An electric vehicle competitive charging method and system

Abstract

The application relates to the technical field of electric vehicle charging, and discloses an electric vehicle competitive charging method and system, which is based on multiple evaluation indexes to evaluate the charging priority of electric vehicle users, takes minimizing the charging cost of the users and maximizing the satisfaction of the charging demand of the users as the optimization target, takes the SOC of the electric vehicle battery after charging being satisfied with the expected value of the users and the number of interruptions in the charging process being not more than twice as the constraint condition, establishes a multi-objective optimization mathematical model, obtains the optimal competitive right and interest charging queue of the electric vehicle users accessing the charging pile by solving the optimal solution of the multi-objective optimization mathematical model, and solves the technical problems of the unordered charging of the electric vehicle, the influence on the safe and stable operation of the power grid and the satisfaction of the users when traveling, the frequent interruption of the charging, the increase of the waiting time of the electric vehicle users or the charging failure, and the influence on the service life of the electric vehicle battery.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging technology, and in particular to a competitive charging method and system for electric vehicles. Background Technology

[0002] Electric vehicles, as a green and environmentally friendly mode of transportation, have become an important part of urban transportation. However, the large-scale integration of electric vehicles into low-voltage distribution areas has led to frequent charging due to low energy utilization and increasingly disorderly charging behavior, seriously affecting the safe and stable operation of the power grid and user satisfaction. Furthermore, disorderly charging can cause frequent charging interruptions, increasing waiting times or charging failures for electric vehicle users and impacting battery lifespan. Therefore, guiding electric vehicle users to charge in an orderly manner can reduce the risks to power grid operation and improve its economic efficiency. Summary of the Invention

[0003] This invention provides a competitive charging method and system for electric vehicles to address the technical problems of disordered charging of electric vehicles, which affects the safe and stable operation of the power grid and user satisfaction during travel. It also easily causes frequent charging interruptions, leading to increased waiting time or charging failures for electric vehicle users and affecting the lifespan of electric vehicle batteries.

[0004] In view of this, the first aspect of the present invention provides a competitive charging method for electric vehicles, comprising:

[0005] Information is collected from electric vehicle users who are connected to charging stations;

[0006] Based on multiple evaluation indicators and information about electric vehicle users, the charging priority of electric vehicle users is evaluated, and an initial competitive charging queue for electric vehicle users to access charging piles is generated according to the priority.

[0007] Based on the multi-objective particle swarm optimization algorithm, with the optimization objectives of minimizing user charging costs and maximizing the satisfaction of user charging needs, and with the constraints that the SOC of the electric vehicle battery after charging should meet user expectations and the number of interruptions during charging should not exceed 2, a multi-objective optimization mathematical model is established.

[0008] Solve the optimal solution of the multi-objective optimization mathematical model to obtain the optimal competitive charging queue for electric vehicle users connected to the charging pile;

[0009] Electric vehicle users connected to the charging station are charged according to the optimal competitive rights charging queue.

[0010] Optionally, multiple evaluation metrics include the electric vehicle user's return time, the electric vehicle user's expected departure time, the electric vehicle's state of charge upon return, the electric vehicle user's expected state of charge, the electric vehicle's battery capacity, and the charging power of the charging station.

[0011] Optionally, the multi-objective optimization mathematical model is as follows:

[0012]

[0013]

[0014]

[0015]

[0016] ,

[0017] in, For charging decision variables, The charging power for electric vehicles, where T is the total number of time periods. The electricity price for time period t. For the load during time period t, For average load, The charging power for the i-th electric vehicle. For the charging efficiency of electric vehicles. The length of the time period. Let i be the desired state of charge of the electric vehicle for the i-th electric vehicle user. Let represent the state of charge of the i-th electric vehicle when it returns. Let I be the battery capacity of the i-th electric vehicle, I be the number of charging interruptions, and II(·) be the indicator function. Indicates taking j For any value in the given information, the charging decision variable for time period t+j is 1.

[0018] Optionally, the return time of electric vehicle users follows a first normal distribution, and the expected departure time of electric vehicle users follows a second normal distribution. The first normal distribution is as follows:

[0019]

[0020] The second normal distribution is:

[0021]

[0022] in, It is the first normal distribution function. For electric vehicle users' return time, Return the mean of the probability density function at each time step for electric vehicle users. Let Variance be the probability density function of the return time for electric vehicle users. It is the second normal distribution function. For the estimated departure time of electric vehicle users, Let be the mean of the probability density function of the expected departure time for electric vehicle users. The variance of the probability density function for the expected departure time of electric vehicle users.

[0023] Optionally, the formula for calculating the state of charge of the electric vehicle upon return is:

[0024]

[0025] in, Let represent the state of charge of the i-th electric vehicle when it returns. Let represent the state of charge of the i-th electric vehicle when it travels. Let be the electricity consumption per 100 kilometers of the i-th electric vehicle. Let be the battery capacity of the i-th electric vehicle. Let be the daily mileage of the i-th electric vehicle.

[0026] Optionally, the formula for calculating the charging priority of electric vehicle users is:

[0027]

[0028]

[0029] in, Prioritizing charging for electric vehicle users For the estimated departure time of electric vehicle users, The duration of parking time after electric vehicle users return. The time required to charge an electric vehicle.

[0030] Optionally, charging electric vehicle users connected to the charging station is performed according to the optimal competitive rights charging queue of the electric vehicle users connected to the charging station, which also includes the following steps:

[0031] Determine whether the charging station can meet the charging needs of the electric vehicle user during the electric vehicle's stay time. If not, calculate the maximum battery state of charge that the charging station can meet when the electric vehicle user leaves, and let the electric vehicle user choose whether to accept the charging service.

[0032] Optionally, the formula for calculating the maximum state of charge of the battery that the charging station can satisfy at the moment the electric vehicle user leaves is:

[0033]

[0034] in, Let be the maximum state of charge of the battery that the charging station can satisfy at the moment the i-th electric vehicle leaves. Let represent the state of charge of the i-th electric vehicle when it returns. The charging power for the i-th electric vehicle. Let be the battery capacity of the i-th electric vehicle. The length of the time period. The charging efficiency of electric vehicles.

[0035] Optionally, the position and velocity update models for each particle in the multi-objective particle swarm optimization algorithm are as follows:

[0036]

[0037]

[0038] Where k is the number of particle iterations. Let be the velocity of the i-th particle in the (k+1)-th iteration in d-dimensional space. For inertial weights, Let be the velocity of the i-th particle in the d-dimensional space during the k-th iteration. and As a learning factor, and A random number between [0,1] Let be the position of the i-th particle in the (k+1)-th iteration in the d-dimensional space. Let i be the position of the i-th particle in the d-dimensional space during the k-th iteration. Let be the individual extreme value of the particle in the k-th iteration. This is the optimal solution for the particle swarm optimization in the k-th iteration.

[0039] A second aspect of the present invention provides a competitive charging system for electric vehicles, comprising:

[0040] The information collection module is used to collect information from electric vehicle users connected to the charging pile;

[0041] The charging priority evaluation module is used to evaluate the charging priority of electric vehicle users based on multiple evaluation indicators and information of electric vehicle users, and generate an initial competitive charging queue for electric vehicle users to access the charging pile according to the priority.

[0042] The optimization model building module is used to establish a multi-objective optimization mathematical model based on the multi-objective particle swarm optimization algorithm, with the optimization objectives of minimizing user charging costs and maximizing the satisfaction of user charging needs, and the constraints that the SOC of the electric vehicle battery should meet user expectations after charging and the number of interruptions during charging should not exceed 2.

[0043] The solution module is used to solve the optimal solution of the multi-objective optimization mathematical model and obtain the optimal competitive charging queue for electric vehicle users connected to the charging pile.

[0044] The charging module is used to charge electric vehicles connected to the charging pile according to the optimal competitive rights charging queue of electric vehicle users connected to the charging pile.

[0045] As can be seen from the above technical solutions, the electric vehicle competitive charging method provided by the present invention has the following advantages:

[0046] The electric vehicle competitive charging method provided by this invention evaluates the charging priority of electric vehicle users based on multiple assessment indicators. With the optimization objectives of minimizing user charging costs and maximizing user charging demand satisfaction, it prioritizes charging during low-electricity-price periods compared to disordered charging, reducing charging expenses during high-electricity-price periods. It dynamically adjusts charging time allocation to ensure that charging volume meets demand while minimizing total electricity costs, reducing peak-valley differences in the power grid, achieving peak shaving and valley filling, effectively balancing power grid load distribution, preventing grid overload, and improving grid operational stability. Constraints such as the electric vehicle battery SOC meeting user expectations after charging and the number of charging interruptions not exceeding two are used to avoid increased user waiting time or charging failures caused by frequent charging interruptions, thus improving battery lifespan, reducing charging equipment wear, and enhancing system reliability. By solving the optimal solution of the multi-objective optimization mathematical model, the optimal competitive charging queue for electric vehicle users connected to the charging pile is obtained. This solves the technical problems of disordered electric vehicle charging, which affects the safe and stable operation of the power grid and user satisfaction during travel, and also easily leads to frequent charging interruptions, resulting in increased user waiting time or charging failures and affecting the lifespan of electric vehicle batteries.

[0047] Meanwhile, the electric vehicle competitive charging method provided by this invention combines multiple dimensions such as electric vehicle users' charging needs, SOC status, dwell time, and grid load to accurately assess user charging priority, reasonably allocate charging rights, ensure that users with high demand charge first, and at the same time take into account the overall grid load optimization.

[0048] The competitive charging method for electric vehicles provided by this invention employs a multi-objective particle swarm optimization algorithm to optimize the orderly charging of electric vehicles. This not only reduces user charging costs but also minimizes power grid load fluctuations, ensuring that user charging needs are met. Compared to genetic algorithms (GA) and dynamic programming (DP), it requires no complex coding, involves fewer parameter adjustments, is suitable for large-scale charging optimization problems, and has strong adaptability, dynamically adjusting the charging strategy according to changes in power grid load and user demand. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating a competitive charging method for electric vehicles provided in an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of a competitive charging system for electric vehicles provided in an embodiment of the present invention. Detailed Implementation

[0052] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] For easier understanding, please refer to Figure 1 This invention provides an embodiment of a competitive charging method for electric vehicles, comprising:

[0054] Step 101: Collect information from electric vehicle users who have connected to the charging pile.

[0055] It should be noted that, firstly, information is collected from electric vehicle users connected to the charging station. This collected information includes: the user's return time, the user's expected departure time, the state of charge (SOC) upon return, the user's desired SOC, and the battery capacity. After connecting the electric vehicle to the charging station, the user can set their expected departure time and desired SOC through the station's human-machine interface. The charging station, through its charging control system, records the user's return time and reads the electric vehicle's initial SOC and battery capacity to calculate the required charging time.

[0056] The return times of electric vehicle users follow a first normal distribution, while the expected departure times follow a second normal distribution. The first normal distribution is as follows:

[0057]

[0058] The second normal distribution is:

[0059]

[0060] in, It is the first normal distribution function. For electric vehicle users' return time, Return the mean of the probability density function at each time step for electric vehicle users. Let Variance be the probability density function of the return time for electric vehicle users. It is the second normal distribution function. For the estimated departure time of electric vehicle users, Let be the mean of the probability density function of the expected departure time for electric vehicle users. The variance of the probability density function for the expected departure time of electric vehicle users.

[0061] The parking time for the electric vehicle after returning is:

[0062]

[0063] in, This refers to the duration of time an electric vehicle user parks their vehicle after returning home.

[0064] The probability density function of the daily driving distance D of an electric vehicle is:

[0065]

[0066] in, Let D be the probability density function of the daily mileage D of the electric vehicle. Let be the mean of the probability density function of the daily mileage of electric vehicles. Let V be the variance of the probability density function of the daily mileage of electric vehicles.

[0067] The formula for calculating the state of charge of an electric vehicle upon return is:

[0068]

[0069] in, Let represent the state of charge of the i-th electric vehicle when it returns. Let represent the state of charge of the i-th electric vehicle when it travels. Let be the electricity consumption per 100 kilometers of the i-th electric vehicle. Let be the battery capacity of the i-th electric vehicle. Let be the daily mileage of the i-th electric vehicle.

[0070] Step 102: Based on multiple evaluation indicators and the information of electric vehicle users, evaluate the charging priority of electric vehicle users, and generate an initial competitive charging queue for electric vehicle users to access the charging pile according to the priority.

[0071] It should be noted that, based on the electric vehicle user's return time, expected departure time, state of charge (SBC) upon return, desired SBC, battery capacity, and charging power of the charging station, these evaluation indicators, combined with collected user information, are used to calculate the charging priority of electric vehicle users. An initial competitive charging queue is then generated based on this priority. If the charging capacity of the charging station area is sufficient, the electric vehicle can begin charging directly; otherwise, it enters the queue, and its charging priority needs to be calculated.

[0072] The capacity margin of the transformer area during time period t is defined as follows:

[0073]

[0074] in, For the capacity margin of the transformer area, For the power limit of the transformer area, For loads other than electric vehicle charging, Total charging power for electric vehicles.

[0075] The method for calculating the charging time required for an electric vehicle is as follows:

[0076]

[0077] in, The time required to charge an electric vehicle Let represent the state of charge of the i-th electric vehicle when it returns. Let i be the desired state of charge of the electric vehicle for the i-th electric vehicle user. For the charging efficiency of electric vehicles. Let be the battery capacity of the i-th electric vehicle. The charging power of the i-th electric vehicle.

[0078] The formula for calculating the charging priority of electric vehicle users is:

[0079]

[0080]

[0081] in, Prioritizing charging for electric vehicle users For the estimated departure time of electric vehicle users, The duration of parking time after electric vehicle users return. The time required to charge an electric vehicle. When the parking time and charging time of the electric vehicle are equal, the electric vehicle has the highest charging priority and should be charged first. When the charge priority is 0, the electric vehicle has the lowest charging priority and can be scheduled for charging last. Because when... In other words, if a charging station cannot fulfill a user's charging needs within their stay time, the charging station will calculate the maximum state of charge (SOC) that the station can provide at the moment the user leaves. The user then decides whether to accept this offer. If accepted, charging will begin immediately if there is remaining capacity and continue until the user leaves. If not accepted, the charging station will exit the charging station. This approach can disregard user acceptance and not participate in the optimized charging sequence scheduling. Therefore, this only applies to... Priority calculation is performed for cases t. out =24 results in a denominator of zero, here we use t out =0 for calculation. For t out Users with lower values ​​(i.e., those who left earlier) have less room for adjusting their charging time, so they need to be prioritized for charging; while for users with lower values ​​(t...), they need to be prioritized for charging. out Users with higher values, meaning those who leave later, have more room for adjustment in their charging time. The system can schedule them to charge during off-peak hours as much as possible within their acceptable stay period, thereby achieving the goal of peak shaving and valley filling. Therefore, they are given lower priority.

[0082] Considering that during electric vehicle charging, there may be periods where the charging capacity margin of the charging station is insufficient, leading to charging interruptions, when analyzing the daily behavior of electric vehicle users to predict the load of the charging station, it is necessary to segment the load curve and calculate the capacity margin of the charging station for each time period. This ensures that electric vehicles charge during periods with sufficient capacity margin and maximizes the continuity of the charging process, minimizing the negative impact of orderly charging on charging piles and power batteries. The capacity margin for the j-th time period is... The calculation method is as follows:

[0083]

[0084] in, Let j be the start time of the j-th time interval. This is the start time of the (j+1)th time interval. Let be a function of the load in the transformer area as a function of time t. This refers to the power limit for the transformer area.

[0085] Number of time periods required for electric vehicle users to charge Number of parking time periods Defined as:

[0086]

[0087] in, The length of the time period. The floor symbol is used for rounding down. The rounding up symbol.

[0088] Sufficient margin, i.e. The number of time periods is :

[0089]

[0090] in, For set The number of internal elements.

[0091] like If the charging station can meet the user's charging needs during the electric vehicle's dwell time, it will charge the electric vehicle according to the charging priority order. If the charging station cannot meet the user's charging needs during the electric vehicle's stay, the maximum battery state of charge that can be met at the time the user leaves can be calculated. The electric vehicle user can then choose whether to accept the charging service. The formula for calculating the maximum battery state of charge that the charging station can meet at the time the electric vehicle user leaves is as follows:

[0092]

[0093] in, Let be the maximum state of charge of the battery that the charging station can satisfy at the moment the i-th electric vehicle leaves. Let represent the state of charge of the i-th electric vehicle when it returns. The charging power for the i-th electric vehicle. Let be the battery capacity of the i-th electric vehicle. The length of the time period. The charging efficiency of electric vehicles.

[0094] Step 103: Based on the multi-objective particle swarm optimization algorithm, with the optimization objectives of minimizing user charging costs and maximizing the satisfaction of user charging needs, and with the constraints that the SOC of the electric vehicle battery should meet user expectations after charging and the number of interruptions during charging should not exceed 2, a multi-objective optimization mathematical model is established.

[0095] It should be noted that the optimization objective is to maximize the satisfaction of users' charging needs, meaning the state of charge (SOC) after charging should be close to the user's desired SOC; simultaneously, it aims to move electric vehicles to charging during off-peak hours as much as possible to reduce user charging costs and achieve peak shaving and valley filling. This optimization problem can be expressed as a multi-objective optimization problem, where the decision variable is whether to charge in each time period. Let's divide a day into T time periods, and define the charging decision variable as follows: :

[0096] .

[0097] With the optimization objectives of minimizing user charging costs and maximizing user charging needs, the objective function is:

[0098] Minimize charging costs:

[0099]

[0100] Minimize load variance:

[0101]

[0102] Minimize the fulfillment of user needs:

[0103]

[0104] in, For charging decision variables, The charging power for electric vehicles, where T is the total number of time periods. The electricity price for time period t. For the load during time period t, For average load, The charging power for the i-th electric vehicle. For the charging efficiency of electric vehicles. The length of the time period. Let i be the desired state of charge of the electric vehicle for the i-th electric vehicle user. Let represent the state of charge of the i-th electric vehicle when it returns. Let be the battery capacity of the i-th electric vehicle.

[0105] Electric vehicles are parked Within a given time period, considering the possibility of selection... When charging is interrupted due to discontinuous time periods, it affects the battery life of electric vehicles and reduces user satisfaction. This invention addresses this by limiting the number of charging interruptions to no more than two times as a constraint, maximizing the continuity of the charging process and minimizing the negative impact of orderly charging on the charger and power battery. The issue of charging interruptions is represented by a decision variable. The process involves changing the value from 1 to 0, and then from 0 to 1. Therefore, the number of charging interruptions is defined as:

[0106]

[0107] in, The indicator function II(·) takes the value 1 if the condition is true, and 0 otherwise. Indicates taking j For any value in the formula, the charging decision variable for time period t+j is 1. I represents the number of charging interruptions. The condition within the parentheses of the indicator function II(·) represents the charging interruption situation. It takes the value 1 when the condition is met and 0 otherwise. This summation formula represents the total number of charging interruptions during the T time periods when the electric vehicle is stationary. Indicates taking j For any value in the variable, the charging decision variable for time period t+j is 1. Here, a constraint is set on the number of interruptions I during the charging process, so that it cannot exceed 2 times.

[0108] In this invention, the constraints are that the SOC of the electric vehicle battery after charging should meet the user's expectations and the number of charging interruptions should not exceed two. The constraint that the SOC of the electric vehicle battery after charging should meet the user's expectations is expressed as follows:

[0109]

[0110] Step 104: Solve the optimal solution of the multi-objective optimization mathematical model to obtain the optimal competitive charging queue for electric vehicle users connected to the charging pile.

[0111] It should be noted that, through simulation and iterative calculation, the particle swarm optimization algorithm can find the optimal charging start time, end time and other parameters according to user needs, and determine the user's competitive charging rights.

[0112] The process of solving a multi-objective optimization mathematical model is as follows:

[0113] 1) When an electric vehicle connects to a charging station, the system reads the battery information, the user sets the charging requirements and departure time, the system calculates the required charging time, and sets up a charging queue according to priority.

[0114] 2) Initialize the particle velocity and position, and set algorithm parameters, including the number of particles, inertia weight, maximum number of iterations, etc. Each particle represents a charging decision vector, that is, whether to charge or not at a certain time period. The particle dimension d corresponds to the total number of time periods T, that is, the number of time periods into which a day is divided.

[0115] 3) Calculate the initial population fitness value. Each particle has two fitness values: the load fluctuation variance and the user's charging cost.

[0116] 4) Calculate the objective function value to obtain individual extreme values ​​and population extreme values;

[0117] 5) Compare the fitness value of each particle with the individual extreme value. If the fitness value is better, update the individual extreme value.

[0118] 6) Compare the fitness value of each particle with the global optimum; if it is better, update the global optimum.

[0119] 7) Update the position and velocity of each particle. The position and velocity update models for each particle are as follows:

[0120]

[0121]

[0122] Where k is the number of particle iterations. Let be the velocity of the i-th particle in the (k+1)-th iteration in d-dimensional space. For inertial weights, Let be the velocity of the i-th particle in the d-dimensional space during the k-th iteration. and As a learning factor, and A random number between [0,1] Let be the position of the i-th particle in the (k+1)-th iteration in the d-dimensional space. Let i be the position of the i-th particle in the d-dimensional space during the k-th iteration. Let be the individual extreme value of the particle in the k-th iteration. This is the optimal solution for the particle swarm optimization in the k-th iteration.

[0123] 8) Stop when the maximum number of iterations is reached and output the optimal solution; otherwise, return to step 3.

[0124] Step 105: Charge the electric vehicles connected to the charging pile according to the optimal competitive rights charging queue of the electric vehicle users connected to the charging pile.

[0125] It should be noted that, according to the optimal competitive rights charging queue of electric vehicle users connected to the charging pile, electric vehicle users are allowed to connect to the charging pile one by one until the remaining capacity of the charging pile is exhausted.

[0126] The electric vehicle competitive charging method provided by this invention evaluates the charging priority of electric vehicle users based on multiple assessment indicators. With the optimization objectives of minimizing user charging costs and maximizing user charging demand satisfaction, it prioritizes charging during low-electricity-price periods compared to disordered charging, reducing charging expenses during high-electricity-price periods. It dynamically adjusts charging time allocation to ensure that charging volume meets demand while minimizing total electricity costs, reducing peak-valley differences in the power grid, achieving peak shaving and valley filling, effectively balancing power grid load distribution, preventing grid overload, and improving grid operational stability. Constraints such as the electric vehicle battery SOC meeting user expectations after charging and the number of charging interruptions not exceeding two are used to avoid increased user waiting time or charging failures caused by frequent charging interruptions, thus improving battery lifespan, reducing charging equipment wear, and enhancing system reliability. By solving the optimal solution of the multi-objective optimization mathematical model, the optimal competitive charging queue for electric vehicle users connected to the charging pile is obtained. This solves the technical problems of disordered electric vehicle charging, which affects the safe and stable operation of the power grid and user satisfaction during travel, and also easily leads to frequent charging interruptions, resulting in increased user waiting time or charging failures and affecting the lifespan of electric vehicle batteries.

[0127] Meanwhile, the electric vehicle competitive charging method provided by this invention combines multiple dimensions such as electric vehicle users' charging needs, SOC status, dwell time, and grid load to accurately assess user charging priority, reasonably allocate charging rights, ensure that users with high demand charge first, and at the same time take into account the overall grid load optimization.

[0128] The competitive charging method for electric vehicles provided by this invention employs a multi-objective particle swarm optimization algorithm to optimize the orderly charging of electric vehicles. This not only reduces user charging costs but also minimizes power grid load fluctuations, ensuring that user charging needs are met. Compared to genetic algorithms (GA) and dynamic programming (DP), it requires no complex coding, involves fewer parameter adjustments, is suitable for large-scale charging optimization problems, and has strong adaptability, dynamically adjusting the charging strategy according to changes in power grid load and user demand.

[0129] For easier understanding, please refer to Figure 2 This invention provides an embodiment of a competitive charging system for electric vehicles, comprising:

[0130] The information collection module is used to collect information from electric vehicle users connected to the charging pile;

[0131] The charging priority evaluation module is used to evaluate the charging priority of electric vehicle users based on multiple evaluation indicators and information of electric vehicle users, and generate an initial competitive charging queue for electric vehicle users to access the charging pile according to the priority.

[0132] The optimization model building module is used to establish a multi-objective optimization mathematical model based on the multi-objective particle swarm optimization algorithm, with the optimization objectives of minimizing user charging costs and maximizing the satisfaction of user charging needs, and the constraints that the SOC of the electric vehicle battery should meet user expectations after charging and the number of interruptions during charging should not exceed 2.

[0133] The solution module is used to solve the optimal solution of the multi-objective optimization mathematical model and obtain the optimal competitive charging queue for electric vehicle users connected to the charging pile.

[0134] The charging module is used to charge electric vehicles connected to the charging pile according to the optimal competitive rights charging queue of electric vehicle users connected to the charging pile.

[0135] In one embodiment, multiple evaluation metrics include the electric vehicle user's return time, the electric vehicle user's expected departure time, the electric vehicle's state of charge upon return, the electric vehicle user's expected state of charge, the electric vehicle's battery capacity, and the charging power of the charging station.

[0136] In one embodiment, the multi-objective optimization mathematical model is as follows:

[0137]

[0138]

[0139]

[0140]

[0141] ,

[0142] in, For charging decision variables, The charging power for electric vehicles, where T is the total number of time periods. The electricity price for time period t. For the load during time period t, For average load, The charging power for the i-th electric vehicle. For the charging efficiency of electric vehicles. The length of the time period. Let i be the desired state of charge of the electric vehicle for the i-th electric vehicle user. Let represent the state of charge of the i-th electric vehicle when it returns. Let I be the battery capacity of the i-th electric vehicle, I be the number of interruptions during the charging process, and II(·) be the indicator function.

[0143] In one embodiment, the return time of the electric vehicle user follows a first normal distribution, and the expected departure time of the electric vehicle user follows a second normal distribution. The first normal distribution is as follows:

[0144]

[0145] The second normal distribution is:

[0146]

[0147] in, It is the first normal distribution function. For electric vehicle users' return time, Return the mean of the probability density function at each time step for electric vehicle users. Let Variance be the probability density function of the return time for electric vehicle users. It is the second normal distribution function. For the estimated departure time of electric vehicle users, Let be the mean of the probability density function of the expected departure time for electric vehicle users. The variance of the probability density function for the expected departure time of electric vehicle users.

[0148] In one embodiment, the state of charge of the electric vehicle upon return is calculated as follows:

[0149]

[0150] in, Let represent the state of charge of the i-th electric vehicle when it returns. Let represent the state of charge of the i-th electric vehicle when it travels. Let be the electricity consumption per 100 kilometers of the i-th electric vehicle. Let be the battery capacity of the i-th electric vehicle. Let be the daily mileage of the i-th electric vehicle.

[0151] In one embodiment, the formula for calculating the charging priority of electric vehicle users is:

[0152]

[0153]

[0154] in, Prioritizing charging for electric vehicle users For the estimated departure time of electric vehicle users, The duration of parking time after electric vehicle users return. The time required to charge an electric vehicle.

[0155] In one embodiment, charging electric vehicle users connected to the charging station is performed according to the optimal competitive rights charging queue of the electric vehicle users connected to the charging station, and this further includes:

[0156] Determine whether the charging station can meet the charging needs of the electric vehicle user during the electric vehicle's stay time. If not, calculate the maximum battery state of charge that the charging station can meet when the electric vehicle user leaves, and let the electric vehicle user choose whether to accept the charging service.

[0157] In one embodiment, the formula for calculating the maximum state of charge of the battery that can be satisfied by the charging station when the electric vehicle user leaves is as follows:

[0158]

[0159] in, Let be the maximum state of charge of the battery that the charging station can satisfy at the moment the i-th electric vehicle leaves. Let represent the state of charge of the i-th electric vehicle when it returns. The charging power for the i-th electric vehicle. Let be the battery capacity of the i-th electric vehicle. The length of the time period. The charging efficiency of electric vehicles.

[0160] In one embodiment, the position and velocity update models for each particle in the multi-objective particle swarm optimization algorithm are as follows:

[0161]

[0162]

[0163] Where k is the number of particle iterations. Let be the velocity of the i-th particle in the (k+1)-th iteration in d-dimensional space. For inertial weights, Let be the velocity of the i-th particle in the d-dimensional space during the k-th iteration. and As a learning factor, and A random number between [0,1] Let be the position of the i-th particle in the (k+1)-th iteration in the d-dimensional space. Let i be the position of the i-th particle in the d-dimensional space during the k-th iteration. Let be the individual extreme value of the particle in the k-th iteration. This is the optimal solution for the particle swarm optimization in the k-th iteration.

[0164] The electric vehicle competitive charging system provided in this invention is used to execute the electric vehicle competitive charging method provided in this invention. Its technical effects have been described in the embodiments of the electric vehicle competitive charging method provided in this invention, and will not be repeated here.

[0165] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A competitive charging method for electric vehicles, characterized in that, include: Information is collected from electric vehicle users who are connected to charging stations; Based on multiple evaluation indicators and information about electric vehicle users, the charging priority of electric vehicle users is evaluated, and an initial competitive charging queue for electric vehicle users to access charging piles is generated according to the priority. Based on the multi-objective particle swarm optimization algorithm, with the optimization objectives of minimizing user charging costs and maximizing the satisfaction of user charging needs, and with the constraints that the SOC of the electric vehicle battery after charging should meet user expectations and the number of interruptions during charging should not exceed 2, a multi-objective optimization mathematical model is established. Solve the optimal solution of the multi-objective optimization mathematical model to obtain the optimal competitive charging queue for electric vehicle users connected to the charging pile; Electric vehicle users connected to the charging station are charged according to the optimal competitive rights charging queue of the electric vehicle users connected to the charging station; Multiple evaluation metrics include the electric vehicle user's return time, the electric vehicle user's expected departure time, the electric vehicle's state of charge upon return, the electric vehicle user's expected state of charge, the electric vehicle's battery capacity, and the charging power of the charging station. The multi-objective optimization mathematical model is as follows: ； ； ； ； ， ； in, For charging decision variables, The charging power for electric vehicles, where T is the total number of time periods. The electricity price for time period t. For the load during time period t, For average load, The charging power for the i-th electric vehicle. For the charging efficiency of electric vehicles. The length of the time period. Let i be the desired state of charge of the electric vehicle for the i-th electric vehicle user. Let represent the state of charge of the i-th electric vehicle when it returns. Let I be the battery capacity of the i-th electric vehicle, I be the number of charging interruptions, and II(·) be the indicator function. Indicates taking j For any value in the above, the charging decision variable for time period t+j is 1; The formula for calculating the charging priority of electric vehicle users is: ； ； in, Prioritizing charging for electric vehicle users For the estimated departure time of electric vehicle users, The duration of parking time after electric vehicle users return. The time required to charge an electric vehicle.

2. The electric vehicle competitive charging method according to claim 1, characterized in that, The return times of electric vehicle users follow a first normal distribution, while the expected departure times follow a second normal distribution. The first normal distribution is as follows: ； The second normal distribution is: ； in, It is the first normal distribution function. For electric vehicle users' return time, Return the mean of the probability density function at each time step for electric vehicle users. Let Variance be the probability density function of the return time for electric vehicle users. It is the second normal distribution function. For the estimated departure time of electric vehicle users, Let be the mean of the probability density function of the expected departure time for electric vehicle users. The variance of the probability density function for the expected departure time of electric vehicle users.

3. The electric vehicle competitive charging method according to claim 1, characterized in that, The formula for calculating the state of charge of an electric vehicle upon return is: ； in, Let represent the state of charge of the i-th electric vehicle when it returns. Let represent the state of charge of the i-th electric vehicle when it travels. Let be the electricity consumption per 100 kilometers of the i-th electric vehicle. Let be the battery capacity of the i-th electric vehicle. Let be the daily mileage of the i-th electric vehicle.

4. The electric vehicle competitive charging method according to claim 1, characterized in that, Charging is performed on electric vehicles connected to the charging station based on the optimal competitive charging queue for those vehicles. This process previously included: Determine whether the charging station can meet the charging needs of the electric vehicle user during the electric vehicle's stay time. If not, calculate the maximum battery state of charge that the charging station can meet when the electric vehicle user leaves, and let the electric vehicle user choose whether to accept the charging service.

5. The electric vehicle competitive charging method according to claim 4, characterized in that, The formula for calculating the maximum state of charge of a battery that a charging station can satisfy when an electric vehicle user leaves is: ； in, Let be the maximum state of charge of the battery that the charging station can satisfy at the moment the i-th electric vehicle leaves. Let represent the state of charge of the i-th electric vehicle when it returns. The charging power for the i-th electric vehicle. Let be the battery capacity of the i-th electric vehicle. The length of the time period. For the charging efficiency of electric vehicles. This refers to the number of time periods in which the capacity margin is greater than or equal to the charging power of the i-th electric vehicle.

6. The electric vehicle competitive charging method according to claim 1, characterized in that, The position and velocity update models for each particle in the multi-objective particle swarm optimization algorithm are as follows: ； ； Where k is the number of particle iterations. Let be the velocity of the i-th particle in the (k+1)-th iteration in d-dimensional space. For inertial weights, Let be the velocity of the i-th particle in the d-dimensional space during the k-th iteration. and As a learning factor, and A random number between [0,1] Let be the position of the i-th particle in the (k+1)-th iteration in the d-dimensional space. Let i be the position of the i-th particle in the d-dimensional space during the k-th iteration. Let be the individual extreme value of the particle in the k-th iteration. This is the optimal solution for the particle swarm optimization in the k-th iteration.

7. A competitive charging system for electric vehicles, characterized in that, include: The information collection module is used to collect information from electric vehicle users connected to the charging pile; The charging priority evaluation module is used to evaluate the charging priority of electric vehicle users based on multiple evaluation indicators and information of electric vehicle users, and generate an initial competitive charging queue for electric vehicle users to access the charging pile according to the priority. The optimization model building module is used to establish a multi-objective optimization mathematical model based on the multi-objective particle swarm optimization algorithm, with the optimization objectives of minimizing user charging costs and maximizing the satisfaction of user charging needs, and the constraints that the SOC of the electric vehicle battery should meet user expectations after charging and the number of interruptions during charging should not exceed 2. The solution module is used to solve the optimal solution of the multi-objective optimization mathematical model and obtain the optimal competitive charging queue for electric vehicle users connected to the charging pile. The charging module is used to charge electric vehicle users connected to the charging pile according to the optimal competitive rights charging queue of electric vehicle users connected to the charging pile. Multiple evaluation metrics include the electric vehicle user's return time, the electric vehicle user's expected departure time, the electric vehicle's state of charge upon return, the electric vehicle user's expected state of charge, the electric vehicle's battery capacity, and the charging power of the charging station. The multi-objective optimization mathematical model is as follows: ； ； ； ； ， ； in, For charging decision variables, The charging power for electric vehicles, where T is the total number of time periods. The electricity price for time period t. For the load during time period t, For average load, The charging power for the i-th electric vehicle. For the charging efficiency of electric vehicles. The length of the time period. Let i be the desired state of charge of the electric vehicle for the i-th electric vehicle user. Let represent the state of charge of the i-th electric vehicle when it returns. Let I be the battery capacity of the i-th electric vehicle, I be the number of charging interruptions, and II(·) be the indicator function. Indicates taking j For any value in the above, the charging decision variable for time period t+j is 1; The formula for calculating the charging priority of electric vehicle users is: ； ； in, Prioritizing charging for electric vehicle users For the estimated departure time of electric vehicle users, The duration of parking time after electric vehicle users return. The time required to charge an electric vehicle.

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