Electric vehicle cluster control method and system

Abstract

The invention discloses an electric vehicle cluster control method and system, and the method comprises the steps: obtaining the travel data of an electric vehicle, and building a behavior characteristic model of the electric vehicle according to the travel data of the electric vehicle; calculating an operation domain of the electric vehicle participating in the power grid auxiliary service based on the behavior characteristic model; and controlling the electric vehicle cluster to respond to the power grid change auxiliary service demand according to the operation domains of all the electric vehicles. According to the technical scheme, the operation domain of the electric vehicle is determined by determining the behavior characteristic model of the electric vehicle, the available capacity of the electric vehicle cluster can be determined, the output stability of the electric vehicle cluster can be improved by responding to the power grid change auxiliary service requirement based on the operation domain, unnecessary loss of a power battery can be avoided, and the power utilization rate is improved. And the aging cost of the power battery can be reduced.

Description

Technical Field

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

[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 an emerging clean energy means of transportation, have received unprecedented attention. The popularity of electric vehicles has not only promoted green travel, but also led to the rapid development of V2G technology. V2G technology allows electric vehicles to provide surplus energy to the power grid during charging and provide auxiliary services to the power grid, such as regulating the frequency and voltage of the power grid. The emergence of this technology not only improves the flexibility and stability of the power grid, but also provides a new source of income for electric vehicle owners. However, it is not easy for electric vehicles to participate in grid auxiliary services. It is necessary to fully consider the behavioral characteristics and charging needs of electric vehicles, as well as the life loss of power batteries. How to make full use of the adjustable capacity of electric vehicle clusters to output or input stable power to the power grid while meeting the charging needs of electric vehicles, while reducing the life loss of power batteries during the service process, has become a key issue for electric vehicles to participate in grid auxiliary services.

[0003] Although electric vehicles have broad prospects for participating in grid auxiliary services, existing control methods still face many challenges. Some methods do not consider the impact of the aging characteristics of power batteries on the primary frequency regulation provided by electric vehicles to the grid, resulting in poor frequency regulation effects. At the same time, some methods have not built a complete control area for electric vehicles to provide active support to the grid, and the SOC (battery state of charge) range is set too conservatively, resulting in a waste of available capacity of electric vehicles. In addition, existing methods are also insufficient in dealing with the problem of unstable output when electric vehicle clusters provide auxiliary services to the grid, and fail 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 of the invention

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

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention 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 the electric vehicle cluster provides auxiliary services for the power grid.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an electric vehicle cluster control method, comprising:

[0008] Acquire electric vehicle travel data, and establish a behavior characteristic model of the electric vehicle based on the electric vehicle travel data;

[0009] Based on the behavioral characteristic model, calculating the operation domain of the electric vehicle participating in grid auxiliary services;

[0010] According to the operating domains of all electric vehicles, the electric vehicle cluster is controlled to respond to the auxiliary service requirements of the power grid changes.

[0011] As a preferred solution of the electric vehicle cluster control method of the present invention, the operation domain of electric vehicles participating in grid auxiliary services is determined to include:

[0012] Based on the behavior characteristic model, boundary constraints for electric vehicles to participate in grid auxiliary services are designed, and the operation domain for electric vehicles to participate in grid auxiliary services is calculated and obtained by combining the behavior characteristic model and the boundary constraints.

[0013] As a preferred solution of the electric vehicle cluster control method described in the present invention, wherein:

[0014] The demand for ancillary services for power grid changes includes:

[0015] Obtaining the demand for auxiliary services of power grid changes;

[0016] Based on the operating domain of each electric vehicle, it is determined whether the electric vehicle can participate in the grid change auxiliary service.

[0017] As a preferred solution of the electric vehicle cluster control method described in the present invention, wherein:

[0018] Controlling electric vehicle clusters to respond to grid changes Ancillary service requirements include:

[0019] For each electric vehicle participating in the grid change auxiliary service, the charging and discharging states are changed in turn according to the response priority list to provide power to the grid.

[0020] As a preferred solution of the electric vehicle cluster control method described in the present invention, wherein:

[0021] Judging whether electric vehicles can participate in grid change auxiliary services includes:

[0022] Determine whether the electric vehicle is within the discharge boundary. If so, the electric vehicle can participate in the grid change auxiliary service.

[0023] Get the power that the electric vehicles in the cluster can provide and calculate the adjustable power of the cluster, which is expressed as:

[0024]

[0025] Among them, P cl_total is the cluster adjustable power, N is the number of electric vehicles in the cluster within the discharge boundary, The amount of power that can be provided for electric vehicles.

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

[0027] As a preferred solution of the electric vehicle cluster control method described in the present invention, wherein:

[0028] The proportional distribution power output of each electric vehicle cluster corresponding to the available power is expressed as:

[0029]

[0030] in, is the power provided by electric vehicle cluster a to the grid, is the adjustable power of the ath cluster, P cls is the total power provided by all electric vehicle clusters to the grid, P cls_total is the sum of the available power of all electric vehicle clusters.

[0031] As a preferred solution of the electric vehicle cluster control method described in the present invention, wherein:

[0032] The response priority list includes,

[0033] Get the electric vehicle's parameter DIS and remaining charging time;

[0034] Arrange the DIS distance of each electric vehicle in order;

[0035] If the DIS distances are equal, the remaining charging time of each electric vehicle is arranged in order to complete the construction of the response priority list.

[0036] In a second aspect, the present invention provides a system for controlling a cluster of electric vehicles, comprising:

[0037] An acquisition module, used to acquire electric vehicle travel data;

[0038] A calculation module, used to establish a behavior characteristic model of the electric vehicle according to the electric vehicle travel data; and based on the behavior characteristic model, calculate an operation domain of the electric vehicle participating in grid auxiliary services;

[0039] The response module is used to control the electric vehicle cluster to respond to the auxiliary service requirements of the power grid changes according to the operation domains of all electric vehicles.

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

[0041] Memory and processor;

[0042] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the electric vehicle cluster control method are implemented.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the electric vehicle cluster control method.

[0044] Compared with the prior art, the present invention has the following beneficial effects: the technical solution provided by the present invention determines the operating domain of the electric vehicle by determining the behavioral characteristic model of the electric vehicle, thereby clarifying the available capacity of the electric vehicle cluster, and further responding to the auxiliary service needs of the power grid changes based on the operating domain to improve the output stability of the electric vehicle cluster, while avoiding unnecessary loss of power batteries, which helps to reduce the aging cost of power batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0046] Figure 1 It is a schematic diagram of the overall process of the electric vehicle cluster control method according to an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of an operation domain of electric vehicles participating in grid auxiliary services in an electric vehicle cluster control method according to an embodiment of the present invention;

[0048] Figure 3 A schematic diagram of the internal structure of a computer device according to an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of a state trajectory of an electric vehicle in an operation domain of an electric vehicle cluster control method according to an embodiment of the present invention;

[0050] Figure 5A schematic diagram of electric vehicle state distribution selected by an electric vehicle cluster control method according to an embodiment of the present invention and a traditional control strategy;

[0051] Figure 6 A schematic diagram showing a comparison of battery health states when the electric vehicle cluster control method according to an embodiment of the present invention has a 10% discharge depth and the electric vehicle provides auxiliary services for different times;

[0052] Figure 7 The figure is a schematic diagram comparing the power output of an electric vehicle cluster under a control method of an embodiment of the present invention and a traditional control strategy. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0056] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0057] At the same time, in the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0058] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0059] Example 1

[0060] Reference Figure 1-2 , as an embodiment of the present invention, provides an electric vehicle cluster control method, comprising:

[0061] S100: Acquire electric vehicle travel data, and establish a behavior characteristic model of the electric vehicle based on the electric vehicle travel data;

[0062] In an optional embodiment, the electric vehicle travel data may include data such as the electric vehicle travel time, mileage, charging records, and electricity usage preferences;

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

[0064] (1) Each electric vehicle is an independent entity. Electric vehicles do not affect each other. The charging behavior of electric vehicles is determined only by the owner.

[0065] (2) Electric vehicle owners will preset charging time and expected state of charge when charging;

[0066] (3) After being fully charged, electric vehicles will not be disconnected from the power grid, but will remain in an idle state connected to the power grid.

[0067] In the embodiment of the present application, it is assumed that the charging of electric vehicles is mainly divided into two time periods: daytime and nighttime. During the day, charging is mainly concentrated in commercial areas, and at night, charging is mainly concentrated in residential areas.

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

[0069]

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

[0071] The mileage F of electric vehicles conforms to the gamma distribution.

[0072]

[0073] Where x is the driving mileage, f(x) represents the gamma distribution corresponding to the driving mileage, α is the shape parameter of the gamma distribution, β is the inverse scale parameter, and Γ(·) represents the gamma function evaluated at α.

[0074] S102: Calculating the operation domain of electric vehicles participating in grid auxiliary services based on the behavioral characteristic model;

[0075] Preferably, based on the behavior characteristic model, boundary constraints for electric vehicles to participate in grid auxiliary services are designed, and the operation domain for electric vehicles to participate in grid auxiliary services is calculated and obtained by combining the behavior characteristic model and the boundary constraints;

[0076] In the embodiment of the present application, the boundary constraint conditions include a mandatory charging boundary for electric vehicles participating in grid auxiliary services and a discharging boundary for electric vehicles participating in grid auxiliary services.

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

[0078] First, the mandatory charging boundary of electric vehicles participating in grid auxiliary services is constructed. f Start charging at the maximum charging rate at time t until the charging ends at time t end When the electric vehicle SOC (State of Charge) is charged to the expected value, t f That is the forced charging moment of the electric vehicle at this time. The curve formed by the forced charging moments under all SOCs is called the forced charging boundary.

[0079] In the embodiment of the present application, the mandatory charging boundary for electric vehicles to participate in grid auxiliary services is expressed as:

[0080]

[0081] Among them, t f When forced charging is triggered, t end The forced charging end time, SOC f For electric vehicles in t f The power at the moment, SOC exp For electric vehicles in tend The power at the moment, E is the battery capacity of the electric vehicle, P in Input power for mandatory charging of electric vehicles;

[0082] During the time Δt, the increment ΔS of SOC is:

[0083]

[0084] Among them, P in Input power for mandatory charging of electric vehicles.

[0085] When the running state of the electric vehicle reaches the mandatory charging boundary, the electric vehicle is forced to switch to the charging state and start charging to ensure that the electric vehicle SOC reaches the expected value at the end of charging.

[0086] Secondly, the discharge boundary of electric vehicles participating in grid auxiliary services is constructed. Within the time of Δt, the reduction of SOC ΔS' is:

[0087]

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

[0089] In the embodiment 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 located on the discharge boundary are defined as (t d ,SOC d ), which just reaches the mandatory charging boundary at the end of the auxiliary service, that is, the right discharge boundary is expressed as:

[0091]

[0092] Among them, SOC d is the boundary power of electric vehicles participating in grid auxiliary services, P out is the discharge power of electric vehicles participating in grid auxiliary services, t d When electric vehicles participate in grid auxiliary services, SOC f For electric vehicles in t f The amount of electricity at the moment;

[0093] For example, at the same time, considering reducing the life loss of the electric vehicle power battery, the electric vehicle over-discharge value is set, that is, the SOC of the electric vehicle cannot be lower than the over-discharge value during the active support process, that is, the lower discharge boundary is expressed as:

[0094]

[0095] Among them, SOC od The preset over-discharge value for electric vehicles, SOC d is the boundary power of electric vehicles participating in grid auxiliary services, P out is the discharge power of electric vehicles participating in grid auxiliary services, t d When electric vehicles participate in grid auxiliary services, SOC f For electric vehicles in t f The power at the moment.

[0096] According to the mandatory charging boundary and discharging boundary of the present invention, the operation domain of electric vehicles participating in grid auxiliary services is constructed as follows: Figure 2 As shown;

[0097] Figure 2 In, t start is the initial charging time, t end is the end of charging time, t s For battery SOC equal to SOC od The mandatory charging time is: SOC start Indicates the SOC at the start of charging of the electric vehicle, SOC dD Indicates the lower discharge boundary value, SOC od Indicates the overcharge of the power battery.

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

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

[0100] S104: Controlling the electric vehicle cluster to respond to the auxiliary service demand of the power grid change according to the operation domain of all electric vehicles;

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

[0102] The present invention proposes a two-level structure consisting of an electric vehicle control center and an electric vehicle cluster controller to control electric vehicles to participate in 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 operation domain. After determining the active support capacity of the cluster, the priority of electric vehicles participating in grid auxiliary services is sorted 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 demand for auxiliary services for grid changes; based on the operating domain of each electric vehicle, determine whether the electric vehicle can participate in the auxiliary services for grid changes;

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

[0105] Exemplarily, the prediction method is to collect key parameters such as voltage, current, and frequency in real time through sensors and intelligent measuring devices in the power grid, and at the same time combine the real-time data of renewable energy power generation, use big data analysis and machine learning algorithms to process these data, and predict future load change trends and possible imbalances in the power grid. Based on these prediction results, the specific needs of the power grid for ancillary services in different time periods are evaluated.

[0106] In the embodiment of the present application, each cluster collects the state parameters of each electric vehicle in the cluster and forms an operation domain. The state parameters include the battery capacity of the electric vehicle. E , charging time t and the electric vehicle SOC when the power grid has power demand, where the time parameter satisfies t_ dur =t f -t d .

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

[0108] In the embodiment of the present application, the number of electric vehicles N providing power support to the power grid feq satisfy:

[0109]

[0110] Among them, P kEV is the power margin that the kth electric vehicle in the cluster can provide to the grid;

[0111]

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

[0113] Preferably, it is determined whether the electric vehicle is within the discharge boundary, and if so, the electric vehicle can participate in the grid change auxiliary service;

[0114] Preferably, the power that can be provided by the electric vehicles in the cluster is obtained, and the adjustable power of the cluster is calculated, which is expressed as:

[0115]

[0116] Among them, P cl_total is the cluster adjustable power, N is the number of electric vehicles in the cluster within the discharge boundary, The amount of power that can be provided to electric vehicles;

[0117] Preferably, if the power shortage of the power grid is less than the sum of the available powers of the electric vehicle clusters, the power output is distributed according to the proportion of the available powers of each electric vehicle cluster;

[0118] In the embodiment of the present application, the sum of the available powers P of all electric vehicle clusters is compared. cls_total and the grid power shortage P s , expressed as:

[0119]

[0120] Among them, P cls is the total power provided to the grid by all EV clusters;

[0121] In the embodiment of the present application, the proportional distribution power output of each electric vehicle cluster corresponding to the available power is expressed as:

[0122]

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

[0124] In the embodiment of the present application, the steps of responding to the priority list are:

[0125] Get the electric vehicle's parameter DIS and remaining charging time;

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

[0127] In the embodiment of the present application, the acquisition parameter DIS is expressed as:

[0128]

[0129] Among them, DIS k is the DIS distance of the kth electric vehicle, SOC k is the current state of charge of the kth car.

[0130] In the embodiment of the present application, the remaining charging time is obtained as:

[0131]

[0132] Among them, T k is the remaining charging time of the kth electric vehicle, The moment when the kth electric car finishes charging, is the moment when the kth electric vehicle starts to actively provide power to the grid

[0133] Arrange the DIS distance of each electric vehicle in order;

[0134] If the DIS distances are equal, the remaining charging time of each electric vehicle is arranged in order to complete the construction of the response priority list.

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

[0136] L EV =(EV 1 ,...,EV k ,...,EV n );

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

[0138] In the response priority list L EV middle,

[0139] DIS 1 ≥…≥DIS k ≥…≥DIS n ;

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

[0141] In another possible embodiment, the arrangement is performed in sequence, and the method may be weighted sorting, sorting using an intelligent algorithm, etc.

[0142] It should be noted that the present invention realizes the flexible response of electric vehicle clusters to grid demand by comprehensively considering the operation domain of electric vehicles and the auxiliary service demand of grid changes; predicts grid load changes through real-time monitoring and data analysis, and constructs the operation domain based on the battery capacity, charging time and current SOC and other state parameters of electric vehicles to determine whether electric vehicles can participate in auxiliary services; by constructing a response priority list, sorting according to the DIS distance and remaining charging time of electric vehicles, ensuring that electric vehicles with short distances and no urgent charging needs are given priority response. The present invention not only improves the power regulation capability of electric vehicle clusters, but also effectively fills the power shortage of the grid by accurately allocating power output, realizes efficient coordination between the grid and electric vehicle clusters, and improves the stability and reliability of grid operation.

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

[0144] Example 2

[0145] This embodiment provides an electric vehicle cluster control system, including:

[0146] An acquisition module, used to acquire electric vehicle travel data;

[0147] A calculation module is used to establish a behavior characteristic model of electric vehicles based on electric vehicle travel data; based on the behavior characteristic model, the operation domain of electric vehicles participating in grid auxiliary services is calculated;

[0148] The response module is used to control the electric vehicle cluster to respond to the auxiliary service requirements of the power grid changes according to the operation domains of all electric vehicles.

[0149] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned 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, a memory, a communication interface, a display screen and an input system 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, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, an electric vehicle cluster control method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input system of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0152] The present embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements: obtaining electric vehicle travel data, and establishing a behavior characteristic model of the electric vehicle based on the electric vehicle travel data; calculating the operating domain of the electric vehicle participating in grid auxiliary services based on the behavior characteristic model; and controlling the electric vehicle cluster to respond to grid change auxiliary service needs 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 invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0154] Example 4

[0155] Reference Figure 4-7 Table 1 and Table 1 are an embodiment of the present invention, which provides an electric vehicle cluster control method. In order to verify its beneficial effects, comparison results of several solutions are provided.

[0156] Assume that an electric vehicle cluster consisting of 100 electric vehicles is connected to the power grid. By adopting certain incentives such as giving economic compensation to car owners, electric vehicles are encouraged to connect to the power grid and participate in grid ancillary services to obtain benefits. Assume that electric vehicles are connected to the power grid through charging piles after arriving at commercial areas or workplaces during the day and at residential areas at night to participate in grid ancillary services. The travel time of electric vehicles conforms to Gaussian distribution, and the daily mileage conforms to Gamma distribution.

[0157] According to the operation domain of electric vehicles participating in grid auxiliary services and related simulation parameters proposed in the present invention, it is assumed that three electric vehicles EV1, EV2 and EV3 in different areas of the operation domain participate in grid auxiliary services, and the charging time is 5h. 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] Three electric vehicles, EV1, EV2 and EV3, which are in different states, are first configured to output energy to the grid for 10 minutes to provide grid auxiliary services, and then are charged to the expected SOC value, thereby verifying the effectiveness of the operation domain proposed in the present invention. Figure 4 The state change trajectories of three electric vehicles in the operation domain are shown.

[0161] Figure 4 In the example, EV1 is within the charging boundary of the operation domain at the start of the response. It first outputs energy to the grid for 10 minutes, and the SOC value drops to 48.14% after 10 minutes. Then it starts charging and reaches the expected SOC value at 2.80 hours. It provides stable power output to the grid while meeting the charging needs of electric vehicles.

[0162] At the beginning of the response, EV2 is outside the right discharge boundary DG and inside the mandatory charging boundary EH. First, it outputs energy to the grid for 10 minutes, and touches the mandatory charging boundary EH at 4.0 hours in the response process. At this time, if the charging demand of the electric vehicle is sacrificed to maintain stable output power, the SOC of the electric vehicle drops to 42.22% after 10 minutes of output. At the end of charging, the electric vehicle power does not reach the expected SOC value, but only 63.71%, resulting in a 6.29% shortage, resulting in the charging demand of the electric vehicle not being met.

[0163] At the start of the response, EV3 is outside the lower discharge boundary FG, and the battery is over-discharged SOC od First, the energy is output to the grid for 10 minutes. At 1.73 hours in the response process, the SOC drops to the battery over-discharge value SOC. od At this time, if the electric vehicle power battery life is sacrificed to maintain a stable output power, the SOC of the electric vehicle will drop to 17.74% after 10 minutes of support. Then charging will begin and the expected SOC value will be reached at 3.95 hours.

[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 simultaneously meet the charging needs of the electric vehicle owner or the needs of protecting the power battery life and outputting stable power to participate in the grid auxiliary service. This verifies the effectiveness of the operating domain proposed by the present invention.

[0165] Secondly, the electric vehicle cluster orderly control strategy proposed by the present invention that responds to the auxiliary service demand of the power grid changes (hereinafter referred to as "ordered control strategy") is compared with the traditional control strategy that responds in order of relative SOC size and relative remaining charging time, that is, the strategy that the electric vehicles with larger SOC and longer remaining charging time in the electric vehicle cluster output power first to provide auxiliary services for the power grid (hereinafter referred to as "traditional control strategy"). Assuming that the power grid has a power shortage of 100kW at 10:00, the power grid sends an energy demand of Pcl = 100kW, tdur = 0.5h to the electric vehicle dealer, and compares the aging difference and power output stability of the electric vehicle power batteries selected by the ordered control strategy and the traditional control strategy to participate in the auxiliary service of the power grid.

[0166] According to the experiment, the battery showed the lowest degradation rate when cycling 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 cycling in the high SOC and low SOC intervals. For the battery cycling at an average SOC of around 50%, the minimum aging was detected. Therefore, setting the SOC optThe real-time SOC and remaining charging time distribution of electric vehicles connected to the grid at 10:00 are obtained through simulation, as well as the distribution of electric vehicles participating in grid auxiliary services selected by the traditional control strategy and the ordered control strategy. Figure 5 shown.

[0167] The discharge depth DoD of the electric vehicle for the grid for 0.5h is about 10%. According to the state distribution of the electric vehicle selected by the two control strategies, the changes of SOHave under the two control strategies when the electric vehicle provides different times of auxiliary services (i.e. A is different values) are shown as follows: Figure 4 shown.

[0168] Depend on Figure 6 It can be seen that when A = 180, the SOH of the traditional control strategy is ave =0.9715, using the orderly control strategy proposed by the present invention, its SOH ave =0.9928, compared with 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 with the traditional control strategy. And as the number of times electric vehicles provide auxiliary services to the power grid increases, the SOH reduction ratio of electric vehicle power batteries using the ordered control strategy compared with the traditional control strategy will be greater.

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

[0170] from Figure 7 It can be seen that the traditional control strategy does not construct a reasonable electric vehicle operation domain for participating in grid auxiliary services, and calls electric vehicles outside the discharge boundary to participate in grid auxiliary services, resulting in the electric vehicle reaching the mandatory charging boundary at 23.81min and 27.50min in the response process, and thus being forced to change the charging and discharging state, causing the output power of the electric vehicle cluster to drop sharply, and unable to provide stable output power for the grid. In contrast, the orderly control strategy proposed in the present invention constructs a reasonable electric vehicle operation domain, and uses electric vehicles within the discharge boundary of the operation domain to participate in grid auxiliary services, thus providing stable output power for the grid during the entire response process.

[0171] From the above analysis, it can be seen that the main application scenario of the modeling method proposed in the present invention is that electric vehicles provide auxiliary services to the power grid when the power grid needs energy to participate in active support. This method takes into account the behavioral characteristics, charging needs and battery aging characteristics of electric vehicle owners, defines the mandatory charging boundary, discharge boundary and restricted charging boundary of electric vehicles participating in power grid auxiliary services, and proposes an analysis method. On this basis, the electric vehicle auxiliary service operation domain is constructed, the adjustable range of electric vehicles is determined, and the adjustable capacity of the electric vehicle cluster is fully utilized under the premise of optimizing battery aging, avoiding the waste of adjustable capacity. Then, the present invention proposes an electric vehicle cluster control strategy that responds to the auxiliary service needs of the power grid changes, clarifies the participation priority of electric vehicles in the cluster when responding to different support times and different power requirements of the power grid, and realizes the orderly and stable input or output of the electric vehicle cluster, while reducing the aging cost of the electric vehicle power battery.

[0172] In summary, the technical solution proposed in the present invention determines the operating domain of the electric vehicle by determining the behavioral characteristic model of the electric vehicle, and then improves the utilization rate of the available capacity by clarifying the available capacity of the electric vehicle cluster; further, responding to the auxiliary service needs of the power grid changes based on the operating domain can improve the output stability of the electric vehicle cluster, while avoiding unnecessary loss of power batteries, which helps to reduce the aging cost of power batteries.

[0173] Furthermore, determining the mandatory charging boundaries, discharging boundaries, and restricted charging boundaries for electric vehicles participating in grid ancillary services will help to better improve the output stability of the electric vehicle cluster, avoid unnecessary losses of power batteries, and reduce the aging costs of power batteries; restricting the number of electric vehicles that provide power support to the grid can further ensure 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 behavior characteristic model of the electric vehicle based on the electric vehicle travel data; Based on the behavioral characteristic model, calculating the operation domain of the electric vehicle participating in grid auxiliary services; According to the operating domains of all electric vehicles, the electric vehicle cluster is controlled to respond to the auxiliary service requirements of the power grid changes.

2. The electric vehicle cluster control method according to claim 1, characterized in that: Determine the operation domain of electric vehicles participating in grid ancillary services, including: Based on the behavior characteristic model, boundary constraints for electric vehicles to participate in grid auxiliary services are designed, and the operation domain for electric vehicles to participate in grid auxiliary services is calculated and obtained by combining the behavior characteristic model and the boundary constraints.

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

4. The electric vehicle cluster control method according to claim 3, characterized in that: Controlling electric vehicle clusters to respond to grid changes Ancillary service requirements include: For each electric vehicle participating in the grid change auxiliary service, the charging and discharging states are changed in sequence according to the response priority list to provide power to the grid.

5. The electric vehicle cluster control method according to claim 4, characterized in that: Judging whether electric vehicles can participate in grid change auxiliary services includes: Determine whether the electric vehicle is within the discharge boundary. If so, the electric vehicle can participate in the grid change auxiliary service. Get the power that the electric vehicles in the cluster can provide and calculate the adjustable power of the cluster, which is expressed as: Among them, P cl_total is the cluster adjustable power, N is the number of electric vehicles in the cluster within the discharge boundary, The amount of power that can be provided to electric vehicles; If the power shortage of the power grid is less than the sum of the available powers of the electric vehicle clusters, the power output is allocated according to the proportion of the available powers of each electric vehicle cluster.

6. The electric vehicle cluster control method according to claim 5, characterized in that: The proportional distribution power output of each electric vehicle cluster corresponding to the available power is expressed as: in, is the power provided by electric vehicle cluster a to the grid, is the adjustable power of the ath cluster, P cls is the total power provided by all electric vehicle clusters to the grid, P cls_total is the sum of the available power of all electric vehicle clusters.

7. The electric vehicle cluster control method according to claim 6, characterized in that: The response priority list includes, Get the electric vehicle's parameter DIS and remaining charging time; Arrange the DIS distance of each electric vehicle in order; If the DIS distances are equal, the remaining charging time of each electric vehicle is arranged in order to complete the construction of the response priority list.

8. A system for controlling electric vehicle clusters, characterized in that: include, An acquisition module, used to acquire electric vehicle travel data; A calculation module, used to establish a behavior characteristic model of the electric vehicle according to the electric vehicle travel data; and based on the behavior characteristic model, calculate an operation domain of the electric vehicle participating in grid auxiliary services; The response module is used to control the electric vehicle cluster to respond to the auxiliary service requirements of the power grid changes according to the operation domains of all electric vehicles.

9. 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. When the computer executable instructions are executed by the processor, the steps of the electric vehicle cluster control method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the electric vehicle cluster control method according to any one of claims 1 to 7.

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