Electric vehicle charging method and device based on time-of-use electricity price and related equipment

Abstract

The present application relates to electric vehicle charging technical field, especially to electric vehicle charging method, device and related equipment based on time-of-use electricity price, method includes: according to the travel data of electric vehicle constructs electric vehicle load model;Based on the electric vehicle load model, with the minimum peak-valley difference of total power load as objective function, the ordered charging model of electric vehicle is constructed;Establish the constraint condition corresponding to the ordered charging model of electric vehicle;Based on the objective function and the constraint condition, the ordered charging model of electric vehicle is solved by artificial bee colony algorithm.The present application can reduce the peak-valley difference of power grid, slow down the power grid load fluctuation caused by electric vehicle disorderly charging, improve the stability of power grid, and guide electric vehicle users orderly charging.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging technology, and in particular to electric vehicle charging methods, apparatus and related equipment based on time-of-use pricing. Background Technology

[0002] In recent years, with the depletion of fossil fuels and the increasing severity of environmental problems, electric vehicles have received widespread attention as an emerging industry. As a representative of new energy vehicles, electric vehicles offer advantages over traditional gasoline-powered vehicles, including less pollution, lower noise, lower operating costs, and higher energy conversion efficiency. In today's social and ecological environment, increasing investment in electric vehicles can effectively alleviate the pressure on oil supply and, to some extent, improve the atmospheric environment.

[0003] The rapid increase in the number of electric vehicles has brought about a host of problems related to electric vehicle charging. The large-scale, unregulated connection of electric vehicles to the power grid negatively impacts the safe and stable operation of the grid, causing load fluctuations. Studies show that the charging times of electric vehicle users largely correspond to their daily routines, indicating that the peak-to-valley difference in the grid load curve can be significantly increased due to the large-scale connection of electric vehicles during peak load periods. Therefore, it is urgent to develop reasonable charging strategies to reduce grid load fluctuations. Summary of the Invention

[0004] This invention provides a method for charging electric vehicles based on time-of-use pricing, aiming to reduce the peak-valley difference of the power grid, mitigate grid load fluctuations caused by disorderly charging of electric vehicles, and improve grid stability.

[0005] In a first aspect, embodiments of the present invention provide a method for charging electric vehicles based on time-of-use pricing, comprising the following steps:

[0006] Construct an electric vehicle load model based on electric vehicle travel data;

[0007] Based on the electric vehicle load model, an orderly charging model for electric vehicles is constructed with the objective function of minimizing the peak-valley difference of the total grid load.

[0008] Establish the constraints corresponding to the electric vehicle orderly charging model;

[0009] Based on the objective function and the constraints, the orderly charging model of electric vehicles is solved using the artificial bee colony algorithm.

[0010] Secondly, embodiments of the present invention provide an electric vehicle charging device based on time-of-use pricing, comprising:

[0011] The first building module is used to build an electric vehicle load model based on electric vehicle travel data;

[0012] The second construction module is used to construct an orderly charging model for electric vehicles based on the electric vehicle load model, with the objective function being the minimum peak-valley difference of the total grid load.

[0013] The third construction module is used to establish the constraints corresponding to the electric vehicle orderly charging model.

[0014] The calculation module is used to solve the electric vehicle orderly charging model based on the objective function and the constraints using the artificial bee colony algorithm.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the electric vehicle charging method based on time-of-use pricing provided in embodiments of the present invention.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in the electric vehicle charging method based on time-of-use pricing provided in embodiments of the present invention.

[0017] In this embodiment of the invention, an electric vehicle load model is constructed based on electric vehicle travel data. Then, based on this load model, an orderly charging model for electric vehicles and its constraints are established, with the objective function being the minimum peak-to-valley difference in the total grid load. The artificial bee colony algorithm is used to solve the orderly charging model, ultimately obtaining the minimum value of the objective function, i.e., the minimum peak-to-valley difference in the total grid load. This allows for the use of time-of-use pricing policies to guide users to charge when the grid load is low, thereby reducing grid load fluctuations caused by disorderly charging of electric vehicles, improving grid stability, and achieving orderly charging of electric vehicles. Attached Figure Description

[0018] 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 drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of an electric vehicle charging method based on time-of-use pricing provided in an embodiment of the present invention;

[0020] Figure 2 This is provided by the embodiments of the present invention. Figure 1 The detailed flowchart of step S104;

[0021] Figure 3 This is a schematic diagram of the structure of an electric vehicle charging device based on time-of-use pricing provided in an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0024] like Figure 1 As shown, Figure 1 This is a flowchart of an electric vehicle charging method based on time-of-use pricing provided in an embodiment of the present invention, including the following steps:

[0025] S101. Construct an electric vehicle load model based on electric vehicle travel data.

[0026] The electric vehicle charging method based on time-of-use pricing provided in this embodiment allows the electronic devices running on it to connect to other electronic devices via wired or wireless connections for data transmission, etc. Wireless connection methods may include, but are not limited to, 3G / 4G connections, WiFi (Wireless-Fidelity) connections, Bluetooth connections, WiMAX (Worldwide Interoperability for Microwave Access) connections, Zigbee (Low Power Local Area Network Protocol, also known as Zigbee Protocol), UWB (Ultra Wideband) connections, and other currently known or future wireless connection methods.

[0027] In this embodiment, based on the environment in which the electric vehicle charging method based on time-of-use pricing provided in this application is applied, travel data corresponding to the charging characteristics of the electric vehicle and the user's travel habits can be obtained. For example, when applied to the electric vehicle charging management system of community A, the travel data of the electric vehicle can be obtained based on the electric vehicle charging management system of that community. The travel data may include data such as the electric vehicle user's trip time, trip end time, and mileage traveled that day. After obtaining the electric vehicle user's travel data, an electric vehicle load model can be constructed based on the travel data, and the electric vehicle load model may include functions corresponding to the electric vehicle user's trip time, trip end time, and mileage traveled that day.

[0028] S102. Based on the electric vehicle load model, construct an orderly charging model for electric vehicles with the objective function of minimizing the peak-valley difference of the total grid load.

[0029] The peak-to-valley difference of the total grid load refers to the difference between the maximum and minimum total grid load. To reduce the adverse effects of grid load fluctuations caused by disorderly charging of electric vehicles (EVs), the minimum peak-to-valley difference of the total grid load can be used as the objective function to construct an orderly charging model for EVs. This model can guide EV users to charge during periods of lower grid load, based on grid load conditions and current time-of-use pricing policies.

[0030] S103. Establish the constraints corresponding to the electric vehicle orderly charging model.

[0031] To enable electric vehicle (EV) users to charge in an orderly manner according to the EV orderly charging model, constraints can be created to constrain the model, better guiding EV users to charge during periods of lower grid load. These constraints can include constraints on the number of EVs charging, the state of charge (SBC) at the end of charging, the SBC of the charged quantity, and charging time, among others.

[0032] S104. Based on the objective function and constraints, the orderly charging model of electric vehicles is solved using the artificial bee colony algorithm.

[0033] Among them, with the objective function as the final goal, under the constraints, the orderly charging model of electric vehicles is solved by artificial bee colony algorithm. The final solution is the optimal solution, which corresponds to the minimum peak-valley difference of the total grid load.

[0034] The Artificial Bee Colony Algorithm (ABC algorithm) is an optimization method that mimics the behavior of bees. It's a specific application of swarm intelligence, characterized by its ability to converge quickly without requiring specific information about the problem. Instead, it compares the merits of different bees, and through the local optimization behavior of individual artificial bees, the global optimum emerges within the colony. Standard ABC algorithms emulate the honey-gathering mechanism of real bees, dividing the colony into three categories: leader bees, follower bees, and scout bees. The goal of the entire colony is to find the optimal food source; in this example, it's finding the optimal solution for the orderly charging model of electric vehicles. In the standard ABC algorithm, leader bees utilize previous food sources to search for new food sources near their respective food sources and share this information with follower bees. Follower bees use this information to find food sources to retain. When a retained food source needs to be abandoned, the leader bee corresponding to that source becomes a scout bee, continuing to search for new valuable food sources until the algorithm terminates and outputs the optimal solution. The output optimal solution is the optimal solution of the electric vehicle orderly charging model under constraints, with the objective of minimizing the peak-valley difference of the total grid load.

[0035] In this embodiment of the invention, an electric vehicle load model is constructed based on electric vehicle travel data. Then, based on this model, an orderly charging model for electric vehicles is built with the objective function of minimizing the peak-to-valley difference in the total grid load, along with constraints. The artificial bee colony algorithm is used to solve this orderly charging model, ultimately yielding the minimum value of the objective function, i.e., the minimum peak-to-valley difference in the total grid load. This allows for the use of time-of-use pricing policies to guide users to charge when the grid load is low, thereby reducing grid load fluctuations caused by disorderly charging of electric vehicles, improving grid stability, and achieving orderly charging of electric vehicles.

[0036] Optional, Figure 1 S101 includes the following steps:

[0037] S201. Obtain the daily trip end time of electric vehicle users and construct the probability density function for the daily mileage end time of electric vehicle users:

[0038]

[0039] In the formula, μ e σ represents the expected daily trip end time for electric vehicle users. e This represents the standard deviation of the daily trip end time for electric vehicle users.

[0040] Specifically, for the daily trip end time of electric vehicle users, a probability density function for the daily mileage end time of electric vehicle users can be constructed, as shown in formula (1) above. By obtaining the trip end times of multiple electric vehicle users each day, the expected value and standard deviation of the daily trip end time of electric vehicle users are calculated. Then, based on the expected value and standard deviation of the daily trip end time, the probability density function for the daily mileage end time of electric vehicle users is constructed. In this embodiment, the expected value μ of the daily trip end time... e =17.47, standard deviation σ e =3.41.

[0041] S202. Obtain the daily travel time of electric vehicle users and construct the probability density function of the earliest departure time of the electric vehicle each day:

[0042]

[0043] In the formula, μ t σ represents the expected earliest departure time for electric vehicle users each day. t The standard deviation of the earliest departure time for electric vehicle users each day.

[0044] Specifically, for the daily travel time (earliest departure time of each day) of electric vehicle users, a probability density function for the earliest departure time of each day can be constructed, as shown in formula (2) above. By obtaining the travel times of multiple electric vehicle users each day, the expected value and standard deviation of the daily travel time of electric vehicle users are calculated. Then, based on the expected value and standard deviation of the daily travel time, a probability density function for the end time of the daily driving mileage of electric vehicle users is constructed. In this embodiment, the expected value μ of the daily travel time is... t = 8.92, standard deviation σ t =3.24.

[0045] S203. Obtain the daily mileage of electric vehicle users and construct the probability density function of daily mileage of electric vehicles:

[0046]

[0047] In the formula, σ r The standard deviation of daily mileage driven by electric vehicle users; μ r Let be the expected daily mileage of an electric vehicle user; r is the daily mileage of an electric vehicle user, in km.

[0048] Specifically, for the daily mileage of electric vehicle users, a probability density function for the daily mileage of electric vehicles can be constructed, as shown in formula (3) above. By obtaining the daily mileage of multiple electric vehicle users each day, the expected value and standard deviation of the daily mileage of electric vehicle users are calculated, and then the probability density function for the daily mileage of electric vehicles is constructed based on the expected value and standard deviation of the daily mileage. In this embodiment, the expected value of the daily mileage σ r =1.14, standard deviation μ r =2.98.

[0049] Optional, Figure 1 Step S101 further includes the following steps:

[0050] S204. Obtain the time when the electric vehicle connects to the power grid and the time when it leaves the power grid, and construct a parking time function for the electric vehicle:

[0051]

[0052] In the formula, T represents the parking time of the electric vehicle; T in T is the time it takes for an electric vehicle to connect to the power grid. out This refers to the time an electric vehicle is disconnected from the power grid.

[0053] Specifically, travel data can also include the time when an electric vehicle connects to the power grid and the time when it leaves the grid. There are two scenarios for calculating the parking time of an electric vehicle: the first is that the electric vehicle user leaves the grid in the morning and connects back in the evening; the second is that the user connects to the grid on the first day and leaves on the second day. Therefore, according to formula (4) above, by obtaining the time the electric vehicle connects to the power grid and the time it leaves the grid, a parking time function for the electric vehicle can be constructed based on these two calculation methods. The parking time function of the electric vehicle can reflect the time the electric vehicle connects to the power grid.

[0054] S205. Obtain the charging power data of the electric vehicle and construct the charging time function for the electric vehicle:

[0055]

[0056] In the formula, t is the charging time required for the electric vehicle; B is the battery capacity of the electric vehicle; and SOC is the state of charge. end Expected charging capacity for electric vehicles; SOC start η represents the initial charge of the electric vehicle; η represents the charging efficiency of the electric vehicle; and P represents the charging power of the electric vehicle.

[0057] Specifically, travel data can also include electric vehicle charging data, which can include the electric vehicle's own battery capacity, expected charging amount, initial charge level, charging efficiency, and charging power. After obtaining the above charging data, the electric vehicle charging time function shown in formula (5) can be constructed. The electric vehicle charging time function can characterize the charging time required for the electric vehicle to connect to the power grid.

[0058] In this embodiment, an electric vehicle load model is constructed based on the daily travel time, daily trip end time, and daily mileage of electric vehicle users, enabling more accurate calculation of the daily charging load of electric vehicles. Furthermore, separate parking time and charging time functions are constructed for electric vehicles, facilitating a more realistic and efficient orderly charging model for electric vehicles. This allows for more accurate utilization of time-of-use pricing policies to guide users to charge when grid load levels are low, thereby reducing grid load fluctuations caused by disorderly charging of electric vehicles, improving grid stability, and achieving orderly charging of electric vehicles.

[0059] Optional, Figure 1 Step S102 specifically includes:

[0060] S301. Construct the total load function of the electric vehicle based on the charging power, charging status, and basic load of the target area at the same time:

[0061]

[0062] In the formula, p s (t) represents the total load of electric vehicles at time t after they are connected to the grid; P in The charging power for electric vehicles; k i Let k be the state of the i-th electric vehicle at time t; i When k = 0, it indicates that the electric vehicle is not charging at this moment; i When p = 1, it indicates that the electric vehicle is in a charging state at this moment; j (t) represents the basic load of the target area during time period t.

[0063] S302. Construct an objective function based on the total load function to minimize the peak-valley difference of the total grid load:

[0064] f = min[max{p s (t)}-min{p s (t)}] (7)

[0065] In the formula, max{p s(t)} represents the maximum total load of electric vehicles at time t after they are connected to the grid; min{p s (t)} represents the minimum total load of the electric vehicle at time t after it is connected to the grid.

[0066] Specifically, as shown in formula (6) above, after each electric vehicle is connected to the grid, the total load function of the electric vehicles at that moment can be constructed by summing the charging status (charging / not charging) and charging power of each electric vehicle at a certain moment, combined with the basic charge of the target area at that moment. Here, the target area can refer to a community, etc. Then, the total load of the electric vehicles at that moment after they are connected to the grid can be monitored, and the difference between the maximum total load and the minimum total load can be taken as the minimum value, that is, the objective function with the goal of minimizing the peak-valley difference of the total grid load can be constructed, as shown in formula (7) above.

[0067] In this embodiment, by constructing the total load function of electric vehicles, and based on the total load function of electric vehicles, an objective function is constructed with the goal of minimizing the peak-valley difference of the total grid load. This allows the use of time-of-use pricing policies to guide users to charge when the grid load level is low, thereby reducing grid load fluctuations caused by disorderly charging of electric vehicles, improving grid stability, and achieving orderly charging of electric vehicles.

[0068] Optional, Figure 1 Step S103 specifically includes:

[0069] S401. Construct constraints on the number of electric vehicle charging stations:

[0070] n≤N (8)

[0071] In the formula, n is the number of electric vehicles in the charging state; N is the total number of electric vehicles in the target area.

[0072] S402. Construct the state-of-charge constraints for the electric vehicle at the end of charging:

[0073]

[0074] In the formula, B i S″ refers to the battery capacity. i S′ represents the state of charge of the i-th electric vehicle when charging is finished. i P represents the state of charge of the i-th electric vehicle before it begins charging; in The charging power for electric vehicles; k i Let Δ be the state of the i-th electric vehicle at time t; t The charging time for an electric vehicle from the start to the end of charging.

[0075] S403. Construct the state-of-charge constraints for the charging capacity of electric vehicles:

[0076] S′ i ≤S(t)≤S″ i ≤S max (10)

[0077] In the formula, S(t) represents the state of charge of the electric vehicle during charging; S max This represents the maximum value of the electric vehicle's state of charge.

[0078] S404. Construct charging time constraints:

[0079] t in，i ≤t i ≤t out，i (11)

[0080] In the formula, t i t represents the time during which the i-th electric vehicle can participate in orderly charging; at other times, the electric vehicle does not participate in orderly charging. in，i The time when the i-th electric vehicle begins charging; t out，i The time when the charging of the i-th electric vehicle ends.

[0081] Specifically, in order to make electric vehicles charge in a more orderly manner, various parameters of electric vehicles can be constrained, as shown in formulas (8) to (11) above. Constraints are constructed for the number of electric vehicles being charged, the state of charge of the electric vehicle at the end of charging, the state of charge of the electric vehicle's charging power, and the charging time, respectively. After constructing the constraints, the orderly charging model of electric vehicles is solved under the constraints by the artificial bee colony algorithm. The optimal solution obtained is more conducive to guiding users to charge when the grid load level is low, reducing grid load fluctuations caused by disorderly charging of electric vehicles, improving grid stability, and realizing orderly charging of electric vehicles.

[0082] Optional, Figure 1 Step S104 provided in the document includes the following steps:

[0083] S501. Randomly initialize the population in the solution space of the electric vehicle ordered charging model and generate N food sources.

[0084] S502. Each leader bee searches for new food sources near its own food source based on the formula shown below;

[0085] x′ id =x id +φ id (x id -x kd (12)

[0086] In the formula, i = 1, 2, ..., N; d = 1, 2, ..., D, where D is the dimension of the solution space; φid It is a random number in the interval [-1, 1]; k ≠ ​​i, that is, x id and x kd Let x be the upper and lower bounds of the search interval; for each solution x i (i = 1, 2, ..., D) represents a D-dimensional vector.

[0087] S503. Calculate the fitness values ​​of the initial food source and the new food source, and perform a greedy selection on the initial food source and the new food source.

[0088] S504. If all the lead bees have completed their search, the follower bees will select a lead bee to follow based on the food source information shared by the lead bees and the follower bees will search in the same way as the lead bees to determine the food source to be retained.

[0089] The formula for calculating the probability of selection is:

[0090]

[0091] In the formula, P i The probability of selecting a leader bee for follower bees; fit i Let be the fitness value of the i-th solution.

[0092] S505. If the lead bee and follower bees complete the search of the solution space, then determine whether the food source to be retained meets the condition for being abandoned.

[0093] S506. If the abandonment condition is met, the retained food source is abandoned, and the leader bee corresponding to the retained food source is transformed into a scout bee, which searches for other food sources using the formula shown below.

[0094]

[0095] In the formula, r is a random number on [0, 1]; and Let the upper and lower bounds of the D-th dimension be defined respectively.

[0096] S507. If the abandonment condition is not met, determine whether the artificial bee colony algorithm meets the iteration termination condition.

[0097] S508. If the iteration termination condition is met, terminate the algorithm and output the optimal solution of the electric vehicle orderly charging model; otherwise, return to step S502 to search again to determine a new food source.

[0098] Specifically, in combination Figure 2 As shown, Figure 2This is a flowchart illustrating the artificial bee colony algorithm solution for the electric vehicle charging method based on time-of-use pricing provided in this embodiment. First, the artificial bee colony algorithm is used to randomly initialize the solution space of the ordered charging model of the electric vehicle, generating an initial population N, including N food sources, N = {x1, x2, x3, ..., x...}. n}

[0099] After initialization, the fitness value is calculated for each initial food source, and the leader bee determines the initial marked food source. N initial food sources can be arranged according to their fitness values. The leader bee can search for the first half of the food sources in the sorted order, while the follower bees and scout bees can search for the second half of the food sources in the sorted order. One leader bee corresponds to one food source. Assuming the space is D-dimensional, the leader bee corresponding to the i-th food source searches for and generates a new food source in a random area near the initial food source according to the above formula (12). After finding a new food source, the fitness values ​​of the initial food source and the new food source are calculated, and a greedy selection is performed based on the fitness values. If the position of the new food source is better than the position of the initial food source, the corresponding leader bee will discard the position of the initial food source and adopt a greedy selection method to find a better food source position.

[0100] During the search process, the lead bee shares food source information with the follower bees. If all lead bees complete the search, each follower bee will calculate the selection probability of each food source shared by the lead bee according to formula (13), which is also the selection probability of a feasible solution. Based on the calculated selection probability, it selects a lead bee to follow. Figure 2 The leader bee recruits followers. If a follower bee is assigned a food source, the best food source location is recorded (the reserved food source); if a follower bee is not assigned a food source, a food source is selected for the follower, and the follower bee searches for food sources in the same way as the leader bee, changes the food marker, and then calculates the fitness value of the searched food source, and continues to greedily select based on the fitness value. The above judgment on whether the reserved food source meets the condition for being abandoned means that if the fitness value of a food source is not improved within a given step, the food source is discarded, and the leader bee corresponding to the food source becomes a scout bee. The scout bee will continue to search for food sources through the above formula (14). If the scout bee finds a new food source and the new food source replaces the old food source, the new food source is marked, and it is judged whether the iteration termination condition is met. The algorithm process is terminated when the iteration termination condition is met, and the optimal solution is output. Otherwise, the food source search process of the leader bee continues.

[0101] The iteration termination condition can include the maximum number of iterations / algorithm convergence. If the number of iterations reaches the maximum number of iterations or the algorithm converges, the optimal solution is output. The optimal solution is the optimal solution of the ordered charging model for electric vehicles with the objective function of minimizing the peak-valley difference of the total grid load.

[0102] In this embodiment of the invention, an electric vehicle load model is constructed based on electric vehicle travel data. Then, based on this model, an orderly charging model for electric vehicles is built with the objective function of minimizing the peak-to-valley difference in the total grid load, along with constraints. The artificial bee colony algorithm is used to solve this orderly charging model, ultimately yielding the minimum value of the objective function, i.e., the minimum peak-to-valley difference in the total grid load. This allows for the use of time-of-use pricing policies to guide users to charge when the grid load is low, thereby reducing grid load fluctuations caused by disorderly charging of electric vehicles, improving grid stability, and achieving orderly charging of electric vehicles.

[0103] like Figure 3 As shown, Figure 3 This is a module structure diagram of an electric vehicle charging device based on time-of-use pricing provided in an embodiment of the present invention. The device 600 includes:

[0104] The first construction module 601 is used to construct an electric vehicle load model based on the electric vehicle travel data;

[0105] The second construction module 602 is used to construct an orderly charging model for electric vehicles based on the electric vehicle load model, with the objective function being the minimum difference between the peak and valley loads of the total power grid.

[0106] The third construction module 603 is used to establish the constraints of the corresponding orderly charging model for electric vehicles;

[0107] The computation module 604 is used to solve the orderly charging model of electric vehicles based on the objective function and constraints using the artificial bee colony algorithm.

[0108] Optionally, travel data includes daily trip start time, daily trip end time, and daily mileage for electric vehicle users. Figure 3 The first building block 601 includes:

[0109] The first function construction unit 6011 is used to obtain the daily trip end time of electric vehicle users and construct the probability density function of the daily mileage end time of electric vehicle users:

[0110]

[0111] In the formula, μ e σ represents the expected daily trip end time for electric vehicle users. e The standard deviation of the daily trip end time for electric vehicle users;

[0112] The second function construction unit 6012 is used to obtain the daily travel time of electric vehicle users and construct the probability density function of the earliest departure time of the electric vehicle each day:

[0113]

[0114] In the formula, μ t σ represents the expected earliest departure time for electric vehicle users each day. t Standard deviation of the earliest departure time for electric vehicle users each day;

[0115] The third function construction unit 6013 is used to obtain the daily mileage of electric vehicle users and construct the probability density function of the daily mileage of electric vehicles:

[0116]

[0117] In the formula, σ r The standard deviation of daily mileage driven by electric vehicle users; μ r Let be the expected daily mileage of an electric vehicle user; r is the daily mileage of an electric vehicle user.

[0118] Optionally, travel data may also include the time the electric vehicle connects to the grid, the time it disconnects from the grid, and the amount of electricity charged by the electric vehicle. Figure 3 The first building block 601 is also used for:

[0119] The fourth function building unit 6014 is used to obtain the time when the electric vehicle connects to the power grid and the time when it leaves the power grid, and to construct the parking time function of the electric vehicle:

[0120]

[0121] In the formula, T represents the parking time of the electric vehicle; T in T is the time it takes for an electric vehicle to connect to the power grid. out The time an electric vehicle spends off the power grid;

[0122] The fifth function construction unit 6015 is used to obtain the charging power data of the electric vehicle and construct the charging time function of the electric vehicle.

[0123]

[0124] In the formula, t is the charging time required for the electric vehicle; B is the battery capacity of the electric vehicle; and SOC is the state of charge. end Expected charging capacity for electric vehicles; SOC start η represents the initial charge of the electric vehicle; η represents the charging efficiency of the electric vehicle; and P represents the charging power of the electric vehicle.

[0125] Optional, Figure 3 The second building block 602 includes:

[0126] The sixth function construction unit 6021 is used to construct the total load function of the electric vehicle based on the charging power, charging status, and basic load of the target area of ​​the electric vehicle at the same time.

[0127]

[0128] In the formula, p s (t) represents the total load of electric vehicles at time t after they are connected to the grid; P in The charging power for electric vehicles; k i Let k be the state of the i-th electric vehicle at time t; i When k = 0, it indicates that the electric vehicle is not charging at this moment; i When p = 1, it indicates that the electric vehicle is in a charging state at this moment; j (t) represents the basic load of the target area during time period t;

[0129] The seventh function construction unit 6022 is used to construct an objective function based on the total load function to minimize the peak-to-valley difference of the total grid load.

[0130] f = min[max{p s (t)}-min{p s (t)}]

[0131] In the formula, max{p s (t)} represents the maximum total load of electric vehicles at time t after they are connected to the grid; min{p s (t)} represents the minimum total load of the electric vehicle at time t after it is connected to the grid.

[0132] Optional, Figure 3 The third building block 603 includes:

[0133] The first constraint unit 6031 is used to construct the constraint conditions for the number of electric vehicle charging units:

[0134] n≤N

[0135] In the formula, n is the number of electric vehicles in the charging state; N is the total number of electric vehicles in the target area;

[0136] The second constraint unit 6032 is used to construct the state-of-charge constraints of the electric vehicle at the end of charging:

[0137] In the formula, B i S″ refers to the battery capacity. i S′ represents the state of charge of the i-th electric vehicle when charging is finished. i P represents the state of charge of the i-th electric vehicle before it begins charging; in The charging power for electric vehicles; ki Let Δ be the state of the i-th electric vehicle at time t; t The charging time for an electric vehicle from the start to the end of charging;

[0138] The third constraint element 6033 is used to construct the state-of-charge constraints for the charging capacity of electric vehicles:

[0139] S′ i ≤S(t)≤S″ i ≤S max

[0140] In the formula, S(t) represents the state of charge of the electric vehicle during charging; S max This represents the maximum value of the electric vehicle's state of charge.

[0141] The fourth constraint element 6034 is used to construct charging time constraints:

[0142] t in，i ≤t i ≤t out，i

[0143] In the formula, t i t represents the time during which the i-th electric vehicle can participate in orderly charging; at other times, the electric vehicle does not participate in orderly charging. in，i The time when the i-th electric vehicle begins charging; t out，i The time when the charging of the i-th electric vehicle ends.

[0144] Optional, Figure 3 The intermediate computing module 604 includes:

[0145] Initialization unit 6041 is used to randomly initialize the population in the solution space of the electric vehicle ordered charging model and generate N food sources;

[0146] The first search unit 6042 is used by each leader bee to search for new food sources near its respective food source based on the formula shown below.

[0147] x′ id =x id +φ id (x id -x kd )

[0148] In the formula, i = 1, 2, ..., N; d = 1, 2, ..., D, where D is the dimension of the solution space; φ id It is a random number in the interval [-1, 1]; k ≠ ​​i, that is, x id and x kd Let x be the upper and lower bounds of the search interval; for each solution x i(i = 1, 2, ..., D) represents a D-dimensional vector;

[0149] The calculation unit 6043 is used to calculate the fitness values ​​of the initial food source and the new food source, and to perform a greedy selection on the initial food source and the new food source;

[0150] The second search unit 6044 is used to select a follower bee based on the food source information shared by the leader bee and the selection probability, and the follower bee searches in the same way as the leader bee to determine the food source to be retained;

[0151] The formula for calculating the probability of selection is:

[0152]

[0153] In the formula, P i The probability of selecting a leader bee for follower bees; fit i Let be the fitness value of the i-th solution;

[0154] The first judgment unit 6045 is used to determine whether the food source to be retained meets the condition of being abandoned if the leading bee and the following bee have completed the search of the solution space.

[0155] The third search unit 6046 is used to abandon the retained food source if the abandonment condition is met, and transform the leader bee corresponding to the retained food source into a scout bee to find other food sources through the formula shown below.

[0156]

[0157] In the formula, r is a random number on [0, 1]; and Let the upper and lower bounds of the D-th dimension be defined respectively.

[0158] The second judgment unit 6047 is used to determine whether the artificial bee colony algorithm meets the iteration termination condition if the abandonment condition is not met.

[0159] Output unit 6048 is used to terminate the algorithm and output the optimal solution of the electric vehicle orderly charging model if the iteration termination condition is met; otherwise, it returns each leading bee to search again near its respective food source to determine a new food source.

[0160] The electric vehicle charging device based on time-of-use pricing provided in this embodiment of the invention can realize the various implementation methods of the electric vehicle charging method based on time-of-use pricing described above, as well as the corresponding beneficial effects. To avoid repetition, these will not be repeated here.

[0161] like Figure 4 As shown, Figure 4This is a structural diagram of an electronic device provided in an embodiment of the present invention. Figure 4 As shown, it includes: a processor 401, a memory 402, a network interface 403, and a computer program stored in the memory 402 and executable on the processor 401, wherein:

[0162] The processor 401 is used to call the computer program stored in the memory 402 and perform the following steps:

[0163] Construct an electric vehicle load model based on electric vehicle travel data;

[0164] Based on the electric vehicle load model, an orderly charging model for electric vehicles is constructed with the objective function of minimizing the peak-valley difference of the total grid load.

[0165] Establish the constraints for the corresponding orderly charging model of electric vehicles;

[0166] Based on the objective function and constraints, the orderly charging model of electric vehicles is solved using the artificial bee colony algorithm.

[0167] Optionally, the travel data includes the daily travel time, daily trip end time, and daily mileage of electric vehicle users. The processor 401 executes a process to construct an electric vehicle load model based on the electric vehicle travel data, including:

[0168] Obtain the daily trip end time of electric vehicle users and construct the probability density function for the daily mileage end time of electric vehicle users:

[0169]

[0170] In the formula, μ e σ represents the expected daily trip end time for electric vehicle users. e The standard deviation of the daily trip end time for electric vehicle users;

[0171] Obtain the daily travel times of electric vehicle users and construct the probability density function for the earliest departure time of the electric vehicle each day:

[0172]

[0173] In the formula, μ t σ represents the expected earliest departure time for electric vehicle users each day. t Standard deviation of the earliest departure time for electric vehicle users each day;

[0174] Obtain the daily mileage of electric vehicle users and construct the probability density function of daily mileage for electric vehicles:

[0175]

[0176] In the formula, σ r The standard deviation of daily mileage driven by electric vehicle users; μ r Let be the expected daily mileage of an electric vehicle user; r is the daily mileage of an electric vehicle user.

[0177] Optionally, the travel data also includes the time the electric vehicle connects to the grid, the time it disconnects from the grid, and the charging amount of the electric vehicle. The processor 401, executing the process of constructing an electric vehicle load model based on the electric vehicle travel data, further includes:

[0178] Obtain the time when the electric vehicle connects to the grid and the time when it leaves the grid, and construct a parking time function for the electric vehicle:

[0179]

[0180] In the formula, T represents the parking time of the electric vehicle; T in T is the time it takes for an electric vehicle to connect to the power grid. out The time an electric vehicle spends off the power grid;

[0181] Obtain the charging power data of the electric vehicle and construct the charging time function for the electric vehicle:

[0182]

[0183] In the formula, t is the charging time required for the electric vehicle; B is the battery capacity of the electric vehicle; and SOC is the state of charge. end Expected charging capacity for electric vehicles; SOC start η represents the initial charge of the electric vehicle; η represents the charging efficiency of the electric vehicle; and P represents the charging power of the electric vehicle.

[0184] Optionally, the processor 401 executes an electric vehicle load model, using the minimum peak-to-valley difference of the total grid load as the objective function, to construct an orderly charging model for electric vehicles, including:

[0185] Construct the total load function of the electric vehicle based on the charging power, charging status, and basic load of the target area at the same time:

[0186]

[0187] In the formula, p s (t) represents the total load of electric vehicles at time t after they are connected to the grid; P in The charging power for electric vehicles; k i Let k be the state of the i-th electric vehicle at time t; i When k = 0, it indicates that the electric vehicle is not charging at this moment; iWhen p = 1, it indicates that the electric vehicle is in a charging state at this moment; j (t) represents the basic load of the target area during time period t;

[0188] Construct an objective function based on the total load function to minimize the peak-to-valley difference of the total grid load:

[0189] f = min[max{p s (t)}-min{p s (t)}]

[0190] In the formula, max{p s (t)} represents the maximum total load of electric vehicles at time t after they are connected to the grid; min{p s (t)} represents the minimum total load of the electric vehicle at time t after it is connected to the grid.

[0191] Optionally, the constraints executed by processor 401 to establish the corresponding orderly charging model for electric vehicles include:

[0192] Construct constraints on the number of electric vehicle charging stations:

[0193] n≤N

[0194] In the formula, n is the number of electric vehicles in the charging state; N is the total number of electric vehicles in the target area;

[0195] Construct the state-of-charge constraints for the electric vehicle at the end of charging:

[0196]

[0197] In the formula, B i S″ refers to the battery capacity. i S′ represents the state of charge of the i-th electric vehicle when charging is finished. i P represents the state of charge of the i-th electric vehicle before it begins charging; in The charging power for electric vehicles; k i Let Δ be the state of the i-th electric vehicle at time t; t The charging time for an electric vehicle from the start to the end of charging;

[0198] Construct the state-of-charge constraints for electric vehicle charging capacity:

[0199] S′ i ≤S(t)≤S″ i ≤S max

[0200] In the formula, S(t) represents the state of charge of the electric vehicle during charging; S max This represents the maximum value of the electric vehicle's state of charge.

[0201] Construct charging time constraints:

[0202] t in，i ≤t i ≤t out，i

[0203] In the formula, t i t represents the time during which the i-th electric vehicle can participate in orderly charging; at other times, the electric vehicle does not participate in orderly charging. in，i The time when the i-th electric vehicle begins charging; t out，i The time when the charging of the i-th electric vehicle ends.

[0204] Optionally, the processor 401 executes a solution to the ordered charging model of electric vehicles based on the objective function and constraints, using the artificial bee colony algorithm, including:

[0205] In the solution space of the ordered charging model of electric vehicles, the population is randomly initialized to generate N food sources;

[0206] Each leader bee searches for new food sources near its own food source based on the formula shown below;

[0207] x′ id =x id +φ id (x id -x kd )

[0208] In the formula, i = 1, 2, ..., N; d = 1, 2, ..., D, where D is the dimension of the solution space; φ id It is a random number in the interval [-1, 1]; k ≠ ​​i, that is, x id and x kd Let x be the upper and lower bounds of the search interval; for each solution x i (i = 1, 2, ..., D) represents a D-dimensional vector;

[0209] Calculate the fitness values ​​of the initial food source and the new food source, and perform a greedy selection on the initial food source and the new food source;

[0210] If all the lead bees have completed their search, the follower bees will select a lead bee to follow based on the food source information shared by the lead bees and the follower bees will use the same method as the lead bees to search and determine the food source to be retained.

[0211] The formula for calculating the probability of selection is:

[0212]

[0213] In the formula, P iThe probability of selecting a leader bee for follower bees; fit i Let be the fitness value of the i-th solution;

[0214] If the lead bee and follower bees complete the search of the solution space, then determine whether the food source to be retained meets the condition for being abandoned;

[0215] If the conditions for abandonment are met, the food source to be retained is abandoned, and the leader bee corresponding to the retained food source is transformed into a scout bee to search for other food sources using the formula shown below.

[0216]

[0217] In the formula, r is a random number on [0, 1]; and Let the upper and lower bounds of the D-th dimension be defined respectively.

[0218] If the abandonment condition is not met, then determine whether the artificial bee colony algorithm meets the iteration termination condition;

[0219] If the iteration termination condition is met, the algorithm terminates and outputs the optimal solution of the electric vehicle orderly charging model; otherwise, each leader bee returns to its original position to search again near its respective food source to determine a new food source.

[0220] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the electric vehicle charging method based on time-of-use pricing provided in this invention and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0221] It should be noted that only components 401-403 are shown in the figure; however, it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead. Those skilled in the art will understand that the electronic device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0222] Electronic device 400 can be a desktop computer, laptop, handheld computer, or cloud server, etc. Electronic device 400 can interact with customers through keyboard, mouse, remote control, touchpad, or voice control devices.

[0223] The memory 402 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 402 may be an internal storage unit of an electronic device, such as the hard disk or memory of the electronic device. In other embodiments, the memory 402 may also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device. Of course, the memory 402 may also include both internal storage units and external storage devices of the electronic device. In this embodiment, the memory 402 is typically used to store the operating system and various application software installed on the electronic device, such as program code for a time-of-use electricity pricing method for electric vehicles. In addition, the memory 402 may also be used to temporarily store various types of data that have been output or will be output.

[0224] In some embodiments, processor 401 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. Processor 401 is typically used to control the overall operation of an electronic device. In this embodiment, processor 401 is used to run program code stored in memory 401 or process data, such as running program code for a time-of-use (TOU) electric vehicle charging method.

[0225] Network interface 403 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between electronic device 400 and other electronic devices.

[0226] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by the processor 401, it implements the various processes of the electric vehicle charging method based on time-of-use pricing provided in this invention and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0227] Those skilled in the art will understand that all or part of the processes in the electric vehicle charging method based on time-of-use pricing in the embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the various methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0228] The terms "first," "second," etc., used in the specification, claims, or accompanying drawings of this application are used to distinguish different objects and not to describe a specific order. The reference to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0229] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for charging electric vehicles based on time-of-use pricing, characterized in that, Includes the following steps: Construct an electric vehicle load model based on electric vehicle travel data; Based on the electric vehicle load model, an orderly charging model for electric vehicles is constructed with the objective function of minimizing the peak-valley difference of the total grid load. Establish the constraints corresponding to the electric vehicle orderly charging model; Based on the objective function and the constraints, the orderly charging model of electric vehicles is solved using the artificial bee colony algorithm; The travel data includes the daily travel time, daily trip end time, and daily mileage of electric vehicle users. The step of constructing an electric vehicle load model based on the electric vehicle travel data includes: Obtain the daily trip end time of the electric vehicle users, and construct the probability density function of the daily mileage end time for the electric vehicle users: In the formula, The expected end time of each day's journey for electric vehicle users; The standard deviation of the daily trip end time for electric vehicle users; Obtain the daily travel times of the electric vehicle users and construct the probability density function for the earliest departure time of the electric vehicle each day: In the formula, The expected earliest departure time for electric vehicle users each day; Standard deviation of the earliest departure time for electric vehicle users each day; Obtain the daily mileage of the electric vehicle users and construct the daily mileage probability density function of the electric vehicles: In the formula, The standard deviation of daily mileage driven by electric vehicle users; Let be the expected daily mileage of an electric vehicle user; r is the daily mileage of an electric vehicle user. The travel data also includes the time the electric vehicle connects to the grid, the time it leaves the grid, and the charging amount of the electric vehicle. The step of constructing an electric vehicle load model based on the electric vehicle travel data further includes: Obtain the time when the electric vehicle connects to the power grid and the time when it leaves the power grid, and construct a parking time function for the electric vehicle: In the formula, T represents the parking time of the electric vehicle; The time for electric vehicles to connect to the power grid; The time an electric vehicle spends off the power grid; Obtain the charging power data of the electric vehicle and construct the charging time function of the electric vehicle: In the formula, t is the time required to charge the electric vehicle; B is the battery capacity of the electric vehicle itself. Expected charging amount for electric vehicles; This is the initial charge level of the electric vehicle; P represents the charging efficiency of the electric vehicle; P represents the charging power of the electric vehicle.

2. The method as described in claim 1, characterized in that, Based on the electric vehicle load model, and with the objective function of minimizing the peak-valley difference of the total grid load, an orderly charging model for electric vehicles is constructed, including: The total load function of the electric vehicle is constructed based on the charging power, charging status, and basic load of the target area at the same time: In the formula, The total load of electric vehicles at time t after they are connected to the grid; The charging power for electric vehicles; Let i be the state of the i-th electric vehicle at time t; This indicates that the electric vehicle is not charging at this moment; This indicates that the electric vehicle is charging at this moment; is the base load of the target area in time period t; N is the total number of electric vehicles in the target area; Based on the total load function, construct the objective function that minimizes the peak-to-valley difference of the total grid load: In the formula, The maximum total load of electric vehicles at time t after they are connected to the grid; Let t be the minimum total load of the electric vehicle after it is connected to the grid.

3. The method as described in claim 2, characterized in that, The constraints for establishing the electric vehicle orderly charging model include: Construct constraints on the number of electric vehicle charging stations: In the formula, n is the number of electric vehicles in the charging state; N is the total number of electric vehicles in the target area; Construct the state-of-charge constraints for the electric vehicle at the end of charging: In the formula, This refers to the battery's capacity. The state of charge of the i-th electric vehicle when it finishes charging. The state of charge of the i-th electric vehicle before it has started charging; The charging power for electric vehicles; Let i be the state of the i-th electric vehicle at time t; The charging time for an electric vehicle from the start to the end of charging; Construct the state-of-charge constraints for electric vehicle charging capacity: In the formula, The state of charge of the electricity used to charge an electric vehicle; This represents the maximum value of the electric vehicle's state of charge. Construct charging time constraints: In the formula, The time during which the i-th electric vehicle can participate in orderly charging, and the time during which the electric vehicle does not participate in orderly charging; The time when the i-th electric vehicle begins charging; The time when the charging of the i-th electric vehicle ends.

4. The method as described in claim 1, characterized in that, The step of solving the ordered charging model of electric vehicles using the artificial bee colony algorithm based on the objective function and the constraints includes: In the solution space of the ordered charging model of the electric vehicle, the population is randomly initialized to generate A food sources; Each leader bee searches for new food sources near its own food source based on the formula shown below; In the formula, j , represents the j-th food source; , Let d represent the d-th dimension in the solution space, where D is the dimension of the solution space; It is an interval Random numbers; ,Right now This represents the value of the j-th food source in the d-th dimension. This represents the value of the k-th food source in the d-th dimension; each food source Represent a D-dimensional vector; Calculate the fitness values ​​of the initial food source and the new food source, and perform a greedy selection on the initial food source and the new food source; If all the lead bees have completed their search, the follower bees will select the lead bee to follow based on the food source information shared by the lead bees and the follower bees will search in the same way as the lead bees to determine the food source to be retained. The formula for calculating the selection probability is: In the formula, The probability of selecting a leader bee for a follower bee; Let be the fitness value of the j-th solution; If the lead bee and the follower bee complete the search of the solution space, then determine whether the retained food source meets the condition for being abandoned; If the abandonment condition is met, the reserved food source is abandoned, and the leader bee corresponding to the reserved food source is transformed into a scout bee to search for other food sources using the formula shown below. In the formula, r is Random numbers; and Let the upper and lower bounds of the D-th dimension be defined respectively. If the abandonment condition is not met, then determine whether the artificial bee colony algorithm meets the iteration termination condition; If the iteration termination condition is met, the algorithm terminates and outputs the optimal solution of the electric vehicle orderly charging model; otherwise, each leader bee returns to its original position to search again near its respective food source to determine a new food source.

5. An electric vehicle charging device based on time-of-use pricing, characterized in that, include: The first building module is used to build an electric vehicle load model based on electric vehicle travel data; The second construction module is used to construct an orderly charging model for electric vehicles based on the electric vehicle load model, with the objective function being the minimum peak-valley difference of the total grid load. The third construction module is used to establish the constraints corresponding to the electric vehicle orderly charging model. The calculation module is used to solve the electric vehicle orderly charging model based on the objective function and the constraints using the artificial bee colony algorithm; The travel data includes the daily travel time, daily trip end time, and daily mileage of electric vehicle users. The step of constructing an electric vehicle load model based on the electric vehicle travel data includes: Obtain the daily trip end time of the electric vehicle users, and construct the probability density function of the daily mileage end time for the electric vehicle users: In the formula, The expected end time of each day's journey for electric vehicle users; The standard deviation of the daily trip end time for electric vehicle users; Obtain the daily travel times of the electric vehicle users and construct the probability density function for the earliest departure time of the electric vehicle each day: In the formula, The expected earliest departure time for electric vehicle users each day; The standard deviation of the earliest departure time for electric vehicle users each day; Obtain the daily mileage of the electric vehicle users and construct the daily mileage probability density function of the electric vehicles: In the formula, The standard deviation of daily mileage driven by electric vehicle users; Let be the expected daily mileage of an electric vehicle user; r is the daily mileage of an electric vehicle user. The travel data also includes the time the electric vehicle connects to the grid, the time it leaves the grid, and the charging amount of the electric vehicle. The step of constructing an electric vehicle load model based on the electric vehicle travel data further includes: Obtain the time when the electric vehicle connects to the power grid and the time when it leaves the power grid, and construct a parking time function for the electric vehicle: In the formula, T represents the parking time of the electric vehicle; The time for electric vehicles to connect to the power grid; The time an electric vehicle is disconnected from the power grid; Obtain the charging power data of the electric vehicle and construct the charging time function of the electric vehicle: In the formula, t is the time required to charge the electric vehicle; B is the battery capacity of the electric vehicle itself. Expected charging amount for electric vehicles; This is the initial charge level of the electric vehicle; P represents the charging efficiency of the electric vehicle; P represents the charging power of the electric vehicle.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the electric vehicle charging method based on time-of-use pricing as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the electric vehicle charging method based on time-of-use pricing as described in any one of claims 1 to 4.

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