Charging power scheduling method, electronic equipment and computer readable storage medium

Abstract

The invention relates to the technical field of new energy charging, in particular to a charging power scheduling method, electronic equipment and a computer readable storage medium, and the charging power scheduling method comprises the steps: obtaining an initial charging SoC, a target charging SoC, a charging curve and a battery capacity of each charging vehicle, determining a plurality of SoC check points of each charging vehicle according to the initial charging SoC and the target charging SoC, determining a plurality of initial charging power groups and a reference charging duration from the initial charging SoC to the target charging SoC according to the charging curve, and iteratively updating each initial charging power group according to a particle swarm optimization algorithm, the battery capacity and the reference charging duration, and obtaining a target charging power group, and scheduling the charging power of each charging vehicle according to the target charging power group. By selecting the power of each charged vehicle at an individual SoC checkpoint for optimization, the computational complexity can be reduced, and timely charging can be ensured according to the target optimization power that minimizes the charging delay of all charged vehicles.

Description

Technical Field

[0001] This invention relates to the field of new energy charging technology, specifically to a charging power scheduling method, electronic device, and computer-readable storage medium. Background Technology

[0002] With the increasing prevalence of electric vehicles, charging infrastructure solutions capable of meeting large-scale dynamic energy demands are becoming increasingly urgent. Some existing charging solutions are effective in certain situations, but they are generally limited in terms of scalability, adaptability, and computational complexity.

[0003] Significant progress has been made in efforts to enhance electric vehicle demand scheduling. However, existing systems still fail to effectively address the scalability and adaptability issues required for large-scale charging demand at charging stations, nor can they cope with the complexity of real-time data processing under dynamic and stochastic conditions. In particular, they suffer from high computational complexity and low computational efficiency when dealing with complex charging scheduling problems, making it difficult to achieve fast and efficient charging scheduling. Summary of the Invention

[0004] One objective of this invention is to provide a charging power scheduling method, an electronic device, and a computer-readable storage medium to solve the technical problem of untimely charging scheduling in related technologies.

[0005] In a first aspect, embodiments of the present invention provide a charging power scheduling method, comprising: Obtain the initial charging SoC, target charging SoC, charging curve, and battery capacity for each charging vehicle; Multiple SoC checkpoints for each charging vehicle are determined based on the initial charging SoC and the target charging SoC, wherein the SoC checkpoints are SoC values ​​distributed between the initial charging SoC and the target charging SoC; Multiple initial charging power groups and a reference charging time from the initial charging SoC to the target charging SoC are determined based on the charging curve. Each initial charging power group includes the initial charging power of each charging vehicle at each SoC checkpoint. Each initial charging power group is iteratively updated according to the particle swarm optimization algorithm, the battery capacity, and the reference charging time to obtain the target charging power group; The charging power of each charging vehicle is scheduled according to the target charging power group.

[0006] Optionally, the step of iteratively updating each initial charging power group according to the particle swarm optimization algorithm, the battery capacity, and the reference charging time to obtain the target charging power group includes: Each charging power group is iteratively updated multiple times according to the particle swarm optimization algorithm to obtain the target charging power group. The initial value of the charging power group is the initial charging power group, and each charging power group corresponds to an agent in the particle swarm. The objective function for iterative updates includes minimizing the average charging delay corresponding to each agent, wherein the average charging delay of each agent is determined based on the battery capacity, the reference charging duration, and the charging power group; The constraints of the objective function include at least one of the following conditions: the charging power of each charging vehicle does not exceed the maximum power limit of the charging pile and the maximum power limit of the charging vehicle, and the sum of the charging power of all charging vehicles does not exceed the maximum power limit of the charging station.

[0007] Optionally, the charging power group is updated in a single iteration according to the particle swarm optimization algorithm, including: Obtain the optimal vector for the group and determine the charging power group vector corresponding to each agent based on the charging power group. Each charging power group vector represents the position of its corresponding agent in the search space. The search space is generated based on the charging curve. The initial value of the optimal vector for the group is a preset vector. Based on the optimal group vector and the charging power group vector, the charging power group vector is updated in the search space to obtain multiple solution vectors; A new charging power group is determined for each agent based on each of the solution vectors.

[0008] Optionally, updating the charging power group vector in the search space based on the group optimal vector and the charging power group vector to obtain multiple solution vectors includes: Calculate the movement speed of each agent in the search space based on the group optimal vector and the charging power group vector; Calculate the current position of each agent within the search space based on the movement speed; Each charging power group vector is updated based on the current position to obtain multiple solution vectors.

[0009] Optionally, updating the charging power group in a single iteration according to the particle swarm optimization algorithm further includes: Interpolation processing is performed on each of the new charging power groups to obtain multiple interpolated charging power groups. Each interpolated charging power group includes the new charging power of each charging vehicle in each charging stage, wherein the charging stage is determined according to a specified SoC increment. Based on the interpolated charging power group and the battery capacity, a segmented charging time group corresponding to each agent is determined, and each segmented charging time group includes the segmented charging time of each charging vehicle in each charging stage. The total charging time corresponding to each agent is calculated based on the segmented charging time group. The total charging time is the cumulative charging time of each charging vehicle from the initial charging SoC to the target charging SoC. The average charging delay corresponding to each agent is determined based on the total charging time and the reference charging time. The interpolated charging power group corresponding to each agent is updated based on the average charging delay.

[0010] Optionally, the specified SoC increment is less than the absolute value of the difference between two adjacent SoC checkpoints and is greater than or equal to 1%.

[0011] Optionally, updating the interpolated charging power group corresponding to each agent based on the average charging delay includes: The optimal charging delay for each agent in the historical individual is updated based on the average charging delay. The interpolated charging power group corresponding to each agent is updated based on the interpolated charging power group corresponding to the optimal charging delay of the historical individual.

[0012] Optionally, after updating the historical optimal charging delay for each agent based on the average charging delay, the charging power scheduling method further includes: The optimal charging delay for the group is determined based on the historical optimal charging delay for individual users. Determine the charging power group corresponding to the optimal charging delay of the group and use the charging power group vector corresponding to the charging power group as the optimal vector of the group; Obtain the optimal vector of the group and determine the charging power group vector corresponding to each agent based on the charging power group corresponding to the optimal charging delay of the historical individual, and return to execute the step of updating the charging power group vector in the search space based on the optimal vector of the group and the charging power group vector to obtain multiple solution vectors.

[0013] Optionally, the charging power scheduling method further includes updating the charging power group in a single iteration according to the particle swarm optimization algorithm: Determine whether the iteration termination condition is met, wherein the iteration termination condition is determined based on the optimal charging delay or the number of iterations for the population; If the iteration termination condition is met, the interpolated charging power group corresponding to the optimal charging delay of the group is determined as the target charging power group. If the iteration termination condition is not met, then return to the step of updating the charging power group vector in the search space based on the group optimal vector and the charging power group vector to obtain multiple solution vectors.

[0014] Optionally, the charging power scheduling method further includes: When a new charging vehicle arrives, acquire the initial charging SoC, target charging SoC, charging curve, and battery capacity of the new charging vehicle. Multiple SoC checkpoints for newly arriving charging vehicles are determined based on the initial charging SoC and the target charging SoC. The first charging power group of the newly arrived charging vehicle and the reference charging time from the initial charging SoC to the target charging SoC are determined based on the charging curve. The first charging power group includes the initial charging power of the newly arrived charging vehicle at each SoC checkpoint. The first charging power is added to the target charging power group to obtain the second charging power group. The second charging power group is iteratively updated according to the particle swarm optimization algorithm, the battery capacity, and the reference charging time. The charging power of newly arriving charging vehicles and other charging vehicles is scheduled according to the second charging power group obtained after the iterative update.

[0015] Optionally, the charging power scheduling method further includes: When a charging vehicle leaves, the target charging power of the departing charging vehicle at each SoC checkpoint is removed from the target charging power group to obtain a third charging power group. Obtain the actual charging status of other charging vehicles; Determine whether the conditions for complete re-optimization are met based on the actual charging status; When the conditions for complete re-optimization are met, the third charging power group is iteratively updated according to the particle swarm optimization algorithm, the battery capacity, and the reference charging time, and the charging power of other charging vehicles is scheduled according to the third charging power group obtained after the iterative update.

[0016] Optionally, the charging power scheduling method further includes: If the conditions for complete re-optimization are not met, determine whether there are charging vehicles whose charging is affected. If it exists, the target charging power of the charging vehicles affected by the charging is extracted from the third charging power group at each SoC checkpoint to obtain the fourth charging power group; The fourth charging power group is iteratively updated based on the particle swarm optimization algorithm, the battery capacity, and the reference charging time, and the charging power of the vehicles affected by the charging is scheduled according to the fourth charging power group obtained after the iterative update.

[0017] Optionally, the target charging power group includes multiple charging powers for each charging vehicle, and the method further includes: A planned charging curve for each charging vehicle is generated based on the multiple charging powers described. Obtain the current actual charging power of each charging vehicle; Calculate the power deviation between the actual charging power and the planned charging curve; Determine whether the conditions for complete re-optimization are met based on the power deviation.

[0018] In a second aspect, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory is connected to the processor, and the processor is configured to execute one or more computer programs stored in the memory, wherein when the processor executes the one or more computer programs, the electronic device implements the charging power scheduling method described in the first aspect above.

[0019] In a third aspect, embodiments of the present invention provide a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the charging power scheduling method as described in the second aspect above.

[0020] Compared with the prior art, the embodiments of the present invention provide a charging power scheduling method, an electronic device, and a computer-readable storage medium. The charging power scheduling method includes: acquiring the initial charging SoC, target charging SoC, charging curve, and battery capacity of each charging vehicle; determining multiple SoC checkpoints for each charging vehicle based on the initial charging SoC and target charging SoC, wherein the SoC checkpoints are SoC values ​​distributed between the initial charging SoC and the target charging SoC; determining multiple initial charging power groups and a reference charging time from the initial charging SoC to the target charging SoC based on the charging curve, wherein each initial charging power group includes the initial charging power of each charging vehicle at each SoC checkpoint; iteratively updating each initial charging power group according to the particle swarm optimization algorithm, battery capacity, and reference charging time to obtain the target charging power group; and scheduling the charging power of each charging vehicle according to the target charging power group. On the one hand, this embodiment optimizes the power of each charging vehicle at individual SoC checkpoints by selecting the power of each charging vehicle, which reduces computational complexity and improves computational efficiency, thereby enabling more timely scheduling of the charging power of each charging vehicle. On the other hand, this embodiment constructs an objective function that minimizes the overall charging delay using battery capacity and reference charging time. When optimizing the power using the particle swarm optimization algorithm, the power of each charging vehicle at each charging SoC level can be optimized according to the objective of minimizing the charging delay of all charging vehicles, thereby ensuring that all charging vehicles can be charged as timely as possible. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention 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.

[0022] Figure 1 This is a schematic diagram of a charging power scheduling system provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a charging power scheduling method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the process of updating the charging power group in a single iteration according to the particle swarm optimization algorithm in a charging power scheduling method provided by an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a charging power scheduling device provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0024] It should be noted that, unless otherwise specified, the various features in the embodiments of this invention can be combined with each other, all of which are within the protection scope of this invention. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. Moreover, the terms "first," "second," and "third" used in this invention do not limit the data or execution order, but only distinguish identical or similar items with essentially the same function and effect.

[0025] This invention provides a charging power scheduling system; please refer to [link / reference]. Figure 1 The charging power scheduling system 100 includes multiple charging piles 101 and electronic devices 102.

[0026] Charging pile 101 is a device used to charge electric vehicle 300 to replenish its power. Its working principle is to receive electrical energy from the power grid 200 and then transmit the electrical energy to electric vehicle 300 through a charging cable to charge the electric vehicle 300. In some embodiments, charging pile 101 can be any type of charging pile, such as a DC charging pile, an AC charging pile, or an AC / DC integrated charging pile.

[0027] Electronic devices 102 are communicatively connected to each charging pile 101, including both wireless and wired communication connections. Electronic devices 102 can be terminal devices, servers, or other types of devices. Terminal devices include, but are not limited to, user equipment (UE) such as smartphones, desktop computers, laptops, digital radio receivers, and portable Android devices (PADs), personal digital assistants (PDAs), handheld devices, in-vehicle devices, wearable devices, computing devices, or other processing devices connected to a wireless modem, mobile stations (MS), and mobile terminals. Servers can include edge servers or cloud servers. An edge server is a hardware or software system deployed close to the data source (such as a charging station) and has data storage, computing, and processing capabilities. The edge server processes data and provides services at the network edge without relying entirely on a remote cloud server. The cloud server is used to centrally manage the charging of each charging pile 101 in the charging power scheduling system 100, including charging control, order data, and billing services. Electronic device 102 serves as the control core of charging power scheduling system 100, and is used to execute the charging power scheduling method described below.

[0028] One existing charging dispatching scheme focuses on ensuring that electric vehicles (EVs) can access charging services promptly upon arrival at charging stations. However, the unpredictability of EV arrivals and departures limits the effectiveness of this scheme, often leading to underutilization of charging resources. Another existing scheme proposes grouping EVs based on their charging needs and waiting times to balance grid load and reduce delays. While this scheme offers practical advantages, it heavily relies on accurate predictions of user behavior and energy demand, exhibiting poor robustness under uncertain conditions. Yet another existing scheme proposes using ordinal optimization techniques to simplify the calculation process. While this reduces computational burden, the simplification leads to a loss of accuracy, potentially impacting user satisfaction and grid efficiency. A final existing scheme emphasizes prioritizing emergency charging needs; however, balancing emergency demands with routine dispatching remains a persistent challenge when dealing with sudden load surges.

[0029] In view of this, embodiments of the present invention provide a charging power scheduling method, which is applied to the electronic device described above. Please refer to [link to relevant documentation]. Figure 2 The charging power scheduling methods include: S21. Obtain the initial charging SoC, target charging SoC, charging curve, and battery capacity for each charging vehicle.

[0030] In this step, the charging vehicle is an electric vehicle that is being charged using a charging station. SoC (State of Charge) represents the remaining battery capacity of the electric vehicle, typically expressed as the ratio of remaining battery capacity to the battery's nominal capacity (percentage of charge). The initial charging SoC is the percentage of charge the vehicle has at the start of charging, the target charging SoC is the percentage of charge the vehicle needs to reach, and the charging curve is the curve showing the changes in current and voltage (power) when the vehicle's battery is charged without any charging station power limitations. The charging process can include a constant current stage, a constant voltage stage, and a trickle charging stage. In the constant current stage, the charging station maintains a constant charging current, and the battery voltage gradually increases. The main purpose of this stage is to quickly charge a large amount of battery capacity. When the battery voltage approaches its rated voltage, the charging station switches to constant voltage mode, maintaining a constant charging voltage, and the current gradually decreases. The main purpose of this stage is to ensure the battery reaches a fully charged state and avoid overcharging. In the trickle charging stage, the charging station charges with a very small current, typically taking 2-3 hours. The main purpose of this stage is to maintain and care for the battery, ensuring it remains in optimal condition. Because charging from a smaller SoC to full takes a long time, some car owners set their target charging SoC to an expected SoC, such as 80%, to save charging time. This not only meets their power needs but also avoids spending a long time charging. Battery capacity refers to the amount of electrical energy a charging vehicle can store, generally the amount of energy stored in the battery when the vehicle is fully charged. The battery capacity usually varies between different charging vehicles.

[0031] S22. Determine multiple SoC checkpoints for each charging vehicle based on the initial charging SoC and the target charging SoC. The SoC checkpoints are SoC values ​​distributed between the initial charging SoC and the target charging SoC.

[0032] In this step, the number of SoC checkpoints for each charging vehicle is limited and does not cover all SoC values ​​between the initial charging SoC and the target charging SoC. Specifically, SoC checkpoints are a small number of SoC values ​​selected from the range between the initial and target charging SoCs. The purpose of selecting SoC checkpoints is to optimize the charging power of each charging vehicle at each SoC checkpoint, significantly reducing computational complexity and the dimensionality of the optimization problem. For example, assuming a charging vehicle charges from 30% to 80%, power values ​​are typically calculated for 50 different SoC values ​​at each 1% SoC increment. If there are 10 charging vehicles, 500 decision variables need to be calculated, resulting in a very large computational load. By selecting fewer SoC checkpoints, we only need to determine the power allocation at these few checkpoints and then complete the power allocation for the remaining SoC values. This significantly reduces the number of optimization variables and enables power scheduling for each small SoC increment within the range between the initial and target charging SoCs for the charging vehicle.

[0033] In some embodiments, the SoC checkpoint includes the initial charging SoC and the target charging SoC.

[0034] For example, the charging station currently has three vehicles being charged: EV1, EV2, and EV3. EV1 has an initial charging SoC and a target charging SoC of 30% and 80%, respectively; EV2 has 20% and 80%, respectively; and EV3 has 10% and 80%, respectively. Assuming each vehicle has K SoC checkpoints evenly distributed between the initial and target charging SoCs, and including both the initial and target SoCs, the electronic equipment can determine the SoC checkpoints for each vehicle using the following formula: Among them, SoC i For the i-th charging vehicle, the calculation result at each SoC checkpoint is rounded to the nearest integer when the result is a decimal. max SoC for charging target min Let K be the initial charging SoC, and K be the number of SoC checkpoints. As mentioned earlier, assuming K is 10, meaning each charging vehicle has 10 SoC checkpoints, the distribution of SoC checkpoints for each charging vehicle is shown in Table 1 below: Table 1 S23. Determine multiple initial charging power groups and reference charging time from the initial charging SoC to the target charging SoC based on the charging curve. Each initial charging power group includes the initial charging power of each charging vehicle at each SoC checkpoint.

[0035] In this step, the charging curve for each charging vehicle defines the maximum and minimum power that the vehicle can accept at each SoC value. The charging curve not only determines the feasible power range but can also be used to infer the charging rate at each SoC value during the charging process. The charging curve can be obtained through retrieval or predicted from an electric vehicle model database using a preceding machine learning algorithm. The electronic device can extract the feasible power range for each SoC checkpoint based on the charging curve. The feasible power range represents the upper and lower limits of the acceptable charging power at each SoC checkpoint, used to initialize the charging power at each SoC checkpoint. The electronic device can generate a feasible power range set based on the feasible power range for each SoC checkpoint. For example, a feasible power range set is shown in Table 2 below: Table 2 The electronic device can randomly select a power value as the initial charging power for each SoC checkpoint of each charging vehicle based on the feasible power range, thus obtaining an initial charging power group. This selection can be repeated multiple times to obtain multiple initial charging power groups. For example, an initial charging power group is shown in Table 3 below: Table 3 Understandably, the initial charging power of each charging vehicle at each SoC checkpoint must be within the feasible power range for that SoC checkpoint.

[0036] The reference charging time is the charging time required for a vehicle to charge from the initial charging SoC to the target charging SoC under the condition that there are no power limitations at charging stations. It is understandable that during the actual charging process, due to various power limitations, such as the power limitations of charging stations, the charging power limitations of individual charging guns, and the maximum power limitations of the charging vehicle itself, the total charging time actually required for a vehicle to charge from the initial charging SoC to the target charging SoC is usually greater than the reference charging time. This results in a charging delay, which is the difference between the total charging time and the reference charging time.

[0037] S24. Iteratively update each initial charging power group according to the particle swarm optimization algorithm, battery capacity and reference charging time to obtain the target charging power group.

[0038] In this step, Particle Swarm Optimization (PSO) is a swarm-based metaheuristic algorithm. This swarm comprises multiple agents, also referred to as particles. Each agent represents a candidate solution, which is the set of charging powers for each charging vehicle at each SoC checkpoint. The electronic device can iteratively optimize these candidate solutions using PSO through local and global information sharing, thereby finding the optimal charging power for each charging vehicle at each SoC checkpoint. Each initial charging power set can serve as an initial solution and also as an agent at the particle swarm level. For example, when there are three initial charging power sets, the particle swarm includes three agents. It can be understood that in the particle swarm, each agent has a corresponding position representing the coordinates or parameter combination of a candidate solution in the current solution space.

[0039] Battery capacity and reference charging time are used to construct the objective function, which minimizes the charging delay of each charging vehicle. More precisely, the objective function minimizes the average charging delay, which is the average charging delay of all charging vehicles, i.e., the sum of the charging delays of all charging vehicles divided by the number of charging vehicles. After the electronic device performs one iteration update using the particle swarm optimization algorithm, assuming the particle swarm includes three agents, three candidate solutions can be obtained through iterative updates. The electronic device can calculate the average charging delay corresponding to each agent based on each candidate solution and the objective function, and then update the current optimal solution based on the average charging delay. The current optimal solution is then used for subsequent iteration updates. Through continuous iteration updates, a global optimal solution at the swarm level can be found. The charging power group corresponding to this global optimal solution is the target charging power group, which can include the optimal charging power of each charging vehicle at each charging SoC level.

[0040] S25. Schedule the charging power of each charging vehicle according to the target charging power group.

[0041] In this step, the electronic device can control the corresponding charging pile to output the optimal charging power that matches the current charging SoC value to the corresponding charging vehicle based on the target charging power group and the current charging SoC value of each charging vehicle, thereby realizing the charging power scheduling of each charging vehicle throughout the entire charging process.

[0042] In summary, on the one hand, this embodiment optimizes the power of each charging vehicle at individual SoC checkpoints by selecting the power of each charging vehicle, which reduces computational complexity and improves computational efficiency, thereby enabling more timely scheduling of the charging power of each charging vehicle. On the other hand, this embodiment constructs an objective function that minimizes the overall charging delay using battery capacity and reference charging time. When optimizing the power using the particle swarm optimization algorithm, the charging power of each charging vehicle at each charging SoC level can be optimized according to the objective of minimizing the charging delay of all charging vehicles, thereby ensuring that all charging vehicles can be charged as timely as possible.

[0043] In some embodiments, S24 includes: iteratively updating each charging power group multiple times according to the particle swarm optimization algorithm to obtain a target charging power group, wherein the initial value of the charging power group is an initial charging power group, and each charging power group corresponds to an agent in the particle swarm.

[0044] The objective function for iterative updates includes minimizing the average charging delay corresponding to each agent, where the average charging delay of each agent is determined based on the battery capacity, reference charging time, and charging power group.

[0045] In some embodiments, for each agent, the electronic device can determine the average charging delay corresponding to each agent based on the charging delay of all charging vehicles and the number of charging vehicles.

[0046] In some embodiments, the objective function can be expressed by the following formula (1): Where, ∑ i n (T i -T i ref Let T be the overall charging delay for all charging vehicles, and Ti be the total charging time for the i-th charging vehicle. i ref Let n be the reference charging time for the i-th charging vehicle, and n be the number of charging vehicles.

[0047] In some embodiments, the electronic device may construct a method for calculating the total charging time of each charging vehicle based on the initial charging SoC, target charging SoC, battery capacity, and decision variables of each charging vehicle.

[0048] In this embodiment, the decision variable is the charging power allocated to each charging vehicle in each charging phase. A charging phase refers to the period from the current SoC of the charging vehicle to the completion of a specified SoC increment, where the specified SoC increment refers to the amount of SoC value increase that the charging vehicle defines during the charging process.

[0049] The specified SoC increment can be set according to actual needs. For example, the specified SoC increment can be 1%. The specified SoC increment can determine the charging stages for each charging vehicle. As mentioned earlier, for charging vehicle EV1, charging from 30% to 31% is one charging stage, and charging from 31% to 32% is also one charging stage. Therefore, the entire charging process of charging vehicle EV1 from 30% to 80% includes 50 charging stages. As another example, if the specified SoC increment is 2%, as mentioned earlier, for charging vehicle EV2, charging from 20% to 22% is one specified SoC increment, and charging from 22% to 24% is also one specified SoC increment. Therefore, the entire charging process of charging vehicle EV2 from 20% to 80% includes 30 specified SoC increments.

[0050] In some embodiments, the electronic device can decompose the initial charging SoC to the target charging SoC of each charging vehicle into multiple charging stages according to a specified SoC increment. Based on battery capacity and decision variables, it constructs a method for calculating the segmented charging time for each charging vehicle in each charging stage. Then, it sums the segmented charging times for each charging vehicle in each charging stage to construct a method for calculating the total charging time for each charging vehicle. The segmented charging time is the charging time required for each charging vehicle to charge for each specified SoC increment. As mentioned earlier, for charging vehicle EV1, assuming the specified SoC increment is 1%, the charging time required for EV1 to charge from 30% to 31% is one segmented charging time, and the charging time required to charge from 31% to 32% is also one segmented charging time. Therefore, the entire charging process of charging vehicle EV1 requires 50 segmented charging times. It is understood that since the charging power allocated to charging vehicle EV1 in different charging stages is usually different, the different segmented charging times are also usually different.

[0051] In some embodiments, the electronic device may construct a method for calculating the segmented charging time of each charging vehicle in each charging phase according to the following formula (2): Among them, t i,s The charging time required for the i-th charging vehicle to charge from s% to (s+1)% is the segmented charging time of the charging stage from s% to (s+1)%. i,s E represents the charging power allocated to the i-th charging vehicle during the s-th charging phase. i batt Let represent the battery capacity of the i-th charging vehicle.

[0052] For ease of explanation, the s-th charging stage refers to the charging stage from s% to (s+1)%; the s-1-th charging stage refers to the charging stage from (s+1)% to s%, which is the charging stage preceding the s-th charging stage; and the s+1-th charging stage refers to the charging stage from (s+1)% to (s+2)%, which is the charging stage following the s-th charging stage.

[0053] However, since the charging process is not entirely deterministic, in some embodiments, the electronic device introduces an error parameter δ into the above formula (2) to account for uncertainties in the charging behavior. This error parameter... Typically located in the range [0.9, 1.0), and may vary depending on the charging behavior and efficiency of the charging vehicle. For consistency, in this embodiment, the error parameter δ is a constant for all charging vehicles. In some embodiments, after introducing the error parameter δ, the electronic device can construct a formula for calculating the segmented charging time for each charging vehicle in each charging phase based on the following formula (3): It is understandable that for smaller SoC increments, such as when the specified SoC increment is 1%, the above formula (3) can be simplified to the following formula (4): Therefore, this embodiment introduces an error parameter δ to account for the deviation between the planned charging curve and the actual charging curve, providing a more accurate and realistic estimation of charging time. This makes the prediction of when a vehicle will finish charging more reliable, thereby reducing the risk of overcharging or undercharging and ensuring that charging stations operate within their capacity limits.

[0054] In some embodiments, the electronic device can sum the segmented charging times of each charging vehicle in each charging stage to obtain the total charging time of each charging vehicle. In some embodiments, the electronic device can construct the calculation method for the total charging time of each charging vehicle according to the following formula (5): Among them, T i The total charging time required for the i-th charging vehicle to charge from the initial charging SoC to the target charging SoC, SoC i 0 For the initial charging SoC of the i-th charging vehicle, SoC i goal For the target charging SoC of the i-th charging vehicle, t i,s The segmented charging duration for the i-th charging vehicle in the s-th charging phase.

[0055] The constraints of the objective function must include at least one of the following conditions: the charging power of each charging vehicle does not exceed the maximum power limit of the charging pile and the maximum power limit of the charging vehicle; the sum of the charging power of all charging vehicles does not exceed the maximum power limit of the charging station.

[0056] Since each charging station's charging gun has a maximum output power at any given time, the charging power allocated to each vehicle charging at each charging gun must be less than or equal to the maximum output power of that charging gun, i.e., the maximum power limit of the charging station. Therefore, the constraint that the charging power of each vehicle does not exceed the maximum power limit of the charging station can be expressed as: Where, p j max Let J be the maximum output power of the charging gun. This constraint limits the charging power allocated to each charging vehicle to the power limit of the charging gun it is connected to. For each charging stage of each charging vehicle, the charging power allocated to each charging stage when each charging vehicle is charging at its connected charging gun J cannot exceed the maximum output power of the charging gun J.

[0057] It is understood that the charging power allocated to each charging vehicle in each charging stage must be less than or equal to the maximum limited power corresponding to each charging stage of the charging vehicle. The maximum limited power can be referenced from the charging curve of the charging vehicle. Therefore, the constraint that the charging power of each charging vehicle does not exceed the maximum limited power of the charging vehicle can be expressed as: Where, p i,s max The maximum power limit for the i-th charging vehicle during the s-th charging phase.

[0058] Since the total charging power allocated to all charging vehicles in each charging phase must not exceed the maximum power capacity of the charging station, i.e., the maximum power limit of the charging station, the constraint that the sum of the charging power of all charging vehicles does not exceed the maximum power limit of the charging station can be expressed as: Among them, P max This is the maximum power limit of the charging station. This constraint limits the total charging power consumed by all vehicles charging simultaneously at the charging station, ensuring that the total charging power of all vehicles does not exceed the maximum power capacity of the charging station at any time.

[0059] Understandably, for each charging vehicle, s% must be completed before (s+1)%; to generate an easily processed sequence, the electronic devices can construct the start and end times of each charging stage: Where, β i,s α represents the end time of charging vehicle i in the s-th charging phase. i,s The charging start time of vehicle i in the s-th charging phase, t i,s The segmented charging duration for vehicle i in the s-th charging phase.

[0060] Because the charging start time α of the i-th charging vehicle in the s-th charging phase i,s And the charging end time β of the i-th charging vehicle in the s-th charging phase. i,s Both must be non-negative, therefore β i,s and α i,s The following constraints must be met: For each charging vehicle, since s% must be completed before (s+1)%, therefore, β i,s and α i,s The following constraints must be met: Among them, SoC i 0 For the initial charging SoC of the i-th charging vehicle, SoC i goal The target charging SoC for the i-th charging vehicle. This constraint means that for each charging stage of the i-th charging vehicle, the charging start time α of the i-th charging vehicle in the s-th charging stage is... i,s The charging end time β must be greater than or equal to the charging end time β of the previous charging stage (i.e., the (s-1)th charging stage). i,s-1 And the charging end time β i,s-1 It is based on the charging power p allocated to the i-th charging vehicle in the (s-1)-th charging phase. i,s-1 And the battery capacity E of the i-th charging vehicle i batt Calculated.

[0061] It is also understandable that, since charging stops once each charging vehicle reaches the target charging SoC, in some embodiments, the charging power allocated to each charging vehicle when it is greater than or equal to the target charging SoC should be set to zero. This constraint can be expressed as: Since the above constraints collectively define the feasible solution space for charging scheduling optimization, when calculating the segmented charging duration for each charging vehicle in each charging phase, the charging power of each charging vehicle in each charging phase and the charging start time α of each charging vehicle in each charging phase should be considered. i,s and charging end time β i,s By applying the above constraints, we can ensure power allocation efficiency and comply with operational limitations.

[0062] The electronic device iterates and updates each charging power group multiple times according to the particle swarm optimization algorithm to obtain the target charging power group. In the process of updating each charging power group according to the particle swarm optimization algorithm each time, multiple new charging power groups will be obtained. Each new charging power group includes the new charging power after updating the charging power of each charging vehicle at each SoC checkpoint.

[0063] In some embodiments, see Figure 3 The single iteration update of the charging power group according to the particle swarm optimization algorithm includes: S31. Obtain the optimal vector for the group and determine the charging power group vector corresponding to each agent based on the charging power group. Each charging power group vector represents the position of its corresponding agent in the search space. The search space is generated based on the charging curve. The initial value of the optimal vector for the group is a preset vector.

[0064] S32. Update the charging power group vector in the search space based on the group optimal vector and the charging power group vector to obtain multiple solution vectors.

[0065] S33. Determine the new charging power group for each agent based on each solution vector.

[0066] In S31, the swarm optimal vector is the global optimal solution during the iteration process, representing the position of the optimal particle in the entire swarm. The preset vector can be any user-defined vector; for example, the preset vector can be an all-zero vector.

[0067] The charging power vector is used to solve for the swarm optimization vector and the solution vector, representing the position of its corresponding agent in the search space. The solution vector for each agent is its optimal solution in the entire search space; if a better solution is found, it is updated after each position update. The search space is the set of all possible positions of all particles in the particle swarm optimization algorithm. Each particle searches for the optimal solution by updating its position and velocity within the search space. The size and shape of the search space determine the algorithm's search range and efficiency.

[0068] In some embodiments, the search space is defined by a search boundary matrix. The search boundary matrix includes the position boundary of each particle, which determines the range of values ​​for each particle's position.

[0069] In some embodiments, the electronic device can extract the feasible power range of each charging vehicle at each SoC checkpoint based on the charging curve, and then generate a search boundary matrix based on the feasible power range.

[0070] For example, as mentioned earlier, for the feasible power range group shown in Table 2 above, the electronic device can generate the following search boundary matrix based on the feasible power range group: In some embodiments, the electronic device can generate a charging power group matrix corresponding to each agent based on the charging power group, and determine the charging power group vector corresponding to each agent based on the charging power group matrix.

[0071] For example, if there are n charging vehicles and K SoC checkpoints, the electronic device can obtain a K*n charging power matrix, which takes the form shown below: Each row of this matrix represents the charging power of a charging vehicle at each SoC checkpoint.

[0072] For the initial charging power group shown in Table 3 above, the electronic device can generate the following initial charging power matrix based on the initial charging power group: Electronic devices can expand each charging power matrix into a one-dimensional vector of length K*n, and can use a consistent permutation and indexing scheme (e.g., row-major or column-major order). For example, a straightforward approach is to list the first row of the charging power matrix, then the second row, and so on. Taking row-major order as an example, the vectorized initial charging power matrix (charging power group vector) can be represented as: As mentioned earlier, for the charging power matrix P shown above, the electronic device can obtain the following charging power group vector after vectorizing the charging power matrix P: Understandably, using vectors is more suitable for particle swarm optimization (PSO) algorithms than using matrices directly. This approach is advantageous because it simplifies processing and doesn't affect the solution. Therefore, vectorizing each charging power matrix facilitates computation, allowing the electronic device to more easily update each candidate solution using the PSO algorithm.

[0073] In S32, the electronic device can calculate the movement speed of each agent in the search space based on the group optimal vector and the charging power group vector, calculate the current position of each agent in the search space based on the movement speed, update each charging power group vector based on the current position, and obtain multiple solution vectors.

[0074] In this embodiment, firstly, the electronic device can substitute the charging power group vector corresponding to each agent into the following formula (6) to calculate the moving speed of each agent: Among them, v m For the movement speed, in the first step of optimization, the movement speed is initialized as an all-zero vector with the same size as the charging power group vector. ω is the inertia weight, typically ranging from [0,1], used to control the agent's movement speed. The inertia weight ω can be set according to actual needs; for example, it can be set to 0.6. c1 and c2 are the cognitive coefficient and social coefficient, respectively, used to control the information weights of the individual agent and the optimal group agent, typically ranging from [0,4]. The cognitive coefficient c1 and the social coefficient c2 can be set according to actual needs; for example, the cognitive coefficient c1 can be set to 0.8 and the social coefficient c2 to 1.2. r1 and r2 are random vectors with values ​​ranging from [0,1]. m The charging power vector corresponding to each agent m, x m pbest Let x be the individual optimal position used to solve for each agent m in the iterative update, i.e., the individual optimal solution for each agent m in the entire search space. m pbest The swarm optimal vector represents the position of the best particle in the entire swarm used for solving the problem during the iterative update (swarm optimal position), which is the global optimal solution found during the iteration process.

[0075] Next, after the movement speed is updated, the current position of each agent is updated using the following formula (7): Where, x m For each agent m, the current position, v m The current movement speed for each agent.

[0076] After the current position is updated, the electronic device needs to check whether the updated current position exceeds the predefined search space. Specifically, it checks whether each charging power value in the one-dimensional vector representing the current position is within its corresponding feasible power range. If a charging power value is not within its feasible power range, the electronic device can replace that value with the boundary value closest to it within the feasible power range, thus reflecting the agent's current position back into the search space. It can be understood that after each update of the agent's current position, a new one-dimensional vector (solution vector) is generated, representing the potential charging power solution for each charging vehicle at each SoC checkpoint.

[0077] In S33, the electronic device can restore each solution vector to a matrix to obtain a new charging power matrix corresponding to each agent, and update each charging power group according to each new charging power matrix to obtain a new charging power group corresponding to each agent.

[0078] In some embodiments, updating the charging power group in a single iteration according to the particle swarm optimization algorithm further includes: interpolating each new charging power group to obtain multiple interpolated charging power groups, each interpolated charging power group including the new charging power of each charging vehicle in each charging stage; determining a segmented charging time group corresponding to each agent based on the interpolated charging power group and the battery capacity, each segmented charging time group including the segmented charging time of each charging vehicle in each charging stage; calculating the total charging time corresponding to each agent based on the segmented charging time group, the total charging time being the cumulative charging time of each charging vehicle from the initial charging SoC to the target charging SoC; determining the average charging delay corresponding to each agent based on the total charging time and the reference charging time; and updating the interpolated charging power group corresponding to each agent based on the average charging delay.

[0079] In this embodiment, firstly, since each new charging power group includes the charging power value of each charging vehicle at each SoC checkpoint, when the SoC increment span between adjacent SoC checkpoints is relatively large compared to the specified SoC increment, the charging power value of each charging vehicle at each SoC checkpoint cannot cover the charging power of each charging stage. Therefore, the electronic device needs to perform interpolation processing on each new charging power group.

[0080] In some embodiments, the specified SoC increment is less than the absolute value of the difference between two adjacent SoC checkpoints and is greater than or equal to 1%.

[0081] In this embodiment, as described above, for the charging vehicle EV1, the specified SoC increment is 1%. 30% and 36% are two adjacent SoC checkpoints, and the absolute value of the difference between 30% and 36% is 6%, so the specified SoC increment of 1% is less than 6%. 36% and 41% are also two adjacent SoC checkpoints, and the absolute value of the difference between 36% and 41% is 5%, so the specified SoC increment of 1% is also less than 5%. 41% and 47% are also two adjacent SoC checkpoints, and the absolute value of the difference between 41% and 47% is 6%, so the specified SoC increment of 1% is also less than 6%. 47% and 52% are also two adjacent SoC checkpoints, and the absolute value of the difference between 47% and 52% is 5%, so the specified SoC increment of 1% is also less than 5%. 52% and 58% are also adjacent SoC checkpoints. For two adjacent SoC checkpoints, 52% and 58% have an absolute difference of 6%, and a specified SoC increment of 1% is also less than 6%; 58% and 63% are also adjacent SoC checkpoints, and the absolute difference between 58% and 63% is 5%, and a specified SoC increment of 1% is also less than 5%; 63% and 69% are also adjacent SoC checkpoints, and the absolute difference between 63% and 69% is 6%, and a specified SoC increment of 1% is also less than 6%; 69% and 74% are also adjacent SoC checkpoints, and the absolute difference between 69% and 74% is 5%, and a specified SoC increment of 1% is also less than 5%; 74% and 80% are also adjacent SoC checkpoints, and the absolute difference between 74% and 80% is 6%, and a specified SoC increment of 1% is also less than 6%.

[0082] In this embodiment, the specified SoC increment is less than the absolute value of the difference between two adjacent SoC checkpoints and greater than or equal to 1%. Therefore, when optimizing the charging power of each charging vehicle throughout the entire charging process, it is only necessary to optimize the charging power of each charging vehicle under a small number of SoC values ​​throughout the entire charging process. It is not necessary to completely cover the charging power under every SoC value throughout the entire charging process, thereby reducing the optimization dimensionality and improving optimization efficiency.

[0083] The purpose of interpolating each new charging power group is to complete the new charging power for each charging vehicle at each charging stage except for each SoC checkpoint. This facilitates the electronic equipment to calculate the total charging time for each charging vehicle more precisely and to generate a complete SoC-based charging schedule. As mentioned earlier, for charging vehicle EV1, the new charging power group corresponding to EV1 includes the new charging power at each SoC checkpoint, i.e., at 30%, 36%, 41%, 47%, 52%, 58%, 63%, 69%, 74%, and 80%, respectively. Assuming a specified SoC increment of 1%, since these new charging powers change continuously with the charging process, the electronic equipment can perform linear interpolation calculations based on the new charging power at each SoC checkpoint to infer the new charging power for each other SoC value, i.e., at 31%, 32%, 33%, 46%, 41%, 47%, 52%, 58%, 63%, 69%, 74%, and 80%, respectively. The new charging power at percentages of 34%, 35%, 37%, 38%, 39%, 40%, 42%, 43%, 44%, 45%, 46%, 48%, 49%, 50%, 51%, 53%, 54%, 55%, 56%, 57%, 59%, 60%, 61%, 62%, 64%, 65%, 66%, 67%, 68%, 70%, 71%, 73%, 75%, 76%, 77%, 78%, and 79% is calculated. These inferred new charging powers, along with the new charging power at each SoC checkpoint, constitute an interpolated charging power group. Since there are multiple new charging power groups, the electronic device performs the same interpolation process on each group, resulting in multiple interpolated charging power groups. It is understood that the electronic device can limit each new charging power calculated through interpolation based on the charging curve, thereby ensuring that each interpolated new charging power is within the feasible charging power range and the power limit of the charging station.

[0084] Next, the electronic device can substitute the interpolated charging power group corresponding to each agent and the battery capacity of each charging vehicle into the above formula (2) to determine the segmented charging time group corresponding to each agent.

[0085] Next, the electronic device can substitute the segmented charging time group corresponding to each agent into the above formula (5) to determine the total charging time corresponding to each agent.

[0086] Next, the electronic device can substitute the total charging time and reference charging time corresponding to each agent into the above formula (1) to determine the average charging delay corresponding to each agent.

[0087] Finally, the electronic device can compare the average charging delay corresponding to each agent with the individual optimal charging delay, and update the interpolated charging power group corresponding to each agent based on the comparison results. The individual optimal charging delay is the average charging delay with the smallest delay corresponding to each agent up to this update.

[0088] In some embodiments, the electronic device can update the historical individual optimal charging delay for each agent based on the average charging delay, and update the interpolated charging power group for each agent based on the interpolated charging power group corresponding to the historical individual optimal charging delay.

[0089] For example, if the current average charging delay corresponding to a certain agent is less than the historical individual optimal charging delay, the electronic device can update the historical individual optimal charging delay with the current average charging delay and update the historical optimal interpolated charging power group with the current interpolated charging power group. If the current average charging delay corresponding to a certain agent is greater than the historical individual optimal charging delay, the electronic device will discard the current interpolated charging power group. Therefore, each time the electronic device completes the position update of each agent, iteratively updates the individual optimal charging delay and optimal interpolated charging power group of each agent at the individual level, thereby realizing the update of the interpolated charging power group corresponding to each agent.

[0090] In some embodiments, after performing the step of updating the historical individual optimal charging delay corresponding to each agent based on the average charging delay, the electronic device may further determine the group optimal charging delay based on the historical individual optimal charging delay, determine the charging power group corresponding to the group optimal charging delay and take the charging power group vector corresponding to the charging power group as the group optimal vector, obtain the group optimal vector and determine the charging power group vector corresponding to each agent based on the charging power group corresponding to the historical individual optimal charging delay, and return to perform the step of updating the charging power group vector in the search space based on the group optimal vector and the charging power group vector to obtain multiple solution vectors.

[0091] In this embodiment, the electronic device can determine the individual optimal charging delay with the smallest delay from the individual optimal charging delays corresponding to all agents. The individual optimal charging delay with the smallest delay is taken as the group optimal charging delay, and the optimal interpolated charging power group corresponding to the group optimal charging delay is taken as the group optimal interpolated charging power group at the group level. Then, based on the optimal interpolated charging power group corresponding to each agent and the group optimal interpolated charging power group at the group level, subsequent iterative updates are performed. This iterative update is performed continuously until the iteration termination condition is met, and a final optimal interpolated charging power group can be obtained. This optimal interpolated charging power group is the target charging power group.

[0092] The following describes this embodiment in more detail with reference to Tables 4-6. As shown in Table 4, the particle swarm includes a first agent A1, a second agent A2, and a third agent A3. The initial charging power group and initial charging power group vector corresponding to the first agent A1 are P10 and x10, respectively. The initial charging power group and initial charging power group vector corresponding to the second agent A2 are P20 and x20, respectively. The initial charging power group and initial charging power group vector corresponding to the third agent A3 are P30 and x30, respectively. The initial swarm optimal vector (preset vector) and the charging power group vector corresponding to each agent are substituted into... Formulas (6) and (7) above are iteratively updated. After the first iteration, the initial charging power group P10 and the initial charging power group vector x10 are updated to obtain the charging power group P11 and the solution vector x11, respectively. The initial charging power group P20 and the initial charging power group vector x20 are updated to obtain the charging power group P21 and the solution vector x21, respectively. The initial charging power group P30 and the initial charging power group vector x30 are updated to obtain the charging power group P31 and the solution vector x31, respectively. Interpolation processing is performed on the charging power groups P11, P21 and P31 to obtain the first interpolated charging power group. Power groups P11', P21', and P31' have the following average charging delays: P11' calculates a current average charging delay of 10.2 minutes for the first agent A1; P21' calculates a current average charging delay of 9.4 minutes for the second agent A2; and P31' calculates a current average charging delay of 9.8 minutes for the third agent A3. Since this is the first iteration update, the historical optimal charging delay for the first agent A1 is updated to 10.2 minutes, the historical optimal charging delay for the second agent A2 is updated to 9.4 minutes, and the historical optimal charging delay for the third agent A3 is updated to 9.8 minutes. The historical optimal charging delay for agent A3 is updated to 9.8 minutes. Solution vector x11 is the optimal position for the individual corresponding to the first agent A1, solution vector x21 is the optimal position for the individual corresponding to the second agent A2, and solution vector x31 is the optimal position for the individual corresponding to the third agent A3. Since 9.4 minutes is the optimal charging delay among all historical optimal charging delays, it is updated to the group optimal charging delay. The first interpolated charging power group P21' corresponding to the group optimal charging delay of 9.4 minutes is the group optimal interpolated charging power group, and the optimal position x21 is the group optimal position.

[0093] As shown in Table 5 below, the electronic device can substitute the group's optimal position x21 and the optimal positions corresponding to each agent into the above formulas (6) and (7) for iterative updates. After the second iteration update, the charging power group P11 and the individual optimal position x11 are updated to obtain the charging power group P12 and the solution vector x12, respectively. The charging power group P21 and the individual optimal position x21 are updated to obtain the charging power group P22 and the solution vector x22, respectively. The charging power group P31 and the individual optimal position x31 are updated to obtain the charging power group P32 and the solution vector x32, respectively. The charging power groups P12, P22 and P32 are interpolated to obtain the second interpolated charging power groups P12', P22' and P32'. The current average charging delay corresponding to the first agent A1 calculated according to P12' is 9.7 minutes, the current average charging delay corresponding to the second agent A2 calculated according to P22' is 9.5 minutes, and the current average charging delay corresponding to the third agent A3 calculated according to P32' is 9 minutes. After 3 minutes, for the first agent A1, since the current average charging delay of 9.7 minutes is less than the historical individual optimal charging delay of 10.2 minutes, the historical individual optimal charging delay corresponding to the first agent A1 is updated to 9.7 minutes, and the individual optimal position x11 is also updated to x12. For the second agent A2, since the current average charging delay of 9.5 minutes is greater than the historical individual optimal charging delay of 9.4 minutes, the historical individual optimal charging delay corresponding to the second agent A2 remains unchanged at 9.4 minutes, and the individual optimal position x21 also remains unchanged. For the third agent A3, since the current average charging delay of 9.3 minutes is less than the historical individual optimal charging delay of 9.8 minutes, the historical individual optimal charging delay corresponding to the third agent A3 is updated to 9.3 minutes, and the individual optimal position x31 is also updated to x32. Since 9.3 minutes is the best among all historical individual optimal charging delays, 9.3 minutes is updated to the group optimal charging delay, and the second interpolated charging power group P32 corresponds to the group optimal charging delay of 9.3 minutes. ' The optimal interpolation charging power group for the group and the optimal position of the individual (x32) are the optimal positions for the group.

[0094] As shown in Table 6 below, the electronic device can substitute the group's optimal position x32 and the optimal positions corresponding to each agent into the above formulas (6) and (7) for iterative updates. After the second iteration update, the charging power group P12 and the individual optimal position x12 are updated to obtain the charging power group P13 and the solution vector x13, respectively. The charging power group P21 and the individual optimal position x21 are updated to obtain the charging power group P23 and the solution vector x23, respectively. The charging power group P32 and the individual optimal position x32 are updated to obtain the charging power group P33 and the solution vector x33, respectively. The charging power groups P13, P23 and P33 are interpolated to obtain the second interpolated charging power groups P13', P23' and P33'. The current average charging delay corresponding to the first agent A1 calculated according to P13' is 8.4 minutes, the current average charging delay corresponding to the second agent A2 calculated according to P23' is 8.6 minutes, and the current average charging delay corresponding to the third agent A3 calculated according to P33' is 8.5 minutes. For the first agent A1, since the current average charging delay of 8.4 minutes is less than the historical individual optimal charging delay of 9.7 minutes, the historical individual optimal charging delay corresponding to the first agent A1 is updated to 8.4 minutes, and the individual optimal position x12 is also updated to x13. For the second agent A2, since the current average charging delay of 8.6 minutes is less than the historical individual optimal charging delay of 9.4 minutes, the historical individual optimal charging delay corresponding to the second agent A2 is updated to 8.6 minutes, and the individual optimal position x21 is also updated to x23. For the third agent A3, since the current average charging delay of 8.5 minutes is less than the historical individual optimal charging delay of 9.3 minutes, the historical individual optimal charging delay corresponding to the third agent A3 is updated to 8.5 minutes, and the individual optimal position x32 is also updated to x33. Since 8.4 minutes is the best among all historical individual optimal charging delays, 8.4 minutes is updated to the group optimal charging delay. The third interpolated charging power group P13 corresponds to the group optimal charging delay of 8.4 minutes. ' The optimal interpolation charging power group for the group and the optimal position of the individual (x13) are the optimal positions for the group.

[0095] Understandably, by continuously iterating and updating in this way, when the iteration termination condition is met, the final group of optimal interpolated charging power is the target charging power group.

[0096] Table 4 Table 5 Table 6 In some embodiments, updating the charging power group in a single iteration according to the particle swarm optimization algorithm further includes: determining whether the iteration termination condition is met, wherein the iteration termination condition is determined based on the optimal charging delay of the population or a preset iteration number threshold. If the iteration termination condition is met, the interpolated charging power group corresponding to the optimal charging delay of the population is determined as the target charging power group. If the iteration termination condition is not met, the process returns to the step of updating the charging power group vector in the search space according to the optimal population vector and the charging power group vector to obtain multiple solution vectors.

[0097] In this embodiment, the electronic device can determine whether the algorithm has converged based on the optimal charging delay obtained in the most recent N iterations. For example, if the optimal charging delay obtained in the most recent N iterations has not changed, the electronic device can determine that the algorithm has converged. If the optimal charging delay obtained in the most recent N iterations has changed, the electronic device can determine that the algorithm has not converged. When the algorithm has converged, the electronic device can determine that the iteration termination condition is met. When the algorithm has not converged, the electronic device can determine that the iteration termination condition is not met. The value of N can be set according to actual needs and is not specifically limited here. It is understood that if the number of iterations of the algorithm has reached a preset iteration threshold, even if the algorithm has not converged at this time, the electronic device can still determine that the iteration termination condition is met.

[0098] Understandably, as charging vehicles complete their charging and leave, or new charging vehicles arrive, the charging power scheduling system must adapt accordingly. If the number of active charging vehicles at a charging station changes due to departures or arrivals, or if the charging schedule deviates significantly from the planned schedule, the charging power scheduling system will restart to correct or re-optimize the charging schedule. This ensures that the charging schedule remains efficient and up-to-date at all times. Using the optimized SoC checkpoint power values, the electronic equipment will initiate a new location update, interpolation, and evaluation cycle until a new optimal schedule is found.

[0099] In some embodiments, for newly arriving charging vehicles, the electronic devices can perform SoC checkpoint selection and power initialization and incorporate them into the scheduling. Then, the initialized power is vectorized and concatenated with existing location vectors, and an iterative loop is run to find a new scheduling scheme for all charging vehicles in the charging station. This stage is understandably crucial because it ensures that the charging process remains dynamically optimized in real time to accommodate fluctuations in vehicle arrivals, departures, and scheduling adjustments. To accommodate situations where charging vehicles complete charging and depart, or new charging vehicles arrive, the electronic devices may need to adjust the charging schedule to meet system objectives or remain optimal and feasible under dynamic conditions. Furthermore, when the charging process of each charging vehicle deviates significantly from the charging curve, it may lead to unexpected charging delays or violations of constraints; therefore, the electronic devices can also adjust the charging schedule for such situations.

[0100] In some embodiments, for a newly arrived charging vehicle, the electronic device may introduce a new initial charging SoC, target charging SoC, battery capacity, and charging curve for that charging vehicle. In this case, the electronic device must ensure power constraints, such as that the total charging power of all charging vehicles including that charging vehicle does not exceed the maximum power capacity of the charging station, and that the charging power allocated to each charging gun does not exceed the maximum output power of the charging gun used.

[0101] In some embodiments, when a new charging vehicle arrives, the electronic device can acquire the initial charging SoC, target charging SoC, charging curve, and battery capacity of the new charging vehicle. Based on the initial charging SoC and target charging SoC, it determines multiple SoC checkpoints of the new charging vehicle. Based on the charging curve, it determines a first charging power group of the new charging vehicle and a reference charging time from the initial charging SoC to the target charging SoC. The first charging power group includes the initial charging power of the new charging vehicle at each SoC checkpoint. The first charging power is added to the target charging power group to obtain a second charging power group. The second charging power group is iteratively updated based on the particle swarm optimization algorithm, battery capacity, and reference charging time. The charging power of the new charging vehicle and other charging vehicles is scheduled based on the iteratively updated second charging power group.

[0102] In this embodiment, it is particularly important for the electronic device to perform a complete re-optimization strategy when the power allocation pattern changes significantly. In this case, some previous adjustments may have deviated from the global optimum, requiring a comprehensive re-evaluation to align the scheduling scheme with the latest conditions. The optimization process for complete re-optimization is similar to the charging power scheduling process in the above embodiments, so the optimization process for complete re-optimization can refer to the above embodiments and will not be repeated here.

[0103] Understandably, after adding the initial charging power to the target charging power group, the electronic device can use the current global optimum as the starting point for a complete re-optimization, resetting the entire particle swarm optimization algorithm. This means the algorithm no longer merely adjusts some variables or performs incremental updates, but reconsiders all charging vehicles and their scheduling parameters. By calibrating all decision variables under the updated system constraints and requirements, it ensures that no potential improvements are missed due to previous local optimizations. Utilizing the current global optimum can accelerate convergence. The new optimization run starts from an already relatively good solution space because the initialization here inherits the improvements gained by the previous particle swarm optimization algorithm in iterative updates, thereby reducing the search space required to find the optimal solution. It essentially allows the entire charging power scheduling system to "restart with a better baseline," rather than starting from zero or an uninformed state.

[0104] Therefore, using full re-optimization as a periodic or event-triggered strategy can avoid long-term inefficiencies caused by repeated partial adjustments. It provides a systematic calibration mechanism for the entire scheduling framework, enabling the algorithm to remain adaptive and highly responsive even in dynamic environments, thereby optimizing resource allocation and maintaining a high-quality service level within the system. Although full re-optimization is computationally time-consuming, it ensures maximized system performance, making the operation of charging stations more robust and stable.

[0105] In some embodiments, when a charging vehicle leaves, the electronic device can remove the target charging power of the departing charging vehicle at each SoC checkpoint from the target charging power group to obtain a third charging power group, obtain the actual charging status of other charging vehicles, determine whether the conditions for complete re-optimization are met based on the actual charging status, and when the conditions for complete re-optimization are met, iteratively update the third charging power group according to the particle swarm optimization algorithm, battery capacity and reference charging time, and schedule the charging power of other charging vehicles according to the third charging power group obtained after iterative update.

[0106] In this embodiment, when the conditions for complete re-optimization are met, the electronic device can use the current global optimum as the starting point for complete re-optimization, resetting the entire particle swarm optimization algorithm flow. The current global optimum can be used to accelerate convergence, thereby improving optimization efficiency. The beneficial effects of complete re-optimization are described in the above embodiments and will not be repeated here.

[0107] In some embodiments, when the complete re-optimization condition is not met, the electronic device can determine whether there are charging vehicles affected by charging. If so, the target charging power of the charging vehicles affected by charging is extracted from the third charging power group at each SoC checkpoint to obtain the fourth charging power group. The fourth charging power group is iteratively updated according to the particle swarm optimization algorithm, battery capacity and reference charging time, and the charging power of the charging vehicles affected by charging is scheduled according to the fourth charging power group obtained after iterative update.

[0108] In this embodiment, when the conditions for complete re-optimization are not met, the electronic device can execute a partial re-optimization strategy. In this partial re-optimization strategy, the scheduling variables directly affected by the departure of charging vehicles are updated, while the allocation to unaffected charging vehicles remains unchanged. This allows the charging power scheduling system to quickly adapt to real-time changes without incurring significant computational overhead for a complete recalculation of the entire charging schedule. By updating the charging power of affected charging vehicles at each SoC checkpoint, the method still ensures that the constraints of maximum power capacity and the maximum output power limit of a single charging gun are met. This significantly reduces latency in dynamic or high-load scenarios, which is crucial for scenarios requiring rapid decision-making. While this process improves speed and responsiveness, it may not yield a globally optimal solution. Local updates, though efficient, can accumulate locally optimal decisions over time in complex environments, especially in tightly coupled charging tasks, causing the scheduling strategy to gradually deviate from the globally optimal solution. However, the partial re-optimization strategy provides a practical trade-off between computational efficiency and scheduling quality, enabling it to address rapid response scenarios in charging scheduling tasks.

[0109] In some embodiments, the electronic device can generate a planned charging curve for each charging vehicle based on multiple charging power, obtain the current actual charging power of each charging vehicle, calculate the power deviation between the actual charging power and the planned charging curve, and determine whether the conditions for complete re-optimization are met based on the power deviation.

[0110] In this embodiment, if the power deviation is greater than or equal to the preset deviation value, the electronic device can determine that the complete re-optimization condition is met; if the power deviation is less than the preset deviation value, the electronic device can determine that the complete re-optimization condition is not met.

[0111] It should be noted that in the above embodiments, there is no necessarily a certain order between the steps. Those skilled in the art can understand from the description of the embodiments of the present invention that the above steps may have different execution orders in different embodiments, that is, they may be executed in parallel or in turn, etc.

[0112] As another aspect of this invention, this embodiment provides a charging power scheduling device. The charging power scheduling device can be a software module, which includes several instructions stored in a memory. A processor can access the memory and execute the instructions to complete the charging power scheduling method described in the various embodiments above.

[0113] In some embodiments, the charging power scheduling device can be constructed from hardware components. For example, the charging power scheduling device can be constructed from one or more chips, which can work in coordination to complete the charging power scheduling method described in the various embodiments above. As another example, the charging power scheduling device can also be constructed from components such as general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), microcontrollers, ARM (Acorn RISC Machines), programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components.

[0114] In some embodiments, see Figure 4 The charging power scheduling device 400 provided in this embodiment of the invention includes an acquisition module 401, a first determination module 402, a second determination module 403, an iterative update module 404, and a scheduling module 405.

[0115] The acquisition module 401 is used to acquire the initial charging SoC, target charging SoC, charging curve and battery capacity of each charging vehicle. The first determination module 402 is used to determine multiple SoC checkpoints of each charging vehicle based on the initial charging SoC and target charging SoC. The SoC checkpoints are SoC values ​​distributed between the initial charging SoC and the target charging SoC. The second determination module 403 is used to determine multiple initial charging power groups and the reference charging time from the initial charging SoC to the target charging SoC based on the charging curve. Each initial charging power group includes the initial charging power of each charging vehicle at each SoC checkpoint. The iterative update module 404 is used to iteratively update each initial charging power group based on the particle swarm optimization algorithm, battery capacity and reference charging time to obtain the target charging power group. The scheduling module 405 is used to schedule the charging power of each charging vehicle based on the target charging power group.

[0116] It should be noted that the above-described charging power scheduling device can execute the charging power scheduling method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in the embodiments of the charging power scheduling device can be found in the charging power scheduling method provided in the embodiments of the present invention.

[0117] Please see Figure 5 , Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. For example... Figure 5 As shown, the electronic device 102 includes one or more processors 1021 and a memory 1022. Figure 5 Take a processor 1021 as an example.

[0118] Processor 1021 is configured to support the computer device in performing the corresponding functions in the methods described in the above method embodiments. Processor 1021 may be a Central Processing Unit (CPU), a Network Processor (NP), a hardware chip, or any combination thereof. The aforementioned hardware chip may be an Application Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The aforementioned PLD may be a Complex Programmable Logic Device (CPLD), a Field-Programmable Gate Array (FPGA), a Generic Array Logic (GAL), or any combination thereof.

[0119] Memory 1022 is used to store program code. Memory 1022 may include volatile memory (VM), such as random access memory (RAM); memory may also include non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); memory 1022 may also include combinations of the above types of memory.

[0120] The memory 1022 can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the charging power scheduling method in the embodiments of the present invention. The processor 1021 executes various functional applications and data processing of the charging power scheduling method and charging power scheduling device by running the non-volatile software programs, instructions, and modules stored in the memory 1022, that is, it realizes the functions of each module or unit of the charging power scheduling method and charging power scheduling device provided in the above method embodiments.

[0121] The memory 1022 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function. The data storage area may store data created based on the use of the charging power scheduling device, etc. In some embodiments, the memory 1022 may optionally include memory remotely configured relative to the processor, which can be connected to the charging power scheduling device via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0122] The one or more modules are stored in the memory 1022. When executed by the one or more processors 1021, they execute the charging power scheduling method in any of the above method embodiments. For example, they execute the method steps described in the above method embodiments to realize the functions of the modules described in the above device embodiments.

[0123] This invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a computer, cause the computer to perform the method described in the foregoing embodiments.

[0124] It will be understood by those skilled in the art that all or part of the processes in the methods of the above 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 methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0125] Finally, it should be noted that the present invention can be implemented in many different forms and is not limited to the embodiments described in this specification. These embodiments are not intended to impose additional limitations on the content of the present invention; their purpose is to provide a more thorough and comprehensive understanding of the disclosure of the present invention. Furthermore, within the framework of the present invention, the above-described technical features can be combined with each other, and many other variations of different aspects of the present invention as described above exist, all of which are considered to be within the scope of the present invention specification. Moreover, those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A charging power scheduling method, characterized in that, include: Obtain the initial charging SoC, target charging SoC, charging curve, and battery capacity for each charging vehicle; Multiple SoC checkpoints for each charging vehicle are determined based on the initial charging SoC and the target charging SoC, wherein the SoC checkpoints are SoC values ​​distributed between the initial charging SoC and the target charging SoC; Multiple initial charging power groups and a reference charging time from the initial charging SoC to the target charging SoC are determined based on the charging curve. Each initial charging power group includes the initial charging power of each charging vehicle at each SoC checkpoint. Each initial charging power group is iteratively updated according to the particle swarm optimization algorithm, the battery capacity, and the reference charging time to obtain the target charging power group; The charging power of each charging vehicle is scheduled according to the target charging power group.

2. The charging power scheduling method according to claim 1, wherein the step of iteratively updating each initial charging power group according to the particle swarm optimization algorithm, the battery capacity, and the reference charging time to obtain the target charging power group includes: Each charging power group is iteratively updated multiple times according to the particle swarm optimization algorithm to obtain the target charging power group. The initial value of the charging power group is the initial charging power group, and each charging power group corresponds to an agent in the particle swarm. The objective function for iterative updates includes minimizing the average charging delay corresponding to each agent, wherein the average charging delay corresponding to each agent is determined based on the battery capacity, the reference charging duration, and the charging power group; The constraints of the objective function include at least one of the following conditions: the charging power of each charging vehicle does not exceed the maximum power limit of the charging pile and the maximum power limit of the charging vehicle, and the sum of the charging power of all charging vehicles does not exceed the maximum power limit of the charging station.

3. The charging power scheduling method according to claim 2, characterized in that, The charging power group is updated in a single iteration according to the particle swarm optimization algorithm, including: Obtain the optimal vector for the group and determine the charging power group vector corresponding to each agent based on the charging power group. Each charging power group vector represents the position of its corresponding agent in the search space. The search space is generated based on the charging curve. The initial value of the optimal vector for the group is a preset vector. Based on the optimal group vector and the charging power group vector, the charging power group vector is updated in the search space to obtain multiple solution vectors; A new charging power group corresponding to each agent is determined based on each of the solution vectors.

4. The charging power scheduling method according to claim 3, characterized in that, The step of updating the charging power group vector in the search space based on the group optimal vector and the charging power group vector to obtain multiple solution vectors includes: Calculate the movement speed of each agent in the search space based on the group optimal vector and the charging power group vector; Calculate the current position of each agent within the search space based on the movement speed; Each charging power group vector is updated based on the current position to obtain multiple solution vectors.

5. The charging power scheduling method according to claim 3, characterized in that, The charging power group is updated in a single iteration based on the particle swarm optimization algorithm, and also includes: Interpolation processing is performed on each of the new charging power groups to obtain multiple interpolated charging power groups. Each interpolated charging power group includes the new charging power of each charging vehicle in each charging stage, wherein the charging stage is determined according to a specified SoC increment. Based on the interpolated charging power group and the battery capacity, a segmented charging time group corresponding to each agent is determined, and each segmented charging time group includes the segmented charging time of each charging vehicle in each charging stage. The total charging time corresponding to each agent is calculated based on the segmented charging time group. The total charging time is the cumulative charging time of each charging vehicle from the initial charging SoC to the target charging SoC. The average charging delay corresponding to each agent is determined based on the total charging time and the reference charging time. The interpolated charging power group corresponding to each agent is updated based on the average charging delay.

6. The charging power scheduling method according to claim 5, characterized in that, The specified SoC increment is less than the absolute value of the difference between two adjacent SoC checkpoints and is greater than or equal to 1%.

7. The charging power scheduling method according to claim 5, characterized in that, The step of updating the interpolated charging power group corresponding to each agent based on the average charging delay includes: The optimal charging delay for each agent in the historical individual is updated based on the average charging delay. Update the interpolated charging power group corresponding to each agent based on the interpolated charging power group corresponding to the optimal charging delay of the historical individual.

8. The charging power scheduling method according to claim 7, characterized in that, After updating the historical individual optimal charging delay for each agent based on the average charging delay, the method further includes: The optimal charging delay for the group is determined based on the historical optimal charging delay for individual users. Determine the charging power group corresponding to the optimal charging delay of the group and use the charging power group vector corresponding to the charging power group as the optimal vector of the group; Obtain the optimal vector of the group and determine the charging power group vector corresponding to each agent based on the charging power group corresponding to the optimal charging delay of the historical individual, and return to execute the step of updating the charging power group vector in the search space based on the optimal vector of the group and the charging power group vector to obtain multiple solution vectors.

9. The charging power scheduling method according to claim 8, characterized in that, The charging power group is updated in a single iteration based on the particle swarm optimization algorithm, and also includes: Determine whether the iteration termination condition is met, wherein the iteration termination condition is determined based on the optimal charging delay or the number of iterations for the population; If the iteration termination condition is met, the interpolated charging power group corresponding to the optimal charging delay of the group is determined as the target charging power group. If the iteration termination condition is not met, then return to the step of updating the charging power group vector in the search space based on the group optimal vector and the charging power group vector to obtain multiple solution vectors.

10. The charging power scheduling method according to claim 1, characterized in that, Also includes: When a new charging vehicle arrives, acquire the initial charging SoC, target charging SoC, charging curve, and battery capacity of the new charging vehicle. Multiple SoC checkpoints for newly arriving charging vehicles are determined based on the initial charging SoC and the target charging SoC. The first charging power group of the newly arrived charging vehicle and the reference charging time from the initial charging SoC to the target charging SoC are determined based on the charging curve. The first charging power group includes the initial charging power of the newly arrived charging vehicle at each SoC checkpoint. The first charging power is added to the target charging power group to obtain the second charging power group. The second charging power group is iteratively updated according to the particle swarm optimization algorithm, the battery capacity, and the reference charging time. The charging power of newly arriving charging vehicles and other charging vehicles is scheduled according to the second charging power group obtained after the iterative update.

11. The charging power scheduling method according to claim 1, characterized in that, Also includes: When a charging vehicle leaves, the target charging power of the departing charging vehicle at each SoC checkpoint is removed from the target charging power group to obtain a third charging power group. Obtain the actual charging status of other charging vehicles; Determine whether the conditions for complete re-optimization are met based on the actual charging status; When the conditions for complete re-optimization are met, the third charging power group is iteratively updated according to the particle swarm optimization algorithm, the battery capacity, and the reference charging time, and the charging power of other charging vehicles is scheduled according to the third charging power group obtained after the iterative update.

12. The charging power scheduling method according to claim 11, characterized in that, Also includes: If the conditions for complete re-optimization are not met, determine whether there are charging vehicles whose charging is affected. If it exists, the target charging power of the charging vehicles affected by the charging is extracted from the third charging power group at each SoC checkpoint to obtain the fourth charging power group; The fourth charging power group is iteratively updated based on the particle swarm optimization algorithm, the battery capacity, and the reference charging time, and the charging power of the vehicles affected by the charging is scheduled according to the fourth charging power group obtained after the iterative update.

13. The charging power scheduling method according to claim 11, characterized in that, The target charging power group includes multiple charging powers for each charging vehicle, and the method further includes: A planned charging curve for each charging vehicle is generated based on the multiple charging powers described. Obtain the current actual charging power of each charging vehicle; Calculate the power deviation between the actual charging power and the planned charging curve; Determine whether the conditions for complete re-optimization are met based on the power deviation.

14. An electronic device, characterized in that, The device includes a memory and a processor, the memory being connected to the processor, the processor being configured to execute one or more computer programs stored in the memory, the processor causing the electronic device to implement the charging power scheduling method as described in any one of claims 1 to 13 when executing the one or more computer programs.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the charging power scheduling method as described in any one of claims 1 to 13.

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