A method and system for allocating resources of a charging and swapping station

Abstract

The application relates to a charging and battery replacing station resource allocation method and system, and belongs to the technical field of charging and battery replacing station resource allocation. The method comprises the following steps: obtaining battery replacing resource information, charging resource information and battery state information; determining a charging and battery replacing decision coefficient according to the battery replacing resource information, the charging resource information and the battery state information; judging whether the charging and battery replacing decision coefficient is smaller than a preset charging and battery replacing decision coefficient; if yes, a charging pile power dynamic allocation model based on vehicle travel task demand and vehicle stay duration is constructed; a first constraint condition and a first target function of the charging pile power dynamic allocation model are constructed; under the constraint of the first constraint condition, the charging pile power dynamic allocation model is solved with the minimum first target function as the target, the charging pile power corresponding to each vehicle is dynamically allocated, and charging pile power resource allocation of the charging and battery replacing station is completed; otherwise, the power shortage batteries of each vehicle are replaced with the stock batteries in the charging and battery replacing station, and battery resource allocation of the charging and battery replacing station is completed.

Description

Technical Field

[0001] This invention belongs to the field of resource allocation technology for charging and battery swapping stations, and particularly relates to a resource allocation method and system for charging and battery swapping stations. Background Technology

[0002] Electric mining trucks offer significant advantages in reducing carbon emissions, lowering fuel consumption, and improving the mining environment. However, the complex working environment and high-intensity transportation tasks in mines place more stringent demands on the range, charging and swapping efficiency, and refueling methods of power batteries. To meet the needs of high-frequency transportation and long-term continuous operation of mining trucks, the construction of efficient charging and swapping stations has become a crucial infrastructure for promoting the electrification of mines.

[0003] In existing technologies, traditional charging and battery swapping management systems often only consider time or economic factors in the decision-making process, lacking comprehensive optimization based on battery status, vehicle task requirements, and site resource load. This results in a lack of dynamism and intelligence in the selection of charging and battery swapping solutions for vehicles, making it difficult to adapt to the complex and ever-changing mining operation environment.

[0004] Furthermore, in the charging stage, existing power allocation models generally ignore factors such as grid load fluctuations and peak shaving and valley filling, which can easily put pressure on the local grid. In the battery swapping stage, there is a lack of optimization mechanisms for the management of inventory batteries and the recharging scheduling of depleted batteries, which can easily lead to energy waste and accelerate battery life degradation. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of the invention is to provide a resource allocation method and system for charging and battery swapping stations. It takes into account that traditional charging and battery swapping management systems often only consider time or economic factors in the decision-making process, and lack comprehensive optimization based on battery status, vehicle task requirements and station resource load. This results in a lack of dynamism and intelligence in the selection of charging and battery swapping schemes for vehicles, making it difficult to adapt to the complex and ever-changing mining operation environment. In the charging stage, existing power allocation models generally ignore factors such as grid load fluctuations and peak shaving and valley filling, which can easily put pressure on the local grid. In the battery swapping stage, the lack of optimization mechanisms for inventory battery management and recharging scheduling of depleted batteries can easily lead to energy waste and accelerated battery life degradation.

[0006] In a first aspect, the present invention provides a method for allocating resources at charging and battery swapping stations, the method comprising: S1, obtain battery swapping resource information, charging resource information, and battery status information of each vehicle in the charging and swapping station; S2, determine the charging / swapping decision coefficients based on battery swapping resource information, charging resource information, and battery status information; S3, determine whether the charging / swapping decision coefficient is less than the preset charging / swapping decision coefficient; if yes, perform charging service for each vehicle and proceed to S4; otherwise, perform battery swapping service for each vehicle and proceed to S7. S4, Construct a dynamic power allocation model for charging piles based on vehicle travel task requirements and vehicle dwell time; S5, the first constraint and the first objective function for constructing the dynamic power allocation model of charging piles; S6, under the constraint of the first constraint condition, with the goal of minimizing the first objective function, solve the dynamic allocation model of charging pile power, dynamically allocate the charging pile power corresponding to each vehicle, and complete the allocation of charging pile power resources of the charging and swapping station. S7 replaces the depleted batteries of each vehicle with the stock batteries in the charging and swapping stations, thus completing the battery resource allocation of the charging and swapping stations.

[0007] Furthermore, the battery swapping resource information specifically includes: the total number of battery swapping stations, the number of available battery swapping stations, the hourly service rate of a single battery swapping station, the number of batteries in stock, the state of charge of the batteries in stock, the health status of the batteries in stock, and the capacity of the batteries in stock. The charging resource information specifically includes: the total number of charging piles, the number of available charging piles, the hourly service rate of a single charging pile, and the number of vehicles waiting in line at charging piles; Battery status information specifically includes: battery state of charge, battery health status, and battery rated capacity.

[0008] Furthermore, S2 specifically includes: S201 determines the required replenishment power for each vehicle based on battery status information; S202: Based on battery swapping resource information, the total battery swapping time is determined using the MMC queuing theory model; based on charging resource information and the required replenishment power, the total charging time is determined using the MMC queuing theory model. S203. Determine the total battery swapping cost based on the total battery swapping time; determine the total charging cost based on the total charging time. S204. Determine the charging / battery swapping decision coefficients based on the total cost of battery swapping and the total cost of charging.

[0009] Furthermore, S202 specifically includes: S2021, Determine the battery swapping waiting probability based on battery swapping resource information; S2022, determine the battery swapping waiting time based on the battery swapping waiting probability; S2023, determine the total battery swapping time based on the battery swapping waiting time; S2024, Determine the charging waiting probability based on charging resource information; S2025, determine the charging waiting time based on the charging waiting probability; S2026, determine the total charging time based on the required replenishment power and charging waiting time.

[0010] Furthermore, S203 specifically includes: S2031, calculate the monetary cost of battery swapping based on the required replenishment of electricity; S2032, normalize the total battery swapping time and the battery swapping monetary cost respectively to obtain the normalized total battery swapping time and the normalized battery swapping monetary cost; S2033 introduces dynamic weighting coefficients to perform a weighted sum of the normalized total battery swapping time and the normalized monetary cost of battery swapping to determine the total cost of battery swapping. S2034, calculate the charging currency cost based on the required replenishment of electricity; S2035, normalize the total charging time and charging monetary cost respectively to obtain normalized total charging time and normalized charging monetary cost. S2036 introduces a dynamic weighting coefficient to perform a weighted sum of the normalized total charging time and the normalized charging monetary cost to determine the total charging cost.

[0011] Furthermore, the specific requirements for vehicle travel tasks are as follows: when the battery state of charge of each vehicle is less than the target battery state of charge, the battery state of charge of each vehicle is replenished to the target battery state of charge. The vehicle dwell time is specifically defined as the time interval between the start time of vehicle charging and the start time of the vehicle's trip mission. The dynamic power allocation model for charging piles specifically includes: When the state of charge of each vehicle's battery is less than the target state of charge and the vehicle's charging time is greater than the vehicle's dwell time, the power of the charging pile corresponding to each vehicle is fixed at the maximum power. When the battery state of charge of each vehicle is less than the target battery state of charge and the vehicle charging time is less than or equal to the vehicle dwell time, the charging pile power corresponding to each vehicle is fixed at the maximum power until the battery state of charge of each vehicle is replenished to the target battery state of charge. During the remaining vehicle dwell time, the charging pile power corresponding to each vehicle is dynamically allocated. The remaining vehicle dwell time is specifically the time interval between the time it takes for the battery state of charge of each vehicle to be replenished to the target battery state of charge and the start time of the vehicle's travel mission. When the state of charge of each vehicle's battery is greater than or equal to the target state of charge, the charging power of the corresponding charging pile for each vehicle is dynamically allocated.

[0012] Furthermore, the first constraint specifically includes: vehicle travel task time constraint, vehicle charging power constraint, and vehicle travel task demand constraint. The specific methods for constructing the first objective function include: A first intermediate objective function is constructed with the goal of minimizing the load peak-to-valley difference. A second intermediate objective function is constructed with the goal of minimizing the root mean square error of the load curve fluctuation; To minimize vehicle charging costs, a third intermediate objective function is constructed. Based on the first intermediate objective function, the second intermediate objective function, and the third intermediate objective function, the first objective function is constructed using the linear weighted sum method.

[0013] Furthermore, following S7, it also includes: S8, charge each depleted battery and construct the second constraint and second objective function for each depleted battery during the charging process; S9, under the constraint of the second constraint, dynamically allocates the charging power of each depleted battery during the charging process with the goal of minimizing the second objective function.

[0014] Furthermore, the second constraint specifically includes: the charging power constraint of the depleted battery and the state of charge constraint of the depleted battery; The specific methods for constructing the second objective function include: A fourth intermediate objective function is constructed with the goal of minimizing the power load fluctuation during the charging process of a depleted battery. A fifth intermediate objective function is constructed with the goal of minimizing the electricity cost during the charging process of a depleted battery. A sixth intermediate objective function is constructed with the goal of minimizing the charging loss during the charging process of a depleted battery. Based on the fourth, fifth, and sixth intermediate objective functions, a second objective function is constructed using the linear weighted sum method.

[0015] A second aspect of the present invention provides a resource allocation system for a charging and swapping station, comprising: a memory and a processor; The memory stores an application program adapted to be executed by the processor to implement the charging and swapping station resource allocation method described in the first aspect.

[0016] The beneficial effects of this invention are as follows: In this embodiment of the invention, the decision coefficients for charging and battery swapping are determined based on battery swapping resource information, charging resource information, and battery status information. The decision-making process considers not only time or economic factors but also comprehensive optimization based on battery status, vehicle task requirements, and station resource load. This improves the dynamism and intelligence of vehicle charging and battery swapping scheme selection, enabling it to adapt to complex and changing mining environments. Under the constraint of the first constraint, the dynamic allocation model of charging pile power is solved with the goal of minimizing the first objective function. This dynamically allocates the charging pile power corresponding to each vehicle, completing the allocation of charging pile power resources at the charging and battery swapping station. In the charging stage, existing power allocation models generally consider factors such as grid load fluctuations and peak shaving, which are less likely to put operational pressure on the local grid. By replacing the depleted batteries of each vehicle with the stock batteries in the charging and battery swapping station, the management of stock batteries and the scheduling of recharging depleted batteries in the battery swapping stage have optimization mechanisms, reducing energy waste and accelerating battery life degradation. Attached Figure Description

[0017] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. It is obvious that the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings.

[0018] Figure 1 This is a flowchart illustrating a method for allocating resources at a charging and battery swapping station according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a charging and swapping station resource allocation system provided in an embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts disclosed in this invention.

[0021] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of methods and systems consistent with some aspects of the invention as detailed in the appended claims.

[0023] This invention proposes a resource allocation method and system for charging and battery swapping stations. It addresses the shortcomings of traditional charging and battery swapping management systems, which often only consider time or economic factors in their decision-making process, lacking comprehensive optimization based on battery status, vehicle task requirements, and station resource load. This results in a lack of dynamism and intelligence in vehicle charging and battery swapping scheme selection, making it difficult to adapt to complex and changing mining environments. Furthermore, in the charging stage, existing power allocation models generally ignore factors such as grid load fluctuations and peak shaving, easily putting pressure on the local power grid. In the battery swapping stage, the lack of optimization mechanisms for inventory battery management and recharging scheduling of depleted batteries easily leads to energy waste and accelerated battery life degradation.

[0024] Method Implementation Examples Reference Figure 1 The diagram shows a flowchart of a charging and battery swapping station resource allocation method provided by an embodiment of the present invention.

[0025] This invention provides a method for allocating resources at charging and battery swapping stations, the method comprising: Specifically, the method includes steps S1 to S9.

[0026] S1 obtains information on battery swapping resources, charging resources, and battery status of each vehicle in the charging and swapping station.

[0027] Optionally, the battery swapping resource information specifically includes: the total number of battery swapping stations, the number of available battery swapping stations, the hourly service rate of a single battery swapping station, the number of batteries in stock, the state of charge of the batteries in stock, the health status of the batteries in stock, and the capacity of the batteries in stock.

[0028] Optionally, the charging resource information specifically includes: the total number of charging piles, the number of available charging piles, the hourly service rate of a single charging pile, and the number of vehicles waiting in line at charging piles.

[0029] Optionally, the battery status information specifically includes: battery state of charge, battery health status, and battery rated capacity.

[0030] In this embodiment of the invention, the comprehensive information collection in S1 ensures the scientific rigor and robustness of all subsequent steps. It not only ensures the reliability of the input data for the queuing and cost models but also supports adaptive adjustments to dynamic weights and decision coefficients, thereby enabling more precise vehicle charging / battery swapping selection and guaranteeing the feasibility of execution and the optimality of station-level operations.

[0031] S2 determines the charging / swapping decision coefficients based on battery swapping resource information, charging resource information, and battery status information.

[0032] It should be noted that the charging / swapping decision coefficient is a metric obtained by comprehensively considering the battery swapping resource information, charging resource information, and vehicle battery status information of charging / swapping stations. It is obtained by quantifying, normalizing, and weighting the time and cost required for battery swapping and charging, and using an indexation comparison method. It is used to reflect the degree to which a vehicle is more inclined to choose charging or battery swapping in a specific situation. The larger the value, the more inclined it is to swap batteries, and the smaller the value, the more inclined it is to charge.

[0033] In one possible implementation, S2 specifically includes sub-steps S201 to S204: S201 determines the required replenishment power for each vehicle based on battery status information.

[0034] Optionally, the calculation of the required replenishment power for the vehicle includes two specific methods: The first method, when the vehicle's battery state of charge (SOC) is less than the target SOC, multiplies the vehicle's rated battery capacity by a first SOC difference (the first SOC difference is the difference between the target SOC and the vehicle's battery SOC) to obtain the required replenishment power. The target SOC is specifically the battery SOC that each vehicle is expected to achieve to complete one trip. Here, the rated battery capacity determines the total energy capacity, and the first SOC difference reflects the proportion of the current battery charge shortfall from the target charge level; multiplying this by the rated battery capacity yields the required replenishment power. The second method, when the vehicle's battery SOC is greater than or equal to the target SOC, multiplies the vehicle's rated battery capacity by a second SOC difference (the second SOC difference is the difference between the full battery SOC, i.e., 100% battery SOC, and the vehicle's battery SOC) to obtain the required replenishment power. The second SOC difference reflects the proportion of the current battery charge shortfall from a fully charged state; multiplying this by the rated battery capacity yields the required replenishment power to fully charge the battery.

[0035] S202: Based on battery swapping resource information, the total battery swapping time is determined using the MMC queuing theory model. Based on charging resource information and the required replenishment power, the total charging time is determined using the MMC queuing theory model.

[0036] It should be noted that the MMC queuing model refers to a classic multi-service station queuing model, where M represents the arrival process following a Poisson distribution (Markovian arrival), M represents the service time following an exponential distribution (Markovian service), and C represents the number of parallel service stations (i.e., multiple service facilities) in the system. This model is often used to analyze the system's waiting probability, average waiting time, and total service time when vehicles arrive randomly, service times are random, and multiple charging piles or battery swapping stations exist, thus providing a mathematical basis for charging and battery swapping resource scheduling and efficiency evaluation.

[0037] In one possible implementation, S202 specifically includes sub-steps S2021 to S2026: S2021, Based on battery swapping resource information, determine the battery swapping waiting probability: Among them, P sw S represents the probability of waiting for a vehicle to battery swap. sw λ represents the total number of battery swapping stations. sw μ represents the number of battery swapping vehicles per hour. sw This represents the hourly service rate of a single battery swapping station (the average number of battery swaps that a single battery swapping station can complete per hour, reflecting the operational efficiency of the battery swapping station), where m represents the m-th battery swapping station. ρ represents the factorial operation. sw This indicates the utilization rate of the battery swapping station.

[0038] S2022, determine the battery swapping waiting time based on the battery swapping waiting probability.

[0039] Optionally, the battery swapping waiting time is obtained by dividing the battery swapping waiting probability by the difference between the total service capacity of the battery swapping station and the number of battery swapping vehicles per hour. The total service capacity of the battery swapping station is obtained by multiplying the number of battery swapping stations by the service rate of a single battery swapping station per hour.

[0040] S2023, determine the total battery swapping time based on the battery swapping waiting time.

[0041] Optionally, the total battery swapping time is obtained by adding the battery swapping waiting time to the average operation time required for a single battery swapping station to complete one battery swapping service. The average operation time of a single battery swapping station is the reciprocal of the hourly service rate of a single battery swapping station, thus obtaining the total battery swapping time.

[0042] S2024, Based on charging resource information, determine the charging waiting probability: Among them, P ch S represents the probability of waiting for the vehicle to charge. ch λ represents the total number of charging stations. ch μ represents the number of vehicles being charged per hour. ch ρ represents the hourly service rate of a single charging station (the average number of charging sessions that a single charging station can complete per hour, reflecting the service efficiency of the charging station), where n represents the nth charging station. ch This indicates the utilization rate of the charging pile.

[0043] S2025, determine the charging waiting time based on the charging waiting probability.

[0044] Optionally, the charging waiting time is obtained by dividing the charging waiting probability by the difference between the total service capacity of the charging piles and the number of vehicles charging per hour. The total service capacity of the charging piles is obtained by multiplying the number of charging piles by the service rate of a single charging pile per hour.

[0045] S2026, determine the total charging time based on the required replenishment power and charging waiting time.

[0046] Optionally, the total charging time is obtained by adding the charging waiting time to the average working time required for a single charging pile to complete one charging service. The average working time is obtained by dividing the required replenished electricity by the product of the charging efficiency and the allocable average charging power. The charging efficiency refers to the proportion of electrical energy input from the grid that is converted into the actual stored electrical energy in the battery during the charging process. The allocable average charging power refers to the average charging power that can be allocated to a single vehicle under the conditions of total power limitation of the charging and battery swapping station and concurrent use of charging piles.

[0047] In this embodiment of the invention, through the design of S2021 to S2026, the waiting probability, waiting time, and total time for battery swapping and charging can be scientifically quantified based on the M / M / C queuing theory model. This considers site resource factors such as the number of workstations and charging piles, service rate, and arrival rate, while also incorporating the vehicle's actual energy replenishment needs, charging efficiency, and power allocation constraints, thus comprehensively reflecting the actual time consumption under different modes. This method ensures the consistency of the calculation methods for charging and battery swapping times and the comparability of the results, avoiding the one-sidedness of empirical estimations. This makes subsequent cost calculations and decision coefficient determination more accurate and reliable, improving the scientific nature and feasibility of selecting vehicle energy replenishment methods.

[0048] S203. Determine the total battery swapping cost based on the total battery swapping time. Determine the total charging cost based on the total charging time.

[0049] In one possible implementation, S203 specifically includes sub-steps S2031 to S2036: S2031, calculate the monetary cost of battery swapping based on the required replenishment capacity: Among them, C sw α represents the monetary cost of battery swapping, α0 represents the basic service fee for battery swapping, and α E This represents the electricity-based billing factor, ΔE represents the required supplementary electricity, and α represents the amount of electricity needed. SOH This represents the quality premium coefficient indicating the battery's state of health. This indicates the average battery health status of the batteries available for allocation at the charging and battery swapping station. (Available batteries refer to battery cells stored at the charging and battery swapping station that, after being assessed by the testing and management system, are currently capable of providing battery swapping services for vehicles. These batteries typically meet certain requirements for State of Health (SOH), State of Charge (SOC), and capacity thresholds. They pose no safety hazards and are in a state that can be directly scheduled or allocated, allowing them to be immediately replaced and put into use when a vehicle performs a battery swapping operation.) cur Indicates the vehicle's battery health status, D sw This indicates a battery swap discount.

[0050] S2032, normalize the total battery swapping time and the monetary cost of battery swapping respectively to obtain the normalized total battery swapping time and the normalized monetary cost of battery swapping.

[0051] Optionally, the total battery swapping time and the monetary cost of battery swapping can be normalized by either maximum-minimum or mean-standard-deviation normalization to obtain normalized total battery swapping time and normalized monetary cost.

[0052] S2033 introduces a dynamic weighting coefficient to perform a weighted sum of the normalized total battery swapping time and the normalized monetary cost of battery swapping to determine the total cost of battery swapping.

[0053] S2034, calculate the charging currency cost based on the required replenishment of electricity: Among them, C ch β0 represents the basic charging service fee, and β represents the charging monetary cost. E D represents the real-time electricity price, η represents the charging efficiency (charging efficiency refers to the conversion efficiency of energy input from the grid to the actual energy stored in the battery), and D represents the charging efficiency. ch This indicates a charging discount.

[0054] S2035, normalize the total charging time and charging monetary cost respectively to obtain normalized total charging time and normalized charging monetary cost.

[0055] Optionally, the total charging time and charging monetary cost can be normalized by either maximum-minimum or mean-standard-deviation normalization to obtain normalized total charging time and normalized charging monetary cost.

[0056] S2036 introduces a dynamic weighting coefficient to perform a weighted sum of the normalized total charging time and the normalized charging monetary cost to determine the total charging cost.

[0057] In this embodiment of the invention, through the design of S2031 to S2036, factors such as required replenishment energy, electricity price, service fee, and battery health status are first incorporated into the cost calculation of battery swapping and charging to ensure the accuracy and comprehensiveness of the cost results. Subsequently, the duration and cost are normalized to eliminate dimensional differences, allowing for comparison across different dimensions within a unified framework. Furthermore, combined with dynamic weighting coefficients, the weights of time and cost are adaptively adjusted based on workstation utilization and task urgency, achieving a flexible balance between efficiency during peak periods and economy during off-peak periods. This scheme comprehensively reflects the overall cost of battery swapping and charging, ensuring that the calculation results are scientifically sound, comparable, and usable, providing a reliable basis for determining subsequent charging and swapping decision coefficients.

[0058] Optionally, the methods for determining the dynamic weighting coefficients specifically include: Based on the utilization rate of the battery swapping workstations, the time urgency factor is determined: Where k represents the time urgency factor, min represents taking the minimum value, γ represents the emergency task weight coefficient, and Q emergency N represents the number of emergency response vehicles. total This indicates the number of vehicles in the charging and battery swapping station.

[0059] It should be noted that those skilled in the art can set the size of the emergency task weight coefficient according to actual needs, and this invention does not limit it.

[0060] Determine the dynamic weighting coefficients based on the time urgency factor: Where ω represents the dynamic weighting coefficient, ω min ω represents the minimum weight lower bound. max This indicates the maximum weight limit.

[0061] It should be noted that those skilled in the art can set the minimum weight lower limit and the maximum weight upper limit according to actual needs, and this invention does not impose any limitations on them.

[0062] Furthermore, in S2033, the normalized total battery swapping time is weighted using dynamic weighting coefficients, and the normalized monetary cost of battery swapping is weighted using 1-ω, and the summation is used to obtain the total battery swapping cost. In S2036, the normalized total charging time is weighted using dynamic weighting coefficients, and the normalized monetary cost of charging is weighted using 1-ω, and the summation is used to obtain the total charging cost.

[0063] It should be noted that the dynamic weighting coefficient refers to a weighting factor that is adaptively adjusted based on the operating status of the charging and battery swapping station and the urgency of the vehicle task. It is dynamically calculated by combining factors such as the utilization rate of battery swapping stations and the proportion of vehicles with urgent tasks. It is used to balance the importance of time factors and cost factors in the comprehensive evaluation, so that the calculation result of the total cost of battery swapping or charging can better reflect the actual operating needs and task priorities.

[0064] In this embodiment of the invention, by introducing a dynamic weighting coefficient, an adaptive trade-off can be made between "time priority" and "cost priority" in real time based on the utilization of site resources and the urgency of vehicle tasks. This makes the calculation result of the total cost of charging and swapping more in line with actual operational needs and task priorities, taking into account both user experience and operational efficiency.

[0065] S204. Determine the charging / battery swapping decision coefficients based on the total cost of battery swapping and the total cost of charging.

[0066] Optionally, the calculation process for the charging / swapping decision coefficient is as follows: The negative values ​​of the total charging cost and the negative values ​​of the total battery swapping cost are respectively indexed. The index value corresponding to the negative total charging cost is then divided by the sum of the index values ​​of the negative total charging cost and the negative total charging cost, thus obtaining the charging / swapping decision coefficient. Specifically, when the total battery swapping cost is low, the charging / swapping decision coefficient is larger, indicating that the vehicle is more inclined to choose the battery swapping option. Conversely, when the total charging cost is low, the charging / swapping decision coefficient is smaller, indicating that the vehicle is more inclined to choose the charging option.

[0067] In this embodiment of the invention, by sequentially determining the required replenishment power for the vehicle in steps S201 to S204, calculating the total charging and swapping time based on the M / M / C queuing theory model, combining the time and monetary cost and introducing a dynamic weighting coefficient to obtain the total charging and swapping cost, and then using an indexed comparison method to determine the charging and swapping decision coefficients, this method can not only comprehensively reflect the vehicle's battery status and the utilization of resources within the station, but also scientifically quantify time and cost factors, achieving a unified evaluation of charging and swapping schemes. Furthermore, the method can adapt to different travel task objectives and station operating states, ensuring that the decision results are more reasonable, flexible, and executable, thereby improving the accuracy of vehicle replenishment selection and the overall efficiency and economy of the charging and swapping station operation.

[0068] S3: Determine if the charging / swapping decision coefficient is less than the preset charging / swapping decision coefficient. If yes, execute charging service for each vehicle and proceed to S4. Otherwise, execute battery swapping service for each vehicle and proceed to S7.

[0069] It should be noted that those skilled in the art can set the preset charging and swapping decision coefficient according to actual needs, and this invention does not limit this.

[0070] In this embodiment of the invention, S3 transforms the complex multi-factor comprehensive judgment into a simple and clear two-option decision by setting a preset charging and swapping decision coefficient threshold. This not only improves decision-making efficiency and interpretability, but also flexibly adapts to different operating strategies and site requirements, achieving dual optimization of user experience and resource utilization.

[0071] S4. Construct a dynamic power allocation model for charging piles based on vehicle travel task requirements and vehicle dwell time.

[0072] It should be noted that the dynamic power allocation model for charging piles refers to a mathematical model that adjusts and optimizes the power allocation of charging piles for different vehicles in real time, based on the vehicle's battery state of charge, target power, and the power resources of the charging station, while meeting the needs of vehicle travel tasks and the constraints of dwell time. Its core purpose is to improve charging efficiency, balance grid load, and reduce overall operating costs while ensuring that vehicles complete their tasks on time.

[0073] Optionally, the specific requirements for vehicle travel tasks are: when the state of charge of each vehicle's battery is less than the target state of charge, the state of charge of each vehicle's battery is replenished to the target state of charge.

[0074] Optionally, the vehicle dwell time is specifically defined as the time interval between the start time of vehicle charging and the start time of the vehicle's travel mission.

[0075] Optionally, the dynamic power allocation model for charging piles specifically includes: When the state of charge of each vehicle's battery is less than the target state of charge and the vehicle's charging time is greater than the vehicle's dwell time, the power of the charging pile corresponding to each vehicle is fixed at the maximum power.

[0076] When the battery state of charge of each vehicle is less than the target battery state of charge and the vehicle charging time is less than or equal to the vehicle dwell time, the charging power of the charging pile corresponding to each vehicle is fixed at the maximum power until the battery state of charge of each vehicle is replenished to the target battery state of charge. During the remaining vehicle dwell time, the charging power of the charging pile corresponding to each vehicle is dynamically allocated. The remaining vehicle dwell time is specifically the time interval between the time it takes for the battery state of charge of each vehicle to be replenished to the target battery state of charge and the start time of the vehicle's travel mission.

[0077] When the state of charge of each vehicle's battery is greater than or equal to the target state of charge, the charging power of the corresponding charging pile for each vehicle is dynamically allocated.

[0078] It's important to note that vehicle charging time refers to the time required for a vehicle's battery to increase from its current state of charge (SOC) to a target SOC or full charge under specific charging power conditions. Its calculation is typically based on the vehicle's battery's rated capacity, the difference between the current SOC and the target SOC, and the charging efficiency and average available power during the charging process. In other words, vehicle charging time can be obtained by dividing the required amount of electricity by the product of the charging efficiency and the charging power, thus reflecting the expected duration for the vehicle to complete its charging at a charging station.

[0079] It should be noted that, based on the first constraint conditions and the first objective function constructed subsequently, the dynamic power allocation model of the charging pile is solved to obtain the real-time optimal power allocation decision, and the power of the charging pile corresponding to each vehicle is allocated according to the real-time optimal power allocation decision, thereby completing the dynamic allocation of the power of the charging pile corresponding to each vehicle.

[0080] In this embodiment of the invention, by constructing a dynamic power allocation model for charging piles based on vehicle travel needs and dwell time, it is possible to improve charging efficiency and the utilization rate of charging station power resources while ensuring that vehicles replenish the required electricity within a limited time and that tasks are completed on time. This model can adaptively adjust power allocation according to the vehicle's SOC, target battery level, and dwell time, prioritizing the allocation of maximum power to vehicles with emergency tasks and dynamically adjusting power for general vehicles, thus achieving a balance between fairness and efficiency. Furthermore, it can balance grid load, reduce operating costs, and make the charging process more intelligent, economical, and feasible.

[0081] S5, the first constraint and the first objective function for constructing the dynamic power allocation model of charging piles.

[0082] Optionally, the first constraint conditions specifically include: vehicle travel task time constraint conditions, vehicle charging power constraint conditions, and vehicle travel task demand constraint conditions.

[0083] Optionally, the specific time constraints for vehicle travel tasks are as follows: Where t represents the current time period, t ie p represents the period during which the i-th vehicle begins charging. ij t represents the charging power of the i-th vehicle in the j-th time period. id This represents the period during which the i-th vehicle stops charging. (Note that j refers to the current charging period of the vehicle.) Optionally, the specific constraints on vehicle charging power are as follows: Where, p max This represents the maximum allowable charging power of the vehicle, where N represents a value ranging from [0, p]. max The set of non-negative integers.

[0084] Optionally, the specific constraints on vehicle travel task requirements are as follows: Among them, B i Let Δt represent the battery capacity of the i-th vehicle, Δt represent the vehicle charging time, and SOC represent the battery capacity of the i-th vehicle. i0 This represents the initial state of charge of the i-th vehicle when it is being charged.

[0085] In one possible implementation, the first objective function is constructed in the following ways: To minimize the load peak-to-valley difference, the first intermediate objective function is constructed as follows: Where f1 represents the first intermediate objective function, L max L represents the maximum total load of the local area distribution network supplying power to the charging and battery swapping station. min L represents the minimum total load of the local distribution network supplying power to the charging and battery swapping station, while max represents the maximum value. j This represents the regular load (i.e., the basic load excluding electric vehicle charging load) of the local area distribution network that supplies power to the charging and swapping station during the j-th time period, n represents the total number of vehicles, and 24 refers to 24 hours in a day.

[0086] To minimize the root mean square error of the load curve fluctuation, a second intermediate objective function is constructed: Where f2 represents the second intermediate objective function, L av This represents the average load of the local distribution network supplying power to the charging and battery swapping station.

[0087] To minimize vehicle charging costs, a third intermediate objective function is constructed: Where f3 represents the third intermediate objective function, b j This represents the vehicle charging electricity price during the j-th time period.

[0088] Based on the first, second, and third intermediate objective functions, the first objective function is constructed using the linear weighted sum method: Where F1 represents the first objective function, λ1 represents the weight coefficients of the first intermediate objective function, and f 1max λ represents the maximum load peak-to-valley difference generated by all vehicles without dynamic allocation, λ2 represents the weighting coefficient of the second intermediate objective function, and f 2max λ3 represents the root mean square error of the maximum load curve fluctuation generated by all vehicles without dynamic allocation, and f represents the weighting coefficient of the third intermediate objective function. 3max This represents the maximum vehicle charging cost incurred by all vehicles without dynamic allocation.

[0089] It should be noted that those skilled in the art can set the weight coefficients of the first intermediate objective function, the second intermediate objective function, and the third intermediate objective function according to actual needs, and this invention does not limit these settings.

[0090] It should be noted that the linear weighted sum method is a multi-objective comprehensive evaluation method. Its basic idea is as follows: when a system has multiple evaluation objectives (such as peak-to-valley difference, load fluctuation, and charging cost), the objective functions are first normalized to make them comparable under the same dimensions. Then, corresponding weight coefficients are set according to the importance of different objectives. Finally, a comprehensive objective function is obtained through weighted summation. In this way, the complex multi-objective optimization problem is transformed into a single-objective optimization problem, and the overall optimal solution can be obtained by balancing different factors.

[0091] In this embodiment of the invention, a dynamic power allocation model for charging piles based on vehicle travel task requirements and dwell time is constructed, and constraints such as task time, charging power, and power demand are set. At the same time, a comprehensive objective function is established with the goals of peak shaving and valley filling, load smoothing, and reducing charging costs. This achieves a balance between user experience, site operation, and grid stability, while ensuring that vehicles obtain the required power on time. It also improves charging efficiency and pile utilization, reduces operating costs, and mitigates grid impact. It has good scalability and adaptability.

[0092] S6, under the constraint of the first constraint, with the goal of minimizing the first objective function, solve the dynamic allocation model of charging pile power, dynamically allocate the charging pile power corresponding to each vehicle, and complete the allocation of charging pile power resources of the charging and swapping station.

[0093] In this embodiment of the invention, S6 achieves dynamic allocation of charging pile power by solving the optimal power allocation scheme under constraints. This ensures the completion of individual vehicle tasks while also taking into account grid stability and operational cost optimization, thereby making the allocation of charging pile resources in the charging and battery swapping station more efficient, safe, and intelligent.

[0094] S7 replaces the depleted batteries of each vehicle with the stock batteries in the charging and swapping stations, thus completing the battery resource allocation of the charging and swapping stations.

[0095] Specifically, the depleted batteries of each vehicle are replaced with available stock batteries from the charging and swapping stations, thus completing the allocation of battery resources at the charging and swapping stations.

[0096] In this embodiment of the invention, S7 can significantly shorten the vehicle recharging time and improve the user experience by replacing the vehicle's depleted battery with a stock battery. At the same time, it can improve the service capacity and resource turnover efficiency of the station, and support centralized battery management and grid load optimization, thereby making the entire charging and swapping system more efficient, safe and intelligent.

[0097] In one possible implementation, steps S8 and S9 are further included after S7: S8 charges each depleted battery and constructs the second constraint and second objective function for each depleted battery during the charging process.

[0098] Optionally, the second constraint conditions specifically include: a charging power constraint condition for a depleted battery and a state of charge constraint condition for a depleted battery.

[0099] Optionally, the charging power constraint for a depleted battery is as follows: in, In the nth time period, the first... The charging power of a depleted battery, CP max This indicates the maximum charging power of a single charger in the charging and battery swapping station. In the nth time period, the first... The charging state of a depleted battery, when When =1, the depleted battery is in a charging state. When n = 0, the depleted battery is either not charging or fully charged. (It should be noted that n refers to the current charging period of the depleted battery.) Optionally, the specific constraints on the state of charge of the depleted battery are as follows: Among them, SOC thr This represents the state of charge threshold considered as fully charged (i.e., when SOC is full). thr When the state of charge of a depleted battery reaches or exceeds 95%, it is considered fully charged. Indicates the first The initial state of charge of a depleted battery during the charging process, where T represents the set of all time periods used for dynamic allocation of charging power to the depleted battery. Indicates the first The rated capacity of a depleted battery, ΔT represents the duration of each time period (optionally, the duration of each time period is 5 minutes).

[0100] In one possible implementation, the second objective function is constructed in the following ways: To minimize the power load fluctuations during the charging process of a depleted battery, a fourth intermediate objective function is constructed: Where f4 represents the fourth intermediate objective function, I represents the average power load over all time periods, and I represents the set of all depleted batteries that currently require dynamic allocation of charging power.

[0101] To minimize the electricity cost during the charging process of a depleted battery, a fifth intermediate objective function is constructed: Where f5 represents the fifth intermediate objective function, EP n This represents the electricity price for the nth time period.

[0102] To minimize charging losses during the charging process of a depleted battery, a sixth intermediate objective function is constructed: Where f6 represents the sixth intermediate objective function, and a, b, and c are all empirical fitting coefficients used to calculate battery charging loss. In the nth time period, the first... The charging rate of a depleted battery. Indicates the first The total number of charging periods for each depleted battery Indicates the first The charging completion time of a depleted battery. Indicates the first The charging start time of a depleted battery.

[0103] Based on the fourth, fifth, and sixth intermediate objective functions, a second objective function is constructed using the linear weighted sum method: Where F2 represents the second objective function, λ4 represents the weight coefficients of the fourth intermediate objective function, and f 4max λ5 represents the maximum power load fluctuation value generated by all depleted batteries without dynamic allocation, and f represents the weight coefficient of the fifth intermediate objective function. 5max λ6 represents the maximum electricity cost generated by all depleted batteries without dynamic allocation, and f represents the weight coefficients of the sixth intermediate objective function. 6max This represents the maximum charging loss value generated by all depleted batteries without dynamic allocation.

[0104] It should be noted that those skilled in the art can set the weight coefficients of the fourth intermediate objective function, the fifth intermediate objective function, and the sixth intermediate objective function according to actual needs, and this invention does not limit these settings.

[0105] In this embodiment of the invention, by acquiring vehicle battery status and charging / swapping station resource information, and combining queuing theory models to calculate waiting and service times, dynamic weighting coefficients are introduced to weight time and cost to determine charging / swapping decision coefficients, enabling intelligent selection of charging or swapping. In the charging path, a power allocation model that meets task requirements and dwell time is constructed, and peak shaving and valley filling are achieved through constraint and objective function optimization to reduce costs. In the swapping path, rapid replacement of existing batteries shortens recharge time, and centralized charging of depleted batteries optimizes load and cost, extends battery life, and comprehensively improves efficiency and reliability.

[0106] S9, under the constraint of the second constraint, dynamically allocates the charging power of each depleted battery during the charging process with the goal of minimizing the second objective function.

[0107] In this embodiment of the invention, S9 dynamically allocates the charging power of the depleted battery under constrained conditions, which not only ensures the safety and rationality of the charging process, but also balances the grid load, reduces electricity costs, reduces energy loss and extends battery life, thereby significantly improving the overall operating efficiency and intelligence level of the charging and swapping station.

[0108] The beneficial effects of this invention are as follows: In this embodiment of the invention, the decision coefficients for charging and battery swapping are determined based on battery swapping resource information, charging resource information, and battery status information. The decision-making process considers not only time or economic factors but also comprehensive optimization based on battery status, vehicle task requirements, and station resource load. This improves the dynamism and intelligence of vehicle charging and battery swapping scheme selection, enabling it to adapt to complex and changing mining environments. Under the constraint of the first constraint, the dynamic allocation model of charging pile power is solved with the goal of minimizing the first objective function. This dynamically allocates the charging pile power corresponding to each vehicle, completing the allocation of charging pile power resources at the charging and battery swapping station. In the charging stage, existing power allocation models generally consider factors such as grid load fluctuations and peak shaving, which are less likely to put operational pressure on the local grid. By replacing the depleted batteries of each vehicle with the stock batteries in the charging and battery swapping station, the management of stock batteries and the scheduling of recharging depleted batteries in the battery swapping stage have optimization mechanisms, reducing energy waste and accelerating battery life degradation.

[0109] System Implementation Examples Reference manual attached Figure 2 The diagram shows a structural schematic of a charging and swapping station resource allocation system provided in an embodiment of the present invention.

[0110] The present invention proposes a resource allocation system 30 for charging and swapping stations, comprising: a memory 303 and a processor 301.

[0111] The memory 303 stores an application program adapted to be executed by the processor 301 to implement the charging and swapping station resource allocation method of the method embodiment.

[0112] The charging and battery swapping station resource allocation system 30 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302.

[0113] The structure of the charging and swapping station resource allocation system 30 does not constitute a limitation on the embodiments of the present invention.

[0114] Processor 301 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0115] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI bus or an EISA bus, etc. Bus 302 may be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the figure, but this does not mean that there is only one bus or one type of bus.

[0116] The memory 303 may be a ROM or other type of static storage device capable of storing static information and instructions, RAM or other type of dynamic storage device capable of storing information and instructions, or it may be an EEPROM, CD-ROM or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0117] Computer-readable storage medium embodiments The present invention proposes a computer-readable storage medium having a computer program stored thereon, the computer program being loaded and executed by a processor for the first aspect of the charging and swapping station resource allocation method.

[0118] The applicant of this invention has provided a detailed description of the embodiments of the invention in conjunction with the accompanying drawings. However, those skilled in the art should understand that the above embodiments are merely preferred embodiments of the invention. The detailed description is only intended to help readers better understand the spirit of the invention and is not intended to limit the scope of protection of the invention. On the contrary, any improvements or modifications made based on the inventive spirit of the invention should fall within the scope of protection of the invention.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A method for allocating resources for charging and battery swapping stations, characterized in that, The resource allocation method for the charging and swapping stations includes: S1, obtain battery swapping resource information, charging resource information, and battery status information of each vehicle in the charging and swapping station; S2, determine the charging / swapping decision coefficient based on the battery swapping resource information, the charging resource information, and the battery status information; S3, determine whether the charging / swapping decision coefficient is less than the preset charging / swapping decision coefficient; if yes, perform charging service for each vehicle and proceed to S4; otherwise, perform battery swapping service for each vehicle and proceed to S7. S4, Construct a dynamic power allocation model for charging piles based on vehicle travel task requirements and vehicle dwell time; S5, Construct the first constraint conditions and the first objective function of the dynamic power allocation model of the charging pile; S6. Under the constraints of the first constraint, with the goal of minimizing the first objective function, the dynamic power allocation model of the charging pile is solved, and the power of the charging pile corresponding to each vehicle is dynamically allocated to complete the power resource allocation of the charging and swapping station. S7, replace the depleted batteries of each of the vehicles with the stock batteries in the charging and swapping station, and complete the battery resource allocation of the charging and swapping station.

2. The resource allocation method for charging and battery swapping stations according to claim 1, characterized in that, The battery swapping resource information specifically includes: the total number of battery swapping stations, the number of available battery swapping stations, the hourly service rate of a single battery swapping station, the number of batteries in stock, the state of charge of the batteries in stock, the health status of the batteries in stock, and the capacity of the batteries in stock. The charging resource information specifically includes: the total number of charging piles, the number of available charging piles, the hourly service rate of a single charging pile, and the number of vehicles waiting in line at charging piles. The battery status information specifically includes: battery state of charge, battery health status, and battery rated capacity.

3. The resource allocation method for charging and battery swapping stations according to claim 1, characterized in that, S2 specifically includes: S201, Based on the battery status information, determine the required additional power for each of the vehicles; S202, based on the battery swapping resource information, determine the total battery swapping time using the MMC queuing theory model; based on the charging resource information and the required replenishment power, determine the total charging time using the MMC queuing theory model. S203, determine the total battery swapping cost based on the total battery swapping time; determine the total charging cost based on the total charging time; S204, determine the charging / swapping decision coefficient based on the total battery swapping cost and the total charging cost.

4. The resource allocation method for charging and battery swapping stations according to claim 3, characterized in that, S202 specifically includes: S2021, Determine the battery swapping waiting probability based on the battery swapping resource information; S2022, Determine the battery swapping waiting time based on the battery swapping waiting probability; S2023, Determine the total battery swapping time based on the battery swapping waiting time; S2024, Determine the charging waiting probability based on the charging resource information; S2025, Determine the charging waiting time based on the charging waiting probability; S2026, Determine the total charging time based on the required replenishment power and the charging waiting time.

5. The resource allocation method for charging and battery swapping stations according to claim 3, characterized in that, S203 specifically includes: S2031, Calculate the monetary cost of battery swapping based on the required replenishment power; S2032, normalize the total battery swapping time and the battery swapping monetary cost respectively to obtain normalized total battery swapping time and normalized battery swapping monetary cost; S2033, Introduce dynamic weighting coefficients to perform a weighted summation of the normalized total battery swapping time and the normalized monetary cost of battery swapping to determine the total cost of battery swapping; S2034, Calculate the charging currency cost based on the required replenishment power; S2035, normalize the total charging time and the charging monetary cost respectively to obtain normalized total charging time and normalized charging monetary cost. S2036, Introduce the dynamic weighting coefficient, and perform a weighted summation of the normalized total charging time and the normalized charging monetary cost to determine the total charging cost.

6. The resource allocation method for charging and battery swapping stations according to claim 1, characterized in that, The specific requirements for the vehicle travel task are as follows: when the state of charge of the batteries of each vehicle is less than the target state of charge, the state of charge of the batteries of each vehicle is replenished to the target state of charge. The vehicle dwell time is specifically the time interval between the start time of vehicle charging and the start time of vehicle travel mission. The dynamic power allocation model for charging piles specifically includes: When the state of charge of the battery of each vehicle is less than the state of charge of the target battery and the charging time of the vehicle is greater than the dwell time of the vehicle, the power of the charging pile corresponding to each vehicle is fixedly allocated to the maximum power. When the battery state of charge of each vehicle is less than the target battery state of charge and the vehicle charging time is less than or equal to the vehicle dwell time, the charging pile power corresponding to each vehicle is fixedly allocated to the maximum power until the battery state of charge of each vehicle is replenished to the target battery state of charge. During the remaining vehicle dwell time, the charging pile power corresponding to each vehicle is dynamically allocated. The remaining vehicle dwell time is specifically the time interval between the time it takes for the battery state of charge of each vehicle to be replenished to the target battery state of charge and the start time of the vehicle travel task. When the battery state of charge of each vehicle is greater than or equal to the target battery state of charge, the charging power of the charging pile corresponding to each vehicle is dynamically allocated.

7. The resource allocation method for charging and battery swapping stations according to claim 1, characterized in that, The first constraint conditions specifically include: vehicle travel task time constraint conditions, vehicle charging power constraint conditions, and vehicle travel task demand constraint conditions. The specific methods for constructing the first objective function include: A first intermediate objective function is constructed with the goal of minimizing the load peak-to-valley difference. A second intermediate objective function is constructed with the goal of minimizing the root mean square error of the load curve fluctuation; To minimize vehicle charging costs, a third intermediate objective function is constructed. Based on the first intermediate objective function, the second intermediate objective function, and the third intermediate objective function, the first objective function is constructed using a linear weighted sum method.

8. The resource allocation method for charging and battery swapping stations according to claim 1, characterized in that, Following S7, the following is also included: S8, charge each of the depleted batteries and construct the second constraint condition and the second objective function for each of the depleted batteries during the charging process; S9, under the constraint of the second constraint, with the goal of minimizing the second objective function, dynamically allocate the charging power of each of the depleted batteries during the charging process.

9. The resource allocation method for charging and battery swapping stations according to claim 8, characterized in that, The second constraint specifically includes: the charging power constraint of the depleted battery and the state of charge constraint of the depleted battery; The second objective function is constructed in the following ways: A fourth intermediate objective function is constructed with the goal of minimizing the power load fluctuation during the charging process of the depleted battery. A fifth intermediate objective function is constructed with the goal of minimizing the electricity cost during the charging process of the depleted battery. A sixth intermediate objective function is constructed with the goal of minimizing the charging loss during the charging process of the depleted battery. Based on the fourth, fifth, and sixth intermediate objective functions, the second objective function is constructed using a linear weighted sum method.

10. A resource allocation system for charging and battery swapping stations, characterized in that, include: Memory and processor; The memory stores an application program adapted to be executed by the processor to implement the charging and swapping station resource allocation method according to any one of claims 1 to 9.

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