A vehicle-to-grid interaction service method and system considering inductive charging and discharging of electric vehicles

Abstract

The application discloses a kind of car-network interaction service method and system considering electric vehicle non-inductive charging and discharging, the method of the present application includes obtaining time-of-use electricity price Cost and power grid voltage V from power distribution network operator;Utilize first fuzzy controller to obtain the energy flow perception index SPF of electric vehicle to determine the connection mode of electric vehicle;The power percentage of charging or discharging of electric vehicle is obtained by using second fuzzy controller to the energy flow perception index SPF of electric vehicle, the state of charge SOC of electric vehicle and the remaining departure time TRD;According to the power percentage of charging or discharging of electric vehicle, the charging pile accessed by electric vehicle provides charging or discharging for electric vehicle based on the power percentage.The present application aims to realize efficient and autonomous energy exchange between electric vehicle and power grid by considering electric vehicle non-inductive charging and discharging, and ensures that users participate in power grid charging and discharging without feeling.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicles, in particular to a vehicle-to-grid interactive service method and system considering inductive charging and discharging of electric vehicles. BACKGROUND

[0002] Electric vehicles have received widespread attention due to their advantages in using clean energy, reducing dependence on fossil fuels, and achieving zero greenhouse gas emissions. However, as the number of electric vehicles grows rapidly, they pose new challenges to existing power grid infrastructure. Large-scale access of electric vehicles can cause power grid overload, thereby causing problems in safe operation of the power grid. It is predicted that by 2030, the global electric vehicle ownership will exceed 250 million, and at that time, the additional power demand brought by these vehicles is expected to reach 1.1 PWh. At the same time, electric vehicles are no longer just simple means of transportation, but also mobile energy storage units with energy storage potential. Through the advancement of bidirectional charging technology, electric vehicles can achieve energy exchange with the power grid, i.e., Vehicle-to-Grid (V2G). This mode allows electric vehicle users to feed back unused electric energy in their vehicles to the power grid, thereby alleviating power demand during peak periods to some extent and obtaining economic benefits. Although the Vehicle-to-Grid V2G technology brings many benefits, there are still some obstacles, including how to effectively manage and dispatch these dispersed energy storage resources so that electric vehicle users can participate in energy interaction with the power grid without excessive intervention. SUMMARY

[0003] The technical problem solved by the present application: In view of the above problems of the prior art, the present application provides a vehicle-to-grid interactive service method and system considering inductive charging and discharging of electric vehicles. The present application aims to realize efficient and autonomous energy exchange between electric vehicles and the power grid through vehicle-to-grid interactive service considering inductive charging and discharging of electric vehicles, and to ensure that users participate in inductive charging and discharging of the power grid.

[0004] To solve the above technical problems, the technical scheme adopted by the present application is:

[0005] A vehicle-to-grid interactive service method considering inductive charging and discharging of electric vehicles, comprising the following steps:

[0006] S1, obtaining time-of-use electricity price Cost and power grid voltage V from a power distribution network operator;

[0007] S2, obtaining an energy flow perception index SPF of the electric vehicle by using a first fuzzy controller with the time-of-use electricity price Cost and the grid voltage V for determining the connection mode of the electric vehicle; obtaining a power percentage of charging or discharging of the electric vehicle by using a second fuzzy controller with the energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD;

[0008] S3, controlling the charging pile accessed by the electric vehicle according to the power percentage of charging or discharging of the electric vehicle to provide charging or discharging for the electric vehicle based on the power percentage.

[0009] Optionally, the obtaining of the energy flow perception index SPF of the electric vehicle by using the first fuzzy controller with the time-of-use electricity price Cost and the grid voltage V for determining the working mode of the electric vehicle comprises:

[0010] S101, converting the accurate time-of-use electricity price Cost and the grid voltage V to obtain a fuzzy grid voltage V-fuzzy and a fuzzy electricity price Cost-fuzzy by using a preset membership function respectively;

[0011] S102, obtaining a fuzzy state of the connection mode of the electric vehicle and the priority thereof by using fuzzy reasoning according to fuzzy rules in a pre-defined fuzzy rule base with the fuzzy grid voltage V-fuzzy and the fuzzy electricity price Cost-fuzzy;

[0012] S103, de-fuzzifying the fuzzy state of the connection mode of the electric vehicle and the priority thereof to obtain the energy flow perception index SPF of the electric vehicle for determining the connection mode of the electric vehicle, wherein the obtained energy flow perception index SPF of the electric vehicle is a number with a value range of -1 to 1, and the energy flow perception index SPF of the electric vehicle is represented by the positive and negative characteristics of the value to indicate that the connection mode of the electric vehicle is “charging” or “discharging”, and the energy flow perception index SPF of the electric vehicle is 0 to indicate that the electric vehicle “maintains the status quo” without charging or discharging.

[0013] Optionally, the step S101 of converting the accurate time-of-use electricity price Cost and the grid voltage V into the fuzzy grid voltage V-fuzzy and the fuzzy time-of-use electricity price Cost-fuzzy respectively using the preset membership function, wherein the time-of-use electricity price Cost comprises the membership degrees of three levels corresponding to the low time-of-use electricity price region DT, the medium time-of-use electricity price region OP and the high time-of-use electricity price region PT respectively, the grid voltage V comprises the membership degrees of five levels representing the low L, the medium low ML, the medium M, the medium high MH and the high H of the grid voltage respectively, and the membership function is a triangular membership function or a trapezoidal membership function; and the step S102 of obtaining the fuzzy state of the electric vehicle connection mode and the priority thereof by fuzzy reasoning the fuzzy grid voltage V-fuzzy and the fuzzy time-of-use electricity price Cost-fuzzy according to the fuzzy rules in the predefined fuzzy rule base, wherein the fuzzy state comprises five different levels of the very negative VN, the negative N, the maintain M, the positive P and the very positive VP, the fuzzy state output is the very positive VP when the time-of-use electricity price Cost is the low time-of-use electricity price region DT and the grid voltage V is the low L, the fuzzy state output is the very negative VN when the time-of-use electricity price Cost is the high time-of-use electricity price region PT and the grid voltage V is the high H, and the fuzzy state output is one of the negative N, the maintain M and the positive P when the time-of-use electricity price Cost is the medium time-of-use electricity price region OP and the grid voltage V is one of the medium low ML, the medium M and the medium high MH.

[0014] Optionally, the step of obtaining the power percentage of the charging or discharging of the electric vehicle and controlling the charging pile accessed by the electric vehicle based on the power percentage by using the second fuzzy controller on the energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD comprises:

[0015] S201, converting the accurate energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD into the fuzzy energy flow perception index SPFSPF_Output_fuzzy, the fuzzy state of charge SOC_fuzzy and the fuzzy remaining departure time TRD_fuzzy respectively using the preset membership function;

[0016] S202, the fuzzy energy flow perception index SPFSPF_Output_fuzzy, the fuzzy state of charge SOC_fuzzy and the fuzzy residual departure time TRD_fuzzy are subjected to fuzzy reasoning according to the fuzzy rules in the pre-defined fuzzy rule base to obtain a fuzzy state of the electric vehicle output power level;

[0017] S203, the fuzzy state of the electric vehicle output power level is de-fuzzied to obtain the power percentage of the charging or discharging of the electric vehicle.

[0018] Optionally, the accurate energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD are converted into the fuzzy energy flow perception index SPFSPF_Output_fuzzy, the fuzzy state of charge SOC_fuzzy and the fuzzy remaining departure time TRD_fuzzy respectively by using the preset membership functions in step S201; the energy flow perception index SPF includes five membership functions corresponding to five membership values of very negative VN, negative N, maintain M, positive P and very positive VP; the state of charge SOC includes five membership functions corresponding to five membership values of very low VL, low L, medium M, high H and very high VH; the remaining departure time TRD includes three membership functions corresponding to three membership values of short S, medium M and long L; the membership functions are triangular membership functions or trapezoidal membership functions; the fuzzy states of the electric vehicle charging and discharging power are obtained by fuzzy reasoning of the fuzzy energy flow perception index SPFSPF_Output_fuzzy, the fuzzy state of charge SOC_fuzzy and the fuzzy remaining departure time TRD_fuzzy according to the fuzzy rules in the predefined fuzzy rule base in step S202; the fuzzy states of the electric vehicle output power level include five fuzzy states of very positive VP, positive P, maintain M, negative N and very negative VN; ① when the membership value of the energy flow perception index SPF is very negative VN: if the membership value of the remaining departure time TRD is short S, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, VP, VP, VP and P; if the membership value of the remaining departure time TRD is medium M, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, VP, VP, P and P; if the membership value of the remaining departure time TRD is long L, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, VP, P, P and P; ② when the membership value of the energy flow perception index SPF is negative N: if the membership value of the remaining departure time TRD is short S, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, VP, VP, VP and P; if the membership value of the remaining departure time TRD is medium M, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, VP, VP, P and M; if the membership value of the remaining departure time TRD is long L, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, VP, P, M and M.③ When the membership value of the energy flow awareness index SPF is maintenance M: if the membership value of the remaining departure time TRD is short S, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, VP, VP, P, M respectively; if the membership value of the remaining departure time TRD is medium M, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, P, P, P, M respectively; if the membership value of the remaining departure time TRD is long L, then the output power levels corresponding to the five membership values of the state of charge SOC are P, P, P, M, M respectively; ④ When the membership value of the energy flow awareness index SPF is positive P: if the membership value of the remaining departure time TRD is short S, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, VP, P, M, N respectively; if the membership value of the remaining departure time TRD is medium M, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, P, M, M, N respectively; if the membership value of the remaining departure time TRD is long L, then the output power levels corresponding to the five membership values of the state of charge SOC are P, P, M, N, VN respectively; ⑤ When the membership value of the energy flow awareness index SPF is very positive VP: if the membership value of the remaining departure time TRD is short S, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, VP, P, N, VN respectively; if the membership value of the remaining departure time TRD is medium M, then the output power levels corresponding to the five membership values of the state of charge SOC are P, P, M, N, VN respectively; if the membership value of the remaining departure time TRD is long L, then the output power levels corresponding to the five membership values of the state of charge SOC are M, N, VN, VN, VN respectively.

[0019] Optionally, the state of charge SOC of the electric vehicle is obtained based on a given state of charge SOC-time curve at a current time, the given state of charge SOC-time curve being a piecewise linear function of the state of charge SOC with respect to time; and the remaining departure time TRD is a time difference between the current time and a charging departure time, wherein the charging departure time is calculated according to a function expression as follows:

[0020] ,

[0021] In the above formula, is the charging departure time of the ith electric vehicle, is the start charging time of the ith electric vehicle, for a charging time interval, for an electric vehicle capacity, for an initial state of charge of the ith electric vehicle, for an electric vehicle charging efficiency, for a charging or discharging power of the ith electric vehicle.

[0022] Optionally, before the step S1 of obtaining the time-of-use electricity price Cost and the grid voltage V from the power distribution network operator, the method further comprises: optimizing and solving, by the power distribution network operator, the time-of-use electricity price Cost and the initial time thereof according to a time-of-use electricity price model shown in the following formula:

[0023] ,

[0024] In the above formula, is an objective function of the time-of-use electricity price model, represents taking a minimum value of the objective function of the time-of-use electricity price model, is a number of power distribution network regions, represents a load fluctuation rate of a superimposed load of a region ; represents a load peak-valley difference of a superimposed load of a region .

[0025] In addition, the present application also provides a vehicle-to-grid interactive service system considering inductive charging and discharging of electric vehicles, comprising a microprocessor and a memory connected to each other, the microprocessor being programmed or configured to execute the vehicle-to-grid interactive service method considering inductive charging and discharging of electric vehicles.

[0026] In addition, the present application also provides a computer readable storage medium, the computer readable storage medium storing a computer program or instructions, the computer program or instructions being programmed or configured to execute the vehicle-to-grid interactive service method considering inductive charging and discharging of electric vehicles by a processor.

[0027] In addition, the present application also provides a computer program product, comprising a computer program or instructions, the computer program or instructions being programmed or configured to execute the vehicle-to-grid interactive service method considering inductive charging and discharging of electric vehicles by a processor.

[0028] Compared with the prior art, the present application has the following advantages: the method of the present application comprises obtaining the time-of-use electricity price Cost and the grid voltage V from the power distribution network operator; obtaining the energy flow perception index SPF of the electric vehicle by using a first fuzzy controller to determine the connection mode of the electric vehicle; obtaining the power percentage of the electric vehicle for charging or discharging by using a second fuzzy controller with the energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD; and controlling the charging pile accessed by the electric vehicle to provide charging or discharging for the electric vehicle based on the power percentage, so that the present application realizes efficient and automatic energy exchange between the electric vehicle and the power grid by taking into account the vehicle-grid interaction service of the electric vehicle for inductive charging and discharging, and ensures that the user participates in the inductive charging and discharging of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 It is a basic flowchart of the method of the embodiment of the present application.

[0030] Figure 2 It is a design flowchart of the vehicle-grid interaction service system in the embodiment of the present application.

[0031] Figure 3 It is an application flowchart of the SPF stage of the second cascade fuzzy controller in the embodiment of the present application.

[0032] Figure 4 It is an application flowchart of the PP stage of the second cascade fuzzy controller in the embodiment of the present application.

[0033] Figure 5 It is the relationship between the cumulative percentage of the vehicle and the daily average travel distance in the embodiment of the present application.

[0034] Figure 6 It is the SOC change of the electric vehicle that can be charged and discharged under a typical day in the embodiment of the present application.

[0035] Figure 7 It is the relationship between the SOC and the battery power of the electric vehicle that can be charged and discharged in the embodiment of the present application.

[0036] Figure 8 It is a function graph of the type I fuzzy logic used in the embodiment of the present application.

[0037] Figure 9 It is a function graph of the type II fuzzy logic used in the embodiment of the present application.

[0038] Figure 10 It is a vehicle-grid interaction principle diagram of the electric vehicle charging and discharging with an aggregator in the embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor are within the protection scope of the present application.

[0040] As shown in Figure 1 The vehicle-grid interaction service method considering the inductive charging and discharging of the electric vehicle in the embodiment comprises the following steps:

[0041] S1, obtaining the time-of-use price Cost and the grid voltage V from the power grid operator;

[0042] S2, obtaining the energy flow perception index SPF of the electric vehicle by using the first fuzzy controller with the time-of-use price Cost and the grid voltage V, so as to determine the connection mode of the electric vehicle; obtaining the power percentage of the charging or discharging of the electric vehicle by using the second fuzzy controller with the energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD;

[0043] S3, controlling the charging pile accessed by the electric vehicle to provide the charging or discharging for the electric vehicle based on the power percentage of the charging or discharging of the electric vehicle according to the power percentage.

[0044] The execution subject of steps S1-S3 in the embodiment is the electric vehicle charging and discharging agent, Figure 2 The design flowchart of the vehicle-grid interaction service system in the embodiment comprises: information collection and arrangement: collecting and arranging information for the vehicle-grid interaction service system. Building a charging system: building a two-stage inductive charging and discharging system based on the two-stage fuzzy system of SPF and PP, i.e. the execution subject of the vehicle-grid interaction service method considering the inductive charging and discharging of the electric vehicle in the embodiment; building an interaction framework: building a three-stage interaction framework among the electric vehicle owner, the power grid operator and the electric vehicle charging and discharging agent; the power grid operator establishes a time-of-use price optimization model; the electric vehicle owner and the agent establish a multi-time and space charging and discharging strategy optimization model; through the game between the electric vehicle owner and the power grid operator, the economic win-win of both parties is realized. Building an interaction service system: finally, the vehicle-grid interaction service system considering the inductive charging and discharging of the electric vehicle is established.

[0045] To realize a multi-temporal interactive service system for vehicles and the grid based on electricity pricing, a three-tiered interactive framework needs to be constructed first, involving electric vehicle owners, distribution network operators, and electric vehicle charging / discharging agents. This framework allows electric vehicle owners to authorize agents to schedule their charging and discharging behavior when the vehicle is not in use. Distribution network operators set differentiated regional time-of-use (TOU) electricity prices to guide the charging and discharging behavior of electric vehicles, while agents help owners formulate charging and discharging plans based on regional electricity prices. The distribution network operator sets regional TOU electricity prices with the goal of minimizing regional peak-valley differences, and calculates the optimal start and end times for peak-valley electricity price periods in each region by optimizing the TOU electricity price model GW. Through differentiated regional TOU electricity prices, electric vehicles are incentivized to charge during off-peak hours and discharge during peak hours, thereby achieving peak shaving and valley filling of the grid load and reducing grid load fluctuations. This model can help distribution network operators formulate effective electricity pricing policies to incentivize electric vehicle users to participate in grid load management. As an optional implementation, in step S1 of this embodiment, before obtaining the time-of-use electricity price (Cost) and grid voltage (V) from the distribution network operator, the distribution network operator further optimizes and solves for the time-of-use electricity price (Cost) and its starting time according to the time-of-use electricity price model shown in the following formula:

[0046] ,

[0047] In the above formula, The objective function of the time-of-use electricity pricing model is... This indicates taking the minimum value of the objective function of the time-of-use pricing model. For the number of distribution network areas, Indicates the region Load fluctuation rate of superimposed loads; Indicates the region The peak-to-valley difference of the superimposed load. To develop an optimal charging and discharging plan for electric vehicle dealers to maximize the economic benefits for electric vehicle users and the grid's peak-shaving subsidies, dealers formulate a model with the objective function of maximizing the total revenue of both electric vehicle users and dealers. The objective function is:

[0048] ,

[0049] In the formula: This indicates the operating revenue of electric vehicle dealerships; Indicates the commission rate for agents; This indicates the total number of electric vehicles; This represents the benefits gained by a vehicle during V2G (discharge) processes within k days. The subsidy represents the compensation given by the distribution network operator to the agent. Then the solution is obtained by particle swarm optimization algorithm, and finally the optimal charging and discharging state of each electric private car can be determined. By coordinating the charging and discharging behavior of electric private cars, the peak-valley difference of power grid load is reduced, and economic value is created for electric private car users and agents. Then, for the regional time-of-use electricity price optimization model and the electric private car charging and discharging strategy optimization model, a double-layer alternating solution method based on improved genetic algorithm and particle swarm optimization algorithm is used. The outer layer transmits the electricity price optimization result to the inner layer, and the inner layer optimizes the charging and discharging plan of the electric private car participating in multi-time and space interaction according to the electricity price optimization result, and feeds back to the outer layer. The outer layer adjusts the regional time-of-use electricity price according to the superimposed load of the superimposed charging and discharging load of the electric private car in each region, and the inner and outer layers are iteratively solved alternately until a satisfactory result is obtained. This method can ensure that the charging and discharging behavior of electric private cars can effectively respond after the implementation of the electricity price policy.

[0050] In order to make the electric vehicle inductive charging and discharging vehicle network interaction service system effectively run, the step S2 of the electric vehicle inductive charging and discharging vehicle network interaction service method in the embodiment proposes to design based on a two-stage cascade fuzzy controller. The operation principle of the two-stage cascade fuzzy controller is that the controller receives input data from electric vehicles (EV) and power grids, such as battery state, power grid voltage, electricity price, etc.; then the accurate input data is converted into a fuzzy set, and the membership function is used to represent the degree to which these data belong to different fuzzy concepts; finally, the controller reasons the input data after fuzzification according to the rules in the fuzzy rule base, generates fuzzy output, and converts the fuzzy output into accurate numerical value for use in actual application. In the first stage, the controller determines the suitability of each electric vehicle to supply or consume energy according to the power grid voltage condition and energy price cost. SPF (Sense of the Power Flow, energy flow perception index) is a number ranging from -1 to 1, which determines the connection mode (charging / discharging) of the electric vehicle and its priority. In the second stage, called power percentage (PP), this system determines the percentage of power that the electric vehicle will consume or supply, and is completed through the use of a mobile application or a car console. The input data required by the PP level system is the output of the SPF controller (V2G), SOC and TRD. It can be observed that the SPF level uses information from the power grid, while PP relies on the SOC level of the electric vehicle and the expected behavior of the user. SPF determines whether to consume / provide energy, and PP determines how much energy to exchange.

[0051] In the SPF stage, there are two inputs in the first level fuzzy controller: the cost and the grid voltage (V). In the input data collection stage, the grid voltage (V) data is collected from the electric vehicle charging station in real time, and the current cost (Cost) information. In the fuzzification stage, the precise grid voltage and cost are converted into fuzzy sets using membership functions, for example, the grid voltage can be converted into "low", "medium", "high" and other fuzzy states; its input is the grid voltage (V), the cost (Cost), and the output is the fuzzy grid voltage (V-fuzzy) and the fuzzy cost (Cost-fuzzy). In the fuzzy inference stage, according to the pre-defined fuzzy rule base, the fuzzy states of the grid voltage and the cost are evaluated to determine whether to charge; the input is the fuzzy grid voltage (V-fuzzy) and the fuzzy cost (Cost-fuzzy), and the output is the fuzzy inference output. In the defuzzification stage, the output of the fuzzy inference is converted into a specific decision, such as "charge", "discharge" or "maintain the status quo", i.e. the energy flow awareness index, whose input is the fuzzy inference output and the output is the defuzzified decision. As shown in Figure 3 the first level fuzzy controller, the time-of-use price Cost and the grid voltage V are used to obtain the energy flow awareness index SPF of the electric vehicle to determine the working mode of the electric vehicle, including:

[0052] S101, the precise time-of-use price Cost and the grid voltage V are converted into fuzzy grid voltage V-fuzzy and fuzzy cost Cost-fuzzy using the pre-defined membership function, respectively;

[0053] S102, the fuzzy grid voltage V-fuzzy and the fuzzy cost Cost-fuzzy are subjected to fuzzy inference according to the fuzzy rules in the pre-defined fuzzy rule base to obtain the fuzzy state of the electric vehicle connection mode and its priority;

[0054] S103, the fuzzy state of the electric vehicle connection mode and its priority is defuzzified to obtain the energy flow awareness index SPF of the electric vehicle to determine the connection mode of the electric vehicle, and the obtained energy flow awareness index SPF of the electric vehicle is a number with a value range of -1 to 1, and the energy flow awareness index SPF of the electric vehicle is represented by the positive and negative characteristics of the value to indicate that the connection mode of the electric vehicle is "charging" or "discharging" (V2G or G2V), and the energy flow awareness index SPF is 0, indicating that the electric vehicle "maintains the status quo" and does not charge or discharge.

[0055] In step S101 of the embodiment, the precise time-of-use electricity price Cost and the grid voltage V are converted into the fuzzy grid voltage V-fuzzy and the fuzzy electricity price Cost-fuzzy using the preset membership functions, respectively. The time-of-use electricity price Cost includes the membership degrees of three levels corresponding to the low time-of-use electricity price region DT (Down Time), the medium time-of-use electricity price region OP (Off-Peak), and the high time-of-use electricity price region PT (Peak Time), respectively. The energy cost of the three ranges is typical in the current power system, in which the energy cost mainly includes the vehicle-grid multi-time-space interaction considering the regional time-of-use electricity price.

[0056] The grid voltage V includes the membership degrees of five levels, i.e., low L (Low) representing the case that the grid voltage is lower than the normal or expected level, medium low ML (Medium Low) representing the case that the grid voltage is slightly lower than the medium level, medium M (Medium) representing the case that the grid voltage is at the normal or expected medium level, medium high MH (Medium High) representing the case that the grid voltage is slightly higher than the medium level, and high H (High) representing the case that the grid voltage is higher than the normal or expected level. The membership function is a triangular membership function or a trapezoidal membership function.

[0057] In the embodiment, in step S102, the fuzzy grid voltage V-fuzzy and the fuzzy electricity price Cost-fuzzy are subjected to fuzzy reasoning according to the fuzzy rules in the predefined fuzzy rule base to obtain the fuzzy state of the electric vehicle connection mode and its priority. The fuzzy state includes five different levels of fuzzy state, i.e., very negative VN, negative N, maintain M, positive P, and very positive VP. The very negative level means that the electric vehicle is completely discharged in the grid; the negative level means that the electric vehicle is discharged; at the "maintain" level, the electric vehicle is neither charged nor discharged; finally, the positive and very positive levels mean that the electric vehicle is charged, which is contrary to the negative and very negative levels.

[0058] In this embodiment, the fuzzy rules in the predefined fuzzy rule base include: when the time-of-use electricity price Cost is in the low time-of-use electricity price region DT, and the grid voltage V is low L, the output fuzzy state is very positive VP; when the time-of-use electricity price Cost is in the high time-of-use electricity price region PT, and the grid voltage V is high H, the output fuzzy state is very negative VN; when the time-of-use electricity price Cost is in the medium time-of-use electricity price region OP, the output fuzzy state is one of negative N, maintain M, and positive P; when the grid voltage V is one of low-medium ML, medium M, and high-medium MH, the output fuzzy state is one of negative N, maintain M, and positive P. When the price is low (DT) and the voltage is low (L), the output is Very Positive (VP), which means that the EV should charge as much as possible. This is because the charging cost is low when the price is low, and the low voltage may mean that the grid needs more power to raise the voltage, so charging at this time is beneficial to both the grid and the user. When the price is high (PT) and the voltage is high (H), the output is Very Negative (VN), which means that the EV should discharge as much as possible. This is because selling electricity to the grid at a high price can generate higher revenue, and a high voltage may mean that the grid is heavily loaded and needs to be relieved by discharging. For the case of medium price and voltage, the output value varies between Negative and Positive, indicating that the EV can discharge or charge appropriately according to the specific situation of the grid voltage and price. The knowledge base rules are defined in a clear and precise manner, making them easy to understand. The design of fuzzy rules is based on expert knowledge. Expert knowledge includes configuring all parameters of the fuzzy system using the actual experience of the research team. Therefore, by studying the behavior of the electric vehicle charging and discharging process and the variables that the controller can measure, fuzzy rules are developed to make realistic decisions for V2G or G2V mode. For example, if the price is DT (low price), and the grid voltage is L (lower than the reference value), the output of the controller is VP, i.e. the battery should be charged with a very high probability. The output of this level is the input of the next level. The rule can be explained as follows: under the first premise, the user benefits, and under the second premise, the grid load increases, thereby also increasing the voltage.

[0059] The execution process of the two-stage cascade fuzzy controller in the PP phase is as follows Figure 3The current battery state data is collected from the battery management system of the electric vehicle during the input data collection stage, while receiving the output of the SPF stage as the preliminary decision of charging or discharging, and obtaining the remaining departure time specified by the user through the mobile application or the vehicle-mounted system. In the fuzzification stage, the precise battery state, the remaining departure time and the output of the SPF stage are converted into fuzzy sets using the membership function; the input is the battery state (SOC), the user-specified remaining departure time (TRD), the output of the SPF stage (SPF Output), and the output is the fuzzified battery state (SoC_fuzzy), the fuzzified remaining departure time (TRD_fuzzy), and the fuzzified SPF output (SPF_Output_fuzzy). In the fuzzy reasoning stage of applying fuzzy rules, the appropriate charging and discharging capacity is evaluated according to the pre-defined fuzzy rule base, combined with the battery state, the remaining departure time and the output of the SPF stage; the input is the fuzzified battery state (SoC_fuzzy), the fuzzified remaining departure time (TRD_fuzzy), and the fuzzified SPF output (SPF_Output_fuzzy), and the output is the fuzzy reasoning output. In the defuzzification stage, the output of the fuzzy reasoning is converted into a specific decision, such as the power percentage of charging or discharging; the input is the fuzzy reasoning output, and the output is the defuzzified decision. As shown in Figure 4 In this embodiment, the energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD are used to obtain the power percentage of charging or discharging of the electric vehicle and control the charging pile accessed by the electric vehicle based on the power percentage by using the second fuzzy controller, including:

[0060] S201, the precise energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD are respectively converted into the fuzzified energy flow perception index SPF SPF_Output_fuzzy, the fuzzified state of charge SOC_fuzzy and the fuzzified remaining departure time TRD_fuzzy using the pre-defined membership function;

[0061] S202, the fuzzified energy flow perception index SPF SPF_Output_fuzzy, the fuzzified state of charge SOC_fuzzy and the fuzzified remaining departure time TRD_fuzzy are subjected to fuzzy reasoning according to the fuzzy rules in the pre-defined fuzzy rule base to obtain the fuzzy state of the electric vehicle output power level;

[0062] S203, the fuzzy state of the electric vehicle output power level is defuzzified to obtain the power percentage of charging or discharging of the electric vehicle.

[0063] In step S201 of this embodiment, when using preset membership functions to convert the precise electric vehicle energy flow perception index SPF, electric vehicle state of charge (SOC), and remaining departure time (TRD) into fuzzy energy flow perception index SPF_Output_fuzzy, fuzzy state of charge (SOC)_fuzzy, and fuzzy remaining departure time (TRD)_fuzzy, respectively; the energy flow perception index SPF includes five membership functions, corresponding to five membership values: extremely negative VN, negative N, maintenance M, positive P, and extremely positive VP; the state of charge (SOC) includes five membership functions, corresponding to very low VL, low L, medium M, high H, and very high VL. H has five membership values, and the remaining departure time TRD includes three membership functions, corresponding to short S, medium M, and long L membership values ​​respectively. The membership functions are triangular membership functions or trapezoidal membership functions. In step S202, when the fuzzy energy flow perception index SPFSPF_Output_fuzzy, the fuzzy state of charge SOC_fuzzy, and the fuzzy remaining departure time TRD_fuzzy are fuzzy reasoned according to the fuzzy rules in the predefined fuzzy rule base to obtain the fuzzy state of the electric vehicle's charging and discharging power, the fuzzy state of the electric vehicle's output power level includes five fuzzy states: extremely positive VP, positive P, sustained M, negative N, and extremely negative VN.

[0064] Electric vehicles are not always stationary; at any given time, they may be dispersed throughout an area. This embodiment considers the scenario of electric vehicles being used for daily commuting, and the relationship between the cumulative percentage of vehicles and the average daily mileage is as follows: Figure 5 As shown. For commuting electric vehicles, it is assumed that the vehicles are idle for an average of 22 hours per day. When the commuting distance is less than the potential range of the electric vehicle, not all the energy in the battery is consumed by the commuter. Each electric vehicle can be considered as a potential source of energy and available capacity, which can be utilized by the grid in addition to providing load for the electric vehicle. Different types of charging load probability parameters are shown. for:

[0065] ,

[0066] In the formula: The charging start time for the i-th electric vehicle; Standard deviation of the charging start time for electric vehicles; The average starting time for charging this type of electric vehicle. Considering the state of charge (SOC) of an electric vehicle, which typically decreases as energy is withdrawn from the battery and increases as energy is absorbed, the SOC will follow a similar pattern throughout the day. For example, if an electric vehicle owner goes to work in the morning, parks the vehicle, returns home in the afternoon, and then charges the vehicle in the evening, the SOC will follow a similar pattern.Figure 5 The state of charge SOC of the electric vehicle is obtained based on the current time querying the given state of charge SOC-time curve, as shown in the following formula: Figure 6 As shown in the figure, the given state of charge SOC-time curve is a piecewise linear function of the state of charge SOC (s.o.c.) with respect to time; the remaining departure time TRD is the time difference between the current time and the charging departure time, wherein the calculation function expression of the charging departure time is: Figure 6

[0067] ,

[0068] In the above formula, is the charging departure time of the i-th electric vehicle, is the start charging time of the i-th electric vehicle, is the charging time interval, is the electric vehicle capacity, is the initial state of charge of the i-th electric vehicle, is the electric vehicle charging efficiency, is the charging or discharging power of the i-th electric vehicle. When the state of charge SOC of the electric vehicle exceeds 60%, BV acts as a supply-side resource, and the battery releases energy; when the state of charge SOC is lower than 60%, BV acts as a demand-side resource, and the battery absorbs energy. Considering the battery as a supply-side and demand-side resource as a function of the state of charge SOC, Figure 7 a schematic diagram of BV is shown in the figure, wherein BV represents the electric vehicle, and C is the total state of charge SOC of the electric vehicle. When the electric vehicle is connected to the power grid, the battery power of the electric vehicle is transmitted to the power grid in reverse, and the following constraints need to be met:

[0069] ,

[0070] In the above formula, is the maximum value of the initial state of charge of the i-th electric vehicle.

[0071] ​The rule base of the PP stage is defined in five different tables, each corresponding to one membership function of the input variable V2G. In these tables, the final singleton outputs of the two-level cascade controller can be seen as: very positive (VP), positive (P), maintain (M), negative (N) and very negative (VN). In the SPF stage, the connection mode and priority of the EV has already been decided according to the grid voltage and electricity price, and then the operation is further refined on this basis. For example, if the first stage decides that the EV should discharge (V2G is VN), then according to different SOC and TRD values, the controller will decide the specific proportion of discharge. If the SOC of the EV is very high and the TRD is long, then the EV can discharge more (for example, VN level), because the user does not need to use the vehicle in the short term, and the battery has enough power to discharge. Conversely, if the SOC is low and the TRD is short, then the EV can only need a small amount of discharge (for example, N level) to ensure that the user has enough power when he leaves. The fuzzy rules in the pre-defined fuzzy rule base in this embodiment are as follows: ① When the membership value of the energy flow perception index SPF is very negative VN: if the membership value of the remaining departure time TRD is short S, then the output power levels corresponding to the VL, L, M, H and VH membership values of the state of charge SOC are VP, VP, VP, VP, P respectively; if the membership value of the remaining departure time TRD is medium M, then the output power levels corresponding to the VL, L, M, H and VH membership values of the state of charge SOC are VP, VP, VP, P, P respectively; if the membership value of the remaining departure time TRD is long L, then the output power levels corresponding to the VL, L, M, H and VH membership values of the state of charge SOC are VP, VP, P, P, P respectively; ② When the membership value of the energy flow perception index SPF is negative N: if the membership value of the remaining departure time TRD is short S, then the output power levels corresponding to the VL, L, M, H and VH membership values of the state of charge SOC are VP, VP, VP, VP, P respectively; if the membership value of the remaining departure time TRD is medium M, then the output power levels corresponding to the VL, L, M, H and VH membership values of the state of charge SOC are VP, VP, VP, P, M respectively; if the membership value of the remaining departure time TRD is long L, then the output power levels corresponding to the VL, L, M, H and VH membership values of the state of charge SOC are VP, VP, P, M, M respectively; ③ When the membership value of the energy flow perception index SPF is maintain M: if the membership value of the remaining departure time TRD is short S, then the output power levels corresponding to the VL, L, M, H and VH membership values of the state of charge SOC are VP, VP, VP, P, M respectively; if the membership value of the remaining departure time TRD is medium M, then the output power levels corresponding to the VL, L, M, H and VH membership values of the state of charge SOC are VP, P, P, P, M respectively;If the membership value of the remaining departure time TRD is long L, then the output power levels corresponding to the five membership values of the state of charge SOC are P, P, P, M, M, respectively; 4) when the membership value of the energy flow perception index SPF is positive P: if the membership value of the remaining departure time TRD is short S, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, VP, P, M, N, respectively; if the membership value of the remaining departure time TRD is medium M, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, P, M, M, N, respectively; if the membership value of the remaining departure time TRD is long L, then the output power levels corresponding to the five membership values of the state of charge SOC are P, P, M, N, VN, respectively; 5) when the membership value of the energy flow perception index SPF is very positive VP: if the membership value of the remaining departure time TRD is short S, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, VP, P, N, VN, respectively; if the membership value of the remaining departure time TRD is medium M, then the output power levels corresponding to the five membership values of the state of charge SOC are P, P, M, N, VN, respectively; if the membership value of the remaining departure time TRD is long L, then the output power levels corresponding to the five membership values of the state of charge SOC are M, N, VN, VN, VN, respectively.

[0072] The two-stage fuzzy controller design is adopted in this embodiment, and the two-stage fuzzy controller can be equipped as type I and type II fuzzy controllers. The main difference between the two controllers is that type II fuzzy logic is more complex and powerful than the traditional type I fuzzy logic system. Type II fuzzy logic system can handle the uncertainty in the input data, which is particularly important in scenarios such as grid voltage measurement or user behavior estimation, because the data in these scenarios is often inaccurate or error-prone. Both fuzzy controllers use the “Takagi-Sugeno-Kang (TKS)” method. This method is different from other methods because the output of the TKS fuzzy system is defined as a set of linear functions, while in other systems, the output is simply modeled with one or more independent variables. The fuzzy logic system is characterized by the membership functions of the inputs. The input variables are related to each other through so-called rules to obtain the output. The rules are stored in the knowledge base (KB). Therefore, in the fuzzy system, the input is fuzzified with the fuzzy sets defined in the membership functions, and is combined with the KB rules, and finally the output is obtained and de-fuzzified to get the final crisp value. Type II fuzzy logic is used in applications with very high uncertainty, so the convenience of this type of fuzzy logic system needs to be evaluated. The main difference between type II fuzzy logic and type I fuzzy logic is the shape of the membership function. Type I fuzzy logic: as shown in Figure 8 , where the ordinate is the membership value and the abscissa is the state of charge SOC. The membership function of type I fuzzy logic is accurate, and is usually defined as a fixed geometric shape, such as a triangle, trapezoid or Gaussian shape. The membership of each element is clear and single, that is, at any given point in time, the membership of an element to a certain fuzzy set is a certain numerical value. Type II fuzzy logic: as shown in Figure 9As shown, the membership functions of Type II fuzzy logic are inherently uncertain and are described by another fuzzy set called the Uncertainty Distribution Function (UDF), which can be specified as needed. Instead of a single numerical value, the membership degree of each element is now an interval, defined by the Uncertainty Distribution Function (UDF), which is itself a fuzzy set. This design allows for more flexible handling of uncertainty and fuzziness, especially in cases where the input data is highly uncertain. The fuzzy set consists of so-called footprints of uncertainty (FOU), which are associated through the rules contained in the knowledge base in the same way as for Type I. The output is also obtained in a similar manner, by combining the inputs and outputs with the knowledge rules, and the mathematical function of the output is more complex. Type II fuzzy logic systems quantify and analyze the uncertainty in the input data through the concept of footprints of uncertainty (FOU). Instead of a single membership function, this system constructs a fuzzy set consisting of multiple possible membership functions, each representing the range of uncertainty for the input data at a specific value. In this way, Type II fuzzy logic systems can more comprehensively capture and express the uncertainty of data. During the construction of the rule base, these rules take into account the uncertainty and define the output based on the FOU. After the input data is fuzzified, it is associated with the membership functions in the FOU, converting precise values into fuzzy values. The system evaluates each rule in the rule base, a process that involves fuzzy reasoning on the premise part of each rule, considering all possible combinations of membership functions. After rule evaluation, the system obtains a set of fuzzy outputs, which represent the conclusion part of the rules. Then, the system needs to aggregate these fuzzy outputs to obtain a comprehensive fuzzy output. Finally, through centroid calculation, the system converts the fuzzy output into a definite output value, a process called defuzzification. Defuzzification is the process of converting the output of a fuzzy logic system into a specific numerical value that can be used for actual control or decision-making, making Type II fuzzy logic systems particularly advantageous in handling high-uncertainty scenarios. Through the flow of funds and energy, the interaction between components is reflected, and the computer tracks the set of services received by the BV from the aggregator and the corresponding discounts, as well as the set of services provided by the BV when parking and connecting to the grid, as shown. The design of the vehicle-to-grid interactive service system for electric vehicle induction charging and discharging is realized based on the above two-level fuzzy control. Figure 10

[0073] In addition, the embodiment also provides a vehicle-to-grid interactive service system considering electric vehicle induction charging and discharging, which comprises a microprocessor and a memory connected with each other, and the microprocessor is programmed or configured to execute the vehicle-to-grid interactive service method considering electric vehicle induction charging and discharging.​

[0074] Further, the embodiment also provides a computer readable storage medium, wherein a computer program or instructions are stored, and the computer program or instructions are programmed or configured to execute the vehicle-network interaction service method considering the non-inductive charging and discharging of the electric vehicle by a processor.

[0075] Further, the embodiment also provides a computer program product, comprising a computer program or instructions, which are programmed or configured to execute the vehicle-network interaction service method considering the non-inductive charging and discharging of the electric vehicle by a processor.

[0076] Those skilled in the art should understand that the technical solutions provided by the embodiments of the present application can be in the form of a method, a system, or a computer program product. Therefore, the present application can be in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer readable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer usable program code. The present application is described with reference to flowcharts and / or block diagrams of the methods, apparatus (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus with a means for performing the functions specified in the flowcharts and / or block diagrams. These computer program instructions can also be stored in a computer readable storage medium that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a manufactured product including an instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus with a means for performing the functions specified in the flowcharts and / or block diagrams. These computer program instructions can also be stored in a computer readable storage medium that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a manufactured product including an instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus with a means for performing the functions specified in the flowcharts and / or block diagrams. These computer program instructions can also be stored in a computer readable storage medium that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a manufactured product including an instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams.

[0077] The above merely describes the preferred embodiments of the present application, and the protection scope of the present application is not limited to the above-described embodiments. Any technical solution falling within the concept of the present application shall fall within the protection scope of the present application. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the present application shall also be considered as falling within the protection scope of the present application.

Claims

1. A vehicle-to-grid interaction service method considering inductive charging and discharging of an electric vehicle, characterized in that, The method comprises the following steps: S1, obtaining the time-of-use electricity price Cost and the grid voltage V from a power grid operator; S2, obtaining the energy flow perception index SPF of the electric vehicle by using the first fuzzy controller with the time-of-use electricity price Cost and the grid voltage V for determining the connection mode of the electric vehicle; obtaining the power percentage of the electric vehicle for charging or discharging by using the second fuzzy controller with the energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD; the energy flow perception index SPF of the electric vehicle is a number ranging from -1 to 1, and the energy flow perception index SPF of the electric vehicle indicates the connection mode of the electric vehicle as "charging" or "discharging" through the positive and negative characteristics of the value, and the energy flow perception index SPF of 0 indicates that the electric vehicle "maintains the status quo" without charging or discharging; the state of charge SOC of the electric vehicle is obtained by querying the given state of charge SOC-time curve based on the current time, and the given state of charge SOC-time curve is a piecewise linear function of the state of charge SOC with respect to time; the remaining departure time TRD is the time difference between the current time and the charging departure time, wherein the calculation function expression of the charging departure time is: ， in the above formulae, is the charging departure time of the i-th electric vehicle, is the start charging time of the i-th electric vehicle, is the charging time interval, is the electric vehicle capacity, is the initial state of charge of the i-th electric vehicle, is the electric vehicle charging efficiency, is the charging or discharging power of the i-th electric vehicle; S3, controlling the charging pile accessed by the electric vehicle according to the power percentage of the electric vehicle for charging or discharging to provide the electric vehicle with charging or discharging based on the power percentage. 2.The vehicle-to-grid interactive service method of claim 1, wherein, The method for obtaining the energy flow perception index SPF of the electric vehicle by using the first fuzzy controller with the time-of-use electricity price Cost and the grid voltage V for determining the working mode of the electric vehicle comprises: S101, converting the accurate time-of-use electricity price Cost and the grid voltage V to obtain the fuzzy grid voltage V-fuzzy and the fuzzy electricity price Cost-fuzzy by using the preset membership function respectively; S102, obtaining the fuzzy state of the connection mode of the electric vehicle and its priority by fuzzy reasoning according to the fuzzy rules in the pre-defined fuzzy rule base with the fuzzy grid voltage V-fuzzy and the fuzzy electricity price Cost-fuzzy; S103, de-fuzzing the fuzzy state of the connection mode of the electric vehicle and its priority to convert the energy flow perception index SPF of the electric vehicle for determining the connection mode of the electric vehicle. 3.The vehicle-to-grid interactive service method of claim 2, wherein, In step S101, the accurate time-of-use electricity price Cost and the grid voltage V are converted into the fuzzy grid voltage V-fuzzy and the fuzzy time-of-use electricity price Cost-fuzzy by using the preset membership functions, respectively. The time-of-use electricity price Cost includes the membership degrees of three levels corresponding to the low time-of-use electricity price region DT, the medium time-of-use electricity price region OP and the high time-of-use electricity price region PT, respectively. The grid voltage V includes the membership degrees of five levels corresponding to the low L, the low-medium ML, the medium M, the high-medium MH and the high H, respectively. The membership functions are the triangular membership functions or the trapezoidal membership functions. In step S102, the fuzzy grid voltage V-fuzzy and the fuzzy time-of-use electricity price Cost-fuzzy are subjected to fuzzy reasoning according to the fuzzy rules in the predefined fuzzy rule base to obtain the fuzzy state of the electric vehicle connection mode and the priority. The fuzzy state includes five different levels of the very negative VN, the negative N, the maintenance M, the positive P and the very positive VP. When the time-of-use electricity price Cost is the low time-of-use electricity price region DT and the grid voltage V is the low L, the output fuzzy state is the very positive VP. When the time-of-use electricity price Cost is the high time-of-use electricity price region PT and the grid voltage V is the high H, the output fuzzy state is the very negative VN. When the time-of-use electricity price Cost is the medium time-of-use electricity price region OP, the output fuzzy state is one of the negative N, the maintenance M and the positive P. When the grid voltage V is one of the low-medium ML, the medium M and the high-medium MH, the output fuzzy state is one of the negative N, the maintenance M and the positive P. 4.The vehicle-to-grid interactive service method of claim 1, wherein, The power percentage of the charging or discharging of the electric vehicle is obtained by using the second fuzzy controller based on the energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD, and the charging pile accessed by the electric vehicle is controlled based on the power percentage, and the method comprises the following steps: S201, the accurate energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD are converted into the fuzzy energy flow perception index SPFSPF_Output_fuzzy, the fuzzy state of charge SOC_fuzzy and the fuzzy remaining departure time TRD_fuzzy by using the preset membership functions, respectively; S202, the fuzzy energy flow perception index SPFSPF_Output_fuzzy, the fuzzy state of charge SOC_fuzzy and the fuzzy remaining departure time TRD_fuzzy are subjected to fuzzy reasoning according to the fuzzy rules in the predefined fuzzy rule base to obtain the fuzzy state of the electric vehicle output power level. S203, defuzzifying the fuzzy state of the electric vehicle output power level to obtain the power percentage of the electric vehicle charging or discharging. 5.The vehicle-to-grid interactive service method considering non-inductive charging and discharging of an electric vehicle according to claim 4, wherein, In step S201, the accurate energy flow perception index SPF of the electric vehicle, the state of charge SOC of the electric vehicle and the remaining departure time TRD are respectively converted into the fuzzy energy flow perception index SPFSPF_Output_fuzzy, the fuzzy state of charge SOC_fuzzy and the fuzzy remaining departure time TRD_fuzzy by using the preset membership functions; the energy flow perception index SPF corresponds to five membership functions, which correspond to five membership values of very negative VN, negative N, maintaining M, positive P and very positive VP; the state of charge SOC corresponds to five membership functions, which correspond to five membership values of very low VL, low L, middle M, high H and very high VH; the remaining departure time TRD corresponds to three membership functions, which correspond to three membership values of short S, middle M and long L; the membership functions are triangular membership functions or trapezoidal membership functions; in step S202, the fuzzy energy flow perception index SPFSPF_Output_fuzzy, the fuzzy state of charge SOC_fuzzy and the fuzzy remaining departure time TRD_fuzzy are subjected to fuzzy reasoning according to the fuzzy rules in the predefined fuzzy rule base to obtain the fuzzy state of the electric vehicle charging and discharging power; the fuzzy state of the electric vehicle output power level includes five fuzzy states of very positive VP, positive P, maintaining M, negative N and very negative VN; ① when the membership value of the energy flow perception index SPF is very negative VN: if the membership value of the remaining departure time TRD is short S, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, VP, VP, VP and P; if the membership value of the remaining departure time TRD is middle M, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, VP, VP, P and P; if the membership value of the remaining departure time TRD is long L, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, VP, P, P and P; ② when the membership value of the energy flow perception index SPF is negative N: if the membership value of the remaining departure time TRD is short S, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, VP, VP, P and P; if the membership value of the remaining departure time TRD is middle M, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, VP, P, P and M; if the membership value of the remaining departure time TRD is long L, the output power levels corresponding to the five membership values of VL, L, M, H and VH of the state of charge SOC are VP, P, P, M and M.③ When the membership value of the energy flow awareness index SPF is maintenance M: if the membership value of the remaining departure time TRD is short S, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, VP, VP, P, M respectively; if the membership value of the remaining departure time TRD is medium M, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, P, P, P, M respectively; if the membership value of the remaining departure time TRD is long L, then the output power levels corresponding to the five membership values of the state of charge SOC are P, P, P, M, M respectively; ④ When the membership value of the energy flow awareness index SPF is positive P: if the membership value of the remaining departure time TRD is short S, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, VP, P, M, N respectively; if the membership value of the remaining departure time TRD is medium M, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, P, M, M, N respectively; if the membership value of the remaining departure time TRD is long L, then the output power levels corresponding to the five membership values of the state of charge SOC are P, P, M, N, VN respectively; ⑤ When the membership value of the energy flow awareness index SPF is very positive VP: if the membership value of the remaining departure time TRD is short S, then the output power levels corresponding to the five membership values of the state of charge SOC are VP, VP, P, N, VN respectively; if the membership value of the remaining departure time TRD is medium M, then the output power levels corresponding to the five membership values of the state of charge SOC are P, P, M, N, VN respectively; if the membership value of the remaining departure time TRD is long L, then the output power levels corresponding to the five membership values of the state of charge SOC are M, N, VN, VN, VN respectively. 6.The vehicle-to-grid interactive service method considering non-inductive charging and discharging of an electric vehicle according to claim 1, wherein, Before obtaining the time-of-use electricity price Cost and the grid voltage V from the distribution network operator in step S1, the distribution network operator further optimizes the time-of-use electricity price Cost and its starting time according to a time-of-use electricity price model shown in the following formula: ， In the above formula, is an objective function of the time-of-use electricity price model, represents the minimum value of the objective function of the time-of-use electricity price model, is the number of distribution network regions, represents the region the load fluctuation rate of the superimposed load; represents the region the load peak-valley difference of the superimposed load.

7. A vehicle-to-grid interactive service system considering inductive charging and discharging of an electric vehicle, comprising a microprocessor and a memory connected to each other, characterized in that, The microprocessor is programmed or configured to execute the vehicle-to-grid interaction service method considering the inductive charging and discharging of the electric vehicle according to any one of claims 1-6.

8. A computer-readable storage medium having stored therein a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute the vehicle-to-grid interaction service method considering the inductive charging and discharging of the electric vehicle according to any one of claims 1-6 by the processor.

9. A computer program product comprising computer programs or instructions, characterized in that, The computer program or instructions are programmed or configured to execute the vehicle-to-grid interaction service method considering the inductive charging and discharging of the electric vehicle according to any one of claims 1-6 by the processor.

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