Electric vehicle charging method and system based on hierarchical optimization

Abstract

The invention discloses an electric vehicle charging method and system based on hierarchical optimization, and relates to the field of new energy, and the method comprises the steps that a server obtains the charging duration according to the charging information inputted by a user, calculates the charging cost, and sends the charging cost to a user side for the user to make a decision; and according to the charging decision of the user, the server optimizes the charging strategy of the electric vehicle by taking the minimum total load variance of the power distribution network as an objective function and taking the optimal charging time period as a constraint condition, and issues the optimal charging time period to the charging pile, and the optimal charging time period is used for representing the charging time period range corresponding to the minimum charging cost of the user. The charging cost of the user and the overall stability of the power grid are both considered, the calculated amount and the communication requirement of the system are low, and the system cost is not increased.

Description

Technical Field

[0001] The present invention relates to the field of new energy, and in particular to an electric vehicle charging method and system based on hierarchical optimization. Background Art

[0002] As a new energy vehicle, electric vehicles have the characteristics of transformation and cleanliness. They have great advantages in alleviating energy shortages and reducing environmental pollution. At present, they have become the strategic development goal of countries around the world. At the same time, large-scale electric vehicle charging will bring challenges to the safe operation of the distribution network. If proper regulation is not carried out, the load fluctuation of the distribution network will intensify, and it will bring about adverse effects such as equipment overload and voltage power factor reduction. The significance of studying the orderly charging strategy of electric vehicles is mainly as follows: (1) The orderly charging strategy can help balance charging demand and supply, maximize the use of renewable energy and electricity during off-peak hours, and improve energy efficiency. (2) Through the orderly charging strategy, the overload and peak demand of the power grid can be avoided, which helps to maintain the stable operation of the power grid. (3) Improve the convenience and reliability of user charging and avoid charging congestion and long waiting time.

[0003] In order to ensure the safe operation of the distribution network and reduce the impact of disorderly charging of electric vehicles, scholars at home and abroad have carried out research on the optimization strategy of electric vehicle charging. At present, the charging optimization strategy of electric vehicles is mainly divided into two types: centralized charging optimization and hierarchical charging optimization. Among them, the centralized charging optimization method refers to the centralized optimization of all electric vehicles connected in the current period according to the established charging optimization model. This optimization method has a large amount of calculation and usually requires a large amount of information transmission, which is not conducive to the real-time optimization of electric vehicle charging. Although the common hierarchical charging optimization strategy can reduce the difficulty of communication, it is easy to fall into the problem of ignoring the charging cost and the complexity of information transmission. Therefore, it is necessary to conduct in-depth research on the hierarchical charging optimization strategy of electric vehicles. In the existing research on electric vehicle charging optimization strategy, indicators such as power grid stability, cost-effectiveness, energy utilization efficiency and environmental impact are usually used for measurement. The present invention takes into account both power grid stability and cost-effectiveness, with the goal of minimizing user charging costs and minimizing distribution network load variance. Summary of the invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide an orderly charging method for electric vehicles with the goal of minimizing user charging costs and minimizing distribution network load variance, taking into account both user charging costs and the overall stability of the power grid.

[0005] In order to achieve the above technical objectives, the present application provides a method for charging an electric vehicle based on hierarchical optimization, comprising the following steps:

[0006] In response to the user's decision on the charging plan, the server obtains the charging time according to the charging information entered by the user, calculates the charging fee, and sends it to the user for the user to decide;

[0007] In response to executing the user's charging decision, according to the user's charging decision, the server takes the minimum variance of the total load of the distribution network as the objective function, takes the optimal charging period as a constraint, optimizes the electric vehicle charging strategy, and sends it to the charging pile, where the optimal charging period is used to represent the charging period range corresponding to the lowest charging cost for the user.

[0008] Preferably, in the process of acquiring the charging information, the time of accessing and leaving the charging pile and the battery power expected to be reached after charging are acquired as the charging information.

[0009] Preferably, when obtaining the charging time, the charging time is expressed as:

[0010]

[0011] In the formula, the initial power percentage of the electric vehicle when it is connected to the charging pile is expressed as SOC c SOC is the percentage of power that users expect their vehicles to reach after charging. e The vehicle battery capacity is represented by ET, and the charging power is represented by P c express.

[0012] Preferably, when calculating the charging cost, the peak electricity price period and the electricity price of the corresponding period, the normal electricity price period and the electricity price of the corresponding period, and the valley electricity price period and the electricity price of the corresponding period are taken into consideration; if the local electricity price model adopts a two-stage electricity price, the normal electricity price and the valley electricity price are set to the same.

[0013] Preferably, when calculating the charging fee, the charging time range and charging fee with the lowest charging fee for the user are calculated, and the charging time range and charging fee are transmitted to the user, wherein the charging fee is expressed as:

[0014]

[0015] In the formula, J b The time period to which the vehicle is connected to the charging pile; J e The time period to which the user sets the time to leave the charging station; Δt is the duration of each time period; c j is the charging electricity price in the jth period; P j is the charging power of the electric vehicle in time period j, where if the vehicle is charged in time period j, then P j Equal to P c , if the vehicle does not charge in time period j, then P j is equal to 0.

[0016] Preferably, when calculating the charging cost, the constraint condition of the battery capacity of the electric vehicle after charging is:

[0017]

[0018] Preferably, in the process of obtaining the objective function, the objective function is expressed as:

[0019]

[0020] Where T is the total number of time periods in a 24-hour day; N is the total number of electric vehicles connected to the charging pile in the jth time period; P L,j is the conventional load of the distribution network in the jth period; P i,j is the charging power of vehicle i in time period j, P avr is the average value of the total load of the distribution network during the optimization period, and the expression is:

[0021]

[0022] Assume that the vehicle charging period range obtained by the optimization method in step 2 is [J cb,i ,J ce,i ], then the constraints satisfied by the charging period of electric vehicles are:

[0023] J cb,i ≤J i ≤J ce,i

[0024] In the formula, J i is the charging period of vehicle i; at the same time, the battery capacity of the electric vehicle also meets the constraint condition of the battery capacity of the electric vehicle after charging is completed.

[0025] Preferably, in the process of optimizing the electric vehicle charging strategy, an improved particle swarm algorithm is used to solve the optimal charging strategy.

[0026] The present invention also discloses an electric vehicle charging system based on hierarchical optimization, which is used to implement the above-mentioned electric vehicle charging method based on hierarchical optimization, comprising:

[0027] The charging planning module is used to respond to the user's decision on the charging plan. The server obtains the charging time and calculates the charging fee based on the charging information entered by the user, and sends it to the user for the user to make a decision;

[0028] The charging strategy optimization module is used to respond to and execute the user's charging decision. According to the user's charging decision, the server takes the minimum variance of the total load of the distribution network as the objective function and the optimal charging period as the constraint condition to optimize the electric vehicle charging strategy and send it to the charging pile. Among them, the optimal charging period is used to indicate the charging period range corresponding to the lowest charging cost for the user.

[0029] The present invention discloses the following technical effects:

[0030] The present invention establishes a hierarchical optimization model for electric vehicles that includes minimizing user charging costs and minimizing the variance of the total load of the distribution network; the upper layer takes minimizing the charging cost as the goal, and optimizes the charging time range of the vehicle according to the principle that the longer the vehicle charges during the low-price period, the lower the charging cost, so as to obtain the optimal solution that satisfies the minimum charging cost, and uses it as the constraint condition for the optimization of the lower layer, thereby reducing the calculation burden of the charging optimization of the lower layer; the lower layer takes minimizing the variance of the total load of the distribution network as the goal, and solves the optimization model through the improved particle swarm algorithm, thereby improving the optimization effect of the particle swarm algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 is a flow chart of the charging method of the present invention;

[0033] Figure 2 It is a schematic diagram of the reverse learning initialization population process described in the present invention. DETAILED DESCRIPTION

[0034] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0035] like Figure 1 As shown, the present invention provides a vehicle charging technology based on hierarchical optimization, which specifically includes the following processes:

[0036] Step 1: Before going to the charging station, the user first enters the charging information in the local operator's mini program, including the time set by the user to connect to and leave the charging station, the battery power expected by the vehicle after charging is completed, and other information. The operator stores the data in the database, and after the battery is connected to the smart charging station, the above data can be read and the charging time of the vehicle can be calculated.

[0037] Step 2: Calculate the charging time range and charging cost with the lowest charging cost for the user, and pass the charging time range and charging cost to the user.

[0038] Step 3: Determine whether the user complies with the charging plan. If the user complies with the charging plan, the charging pile will upload the vehicle's charging time range, battery status and user-set information to the local operator. If the user does not comply with the charging plan, charging will start immediately and the charging pile will upload the charging time to the local operator.

[0039] Step 4: The local operator uses the improved particle swarm algorithm to optimize the charging strategy of electric vehicles with the minimum variance of the total load of the distribution network as the objective function, and sends the optimized charging start time to the charging pile. The battery status of the vehicle in step 1, the time set by the user to go to and leave the charging pile, and the power expected by the user can be obtained from the database. The improved PSO algorithm includes the following steps:

[0040] Step 4.1 Set the algorithm parameters, including inertia weight, population size, learning factor, solve the fitness of each particle, and record the position with the best historical fitness of each particle and the position with the best global fitness in the population.

[0041] Step 4.2 Reverse learning to initialize the population. First, initialize the population and encode the S trams to get the vehicle numbers. Then use the reverse learning idea to generate a new reverse population and select the better individuals from the two populations to achieve the purpose of initializing the population.

[0042] Step 4.3 updates the particle speed and position according to the original update formula of the particle swarm.

[0043] Step 4.4 updates the position of the particle according to Gaussian variation theory.

[0044] In step 4.5, if the set maximum number of iterations is met, as the termination condition of the algorithm, the optimal charging strategy for the tram is output: the optimal number of trams is obtained to minimize the total load variance of the distribution network.

[0045] Step 5: The charging pile executes the charging plan according to the vehicle's charging start time, and the local operator updates the load data of the distribution network in each period.

[0046] Embodiment: The present invention is applicable to application scenarios where the charging power provided by each charging pile in the same charging station is the same and the battery capacities of different electric vehicles are evenly distributed between 20-40Kwh. The present invention is further described in detail below with reference to the accompanying drawings and examples.

[0047] according to Figure 1 The public process is as follows: first, before charging, the user will enter the charging information through the application provided by the local operator (such as WeChat applet). The application will store the data in the server database, and calculate the charging time and minimum charging cost based on the information entered by the user and the battery information read by the charging pile, and provide the user with a charging plan. The application will then provide the charging plan to the operator's server. The server will model the distribution network load with the minimum variance, and use the particle swarm algorithm to solve the optimal charging strategy, and finally send the optimal charging strategy to the charging pile.

[0048] The present invention generally comprises the following four steps:

[0049] Step 1. Before using the charging pile, the user first enters the charging information in the application, including the time set by the user to connect to and leave the charging pile, and the battery power expected to be reached after charging. After the battery is connected to the smart charging pile, the system reads the data of the vehicle's battery management system. The application stores the above data in the database and calculates the charging time of the vehicle.

[0050] In step 1, the application program uses the set data including: peak electricity price period and the electricity price of the corresponding period, normal electricity price period and the electricity price of the corresponding period, valley electricity price period and the electricity price of the corresponding period; if the local electricity price model adopts a two-stage electricity price, the normal electricity price and the valley electricity price are set to be the same.

[0051] As shown in Table 1, the charging information entered by the user includes: the percentage of power that the user expects the vehicle to reach after charging, and the time set by the user to connect to and leave the charging pile. The data read by the charging pile from the battery management system of the vehicle includes: the initial battery power percentage when connecting to the charging pile, the battery capacity of the vehicle, and the charging power.

[0052] Table 1

[0053]

[0054]

[0055] The initial power percentage of an electric vehicle when it is connected to a charging station is expressed in SOC. c SOC is the percentage of power that users expect their vehicles to reach after charging. e The vehicle battery capacity is represented by ET, and the charging power is represented by P c express.

[0056] The formula for calculating the vehicle charging time is shown below.

[0057]

[0058] Step 2: Calculate the charging time range and charging cost with the lowest charging cost for the user, and pass the charging time range and charging cost to the user.

[0059] The expression for calculating the charging cost in step 2 is shown in formula (2).

[0060]

[0061] In formula (2), J b The time period to which the vehicle is connected to the charging pile; J e The time period to which the user sets the time to leave the charging station; Δt is the duration of each time period; c j is the charging electricity price in the jth period; P j is the charging power of the electric vehicle in time period j (if the vehicle is charged in time period j, then P j Equal to P c , if the vehicle does not charge in time period j, then P j equal to 0).

[0062] The electricity prices at different time periods are pre-set in the charging pile billing system. Assuming that a charging pile in a certain place adopts the peak-valley billing method, the peak-valley electricity prices are shown in Table 2. At the same time, the battery capacity of the electric vehicle after charging should also meet the constraints of formula (3).

[0063] Table 2

[0064] Time Electricity price (yuan / Kwh) 0:00-08:00 0.36 08:00-23:00 0.56 23:00-24:00 0.36

[0065]

[0066] In step 2, the smart charging pile aims to minimize the user's charging cost. According to the time-of-day electricity price, the longer the vehicle is in the lowest electricity price period, the lower the charging cost. Therefore, the charging period is considered in different situations. Assume that the charging price valley period is [J lb ,J le ], the vehicle access charging pile time is J b , leaving the charging pile time is J e , charging time is T c , analyze the relationship between all possible vehicle stay periods and electricity price valley periods. The following are ten possible vehicle charging periods. When different conditions are met, the optimal charging period range is as follows. The calculated optimal charging period will be provided to the user in the program interface, and the user can choose whether to follow the charging period recommendation.

[0067] (1) When J b <J 1b , J e <J 1e , J lb +T c <J e When the vehicle can be charged in the valley period, the optimal charging period is [J ib ,J e ].

[0068] (2) When J b <J 1b , J e <J 1e , J lb +T c ≥J e The vehicle can charge at most during the electricity price valley period (J e -J lb ) duration, the optimal charging period range is [J e -T c ,J e ].

[0069] (3) When J b <J lb , J e ≥J le , J lb +T c <J le When the vehicle can be charged in the valley period, the optimal charging period range is [J lb ,J le ].

[0070] (4) When J b <J lb , J e ≥J le , J le ≤J lb +T c <J e , J b +T c <J le When the vehicle is charged at most during the valley period (J le -J lb ) duration, the optimal charging period range is [J le -T c ,J lb +T c ].

[0071] (5) When J b <J lb , J e ≥J le, J le ≤J lb +T c <J e , J b +T c ≥J le When the vehicle is charged at most during the valley period (J le -J lb ) duration, the optimal charging period range is [J b ,J lb +T c ].

[0072] (6) When J b <J lb , J e ≥J le , J e ≤J lb +T c , J b +T c <J le When the vehicle is charged at most during the valley period (J le -J lb )

[0073] The optimal charging time range is [J le -T c ,J e ].

[0074] (7) When J b <J lb , J e ≥J le , J e ≤J lb +T c , J b +T c ≥J le When the vehicle is charged at most during the valley period (J le -J lb )

[0075] The optimal charging time range is [J b ,J e ].

[0076] (8) When J b ≥J lb , J e <J le When the vehicle can be charged during the off-peak period, the optimal charging period range is [J b ,J e ].

[0077] (9) When Jb ≥J lb , J e ≥J le , J b +T c <J le When the vehicle can be charged during the off-peak period, the optimal charging period range is [J b ,J le -T c ].

[0078] (10) When J b ≥J lb , J e ≥J le , J b +T c ≥J le The vehicle can be charged at most during the valley period (J le -J b ) duration, the optimal charging period range is [J b ,J b +T c ].

[0079] By selecting the above optimal charging period range, the user can obtain the charging period with the lowest charging cost recommended by the system. Using the optimal charging period as a constraint condition for optimizing the load variance of the distribution network can reduce the amount of optimization calculations.

[0080] In step three, users can choose whether to comply with the charging plan based on the charging time range and charging costs proposed by the charging pile. If they comply, the optimal charging time range given in step two will be used as a constraint to optimize the distribution network load variance, and the vehicle's charging time range, battery data and user-set information will be uploaded to the operator's server; if they do not comply, charging will start immediately and the charging time will be uploaded to the operator's server.

[0081] In step 4, considering the impact of electric vehicle charging on grid load fluctuation, the goal is to minimize the total load variance of the distribution network. The objective function is shown in formula (4).

[0082]

[0083] Where T is the total number of time periods in a 24-hour day; N is the total number of electric vehicles connected to the charging pile in the jth time period; P L,j is the conventional load of the distribution network in the jth period; P i,j is the charging power of vehicle i in time period j. avr is the average value of the total load of the distribution network during the optimization period, and its expression is shown in formula (5).

[0084]

[0085] Assume that the vehicle charging period range obtained by the optimization method in step 2 is [J cb,i ,J ce,i ], the charging period of the electric vehicle needs to meet the constraints shown in formula (6).

[0086] J cb,i ≤J i ≤J ce,i (6)

[0087] In the formula, J i is the charging period of vehicle i. In addition, the battery capacity of each electric vehicle should also meet the constraints shown in formula (3).

[0088] Step 4 takes the minimum variance of the total load of the distribution network as the objective function, takes the optimal charging period obtained in step 2 as the constraint condition, and uses the improved particle swarm algorithm (PSO) to solve the optimal charging strategy. The position of the particle corresponds to the number of electric vehicles that are charged simultaneously in each period. The improved PSO algorithm includes the following steps:

[0089] Step 4.1, set the algorithm parameters (including inertia weight, population size), learning factor, solve the fitness of each particle according to formula (4), and record the position with the best historical fitness of each particle and the position with the best global fitness in the population.

[0090] Step 4.2: Initialize the population by reverse learning. First, initialize the population, encode the S electric vehicles that comply with the charging plan to obtain the vehicle numbers, and then use the reverse learning method to generate a new reverse population and select the better individuals in the two populations to achieve the purpose of initializing the population.

[0091] The process is as Figure 2 As shown, the initialization steps of the improved PSO algorithm are as follows:

[0092] 1. Randomly generate a population P(S), which is the solid circle in the figure. Each individual in the population contains a random initial position x i and random velocity v i .

[0093] 2. According to the above definition, calculate the symmetric individual of each individual in the population P(S) (including the symmetry of speed and position) to generate the symmetric population OP(S) of P(S), which is the hollow circle in the figure.

[0094]

[0095] 3. Substitute the fitness function values ​​of each particle in different populations into formula (4) and sort them, and select the higher half of the particles to form a new initialization population.

[0096] Step 4.3: For each particle S, set its current position X s k The fitness value of (kth iteration) and its best historical position (i.e. the position of the current particle with the best charging number in the kth generation and before, indicating the best position reached by the particle in the past search process) and the corresponding fitness value. If the fitness value of the current position is higher, the current position is used to update its historical optimal position; for each particle S, its current position X s k The fitness value of (kth iteration) and its global best position The corresponding fitness values ​​are compared (that is, the position corresponding to the charging number with the best fitness value of all particles in the kth generation and before, indicating the best position that all particles can reach). If the fitness value of the current position is higher, the current position is used to update the global optimal position.

[0097] Step 4.4: Update the particle speed and position according to the original update formula of the particle swarm algorithm.

[0098] Step 4.5: Update the position of the particle according to Gaussian variation theory.

[0099] Gaussian distribution is also called normal distribution, which is expressed as follows:

[0100]

[0101] Gaussian mutation is to add a Gaussian distributed random disturbance term to the original individual, N i =(n i1 ,n i2 ,…n iD ) represents the D-dimensional Gaussian mutation operator, where N i It obeys the standard Gaussian distribution N(0,1). Then for the individual position x i =(x i1 ,x i2 ,…,x iD ) performs Gaussian mutation as shown below:

[0102] x′ i =x i +x i ·N i (9)

[0103] Among them, x i ·N i =(x i1 ×n i1 ,x i2 ×n i2 ,…,xid ×n id ), N(0,1) represents a random variable generated by a standard normal distribution with a mean of 0 and a variance of 1. Its value can be calculated from the standard normal distribution. i Represents the individual position after Gaussian mutation.

[0104] Step 4.6: If the set maximum number of iterations (the termination condition of the algorithm) is met, the optimal charging strategy for the electric vehicle is output, that is, the optimal number of electric vehicles to start charging in each time period.

[0105] For example, on a certain day, 300 cars go to the charging pile to charge. The read information is consistent, as shown in Table 1. The time of connecting to the charging pile is 18:00, and the time of leaving the charging pile is 9:00. According to formula (1), the charging time of the vehicle is 12 hours. According to step 2, the minimum charging cost of each vehicle is 2952 yuan, and the optimal charging period is 20:00-8:00.

[0106] (1) If all users on that day comply with the charging plan, then according to step 4, the optimal number of charging vehicles between 20:00 and 8:00 is 300. In this case, the minimum charging cost is RMB 2,952. The server backend then issues an instruction to connect the charging piles connected to the vehicles numbered 1 to 300 between 20:00 and 8:00 for charging, while other constraints must be met.

[0107] (2) If 50% of users choose to comply with the charging plan on that day, then the 150 vehicles that do not comply with the charging plan will start charging immediately; according to step 4, the optimal charging strategy is that the optimal number of charging vehicles starting charging between 20:00-8:00 is 150, and the optimal number of charging vehicles starting charging between 18:00-6:00 is 150. In this case, the minimum charging cost is 3072 yuan. The server then gives instructions in the background, and the charging piles numbered 1-150 are put into operation between 18:00-6:00, and the charging piles numbered 151-300 are put into operation between 20:00-8:00.

[0108] In combination with the embodiments above, the optimization results of considering user charging costs and distribution network load under different user responsiveness are compared and analyzed. According to the charging method of the present invention, the minimum user charging cost and the overall stability of the power grid are taken into account, the system's calculation amount and communication requirements are relatively low, and the system cost is not increased.

[0109] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0111] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for charging an electric vehicle based on hierarchical optimization, characterized in that: The following steps are involved: In response to the user's decision on the charging plan, the server obtains the charging time according to the charging information entered by the user, calculates the charging fee, and sends it to the user for the user to decide; In response to executing the user's charging decision, according to the user's charging decision, the server takes the minimum variance of the total load of the distribution network as the objective function, takes the optimal charging period as a constraint, optimizes the electric vehicle charging strategy, and sends it to the charging pile, wherein the optimal charging period is used to represent the charging period range corresponding to the lowest charging cost for the user.

2. The electric vehicle charging method based on hierarchical optimization according to claim 1 is characterized in that: In the process of acquiring the charging information, the time of accessing and leaving the charging pile and the battery power expected to be reached after charging are acquired as the charging information.

3. The electric vehicle charging method based on hierarchical optimization according to claim 2 is characterized in that: When obtaining the charging time, the charging time is expressed as: In the formula, the initial power percentage of the electric vehicle when it is connected to the charging pile is expressed as SOC c SOC is the percentage of power that users expect their vehicles to reach after charging. e The vehicle battery capacity is represented by ET, and the charging power is represented by P c express.

4. The electric vehicle charging method based on hierarchical optimization according to claim 3 is characterized in that: When calculating the charging costs, the peak electricity price period and the electricity price during the corresponding period, the normal electricity price period and the electricity price during the corresponding period, and the valley electricity price period and the electricity price during the corresponding period are taken into consideration; if the local electricity price model adopts a two-stage electricity price, the normal electricity price and the valley electricity price are set to the same.

5. The electric vehicle charging method based on hierarchical optimization according to claim 4 is characterized in that: When calculating the charging fee, the charging time range and charging fee with the lowest charging fee for the user are calculated, and the charging time range and charging fee are transmitted to the user, where the charging fee is expressed as: In the formula, J b The time period to which the vehicle is connected to the charging pile; J e The time period to which the user sets the time to leave the charging station; Δt is the duration of each time period; c j is the charging electricity price in the jth period; P j is the charging power of the electric vehicle in time period j, where if the vehicle is charged in time period j, then P j Equal to P c , if the vehicle does not charge in time period j, then P j is equal to 0.

6. The electric vehicle charging method based on hierarchical optimization according to claim 5 is characterized in that: When calculating the charging cost, the constraint on the battery capacity of the electric vehicle after charging is:

7. The electric vehicle charging method based on hierarchical optimization according to claim 6 is characterized in that: In the process of obtaining the objective function, the objective function is expressed as: Where T is the total number of time periods in a 24-hour day; N is the total number of electric vehicles connected to the charging pile in the jth time period; P L,j is the conventional load of the distribution network in the jth period; P i,j is the charging power of vehicle i in time period j, P avr is the average value of the total load of the distribution network during the optimization period, and the expression is: Assume that the vehicle charging period range obtained by the optimization method in step 2 is [J cb,i ,J ce,i ], then the constraints satisfied by the charging period of electric vehicles are: J cb,i ≤J i ≤J ce,i In the formula, J i is the charging period of vehicle i; at the same time, the battery capacity of the electric vehicle also meets the constraint condition of the battery capacity of the electric vehicle after charging is completed.

8. The electric vehicle charging method based on hierarchical optimization according to claim 7 is characterized in that: In the process of optimizing the electric vehicle charging strategy, an improved particle swarm algorithm is used to solve the optimal charging strategy.

9. An electric vehicle charging system based on hierarchical optimization, used to implement an electric vehicle charging method based on hierarchical optimization as claimed in any one of claims 1 to 8, characterized in that: include: The charging planning module is used to respond to the user's decision on the charging plan. The server obtains the charging time and calculates the charging fee based on the charging information entered by the user, and sends it to the user for the user to make a decision; The charging strategy optimization module is used to respond to and execute the user's charging decision. According to the user's charging decision, the server takes the minimum variance of the total load of the distribution network as the objective function, takes the optimal charging period as the constraint condition, optimizes the electric vehicle charging strategy, and sends it to the charging pile, wherein the optimal charging period is used to represent the charging period range corresponding to the lowest charging cost for the user.

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