate an operating domain of the electric vehicle participating in power grid auxiliary services based on the behavior characteristic model.

[0039] a response module, configured to control the electric vehicle cluster to respond to the change of the power grid auxiliary service demand according to the operating domain of all electric vehicles.

[0040] In a third aspect, the present application provides a computing device, comprising:

[0041] a memory and a processor;

[0042] the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, so as to realize the steps of the electric vehicle cluster control method.

[0043] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions, when executed by a processor, realize the steps of the electric vehicle cluster control method.

[0044] Compared with the prior art, the present application has the following beneficial effects: the technical solution provided by the present application determines the operating domain of the electric vehicle by determining the behavior characteristic model of the electric vehicle, can determine the available capacity of the electric vehicle cluster, further improves the output stability of the electric vehicle cluster based on the operating domain responding to the change of the power grid auxiliary service demand, and can avoid unnecessary loss of the power battery, which is helpful to reduce the aging cost of the power battery. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0046] Figure 1 the overall flowchart of the electric vehicle cluster control method according to an embodiment of the present application;

[0047] Figure 2 the electric vehicle operating domain participating in the power grid auxiliary service of the electric vehicle cluster control method according to an embodiment of the present application;

[0048] Figure 3 the internal structure diagram of the computer device according to an embodiment of the present application;

[0049] Figure 4 the state trajectory diagram of the electric vehicle in the operating domain of the electric vehicle cluster control method according to an embodiment of the present application;

[0050] Figure 5The electric vehicle cluster control method of one embodiment of the present application is compared with the state distribution of the electric vehicle selected by the traditional control strategy.

[0051] Figure 6 The battery health state comparison diagram of the electric vehicle cluster control method of one embodiment of the present application is compared with the state distribution of the electric vehicle selected by the traditional control strategy.

[0052] Figure 7 The electric vehicle cluster control method of one embodiment of the present application is compared with the state distribution of the electric vehicle selected by the traditional control strategy. DETAILED DESCRIPTION

[0053] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work should belong to the protection scope of the present application.

[0054] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application. However, the present application can be implemented in other different manners than those described herein, and those of ordinary skill in the art can make similar generalizations without departing from the spirit and scope of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0055] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or selective embodiment that excludes other embodiments.

[0056] The present application is described in detail in combination with the schematic diagram. In the detailed description of the embodiments of the present application, the cross-sectional view of the device structure is locally enlarged without the general proportion for the convenience of description, and the schematic diagram is only an example, which should not limit the protection scope of the present application herein. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in the actual manufacture.

[0057] Meanwhile, in the description of the present application, it should be noted that the terms "upper, lower, inner and outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the systems or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first, second or third" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0058] Unless otherwise explicitly specified and limited in the present application, the terms "mounting, connection, connection" should be understood broadly, for example: it can be fixed connection, detachable connection or integral connection; It can also be mechanical connection, electrical connection or direct connection, it can also be indirectly connected through intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0059] Embodiment 1

[0060] Reference Figures 1-2 For an embodiment of the present application, an electric vehicle cluster control method is provided, comprising:

[0061] S100: Obtain electric vehicle travel data, and establish a behavior characteristic model of the electric vehicle according to the electric vehicle travel data;

[0062] In an optional embodiment, the electric vehicle travel data can include electric vehicle travel time, secondary driving mileage, charging record and power consumption preference data;

[0063] In the embodiment of the present application, the charging behavior of the electric vehicle conforms to the following characteristics:

[0064] (1) Each electric vehicle is an independent individual, and the charging behavior of the electric vehicle is only determined by the owner;

[0065] (2) The electric vehicle owner will preset the charging time and the expected state of charge when charging;

[0066] (3) The electric vehicle will not be disconnected from the power grid after being fully charged, but will be in an idle state of keeping connected to the power grid.

[0067] In the embodiment of the present application, the charging of the electric vehicle is mainly divided into two time periods, daytime and night. The daytime charging is mainly concentrated in the business district, and the night charging is mainly concentrated in the residential area.

[0068] Therefore, the travel time conforms to the Gaussian distribution N(t), which is expressed as:

[0069]

[0070] wherein t is the travel time, μ is the mathematical expectation of the Gaussian distribution function, and σ is the standard deviation;

[0071] The secondary driving range F of the electric vehicle conforms to a gamma distribution,

[0072]

[0073] wherein x is the secondary driving range, f(x) represents the gamma distribution corresponding to the secondary driving range, a is a shape parameter of the gamma distribution, β is an inverse scale parameter, and Γ(·) represents a gamma function evaluated at a.

[0074] S102: Based on the behavior characteristic model, an operation domain of the electric vehicle participating in the grid auxiliary service is calculated;

[0075] Preferably, based on the behavior characteristic model, a boundary constraint condition of the electric vehicle participating in the grid auxiliary service is designed, and the operation domain of the electric vehicle participating in the grid auxiliary service is calculated and obtained in combination with the behavior characteristic model and the boundary constraint condition.

[0076] In the embodiments of the present application, the boundary constraint condition includes a forced charging boundary of the electric vehicle participating in the grid auxiliary service and a discharging boundary of the electric vehicle participating in the grid auxiliary service.

[0077] In the embodiments of the present application, on the basis of constructing the behavior characteristic model of the electric vehicle, the aging characteristics of the battery of the electric vehicle are considered to determine the adjustable range of the electric vehicle.

[0078] First, the forced charging boundary of the electric vehicle participating in the grid auxiliary service is constructed. When the electric vehicle starts charging at time t f at the maximum charging rate until the charging end time t end , so that the SOC (State of Charge) of the electric vehicle can be charged to the expected value, t f , that is, the forced charging time of the electric vehicle at this time, and the curve composed of the forced charging times under all SOCs is called the forced charging boundary.

[0079] In the embodiments of the present application, the forced charging boundary of the electric vehicle participating in the grid auxiliary service is constructed and represented as:

[0080]

[0081] wherein t f is the forced charging trigger time, t end is the forced charging end time, SOC f is the electric quantity of the electric vehicle at time t f , and SOC exp is the electric quantity of the electric vehicle at time tend E is the power of the electric vehicle at time t, E is the capacity of the electric vehicle battery, P in is the input power of the electric vehicle forced charging;

[0082] The increment of SOC in Δt is ΔS:

[0083]

[0084] P in is the input power of the electric vehicle forced charging.

[0085] When the operating state of the electric vehicle reaches the forced charging boundary, the electric vehicle is forced to change to the charging state to start charging, so that the SOC of the electric vehicle reaches the expected value at the end of charging.

[0086] Secondly, the discharge boundary of the electric vehicle participating in the power grid auxiliary service is constructed, and the decrement of SOC in Δt is ΔS':

[0087]

[0088] The coordinates of the electric vehicle on the discharge boundary are defined as (t d , SOC d ), which just reach the forced charging boundary at the end of the auxiliary service, so that the discharge boundary function relationship relative to the forced charging boundary can be obtained.

[0089] In the embodiments of the present application, the discharge boundary includes a right discharge boundary and a lower discharge boundary;

[0090] Exemplarily, the coordinates of the electric vehicle on the discharge boundary are defined as (t d , SOC d ), which just reach the forced charging boundary at the end of the auxiliary service, that is, the right discharge boundary is represented as:

[0091]

[0092] P d is the boundary power of the electric vehicle participating in the power grid auxiliary service, P out is the discharge power of the electric vehicle participating in the power grid auxiliary service, t d is the time of the electric vehicle participating in the power grid auxiliary service, SOC f is the power of the electric vehicle at t f ;

[0093] Exemplarily, at the same time, in order to reduce the life loss of the power battery of the electric vehicle, the over-discharge value of the electric vehicle is set, that is, the SOC of the electric vehicle cannot be lower than the over-discharge value in the process of active support, that is, the lower discharge boundary is represented as:

[0094]

[0095] SOC od is the preset over-discharge value of the electric quantity of the electric vehicle, SOC d is the boundary electric quantity of the electric vehicle participating in the auxiliary service of the power grid, P out is the discharging power of the electric vehicle participating in the auxiliary service of the power grid, t d is the time of the electric vehicle participating in the auxiliary service of the power grid, SOC f is the electric quantity of the electric vehicle at t f .

[0096] According to the forced charging boundary and the discharging boundary of the present application, the operation domain of the electric vehicle participating in the auxiliary service of the power grid is constructed as shown in Figure 2 .

[0097] Figure 2 In the figure, t start is the starting charging time, t end is the ending charging time, t s is the forced charging time when the battery SOC is equal to SOC od ; SOC start represents the SOC of the electric vehicle at the starting charging time, SOC dD represents the lower discharging boundary value, SOC od represents the over-charge value of the power battery.

[0098] Figure 2 In the figure, point A represents the point at which the electric vehicle starts charging, point B represents the point at which the electric vehicle starts charging from point A to SOC exp , the slope at each position on curve AB represents the charging power at the SOC state; point C represents the discharging boundary value when the SOC is SOC exp , line segment BC represents the upper boundary SOC exp of the operation domain, point D represents the SOC of the electric vehicle at t end , which is charged to SOC exp , and curve CD corresponds to the time at which the electric vehicle cannot participate in the auxiliary service of the power grid when the SOC is the expected value.

[0099] Point E represents the point at which the electric vehicle starts discharging from point A to the lower discharging boundary SOC dD , the slope at each position on curve AE represents the discharging power at the SOC state; point F represents the intersection of the right discharging boundary and the lower discharging boundary, line segment EF is the lower discharging boundary; point G represents the intersection of the forced charging boundary and SOC od ; curve CF represents the right charging boundary, and curve DG represents the forced charging boundary.

[0100] S104: According to the operating domain of all electric vehicles, the electric vehicle cluster is controlled to respond to the power grid change auxiliary service demand;

[0101] It should be noted that when the power grid is disturbed and needs power support, the power grid sends a signal (P, t) signal of the required power P s and the power support duration t dur to energy agents such as energy storage power stations and electric vehicle clusters that want to participate in power grid auxiliary services through a series of processes, and each agent participates in the power grid auxiliary service.

[0102] The present application proposes a two-level structure composed of an electric vehicle control center and an electric vehicle cluster controller to control the participation of electric vehicles in power grid auxiliary services. In different geographical areas, the electric vehicle cluster controller collects the state parameters of each electric vehicle in the electric vehicle cluster under its control and forms an operating domain, determines the cluster's active support capacity, and then sorts the priority of electric vehicles participating in power grid auxiliary services according to the electric vehicle parameters. At the same time, the adjustable capacity of the electric vehicle cluster is sent to the electric vehicle control center.

[0103] Obtain the power grid change auxiliary service demand; based on the operating domain of each electric vehicle, determine whether the electric vehicle can participate in the power grid change auxiliary service;

[0104] In an alternative embodiment, the method of obtaining the power grid change auxiliary service demand can be a real-time monitoring and data analysis prediction method or a demand assessment method based on historical data and pattern recognition;

[0105] For example, the prediction method is to collect key parameters such as voltage, current, and frequency in real time through sensors and intelligent measurement devices in the power grid, and at the same time, combine real-time data of renewable energy generation capacity, use big data analysis and machine learning algorithms to process these data, predict the load change trend and possible imbalance state of the future power grid, and based on these prediction results, assess the specific demand of the power grid for auxiliary services in different time periods.

[0106] In the embodiments of the present application, each cluster collects the state parameters of each electric vehicle in the cluster and forms an operating domain, and the state parameters include the battery capacity E , the charging time t and the SOC of the electric vehicle when the power demand occurs in the power grid, wherein the time parameter satisfies t _dur =t f -t d .

[0107] Preferably, for each electric vehicle participating in the power grid change auxiliary service, the charging and discharging state is sequentially changed according to the response priority list to provide power for the power grid;

[0108] In the embodiments of the present application, the number N of electric vehicles providing power support for the power grid feq satisfies:

[0109]

[0110] wherein, is the power margin that the kth electric vehicle in the cluster can provide for the power grid;

[0111]

[0112] wherein, P k in is the charging power of the kth electric vehicle when the kth electric vehicle does not participate in the power grid change auxiliary service, is the peak power provided by the kth electric vehicle for the power grid when the kth electric vehicle participates in the power grid change auxiliary service.

[0113] Preferably, it is judged whether the electric vehicle is within the discharging boundary, and if the electric vehicle is within the discharging boundary, the electric vehicle can participate in the power grid change auxiliary service;

[0114] Preferably, the power size that the electric vehicles in the cluster can provide is obtained, and the adjustable power of the cluster is calculated and represented as:

[0115]

[0116] wherein, P cl_total is the adjustable power of the cluster, N represents the number of electric vehicles in the cluster that are within the discharging boundary, is the power size that the electric vehicles can provide;

[0117] Preferably, if the power shortage of the power grid is less than the sum of the callable power of the electric vehicle cluster, the power output is allocated according to the proportion of the corresponding callable power of each electric vehicle cluster.

[0118] In the embodiments of the present application, the sum P cls_total of the callable power of all electric vehicle clusters is compared with the power shortage P s of the power grid, and represented as:

[0119]

[0120] wherein, P cls is the total power provided by all electric vehicle clusters for the power grid;

[0121] In the embodiments of the present application, the proportion of the corresponding callable power of each electric vehicle cluster is allocated to the power output, and represented as:

[0122]

[0123] wherein, P is the total power provided by all the electric vehicle clusters to the power grid, P is the adjustable power of the a-th cluster, cls P is the total power provided by all the electric vehicle clusters to the power grid, cls_total P is the sum of the adjustable power of all the electric vehicle clusters.

[0124] In the embodiments of the present application, the response priority list step is:

[0125] The parameter DIS and the remaining charging time of the electric vehicle are obtained;

[0126] In an optional embodiment, the method for obtaining the parameter DIS can be a direct calculation method based on map data, a comprehensive evaluation method based on driving history and prediction model, etc.; the method for obtaining the remaining charging time can be a direct reading method based on the charging management system, a prediction method based on user behavior and charging habits, etc.

[0127] In the embodiments of the present application, the parameter DIS is represented as:

[0128]

[0129] DIS k is the DIS distance of the k-th electric vehicle, k DIS k is the DIS distance of the k-th electric vehicle, k SOC k is the current state of charge of the k-th vehicle, SOC k is the optimal state of charge for minimizing the degradation rate of the battery of the k-th vehicle.

[0130] In the embodiments of the present application, the remaining charging time is represented as:

[0131]

[0132] T k is the remaining charging time of the k-th electric vehicle, k T k is the remaining charging time of the k-th electric vehicle, T k is the time when the k-th electric vehicle finishes charging, T k is the time when the k-th electric vehicle starts to actively provide power to the power grid

[0133] The DIS distance of each electric vehicle is arranged in order;

[0134] If the DIS distance is equal, the remaining charging time of each electric vehicle is arranged in order, and the construction of the response priority list is completed.

[0135] Specifically, the response priority list is determined according to the DIS and the remaining charging time of each electric vehicle in the operating domain. All the electric vehicles in the operating domain are arranged in order according to the DIS from small to large. For the electric vehicles with equal DIS, they are arranged in order according to the remaining charging time from large to small, thereby obtaining the response priority list L.EV :

[0136] L EV =(EV1,...,EV k ,...,EV n );

[0137] wherein k is the number of the electric vehicle in the response priority list, and n is the number of the electric vehicles in the operation domain that can participate in the grid ancillary service.

[0138] In the response priority list L EV ,

[0139] DIS1≥…≥DIS k ≥…≥DIS n ;

[0140] When the grid is disturbed, the electric vehicle cluster controller changes the charging and discharging state according to the instructions transmitted by the electric vehicle control center in the order of the response priority of the electric vehicles in the response priority list L

[0141] In another possible embodiment, the arrangement is in order, and the method can be weighted sorting, sorting using greedy algorithm, etc.

[0142] It should be noted that the present application realizes flexible response of the electric vehicle cluster to the grid demand by comprehensively considering the operation domain of the electric vehicle and the auxiliary service demand of the grid change; the grid load change is monitored and analyzed in real time, and the operation domain is constructed based on the state parameters such as the battery capacity, charging time and current SOC of the electric vehicle, to determine whether the electric vehicle can participate in the auxiliary service; the response priority list is constructed, and the electric vehicles are sorted according to the DIS distance and the remaining charging time, to ensure that the electric vehicles with short distance and non-urgent charging demand respond first. The present application not only improves the power regulation capability of the electric vehicle cluster, but also effectively fills the power shortage of the grid by accurately allocating power output, realizes efficient cooperation between the grid and the electric vehicle cluster, and improves the stability and reliability of the grid operation.

[0143] The above is a schematic scheme of the electric vehicle cluster control method of the present embodiment. It should be noted that the technical scheme of the electric vehicle cluster control system belongs to the same concept as the technical scheme of the electric vehicle cluster control method described above, and the details of the technical scheme of the electric vehicle cluster control system in the present embodiment are not described in detail, which can be referred to the description of the technical scheme of the electric vehicle cluster control method.

[0144] Embodiment 2

[0145] The embodiment provides an electric vehicle cluster control system, comprising:

[0146] The acquisition module is used to acquire electric vehicle travel data;

[0147] The calculation module is used to establish a behavioral characteristic model of electric vehicles based on electric vehicle travel data; and to calculate the operating domain of electric vehicles participating in grid ancillary services based on the behavioral characteristic model.

[0148] The response module is used to control the electric vehicle cluster to respond to ancillary service needs due to grid changes, based on the operating domains of all electric vehicles.

[0149] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0150] Example 3

[0151] This embodiment provides a computer device, which may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, communication interface, display screen, and input system connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for controlling electric vehicle clusters. The display screen can be an LCD screen or an e-ink screen. The input system can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0152] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the following: acquiring electric vehicle travel data; establishing a behavioral characteristic model of electric vehicles based on the electric vehicle travel data; calculating the operating domain of electric vehicles participating in grid ancillary services based on the behavioral characteristic model; and controlling the electric vehicle cluster to respond to grid change ancillary service demands based on the operating domains of all electric vehicles.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

[0154] Embodiment 4

[0155] Referring to Figures 4-7 And Table 1, for an embodiment of the present application, an electric vehicle cluster control method is provided, in order to verify its beneficial effect, the comparison results of several schemes are provided.

[0156] Suppose that an electric vehicle cluster consisting of 100 electric vehicles is connected to the power grid, and by adopting certain incentive measures such as giving economic compensation to the vehicle owners, the electric vehicles are encouraged to connect to the power grid to participate in power grid auxiliary services and obtain benefits. It is assumed that the electric vehicles are connected to the power grid through charging piles to participate in power grid auxiliary services after arriving at commercial areas or work places during the day and arriving at residential areas at night. The travel time of the electric vehicles conforms to Gaussian distribution, and the daily driving distance conforms to Gamma distribution.

[0157] According to the electric vehicle participating in power grid auxiliary service operation domain and related simulation parameters proposed by the present application, it is assumed that three electric vehicles EV1, EV2 and EV3 in different regions of the operation domain participate in power grid auxiliary services, and the charging time is 5h, and the response start time and initial SOC are shown in Table 1.

[0158] Table 1 Simulation parameters of three electric vehicles in different states

[0159]

[0160] Let EV1, EV2 and EV3, three electric vehicles in different states, first output 10min energy to the power grid to provide power grid auxiliary services, and then charge to the SOC expected value, so as to verify the effectiveness of the operation domain proposed by the present application. Figure 4 The state change trajectory of the three electric vehicles in the operation domain is shown.

[0161] Figure 4 In the table, EV1 is in the charging boundary of the operation domain at the response start time. First, 10min energy is output to the power grid, and after 10min, the SOC value drops to 48.14%, and then charging begins, and at 2.80h, the SOC expected value is reached. Under the premise of meeting the charging demand of the electric vehicle, stable power output is provided for the power grid.

[0162] EV2 is outside the right discharge boundary DG at the response starting moment, and is forced to be inside the forced charging boundary EH. First, 10 min energy is output to the power grid, and at the 4.0 h moment in the response process, the forced charging boundary EH is touched, at which time if the charging demand of the electric vehicle is sacrificed to maintain stable output power, the SOC of the electric vehicle decreases to 42.22% after 10 min of output. At the charging ending moment, the electric vehicle power does not reach the SOC expectation value, but only 63.71%, resulting in a shortage of 6.29%, so that the charging demand of the electric vehicle is not met.

[0163] EV3 is outside the lower discharge boundary FG at the response starting moment, and the battery over-discharge value SOC od is above. First, 10 min energy is output to the power grid, and at the 1.73 h moment in the response process, the SOC decreases to the battery over-discharge value SOC od , at which time if the service life of the power battery of the electric vehicle is sacrificed to maintain stable output power, the SOC of the electric vehicle decreases to 17.74% after 10 min of support. Then charging is started, and the SOC expectation value is reached at the 3.95 h moment.

[0164] Through the above analysis, when the state of the electric vehicle is outside the discharge boundary of the operating domain, it is impossible to guarantee that the charging demand of the owner of the electric vehicle or the demand of protecting the service life of the power battery and the stable power output are met at the same time to participate in the auxiliary service of the power grid. Thus, the effectiveness of the operating domain proposed in the application is verified.

[0165] Secondly, the orderly control strategy of the electric vehicle cluster proposed in the application to respond to the auxiliary service demand of the power grid (hereinafter referred to as "orderly control strategy") and the traditional control strategy of responding in order of the relative SOC size and the relative remaining charging time, that is, the strategy of allowing the electric vehicle with a larger SOC and a longer remaining charging time in the electric vehicle cluster to output power to provide auxiliary services for the power grid first (hereinafter referred to as "traditional control strategy") are compared. It is assumed that the power grid produces a 100 kW power shortage at 10:00, and the power grid sends an energy demand of Pcl=100 kW and tdur=0.5 h to the electric vehicle agent, and the aging difference of the power battery of the electric vehicle participating in the auxiliary service of the power grid and the stability of the power output are compared between the orderly control strategy and the traditional control strategy.

[0166] According to the experiment, the battery exhibits the lowest degradation rate near 50% SOC. That is, cycling near 50% SOC will result in lower capacity loss. The results of different SOC ranges show that the battery capacity loss is the largest when cycled in the high SOC and low SOC intervals. For the battery cycled at an average SOC of about 50%, the smallest aging is detected. Therefore, the SOC optThe percentage is 50%. Simulations yielded the real-time State of Charge (SOC) and remaining charging time distribution of electric vehicles connected to the grid at 10:00 AM, as well as the distribution of electric vehicles participating in grid auxiliary services selected under both traditional and ordered control strategies. Figure 5 As shown.

[0167] The electric vehicle provides approximately 10% depth of discharge (DoD) to the grid over 0.5 hours. Based on the electric vehicle state distribution selected under two different control strategies, the changes in SOHave under the two control strategies are shown below when the electric vehicle provides different numbers of auxiliary services (i.e., A has different values). Figure 4 As shown.

[0168] Depend on Figure 6 It can be seen that when A = 180, the SOH using the traditional control strategy... ave =0.9715, using the ordered control strategy proposed in this invention, its SOH ave =0.9928, compared to the traditional control strategy, the SOH reduction is reduced by 2.15%. When A=300, the ordered control strategy reduces the SOH reduction by 5.99% compared to the traditional control strategy. Moreover, the more times the electric vehicle provides ancillary services to the grid, the greater the reduction in SOH of the electric vehicle's power battery using the ordered control strategy compared to the traditional control strategy.

[0169] Secondly, the output power of the electric vehicle cluster under the two control strategies is as follows: Figure 7 As shown.

[0170] from Figure 7 It can be seen that the traditional control strategy, due to the lack of a reasonable operating domain for electric vehicles (EVs) participating in grid ancillary services, calls upon EVs outside the discharge boundary to participate in grid ancillary services. This results in EVs reaching the forced charging boundary at 23.81 minutes and 27.50 minutes during the response process, forcing them to switch from charging to discharging states, leading to a sharp drop in the output power of the EV cluster and an inability to provide stable output power to the grid. In contrast, the ordered control strategy proposed in this invention constructs a reasonable operating domain for EVs and utilizes EVs within the discharge boundary of this operating domain to participate in grid ancillary services, thus providing stable output power to the grid throughout the entire response process.

[0171] From the above analysis, the modeling method proposed in the application is mainly applied to the case that the power grid needs energy participation active support, and the electric vehicle provides auxiliary services for the power grid. The method defines the forced charging boundary, discharging boundary and limited charging boundary of the electric vehicle participating in the auxiliary services of the power grid and proposes an analysis method by considering the behavior characteristics of the electric vehicle owner, charging demand and battery aging characteristics, and on this basis, an electric vehicle auxiliary service operation domain is constructed, and the adjustable range of the electric vehicle is determined. Under the premise of optimizing battery aging, the adjustable capacity of the electric vehicle cluster is fully utilized, and the waste of adjustable capacity is avoided. Then, the application proposes an electric vehicle cluster control strategy responding to the change of auxiliary service demand of the power grid, and clearly defines the priority of electric vehicles in the cluster when responding to different support time and different power demand of the power grid, realizes the orderly and stable input or output of the electric vehicle cluster, and reduces the aging cost of the electric vehicle power battery.

[0172] In summary, the technical solution proposed in the application determines the behavior characteristic model of the electric vehicle to determine its operation domain, and then determines the available capacity of the electric vehicle cluster to improve the utilization rate of the available capacity. Further, based on the operation domain, the output stability of the electric vehicle cluster can be improved in response to the change of auxiliary service demand of the power grid, and unnecessary loss of the power battery can be avoided, which helps to reduce the aging cost of the power battery.

[0173] Further, determining the forced charging boundary, discharging boundary and limited charging boundary of the electric vehicle participating in the auxiliary services of the power grid helps to better improve the output stability of the electric vehicle cluster, avoid unnecessary loss of the power battery, and reduce the aging cost of the power battery. The number of electric vehicles providing power support for the power grid is constrained, which can further guarantee the efficient use of the available capacity of the electric vehicle cluster.

Claims

1. A method for controlling electric vehicle clusters, characterized in that, include: Acquire electric vehicle travel data, and establish a behavioral characteristic model of electric vehicles based on the electric vehicle travel data; Based on the aforementioned behavioral characteristic model, the operating domain for electric vehicles participating in grid ancillary services is calculated. Based on the operating domain of all electric vehicles, control the electric vehicle cluster to respond to ancillary service needs in response to changes in the power grid; For each electric vehicle participating in grid change ancillary services, the charging and discharging states are changed sequentially according to the response priority list to provide power to the grid; Obtain the electric vehicle's DIS parameters and remaining charging time; The parameter DIS is represented as follows: Among them, DIS k Let DIS be the distance to the k-th electric vehicle, and SOC be the distance to the k-th electric vehicle. k Let k be the current state of charge of the kth car. The optimal state of charge that minimizes the degradation rate of the battery of the kth car; Arrange the DIS distances of each electric vehicle in sequence; If the distances between DIS are equal, the remaining charging time of each electric vehicle is arranged in order to complete the construction of the response priority list.

2. The electric vehicle cluster control method as described in claim 1, characterized in that, The operating domains for electric vehicles to participate in grid ancillary services include: Based on the aforementioned behavioral characteristic model, boundary constraints for electric vehicles participating in grid ancillary services are designed. Combining the behavioral characteristic model and boundary constraints, the operating domain for electric vehicles participating in grid ancillary services is calculated and obtained.

3. The electric vehicle cluster control method as described in claim 1 or 2, characterized in that, The demand for ancillary services for power grid changes includes, Obtain ancillary service requirements for power grid changes; Based on the operating domain of each electric vehicle, it is determined whether the electric vehicle can participate in grid change ancillary services.

4. The electric vehicle cluster control method as described in claim 3, characterized in that, Determining whether electric vehicles can participate in grid change ancillary services includes, Determine whether the electric vehicle is within the discharge boundary. If it is within the discharge boundary, the electric vehicle can participate in grid change ancillary services. Obtain the power available from electric vehicles within the cluster, calculate the adjustable power of the cluster, and represent it as follows: Among them, P cl_total The power is adjustable for the cluster, and N represents the number of electric vehicles within the cluster that are within the discharge boundary. Power output available for electric vehicles; If the power deficit in the power grid is less than the sum of the available power of the electric vehicle clusters, then the power output will be allocated according to the proportion of available power of each electric vehicle cluster.

5. The electric vehicle cluster control method as described in claim 4, characterized in that, The proportional allocation of available power for each electric vehicle cluster is expressed as follows: in, The power supplied to the power grid by electric vehicle cluster a For the adjustable power of the a-th cluster, P cls P represents the total power supplied to the grid by all electric vehicle clusters. cls_total This represents the sum of the available power across all electric vehicle clusters.

6. A system for controlling electric vehicle clusters, characterized in that, include, The acquisition module is used to acquire electric vehicle travel data; The calculation module is used to establish a behavioral characteristic model of electric vehicles based on the electric vehicle travel data; and to calculate the operating domain of electric vehicles participating in grid ancillary services based on the behavioral characteristic model. The response module is used to control the electric vehicle cluster to respond to ancillary service needs due to changes in the power grid, based on the operating domain of all electric vehicles. For each electric vehicle participating in grid change ancillary services, the charging and discharging states are changed sequentially according to the response priority list to provide power to the grid; Obtain the electric vehicle's DIS parameters and remaining charging time; The parameter DIS is represented as follows: Among them, DIS k Let DIS be the distance to the k-th electric vehicle, and SOC be the distance to the k-th electric vehicle. k Let k be the current state of charge of the kth car. The optimal state of charge that minimizes the degradation rate of the battery of the kth car; Arrange the DIS distances of each electric vehicle in sequence; If the distances between DIS are equal, the remaining charging time of each electric vehicle is arranged in order to complete the construction of the response priority list.

7. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the electric vehicle cluster control method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the electric vehicle cluster control method according to any one of claims 1 to 5.

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