Vehicle network interactive service method and system considering non-inductive charging and discharging of electric vehicle

Abstract

The invention discloses a vehicle network interactive service method and system considering non-inductive charging and discharging of an electric vehicle. The method comprises the following steps: acquiring a time-of-use electricity price Cost and a power grid voltage V from a power distribution network operator; obtaining an energy flow sensing index SPF of the electric vehicle by using a first-stage fuzzy controller so as to determine a connection mode of the electric vehicle; the charging or discharging power percentage of the electric vehicle is obtained according to 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 of the electric vehicle through a second-stage fuzzy controller; and according to the charging or discharging power percentage of the electric vehicle, controlling a charging pile connected to the electric vehicle to charge or discharge the electric vehicle based on the power percentage. According to the invention, efficient and autonomous energy exchange between the electric vehicle and the power grid is realized by considering the vehicle network interaction service of non-inductive charging and discharging of the electric vehicle, and users are ensured to participate in charging and discharging of the power grid in a non-inductive manner.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a vehicle-grid interactive service method and system taking into account the inductive charging and discharging of electric vehicles. Background Art

[0002] Electric vehicles have attracted widespread attention due to their advantages in using clean energy, reducing dependence on fossil fuels, and achieving zero greenhouse gas emissions. However, with the rapid growth in the number of electric vehicles, they pose new challenges to the existing power grid infrastructure. The large-scale access of electric vehicles may cause grid overload, which in turn causes problems in the safe operation of the grid. It is predicted that by 2030, the number of electric vehicles in the world will exceed 250 million, and the additional electricity 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, they are also regarded as mobile energy storage units with the potential to store energy. Through the advancement of bidirectional charging technology, electric vehicles can realize energy exchange with the grid, the so-called vehicle-to-grid (V2G). This model allows electric vehicle users to feed back the unused electricity in their vehicles to the grid, thereby alleviating the power demand during peak hours to a certain extent and obtaining economic benefits from it. Although vehicle-to-grid V2G technology brings many benefits, there are still some obstacles, including how to effectively manage and dispatch these decentralized energy storage resources so that electric vehicle users can participate in the energy interaction of the grid without too much intervention. Summary of the invention

[0003] Technical problem to be solved by the present invention: In view of the above-mentioned problems in the prior art, a vehicle-grid interactive service method and system taking into account the senseless charging and discharging of electric vehicles are provided. The present invention aims to realize efficient and autonomous energy exchange between electric vehicles and the power grid through vehicle-grid interactive services taking into account the senseless charging and discharging of electric vehicles, thereby ensuring that users participate in the charging and discharging of the power grid senselessly.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: A vehicle-grid interactive service method taking into account the non-inductive charging and discharging of electric vehicles comprises the following steps: S1, obtains the time-of-use electricity price Cost and grid voltage V from the distribution network operator; S2, using the time-of-use electricity price Cost and the grid voltage V to obtain the energy flow perception index SPF of the electric vehicle by using the first-level fuzzy controller to determine the connection mode of the electric vehicle; using 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 to obtain the charging or discharging power percentage of the electric vehicle by using the second-level fuzzy controller; S3, according to the power percentage of charging or discharging of the electric vehicle, controlling the charging pile to which the electric vehicle is connected to provide charging or discharging for the electric vehicle based on the power percentage.

[0005] Optionally, the method of using the first-level fuzzy controller to obtain the energy flow perception index SPF of the electric vehicle by using the time-of-use electricity price Cost and the grid voltage V to determine the working mode of the electric vehicle includes: S101, using a preset membership function to convert the precise 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; S102, performing fuzzy reasoning on the fuzzified grid voltage V-fuzzy and the fuzzified electricity price Cost-fuzzy according to fuzzy rules in a predefined fuzzy rule base to obtain the fuzzy state of the electric vehicle connection mode and its priority; S103, defuzzifying the fuzzy state of the electric vehicle connection mode and its priority level to obtain the energy flow perception index SPF of the electric vehicle for determining the connection mode of the electric vehicle, 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 indicates whether the connection mode of the electric vehicle is "charging" or "discharging" through the positive and negative characteristics of the value, and when the energy flow perception index SPF is 0, it indicates that the electric vehicle "maintains the status quo" and is neither charging nor discharging.

[0006] Optionally, in step S101, when a preset membership function is used to convert the precise 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, the time-of-use electricity price Cost includes three levels of membership corresponding to the area DT with low time-of-use electricity price, the area OP with medium time-of-use electricity price, and the area PT with high time-of-use electricity price, respectively; the grid voltage V includes five levels of membership, namely, low L representing a grid voltage lower than a normal or expected level, medium-low ML representing a grid voltage slightly lower than a medium level, medium M representing a grid voltage at a normal or expected medium level, medium-high MH representing a grid voltage slightly higher than a medium level, and high H representing a grid voltage higher than a normal or expected level, and the membership function is a triangular membership function or a trapezoidal membership function; in step S102, the fuzzy When the grid voltage V-fuzzy and the fuzzified electricity price Cost-fuzzy are fuzzily inferred 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 states, namely, very negative VN, negative N, maintained M, positive P and very positive VP. When the time-of-use electricity price Cost is the area DT with low time-of-use electricity price 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 the area PT with high time-of-use electricity price 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 the area OP with medium time-of-use electricity price, the output fuzzy state is designated as one of negative N, maintained M, and positive P. When the grid voltage V is one of medium-low ML, medium M, and medium-high MH, the output fuzzy state is designated as one of negative N, maintained M, and positive P.

[0007] Optionally, the method of using the second-level fuzzy controller to obtain the charging or discharging power percentage of the electric vehicle 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 controlling the charging pile to which the electric vehicle is connected based on the power percentage includes: S201, respectively using a preset membership function to convert 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 to obtain a fuzzified energy flow perception index SPFSPF_Output_fuzzy, a fuzzified state of charge SOC_fuzzy, and a fuzzified remaining departure time TRD_fuzzy; S202, performing fuzzy reasoning on the fuzzified energy flow perception indicator SPFSPF_Output_fuzzy, the fuzzified state of charge SOC_fuzzy and the fuzzified remaining departure time TRD_fuzzy according to fuzzy rules in a predefined fuzzy rule base to obtain a fuzzy state of the output power level of the electric vehicle; S203, defuzzifying the fuzzy state of the output power level of the electric vehicle to obtain the charging or discharging power percentage of the electric vehicle.

[0008] Optionally, in step S201, the energy flow perception index SPF of the precise 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 SPF SPF_Output_fuzzy, the fuzzy state of charge SOC_fuzzy and the fuzzy remaining departure time TRD_fuzzy respectively using preset membership functions; the energy flow perception index SPF includes five membership functions, corresponding to five membership values ​​of extremely negative VN, negative N, maintained M, positive P and extremely positive VP respectively; the state of charge SOC includes five membership functions, corresponding to very low VL, low L, medium M, high H and very There are five membership values ​​for high VH, and the remaining departure time TRD includes three membership functions, which correspond to three membership values ​​of short S, medium M and long L respectively, and the membership function is a triangular membership function or a trapezoidal membership function; in step S202, the fuzzified energy flow perception indicator SPFSPF_Output_fuzzy, the fuzzified state of charge SOC_fuzzy and the fuzzified remaining departure time TRD_fuzzy are fuzzily inferred according to the fuzzy rules in the predefined fuzzy rule base to obtain the fuzzy state of the charging and discharging power of the electric vehicle, and the fuzzy state of the output power level of the electric vehicle includes five modes: extremely positive VP, positive P, maintaining M, negative N and extremely negative VN. Fuzzy state; ① When the membership value of the energy flow perception index SPF is extremely 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, P respectively; 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, P respectively; 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 respectively , 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, P respectively; 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, M respectively; 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, 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, 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, M respectively; 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, P, P, P, M respectively; if 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, P, P, P, 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 VL, L, M, H and VH of the state of charge SOC are VP, VP, P, M, N; if the membership value of the remaining departure time TRD is medium M, then the state of charge SOC is 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, 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 VL, L, M, H and VH 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 extremely 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 VL, L, M, H and VH of the state of charge SOC are P, P, M, N, VN respectively; , H and VH five membership values ​​corresponding to the output power levels are VP, VP, P, N, VN; if the membership value of the remaining departure time TRD is medium M, then the five membership values ​​of SOC VL, L, M, H and VH five membership values ​​corresponding to the output power levels are P, P, M, N, VN; if the membership value of the remaining departure time TRD is long L, then the five membership values ​​of SOC VL, L, M, H and VH five membership values ​​corresponding to the output power levels are M, N, VN, VN, VN. ;

[0009] Optionally, the state of charge SOC of the electric vehicle is obtained by querying a 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 formula, is the charging departure time of the i-th electric vehicle, is the starting charging time of the i-th electric vehicle, is the charging interval, is the electric vehicle capacity, is the initial state of charge of the ith electric vehicle, Charging efficiency for electric vehicles, is the charging or discharging power of the i-th electric vehicle.

[0010] Optionally, 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 also optimizes and solves 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: , In the above formula, is the objective function of the time-of-use electricity price model, Indicates the minimum value of the objective function of the time-of-use electricity price model. is the number of distribution network areas, Indicates area Load fluctuation rate of superimposed load; Indicates area The load peak-to-valley difference of the superimposed load.

[0011] In addition, the present invention also provides a vehicle-grid interactive service system taking into account the non-contact charging and discharging of electric vehicles, comprising an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the vehicle-grid interactive service method taking into account the non-contact charging and discharging of electric vehicles.

[0012] In addition, the present invention also provides a computer-readable storage medium, in which a computer program or instruction is stored. The computer program or instruction is programmed or configured to execute the vehicle-grid interactive service method taking into account the senseless charging and discharging of electric vehicles through a processor.

[0013] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the vehicle-grid interactive service method taking into account the senseless charging and discharging of electric vehicles through a processor.

[0014] Compared with the prior art, the present invention mainly has the following advantages: the method of the present invention comprises obtaining the time-of-use electricity price Cost and the grid voltage V from the distribution network operator; using the first-level fuzzy controller to obtain the energy flow perception index SPF of the electric vehicle for determining the connection mode of the electric vehicle; using the second-level fuzzy controller to obtain the charging or discharging power percentage of the electric vehicle 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; according to the charging or discharging power percentage of the electric vehicle, the charging pile connected to the electric vehicle is controlled to provide charging or discharging for the electric vehicle based on the power percentage. The present invention realizes efficient and automated energy exchange between the electric vehicle and the grid by taking into account the vehicle-grid interactive service of the electric vehicle's senseless charging and discharging, thereby ensuring that users participate in the charging and discharging of the grid senselessly. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the basic flow of the method of the embodiment of the present invention.

[0016] Figure 2 It is a design flow chart of the vehicle-network interactive service system in an embodiment of the present invention.

[0017] Figure 3 It is an application flow chart of the SPF stage of the two-stage cascade fuzzy controller in an embodiment of the present invention.

[0018] Figure 4 It is an application flow chart of the PP stage of the middle cascade fuzzy controller in an embodiment of the present invention.

[0019] Figure 5 : is the relationship between the cumulative percentage of vehicles and the average daily travel mileage in the embodiment of the present invention.

[0020] Figure 6 Graph 1 shows the SOC change of a chargeable and dischargeable electric vehicle on a typical day in an embodiment of the present invention.

[0021] Figure 7 Schematic diagram showing the relationship between SOC and battery power of a rechargeable and dischargeable electric vehicle in an embodiment of the present invention.

[0022] Figure 8 It is a function diagram of type I fuzzy logic used in the embodiment of the present invention.

[0023] Fig. 9 It is a function diagram of type II fuzzy logic used in the embodiment of the present invention.

[0024] Fig.10 This is a schematic diagram of the vehicle-grid interaction principle of electric vehicle charging and discharging including aggregators in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] like Figure 1 As shown, the vehicle-grid interactive service method for electric vehicle non-inductive charging and discharging in this embodiment includes the following steps: S1, obtains the time-of-use electricity price Cost and grid voltage V from the distribution network operator; S2, using the time-of-use electricity price Cost and the grid voltage V to obtain the energy flow perception index SPF of the electric vehicle by using the first-level fuzzy controller to determine the connection mode of the electric vehicle; using 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 to obtain the charging or discharging power percentage of the electric vehicle by using the second-level fuzzy controller; S3, according to the power percentage of charging or discharging of the electric vehicle, controlling the charging pile to which the electric vehicle is connected to provide charging or discharging for the electric vehicle based on the power percentage.

[0027] In this embodiment, the execution subject of steps S1 to S3 is the electric vehicle charging and discharging agent. Figure 2 This is a design flow chart of the vehicle-grid interactive service system in this embodiment, including: Information collection and organization: Information collection and organization for the vehicle-grid interactive service system. Construction of charging system: SPF and PP two-level senseless charging and discharging system based on two-level fuzzy system, that is, the execution subject of the vehicle-grid interactive service method taking into account senseless charging and discharging of electric vehicles in this embodiment; Construction of interactive framework: Construction of a three-level interactive framework between electric vehicle owners, distribution network operators, and electric vehicle charging and discharging agents; distribution network operators establish a time-of-use electricity price optimization model; electric vehicle owners and agents establish a multi-time and space charging and discharging strategy optimization model; through the game between electric vehicle owners and power grid operators, economic win-win situations for both parties are achieved. Establishment of interactive service system: Finally, a vehicle-grid interactive service system taking into account senseless charging and discharging of electric vehicles is established.

[0028] In order to realize the multi-temporal and spatial interactive service system of vehicle-grid based on electricity price, it is necessary to build a three-level interactive framework between electric private car owners, distribution network operators and electric vehicle charging and discharging agents. This framework allows electric private car owners to authorize agents to dispatch their charging and discharging behaviors when they stop driving. Distribution network operators formulate differentiated regional time-of-use electricity prices to guide the charging and discharging behaviors of electric private cars, and agents help car owners formulate charging and discharging plans based on regional electricity prices. Distribution network operators formulate regional time-of-use electricity prices, while minimizing the peak-valley difference of regional expansion as the goal, and calculate the optimal start and end time of each regional peak-valley electricity price period by optimizing the time-of-use electricity price model GW. Through differentiated regional time-of-use electricity prices, electric private cars are encouraged to charge during the low-load period of the power grid and discharge during the peak period, thereby realizing the peak-shaving and valley-filling of the power grid load and reducing the load fluctuation of the power grid. This model can help distribution network operators formulate effective electricity price policies and encourage electric private car users to participate in the load management of the power grid. As an optional implementation, before obtaining the time-of-use electricity price Cost and the grid voltage V from the distribution network operator in step S1 of this embodiment, the distribution network operator also optimizes and solves 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: , In the above formula, is the objective function of the time-of-use electricity price model, Indicates the minimum value of the objective function of the time-of-use electricity price model. is the number of distribution network areas, Indicates area Load fluctuation rate of superimposed load; Indicates area The peak-to-valley difference of the superimposed load. To develop the optimal charging and discharging plan for electric private car dealers to maximize the economic benefits of electric private car users and the peak-shaving subsidy of the power grid, the dealers develop a model with the total revenue maximization of electric private car users and dealers as the objective function, where the objective function is: , Where: It represents the operating income of electric vehicle dealers; Indicates the agent's commission ratio; represents the total number of electric vehicles; It represents the benefits obtained by the vehicle from charging and discharging during the V2G (discharging) process within k days; Represents the subsidy given by the distribution network operator to the agent. Then, the particle swarm algorithm is used to solve the problem, 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-to-valley difference of the 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 the improved genetic algorithm and the particle swarm algorithm is adopted respectively. The outer layer passes the electricity price optimization result to the inner layer. The inner layer optimizes the charging and discharging plan of the electric private car participating in multi-temporal and spatial interactions based on 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 electric private car charging and discharging load in each region. The inner and outer layers alternately iterate and solve until a satisfactory result is obtained. This method can ensure that the charging and discharging behavior of electric private cars can respond effectively after the implementation of the electricity price policy.

[0029] In order to make the vehicle-grid interactive service system of electric vehicle senseless charging and discharging operate effectively, the step S2 of the vehicle-grid interactive service method of electric vehicle senseless charging and discharging is proposed to be designed based on a two-level cascade fuzzy controller. The operating principle of the two-level cascade fuzzy controller is that the controller receives input data from electric vehicles (EVs) and power grids, such as battery status, grid voltage, electricity price, etc.; then converts the precise input data into fuzzy sets, and uses membership functions to represent the degree to which these data belong to different fuzzy concepts; finally, the controller infers the fuzzified input data according to the rules in the fuzzy rule base, generates fuzzy outputs, and converts the fuzzy outputs into precise numerical values ​​for use in practical applications. At the first level, the controller determines the suitability of each electric vehicle to supply or consume energy based on the grid voltage conditions and energy price costs. SPF (Sense of the Power Flow) is a number ranging from -1 to 1, which determines the connection mode (charging / discharging) and priority of the electric vehicle. At the second level, called power percentage (PP), this layer system determines the percentage of power that the electric vehicle will consume or supply, and is done by using a mobile application or a car console. The input data required by the PP level system are the outputs of the SPF controller (V2G), SOC and TRD. It can be observed that the SPF level uses information from the grid, while the PP depends on the SOC level of the EV and the expected user behavior. The SPF decides whether to consume / provide energy, and the PP decides how much energy is exchanged.

[0030] In the SPF stage, there are two inputs in the first-level fuzzy controller: electricity price (Cost) and grid voltage (V). In the input data collection stage, the grid voltage (V) data and the current electricity price (Cost) information are collected in real time from the electric vehicle charging station. In the fuzzification stage, the membership function is used to convert the precise grid voltage and electricity price into fuzzy sets. For example, the grid voltage may be converted into fuzzy states such as "low", "medium", and "high"; its input is the grid voltage (V) and electricity price (Cost), and the output is the fuzzified grid voltage (V-fuzzy) and the fuzzified electricity price (Cost-fuzzy). In the fuzzy reasoning stage of applying fuzzy rules, the fuzzy state of the grid voltage and electricity price is evaluated according to the predefined fuzzy rule base to determine whether to charge; the input is the fuzzified grid voltage (V-fuzzy) and the fuzzified electricity price (Cost-fuzzy), and the output is the fuzzy reasoning output. In the defuzzification stage, the output of fuzzy reasoning is converted into a specific decision, such as "charging", "discharging" or "maintaining the status quo", that is, the energy flow perception indicator, whose input is the fuzzy reasoning output and the output is the defuzzified decision. Figure 3 As shown, in this embodiment, the time-of-use electricity price Cost and the grid voltage V are used to obtain the energy flow perception index SPF of the electric vehicle by using the first-level fuzzy controller to determine the working mode of the electric vehicle, including: S101, using a preset membership function to convert the precise 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; S102, performing fuzzy reasoning on the fuzzified grid voltage V-fuzzy and the fuzzified electricity price Cost-fuzzy according to fuzzy rules in a predefined fuzzy rule base to obtain the fuzzy state of the electric vehicle connection mode and its priority; S103, defuzzifying the fuzzy state of the electric vehicle connection mode and its priority level to obtain an 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 ranging from -1 to 1, and the energy flow perception index SPF of the electric vehicle indicates whether the connection mode of the electric vehicle is "charging" or "discharging" (V2G or G2V) through the positive and negative characteristics of the value, and when the energy flow perception index SPF is 0, it indicates that the electric vehicle "maintains the status quo" and is neither charged nor discharged.

[0031] In step S101 of this embodiment, when the preset membership function is used to convert the precise 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, the time-of-use electricity price Cost includes three levels of membership corresponding to the area DT (Down Time) with low time-of-use electricity prices, the area OP (Off-Peak) with medium time-of-use electricity prices, and the area PT (Peak Time) with high time-of-use electricity prices. The energy costs in these three ranges are typical in the current power system, where the energy costs mainly include the multi-temporal and spatial interactions of the vehicle grid taking into account the regional time-of-use electricity prices.

[0032] The grid voltage V includes five levels of membership, including low L (Low) representing a grid voltage lower than a normal or expected level, medium low ML (Medium Low) representing a grid voltage slightly lower than a medium level, medium M (Medium) representing a grid voltage at a normal or expected medium level, medium high MH (Medium High) representing a grid voltage slightly higher than a medium level, and high H (High) representing a grid voltage higher than a normal or expected level. The membership function is a triangular membership function or a trapezoidal membership function.

[0033] In this embodiment, in step S102, when the fuzzified grid voltage V-fuzzy and the fuzzified electricity price Cost-fuzzy are fuzzily inferred 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 states: very negative VN, negative N, maintenance M, positive P and very positive VP. The extremely negative level indicates that the electric vehicle is fully discharged in the grid; the negative pole is dielectric discharge; at the "maintenance" level, the electric vehicle is neither charged nor discharged. Finally, the positive and very positive levels both mean that the electric vehicle is charged, which is opposite to the negative and very negative levels.

[0034] In this embodiment, the fuzzy rules in the predefined fuzzy rule base include: when the time-sharing electricity price Cost is the area DT with low time-sharing electricity price and the grid voltage V is low L, the fuzzy state output is very positive VP, when the time-sharing electricity price Cost is the area PT with high time-sharing electricity price and the grid voltage V is high H, the fuzzy state output is very negative VN, when the time-sharing electricity price Cost is the area OP with medium time-sharing electricity price, the fuzzy state output is respectively designated as one of negative N, maintenance M, and positive P, when the grid voltage V is one of medium-low ML, medium M, and medium-high MH, the fuzzy state output is respectively designated as one of negative N, maintenance M, and positive P. When the electricity price is low (DT) and the voltage is low (L), the output is Very Positive (VP), which means that you should charge as much as possible. This is because the charging cost is lower when the electricity price is low, and the low voltage may mean that the grid needs more electricity to increase the voltage, so charging at this time is beneficial to both the grid and the user. When the electricity price is high (PT) and the voltage is high (H), the output is Very Negative (VN), meaning that the battery should be discharged as much as possible. This is because selling electricity to the grid when the electricity price is high can earn higher profits, while high voltage may mean that the grid is heavily loaded and needs to be discharged to relieve the burden on the grid. For the case of medium electricity price and voltage, the output value varies between Negative and Positive, indicating that the EV can be discharged or charged appropriately according to the specific situation of the grid voltage and electricity price. The knowledge base rules are defined in a clear and precise way, making them easy to understand. The design of fuzzy rules is based on expert knowledge. Expert knowledge includes using the actual experience of the research team to configure all the parameters of the fuzzy system. Therefore, by studying the behavior of the charging and discharging process of the electric vehicle and the variables that the controller can measure, fuzzy rules are developed to achieve realistic decisions on the 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, that is, 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, users benefit, and under the second premise, the grid load increases, thereby also increasing the voltage.

[0035] The execution process of the two-stage cascade fuzzy controller in the PP stage is as follows: Figure 3As shown. In the input data collection stage, the current battery status data is collected from the battery management system of the electric vehicle, and the output of the SPF stage is received as a preliminary decision on charging or discharging, and the remaining departure time specified by the user through the mobile application or the vehicle system is obtained. In the fuzzification stage, the membership function is used to convert the precise battery status, remaining departure time and the output of the SPF stage into fuzzy sets; the input is the battery status (SOC), the remaining departure time (TRD) specified by the user, and the output of the SPF stage (SPF_Output), and the output is the fuzzified battery status (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, according to the predefined fuzzy rule base, combined with the battery status, remaining departure time and the output of the SPF stage, the appropriate charging and discharging amount is evaluated; its input is the fuzzified battery status (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 fuzzy reasoning is converted into a specific decision, such as the power percentage of charging or discharging; its input is the fuzzy reasoning output, and the output is the defuzzified decision. Figure 4 As shown, 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 of the electric vehicle are used to obtain the charging or discharging power percentage of the electric vehicle by using the second-level fuzzy controller and control the charging pile connected to the electric vehicle based on the power percentage, including: S201, respectively using a preset membership function to convert 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 to obtain a fuzzified energy flow perception index SPFSPF_Output_fuzzy, a fuzzified state of charge SOC_fuzzy, and a fuzzified remaining departure time TRD_fuzzy; S202, performing fuzzy reasoning on the fuzzified energy flow perception indicator SPFSPF_Output_fuzzy, the fuzzified state of charge SOC_fuzzy and the fuzzified remaining departure time TRD_fuzzy according to fuzzy rules in a predefined fuzzy rule base to obtain a fuzzy state of the output power level of the electric vehicle; S203, defuzzifying the fuzzy state of the output power level of the electric vehicle to obtain the charging or discharging power percentage of the electric vehicle.

[0036] In step S201 of this embodiment, the energy flow perception index SPF, the state of charge SOC and the remaining departure time TRD of the electric vehicle 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 includes five membership functions, which correspond to five membership values ​​of extremely negative VN, negative N, maintained M, positive P and extremely positive VP respectively; the state of charge SOC includes five membership functions, which correspond to very low VL, low L, medium M, high H and very high VL respectively. H has five membership values, and the remaining departure time TRD includes three membership functions, which correspond to three membership values ​​of short S, medium M and long L respectively, and the membership function is a triangular membership function or a trapezoidal membership function; in step S202, the fuzzified energy flow perception indicator SPFSPF_Output_fuzzy, the fuzzified state of charge SOC_fuzzy and the fuzzified remaining departure time TRD_fuzzy are fuzzily inferred according to the fuzzy rules in the predefined fuzzy rule base to obtain the fuzzy state of the charging and discharging power of the electric vehicle, and the fuzzy states of the output power level of the electric vehicle include five fuzzy states of extremely positive VP, positive P, maintaining M, negative N and extremely negative VN.

[0037] Electric vehicles are not always stationary. At any point in time, electric vehicles may be scattered in an area. In this embodiment, electric vehicles are considered to be used for daily commuting. The relationship between the cumulative percentage of vehicles and the average daily travel mileage is as follows: Figure 5 As shown in Figure 2. For commuter electric vehicles, it is assumed that the vehicle is idle for an average of 22 h 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 regarded as a potential source of energy and available capacity that can be used by the grid in addition to providing the load of the electric vehicle. Probability parameters for different types of charging loads for: , Where: is the charging start time of the i-th electric vehicle; is the standard deviation of the charging start time for electric vehicles; is the average charging start time for this type of electric vehicle. Considering the state of charge (SOC) of an electric vehicle, SOC generally decreases when energy is withdrawn from the battery and increases when energy is absorbed by the battery. Therefore, in a day, an electric vehicle owner works in the morning, parks the electric vehicle, returns home in the afternoon, and then charges the electric vehicle in the evening. The SOC will follow the following trend: Figure 5Therefore, in this embodiment, the state of charge SOC of the electric vehicle is obtained by querying a given state of charge SOC-time curve based on the current time, such as Figure 6 As shown, the curve of the given state of charge SOC-time is the state of charge SOC ( Figure 6 The remaining departure time TRD is the time difference between the current time and the charging departure time, where the calculation function expression of the charging departure time is: , In the above formula, is the charging departure time of the i-th electric vehicle, is the starting charging time of the i-th electric vehicle, is the charging interval, is the electric vehicle capacity, is the initial state of charge of the ith electric vehicle, Charging efficiency for electric vehicles, 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. The battery represents the supply-side and demand-side resources as a function of the state of charge (SOC). Figure 7 The schematic diagram summarizes this information, where 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, its own battery power is transmitted back to the power grid, and the following constraints need to be met: , In the above formula, is the maximum value of the initial state of charge of the i-th electric vehicle.

[0038] The rule base for the PP phase is defined in five different tables, each corresponding to a membership function of the input variable V2G. In these tables, the final singleton outputs of the two-stage cascade controller can be seen as: Very Positive (VP), Positive (P), Maintain (M), Negative (N), and Very Negative (VN). The connection mode and priority of the EV have been determined according to the grid voltage and electricity price in the SPF phase, and the operation is further refined on this basis. For example, if the first stage determines that the EV should be discharged (V2G is VN), the controller will then decide the specific proportion of discharge based on different SOC and TRD values. If the EV's SOC is very high and the TRD is long, then the EV can discharge more (e.g., VN level) because the user does not need to use the vehicle in the short term and the battery has enough power to discharge. On the contrary, if the SOC is low and the TRD is short, then the EV may only need to discharge a small amount (e.g., N level) to ensure that the user has enough power when leaving. The fuzzy rules in the predefined fuzzy rule base in this embodiment are as follows: ① When the membership value of the energy flow perception index SPF is extremely negative VN: if the membership value of the remaining departure time TRD is short S, then the five membership values ​​of the state of charge SOC VL, L, M, H and VH correspond to the output power levels VP, VP, VP, VP, P respectively; if the membership value of the remaining departure time TRD is medium M, then the five membership values ​​of the state of charge SOC VL, L, M, H and VH correspond to the output power levels VP, VP, VP, VP, P respectively; The output power levels corresponding to the five membership values ​​of the state of charge SOC are VP, VP, VP, P, P; 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 VL, L, M, H and VH of the state of charge SOC are VP, VP, P, P, P; ② When the membership value of the energy flow perception indicator SPF is negative N: if the membership value of the remaining departure time TRD is short S, then the five membership values ​​of VL, L, M, H and VH of the state of charge SOC are The output power levels corresponding to the five membership values ​​of SOC VL, L, M, H and VH are VP, VP, VP, P, 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 SOC VL, L, M, H and VH are VP, VP, P, M, M. ;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, 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, M respectively; 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, P, P, P, M respectively;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 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, 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, N respectively; 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, P, M, M, N respectively; if the membership value of the remaining departure time TRD is long L, the output power levels corresponding to VL, L, M, H and VH of the state of charge SOC are VP, P, M, M, N respectively. The output power levels corresponding to the five membership values ​​of VL, L, M, H and VH are P, P, M, N, and VN; ⑤ When the membership value of the energy flow perception index SPF is extremely positive VP: 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, P, N, and VN; 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 P, P, M, N, and VN; 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 M, N, VN, VN, and VN. ;

[0039] In this embodiment, a two-stage fuzzy controller design of SPF stage and PP stage is adopted, and the two-stage fuzzy controller of SPF stage and PP stage can be configured 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 uncertainty in input data, which is particularly important in scenarios such as grid voltage measurement or user behavior estimation, because the data in these scenarios are often inaccurate or erroneous. 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. Fuzzy logic systems are characterized by membership functions of inputs. The input variables are related to each other through so-called rules to obtain outputs. The rules are stored in a knowledge base (KB). Therefore, in a fuzzy system, the input is fuzzified with the fuzzy sets defined in the membership function, combined with the KB rules, and finally the output is obtained and defuzzified to obtain the final clear value. Type II fuzzy logic is used in applications where uncertainty is very high and 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: Figure 8 As shown in the figure, the ordinate is the membership value and the abscissa is the state of charge SOC. The membership function of type I fuzzy logic is precise 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 fuzzy set is a definite value. Type II fuzzy logic: Fig. 9As shown in Figure 1, the membership function of type II fuzzy logic is itself uncertain and is described by another fuzzy set called the uncertainty distribution function (UDF), which can be specified according to actual needs. The membership of each element is no longer a single value, but an interval, which is defined by the uncertainty distribution function (UDF), which is itself a fuzzy set. This design allows for more flexible handling of uncertainty and ambiguity, especially when the uncertainty of the input data is high. The fuzzy set consists of so-called footprints of uncertainty (FOU), which are associated through rules contained in the knowledge base in the same way as type I. The output is also obtained in a similar way, by combining the input and output with 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 uncertainty footprint (FOU). This system is not limited to a single membership function, but instead constructs a fuzzy set consisting of multiple possible membership functions, each of which represents the uncertainty range of the input data at a specific value. In this way, type II fuzzy logic systems are able to capture and express the uncertainty of the data more comprehensively. During the construction of the rule base, the rules take uncertainty into account and define outputs based on the FOU. After the input data is fuzzified, it is associated with the membership function in the FOU to convert the exact value into a fuzzy value. The system evaluates each rule in the rule base, a process that involves fuzzy reasoning on the premise part of each rule, taking into account all possible combinations of membership functions. After the rule evaluation, the system will obtain a set of fuzzy outputs, which represent the conclusion part of the rule. The system then needs to aggregate these fuzzy outputs to obtain a comprehensive fuzzy output. Finally, through the centroid calculation, the system converts the fuzzy output into a clear output value, a process called defuzzification. Defuzzification is the conversion of the output of the fuzzy logic system into a specific numerical value that can be used for actual control or decision-making, which gives the Type II fuzzy logic system a clear advantage in dealing with high uncertainty scenarios. Through the flow of funds and energy, reflecting the interaction between the components, the computer tracks the set of services and corresponding discounts received by the BV from the aggregator, as well as the set of services provided by the BV when it is parked and connected to the grid, such as Fig.10 Based on the above two-level fuzzy control, the design of the vehicle-grid interactive service system for electric vehicle non-inductive charging and discharging is realized.

[0040] In addition, this embodiment also provides a vehicle-grid interactive service system taking into account the non-contact charging and discharging of electric vehicles, including an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the vehicle-grid interactive service method taking into account the non-contact charging and discharging of electric vehicles.

[0041] In addition, this embodiment also provides a computer-readable storage medium, in which a computer program or instruction is stored. The computer program or instruction is programmed or configured to execute the vehicle-grid interactive service method taking into account the senseless charging and discharging of electric vehicles through a processor.

[0042] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the vehicle-grid interactive service method taking into account the senseless charging and discharging of electric vehicles through a processor.

[0043] Those skilled in the art should understand that the technical solutions provided by the embodiments of the present application may be in the form of methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, 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 device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0044] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A vehicle-grid interactive service method taking into account the non-inductive charging and discharging of electric vehicles, characterized in that: The steps include: S1, obtains the time-of-use electricity price Cost and grid voltage V from the distribution network operator; S2, using the time-of-use electricity price Cost and the grid voltage V to obtain the energy flow perception index SPF of the electric vehicle by using the first-level fuzzy controller to determine the connection mode of the electric vehicle; using 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 to obtain the charging or discharging power percentage of the electric vehicle by using the second-level fuzzy controller; S3, according to the power percentage of charging or discharging of the electric vehicle, controlling the charging pile to which the electric vehicle is connected to provide charging or discharging for the electric vehicle based on the power percentage.

2. The vehicle-grid interactive service method taking into account the non-inductive charging and discharging of electric vehicles according to claim 1 is characterized in that: The method of using the first-level fuzzy controller to obtain the energy flow perception index SPF of the electric vehicle using the time-of-use electricity price Cost and the grid voltage V to determine the working mode of the electric vehicle includes: S101, respectively using a preset membership function to convert the precise 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; S102, performing fuzzy reasoning on the fuzzified grid voltage V-fuzzy and the fuzzified electricity price Cost-fuzzy according to fuzzy rules in a predefined fuzzy rule base to obtain the fuzzy state of the electric vehicle connection mode and its priority; S103, defuzzifying the fuzzy state of the electric vehicle connection mode and its priority level to obtain the energy flow perception index SPF of the electric vehicle for determining the connection mode of the electric vehicle, 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 indicates whether the connection mode of the electric vehicle is "charging" or "discharging" through the positive and negative characteristics of the value, and when the energy flow perception index SPF is 0, it indicates that the electric vehicle "maintains the status quo" and is neither charged nor discharged.

3. The vehicle-grid interactive service method taking into account the inductive charging and discharging of electric vehicles according to claim 2 is characterized in that: In step S101, when the preset membership function is used to convert the precise 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, the time-of-use electricity price Cost includes three levels of membership corresponding to the area DT with low time-of-use electricity price, the area OP with medium time-of-use electricity price, and the area PT with high time-of-use electricity price, respectively. The grid voltage V includes five levels of membership, namely, low L representing the situation where the grid voltage is lower than the normal or expected level, medium-low ML representing the situation where the grid voltage is slightly lower than the medium level, medium M representing the situation where the grid voltage is at the normal or expected medium level, medium-high MH representing the situation where the grid voltage is slightly higher than the medium level, and high H representing the situation where the grid voltage is higher than the normal or expected level. The membership function is a triangular membership function or a trapezoidal membership function. In step S102, the fuzzy grid voltage V-fuzzy is converted into the fuzzy grid voltage V-fuzzy. When the voltage V-fuzzy and the fuzzified electricity price Cost-fuzzy are fuzzily inferred 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 states, namely, very negative VN, negative N, maintained M, positive P and very positive VP. When the time-of-use electricity price Cost is the area DT with low time-of-use electricity price 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 the area PT with high time-of-use electricity price 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 the area OP with medium time-of-use electricity price, the output fuzzy state is designated as one of negative N, maintained M, and positive P. When the grid voltage V is one of medium-low ML, medium M, and medium-high MH, the output fuzzy state is designated as one of negative N, maintained M, and positive P.

4. The vehicle-grid interactive service method taking into account the inductive charging and discharging of electric vehicles according to claim 1, characterized in that: The method of using the second-level fuzzy controller to obtain the charging or discharging power percentage of the electric vehicle based on the energy flow perception index SPF, the state of charge SOC and the remaining departure time TRD of the electric vehicle and controlling the charging pile to which the electric vehicle is connected based on the power percentage includes: S201, respectively using a preset membership function to convert 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 to obtain a fuzzified energy flow perception index SPFSPF_Output_fuzzy, a fuzzified state of charge SOC_fuzzy, and a fuzzified remaining departure time TRD_fuzzy; S202, performing fuzzy reasoning on the fuzzified energy flow perception indicator SPFSPF_Output_fuzzy, the fuzzified state of charge SOC_fuzzy and the fuzzified remaining departure time TRD_fuzzy according to fuzzy rules in a predefined fuzzy rule base to obtain a fuzzy state of the output power level of the electric vehicle; S203, defuzzifying the fuzzy state of the output power level of the electric vehicle to obtain the charging or discharging power percentage of the electric vehicle.

5. The vehicle-grid interactive service method taking into account the inductive charging and discharging of electric vehicles according to claim 4 is characterized in that: In step S201, 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 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 using the preset membership functions; the energy flow perception index SPF includes five membership functions, which correspond to five membership values ​​of extremely negative VN, negative N, maintained M, positive P and extremely positive VP respectively; the state of charge SOC includes five membership functions, which correspond to very low VL, low L, medium M, high H and very high VL respectively. H has five membership values, and the remaining departure time TRD includes three membership functions, which correspond to three membership values ​​of short S, medium M and long L respectively, and the membership function is a triangular membership function or a trapezoidal membership function; in step S202, the fuzzified energy flow perception indicator SPFSPF_Output_fuzzy, the fuzzified state of charge SOC_fuzzy and the fuzzified remaining departure time TRD_fuzzy are fuzzily inferred according to the fuzzy rules in the predefined fuzzy rule base to obtain the fuzzy state of the charging and discharging power of the electric vehicle, and the fuzzy state of the output power level of the electric vehicle includes five modes: extremely positive VP, positive P, maintaining M, negative N and extremely negative VN. Fuzzy state; ① When the membership value of the energy flow perception index SPF is extremely 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, P respectively; 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, P respectively; 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, 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, 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, P, P, 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, M;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, 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, M respectively; 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, P, P, P, M respectively; if 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, P, P, P, 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 VL, L, M, H and VH of the state of charge SOC are VP, VP, P, M, N; if the membership value of the remaining departure time TRD is medium M, then the state of charge SOC is 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, 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 VL, L, M, H and VH 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 extremely 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 VL, L, M, H and VH of the state of charge SOC are P, P, M, N, VN respectively; , H and VH five membership values ​​corresponding to the output power levels are VP, VP, P, N, VN; if the membership value of the remaining departure time TRD is medium M, then the five membership values ​​of SOC VL, L, M, H and VH five membership values ​​corresponding to the output power levels are P, P, M, N, VN; if the membership value of the remaining departure time TRD is long L, then the five membership values ​​of SOC VL, L, M, H and VH five membership values ​​corresponding to the output power levels are M, N, VN, VN, VN. ; 6. The vehicle-grid interactive service method taking into account the inductive charging and discharging of electric vehicles according to claim 4 is characterized in that: The state of charge SOC of the electric vehicle is obtained by querying a 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 formula, is the charging departure time of the i-th electric vehicle, is the starting charging time of the i-th electric vehicle, is the charging interval, is the electric vehicle capacity, is the initial state of charge of the ith electric vehicle, Charging efficiency for electric vehicles, is the charging or discharging power of the i-th electric vehicle.

7. The vehicle-grid interactive service method taking into account the inductive charging and discharging of electric vehicles according to claim 1 is characterized in that: 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 also optimizes and solves 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: ， In the above formula, is the objective function of the time-of-use electricity price model, Indicates the minimum value of the objective function of the time-of-use electricity price model. is the number of distribution network areas, Indicates area Load fluctuation rate of superimposed load; Indicates area The load peak-to-valley difference of the superimposed load.

8. A vehicle-grid interactive service system taking into account the non-inductive charging and discharging of electric vehicles, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the vehicle-grid interactive service method taking into account the inductive charging and discharging of electric vehicles as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute the vehicle-grid interactive service method taking into account the senseless charging and discharging of electric vehicles as described in any one of claims 1 to 7 through a processor.

10. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute the vehicle-grid interactive service method taking into account the senseless charging and discharging of electric vehicles as described in any one of claims 1 to 7 through a processor.

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