A battery swapping station day-ahead optimization scheduling method based on user battery swapping threshold

Abstract

The application discloses a battery swap station day-ahead optimization scheduling method based on user battery swap threshold, and belongs to the technical field of electric vehicle battery swap station operation optimization. The application comprises the following steps: obtaining the minimum acceptable battery swap state of charge threshold of different categories of users, and constructing a user battery swap demand representation model; dividing the battery state of charge in the battery swap station into multiple charging states, and establishing a service capability mapping relationship between the charging states and the user categories; and taking the minimum total operation cost as the target, constructing an operation cost optimal scheduling model of the battery swap station for solving. The application can effectively realize the accurate matching of the battery resources of the battery swap station and the differentiated demands of the users, significantly reduces the operation cost under the premise of guaranteeing the service quality, and has important significance for the economic and efficient operation of the battery swap station.

Description

Technical Field

[0001] This invention relates to a day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds, belonging to the field of electric vehicle battery swapping station operation optimization technology. Background Technology

[0002] As a key hub connecting the transportation network and the power distribution network, electric vehicle battery swapping stations face two major challenges in their efficient operation: First, users' battery swapping needs have significant spatiotemporal heterogeneity, with different user groups having different requirements for the state of charge of the swapped batteries; second, the battery resources within the station must meet user needs while also taking into account charging economy and service reliability.

[0003] Most existing battery inventory management methods for battery swapping stations adopt a uniform "full charge service" strategy, failing to finely match users' differentiated battery capacity needs. This leads to waste of high-capacity batteries or persistently high charging costs. A few studies have attempted to classify battery states based on SOC levels, but a service capability mapping mechanism between battery charging range and user categories has not yet been established, making it difficult to achieve accurate matching of battery resources with user needs.

[0004] Therefore, there is an urgent need for a day-ahead optimization scheduling method for battery swapping stations that can take into account users' battery swapping thresholds. Summary of the Invention

[0005] This invention provides a day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds, so as to achieve effective matching of battery swapping station resources with differentiated user needs.

[0006] The technical solution of this invention is:

[0007] According to a first aspect of the present invention, a day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds is provided, comprising the following steps:

[0008] Obtain the minimum acceptable battery swapping state threshold for different categories of users, and construct a user battery swapping demand characterization model;

[0009] The battery charge status in the battery swapping station is divided into multiple charging range states, and a service capability mapping relationship is established between the charging range state and the user category.

[0010] To minimize total operating costs, an optimal scheduling model for the operating costs of battery swapping stations is constructed and solved.

[0011] Furthermore, the construction of the user battery swapping demand representation model is specifically as follows:

[0012] Let the set of all user categories be . ,in For the first type of user, For the second type of user, For the third type of user; the total number of battery swapping requests during time period t within the scheduling cycle is Therefore, the battery swapping demand within the battery swapping station can be broken down according to different user categories, as follows:

[0013] ;

[0014] ;

[0015] ;

[0016] In the formula, express Within the time period The number of users requiring battery swapping services; express Among the vehicles arriving at the station within the specified time period, those belonging to the first The proportion of users of this type; Represents the set of scheduling periods within a single scheduling cycle;

[0017] Based on safety margins, the minimum acceptable charge swapping state thresholds for different user groups are constructed, with the specific expression as follows:

[0018] ;

[0019] In the formula, This represents the minimum acceptable charge-swapping state threshold for user class u; This indicates the minimum state-of-charge (SOC) battery threshold required for a battery swapping station to provide services. Indicates the battery's rated capacity. This represents the energy consumption per unit mileage of the vehicle corresponding to user type u; This represents the psychological safety margin reserved for users of type u.

[0020] Battery swapping user demand set throughout the entire scheduling cycle Represented as:

[0021] .

[0022] Furthermore, the step of dividing the battery state of charge within the battery swapping station into multiple charging states and establishing a service capability mapping relationship between charging states and user categories specifically involves:

[0023] The batteries stored in the battery swapping stations are divided into multiple charging range states, as detailed below:

[0024] ;

[0025] In the formula, express Time period The state of charge level of the battery. For the first Block battery in State of charge over a period of time; This indicates the minimum state-of-charge (SOC) battery threshold required for a battery swapping station (BSS) to provide services. , , These represent the minimum acceptable charge swapping state thresholds for users of categories A, B, and C, respectively. , , , These represent the first, second, third, and fourth levels of charge-up status, respectively.

[0026] Establish a service capability mapping relationship between charging status and user category. for:

[0027] .

[0028] Furthermore, the objective function established in the optimal scheduling model for the operation cost of the battery swapping station, with the goal of minimizing the total operating cost, is as follows:

[0029] ;

[0030] In the formula, Indicates total operating costs. This indicates the cost of purchasing electricity from the power grid. This indicates the cost of the battery inventory. This indicates the service penalty cost for failing to meet users' battery swapping needs.

[0031] Furthermore, the expression for the power grid purchase cost is as follows:

[0032] ;

[0033] In the formula, express Electricity price per unit time period; Indicates the duration of a single scheduling period; express The electrical power purchased by the battery swapping station from the power grid during the designated time period.

[0034] Furthermore, the expression for the inventory battery configuration cost is as follows:

[0035] ;

[0036] In the formula, Indicates the discount rate. Indicates the lifespan of a single battery cell. , These represent the purchase price of a new battery per unit capacity and the residual value of a retired battery, respectively. Indicates the battery's rated capacity. This indicates the total number of batteries configured within the scheduling period. This represents the total number of scheduling cycles over the battery's lifespan.

[0037] The expression for the service penalty cost is:

[0038] ;

[0039] In the formula, Indicates the first Penalty factors for similar users Indicates the time period Unsatisfied first The number of users requiring battery swapping services; Represents the set of scheduling periods within a single scheduling cycle; This represents a set of user categories.

[0040] According to a second aspect of the present invention, a day-ahead optimization scheduling system for battery swapping stations based on user battery swapping thresholds is provided, comprising modules of the day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds as described in any one of the preceding claims.

[0041] According to a third aspect of the present invention, a processor is provided for performing operations including the step of performing the day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds as described in any one of the preceding claims.

[0042] The beneficial effects of this invention are:

[0043] To overcome the limitations of traditional models that assume user homogeneity, a refined user battery swapping demand characterization model is constructed based on the minimum acceptable state of charge (SOC) threshold for different user categories, accurately depicting the service sensitivity of different user types in real-world scenarios. Building upon this, a mapping mechanism between battery SOC and dynamic demand categories is established to ensure that battery resources can match the corresponding user needs with appropriate charge levels at the right time. Based on the above modeling, a mixed-integer linear programming model with the objective of minimizing total operating costs is further constructed to formulate a day-ahead charging and swapping scheduling and inventory configuration scheme that precisely matches battery swapping demand. Comparative verification shows that the strategy proposed in this invention reduces daily electricity purchase costs by approximately 23.4% and inventory battery configuration costs by approximately 28.3% compared to traditional strategies. While maintaining an overall service satisfaction rate of 93.1%, it reduces the optimal inventory size from 53 batteries to 38 batteries, achieving dual optimization of operating costs and resource utilization. Attached Figure Description

[0044] Figure 1This is a flowchart of the present invention.

[0045] Figure 2 This shows the battery charging status under different strategies. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0047] Example 1: As Figure 1 As shown, according to a first aspect of the present invention, a day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds includes the following steps:

[0048] Step S1: Obtain the minimum acceptable battery swapping state threshold for different users and construct a user battery swapping demand characterization model;

[0049] Step S2: Divide the battery charge status in the station into multiple charging range states and establish a service capability mapping relationship between charging range states and user categories;

[0050] Step S3: With the goal of minimizing total operating cost, construct an optimal scheduling model for the operating cost of the battery swapping station and solve it, outputting the system's power purchase, battery charging and discharging power, and the distribution of the number of batteries in each charging state for each time period.

[0051] Furthermore, the user battery swapping demand representation model is specifically as follows:

[0052] Let the set of all user categories be . Where A represents the first type of user, B the second type, and C the third type; the total battery swapping demand during time period t within the scheduling cycle is: Therefore, the battery swapping demand within a battery swapping station can be broken down into sub-demands according to different user categories, as detailed below:

[0053] (1)

[0054] (2)

[0055] (3)

[0056] In the formula, This represents the number of battery swapping needs of the u-th type of user within time period t, i.e., sub-demand; This represents the proportion of users of type u among the vehicles arriving at the station during time period t; This represents the set of scheduling periods within a single scheduling cycle; for example, the first type of user is a user with high SOC demand, the second type of user is a user with medium SOC demand, and the third type of user is a user with low SOC demand.

[0057] In the actual operation of BSS, when the battery SOC in the station drops to a threshold At that time, battery swapping services will be discontinued. The minimum available battery energy window for users between two charging sessions is [missing information]. To meet the battery energy requirements within this range, the battery energy should satisfy the user's target driving range. The following energy constraints must be satisfied:

[0058] (4)

[0059] In the formula, This indicates the minimum state-of-charge (SOC) battery threshold required for a battery swapping station's (BSS) to provide services. This represents the minimum acceptable charge-swapping state threshold for user class u; Indicates the battery's rated capacity. This represents the energy consumption per unit mileage for the vehicle corresponding to user class u.

[0060] To alleviate users' battery anxiety while maintaining a certain safety margin, the minimum acceptable charge state threshold for different user groups is constructed, and the specific expression is shown in equation (5).

[0061] (5)

[0062] In the formula, This represents the minimum acceptable charge-swapping electrical state threshold for user class u. Indicates the battery's rated capacity; This represents the psychological safety margin reserved for users of type u, and .

[0063] Based on the above analysis, the set of user demand for battery swapping throughout the entire scheduling cycle can be represented as:

[0064] .

[0065] Furthermore, the step of dividing the battery state of charge within the battery swapping station into multiple charging states and establishing a service capability mapping relationship between charging states and user categories specifically involves:

[0066] Based on the user's differentiated battery swapping power requirements, the inventory batteries in the BSS can be divided into multiple charging states, as shown in equation (6).

[0067] (6)

[0068] In the formula, This indicates the charging state level of the i-th battery during time period t. Let be the state of charge of the i-th battery in time period t; This indicates the minimum state-of-charge (SOC) battery threshold required for a battery swapping station's (BSS) to provide services. Batteries below this threshold are prohibited from being allocated to users for swapping. , , These represent the minimum acceptable charge state thresholds for users of categories A, B, and C, with values ​​of 0.9, 0.8, and 0.5, respectively.

[0069] To characterize the deterministic service relationship between battery charging state and user category, a service capability mapping function is defined, with the following specific definition:

[0070] (7)

[0071] In the formula, This represents the set of battery charging states. Represents a set of user categories. for The power set (representing batteries with different states of charge (SOC) that can serve different types of users). For any , represents the set of user categories for which the system can stably guarantee service during the charging state, that is, the set of users that can be served in this state.

[0072] Given the differences in minimum acceptable service SOC requirements among different user categories, the set of serviceable users corresponding to each charging state is uniquely determined. Therefore, this mapping relationship can be further expressed as:

[0073] (8)

[0074] Further, step S3 specifically includes:

[0075] (1) Objective function

[0076] During BSS operation, the total operating cost mainly consists of three parts: grid power purchase cost, inventory battery configuration cost, and service penalty cost.

[0077] (9)

[0078] In the formula, This represents the total operating cost of the system. This indicates the cost of purchasing electricity from the power grid. This indicates the cost of the battery inventory. This indicates the service penalty cost for failing to meet users' battery swapping needs.

[0079] The cost of electricity purchased by the power grid is determined by the unit electricity price in time period t. Power purchased during the corresponding time period This is a joint decision. Therefore, the formula for calculating electricity purchase costs can be expressed as:

[0080] (10)

[0081] In the formula, Indicates the duration of a single scheduling period; This represents the electrical power purchased by the battery swapping station from the grid during the t-th time period.

[0082] The cost of configuring inventory batteries within a single scheduling cycle is equal to the lifecycle-adjusted battery investment cost, which can be expressed by the following formula:

[0083] (11)

[0084] In the formula, r represents the discount rate. Indicates the lifespan of a single battery cell. , These represent the purchase price of a new battery per unit capacity and the residual value of a retired battery, respectively. Indicates the battery's rated capacity. This indicates the total number of batteries configured within the scheduling period. This represents the total number of scheduling cycles over the battery's lifespan.

[0085] To ensure the reliability of battery swapping services, economic penalties are needed for failures to meet user needs due to insufficient battery resources or mismatched battery conditions. This aims to improve the user's battery swapping experience. The corresponding service penalty cost is defined as follows:

[0086] (12)

[0087] In the formula, This represents the penalty factor for user class u. This represents the number of battery swapping needs of the u-th type of users that were not met within time period t.

[0088] (2) Constraints

[0089] 1) Battery state constraints

[0090] To accurately depict the inventory of batteries during the scheduling cycle The charging and discharging states within the device are introduced into a binary decision variable. This is used to characterize whether the stored batteries in slot s are in a charging state during time period t. ; This represents the state of charge threshold of the batteries stored in battery swapping station slot s during time period t. Define the maximum set of battery slots that the BSS can accommodate, and define a binary variable. This indicates whether slot s is configured with a battery. Therefore, within each time period, if slot s is configured with a battery, it corresponds to only one charging state; if slot s is not configured, it does not belong to any charging state, as shown in the formula:

[0091] (13)

[0092] To further characterize the battery charging and swapping process, it is necessary to describe the coupling relationship between SOC and state of charge, when the batteries stored in slot s are in the state of charge. At that time, its SOC value must satisfy the feasible interval corresponding to that state. The specific expression is as follows.

[0093] (14)

[0094] (15)

[0095] In the formula, This represents the state of charge value of slot s during time period t; Indicates the first The lower limit of SOC corresponding to each charging state This is a sufficiently large positive number used in the mixed-integer optimization model to implement the logical constraint transformation, converting the logical relationship between the battery SOC state and the charging state into a linear constraint. When the state variable... At that time, the constraint limits the battery SOC to the first Each charging state corresponds to a specific interval; when At that time, the constraints are automatically invalidated by the large M term, thereby realizing the association between discrete state variables and continuous SOC variables.

[0096] During charging, the battery's State of Charge (SOC) is continuously updated over time. For any battery slot s, its SOC in time period t is determined by the SOC of the previous time period and the charging power. In the formula, This indicates the battery's charging efficiency. This indicates that the battery in slot s is in the time period The actual charging power; in the battery swapping operation scenario, when slot s is in the time period After the battery swap service is completed, its SOC in the next time period should be the initial charge of the depleted battery replaced by the user. To characterize the SOC state update process of the battery swapping slots, the following is introduced: And linearize it using the Big M method; where, Auxiliary binary variable This indicates whether slot s is assigned to user type u during time period t; that is, it satisfies the following: , , , .

[0097] During battery charging, when the battery in slot s reaches the target charge level... When the charging process is complete, a binary decision variable is introduced. This is used to indicate whether slot s reaches the target power level in time period t, and its internal relationship is established through the Big M linear method, which can be specifically expressed as:

[0098] (16)

[0099] (17)

[0100] Furthermore, the target capacity can only be achieved if a battery is already configured, which can be specifically expressed as:

[0101] (18)

[0102] In the formula, To achieve the target SOC value for charging, This is a 0-1 variable representing whether the battery in slot s reaches the target SOC during time period t. Additionally, to avoid overcharging and slow down battery cycle life degradation, .

[0103] 2) Battery charging constraints

[0104] The battery SOC is divided into 4 consecutive intervals, and the charging power in each interval is defined by a linear expression, as shown below.

[0105] (19)

[0106] In the formula, , , , These represent the maximum charging power in different SOC ranges during battery charging, with values ​​of 40kW, 23kW, 15kW, and 7.5kW respectively.

[0107] The charging power of the batteries stored in slot s during the scheduling cycle can be linearly expressed as:

[0108] (20)

[0109] In the formula, This represents the actual charging power of the battery in slot s during time period t. This represents the allowed charging power for the p-th charging power range; The variable is 0-1, indicating whether slot s selects the charging power range set during time period t. The p-th charging power range within.

[0110] To describe the power transfer characteristics during the charging process and determine the charging interval at a certain time, a linearization method is used to model it. The specific calculation process is shown below.

[0111] (twenty one)

[0112] (twenty two)

[0113] In the formula, This is the charging power interval number of the battery in slot s during time period t; It is a 0-1 variable used to control whether the interval transfer constraint between adjacent time periods is effective.

[0114] 3) Demand allocation constraints

[0115] To characterize battery allocation during the battery swapping process, an auxiliary binary variable is defined. This indicates whether slot s is assigned to user type u during time period t, and can be specifically represented as:

[0116] (twenty three)

[0117] In the formula, This indicates whether the battery in slot s is used for battery swapping service during time period t;

[0118] In addition, to ensure that the configured batteries can be allocated for battery swapping services, the following must also be met:

[0119] (twenty four)

[0120] Furthermore, within any given time period t, the sum of the actual number of batteries allocated to each user category u and the demand gap is always equal to its battery swapping demand, as shown below:

[0121] (25)

[0122] (26)

[0123] In the formula, This represents the number of battery swapping demands from the u-th type of users that were not met within time period t, and is ultimately included in the service penalty cost. . Let t represent the total number of battery swapping requests from users of type u within time period t.

[0124] During the scheduling cycle, the changes in the charging state of batteries in stock within the BSS are mainly determined by the battery swapping service, the state transitions caused by the charging process, and the return of depleted batteries swapped out by users. Therefore, the quantity of batteries in stock at different charging states within the station can be expressed as:

[0125] (27)

[0126] The total number of batteries in BSS inventory during time period t can be expressed as:

[0127] (28)

[0128] Furthermore, the total battery configuration of the BSS is defined as follows:

[0129] (29)

[0130] In the formula, This represents the total number of batteries configured in the BSS during the scheduling period, while the total number of batteries in the BSS inventory remains unchanged at any time during the scheduling period. .

[0131] 4) System power balance constraints

[0132] As an energy node, BSS should satisfy power conservation within any time period t, that is, the total power consumption and the total power supply within the station should be balanced, as shown in the formula.

[0133] (30)

[0134] In the formula, , These represent the charging power and discharging power of the BESS energy storage system during time period t, respectively. This represents the charging power of slot s during time period t. This represents the actual photovoltaic output during time period t. This represents the actual wind power output during time period t. This represents the electrical power that the power exchange station purchases from the grid during time period t.

[0135] To further compare and analyze the effectiveness of the proposed day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds, this embodiment uses a battery swapping station in a city's transportation network as the object for simulation verification. The battery swapping station parameters are as follows: energy storage system capacity 1000 kWh, maximum charging and discharging power 300 kW, 25 charging piles, maximum photovoltaic output 200 kW, and maximum wind power output 200 kW. The time-of-use electricity price settings are shown in Table 1.

[0136] Table 1. Time-of-use pricing and charging electricity price.

[0137]

[0138] The mainstream EV battery used has a rated capacity of 62kWh and a charging efficiency of 0.95. Based on State of Charge (SOC) differences, it is divided into four levels: L1 (0.10–0.50), L2 (0.50–0.80), L3 (0.80–0.90), and L4 (0.90–1.00). Simultaneously, this paper divides the user group into three categories: A, B, and C, corresponding to high SOC demand users L3 and L4, medium SOC demand users L2, and low SOC demand users L1, respectively.

[0139] Following steps S1 to S3, pre-calendar optimization scheduling is performed to obtain the optimal charging plan and inventory configuration. The operational cost comparison of each strategy is shown in Table 2. Compared with the traditional full-charge service strategy, the strategy of this invention reduces the inventory battery configuration cost by approximately 28.3% and the daily electricity purchase cost by 23.4% while only decreasing the service satisfaction rate by 2.1%.

[0140] Table 2 Comparative Analysis of the Overall Benefits of Different Operational Strategies

[0141]

[0142] according to Figure 2 The battery charging performance under different strategies reveals that the traditional strategy requires all swapped batteries to reach charging range L4. Therefore, each battery must complete a charging path from L1 through L2, L3, and finally L4 within its operational cycle, resulting in up to 179 charging range transitions at each stage. In contrast, the strategy proposed in this paper significantly reduces the need for deep charging by allowing batteries with different SOC ranges to be matched to different user needs. In particular, the number of batteries transitioning from L3 to L4 is only 58, far lower than the traditional strategy. This mechanism effectively demonstrates the advantages of the proposed strategy in reducing electricity purchase costs and minimizing high SOC inventory requirements. By avoiding unnecessary charging processes, it not only reduces energy loss and time commitment but also improves battery configuration and turnover efficiency, thereby enhancing battery resource utilization and operational economics.

[0143] According to a second aspect of the present invention, a day-ahead optimization scheduling system for battery swapping stations based on user battery swapping thresholds is provided, comprising modules of the day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds described in any one of the above embodiments. Specifically, it includes: a first module for obtaining the minimum acceptable state of charge threshold for different categories of users and constructing a user battery swapping demand characterization model; a second module for dividing the battery state of charge within the battery swapping station into multiple charging states and establishing a service capacity mapping relationship between charging states and user categories; and a third module for constructing and solving an optimal operating cost scheduling model for the battery swapping station with the objective of minimizing total operating cost. For modules not described in detail, please refer to the corresponding descriptions in this embodiment.

[0144] According to a third aspect of the present invention, a processor is provided, the processor being configured to perform an operation including the step of performing the day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds as described in any one of the preceding embodiments.

[0145] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds, characterized in that, Includes the following steps: Obtain the minimum acceptable battery swapping state threshold for different categories of users, and construct a user battery swapping demand characterization model; The battery charge status in the battery swapping station is divided into multiple charging range states, and a service capability mapping relationship is established between the charging range state and the user category. To minimize total operating costs, an optimal scheduling model for the operating costs of battery swapping stations is constructed and solved.

2. The day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds according to claim 1, characterized in that, The construction of the user battery swapping demand representation model is as follows: Let the set of all user categories be . ,in For the first type of user, For the second type of user, For the third type of user; the total number of battery swapping requests during time period t within the scheduling cycle is The battery swapping demand within the battery swapping station can be broken down according to different user categories, as follows: ； ； ； In the formula, express Within the time period The number of users requiring battery swapping services; express Among the vehicles arriving at the station within the specified time period, those belonging to the first The proportion of users of this type; Represents the set of scheduling periods within a single scheduling cycle; Based on safety margins, the minimum acceptable charge swapping state thresholds for different user groups are constructed, with the specific expression as follows: ； In the formula, This represents the minimum acceptable charge-swapping state threshold for user class u; This indicates the minimum state-of-charge (SOC) battery threshold required for a battery swapping station to provide services. Indicates the battery's rated capacity. This represents the energy consumption per unit mileage of the vehicle corresponding to user type u; This represents the psychological safety margin reserved for users of type u. The set of user demand for battery swapping throughout the entire scheduling cycle is represented as follows: 。 3. The day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds according to claim 1, characterized in that, The process of dividing the battery state of charge within the battery swapping station into multiple charging states and establishing a service capability mapping relationship between charging states and user categories is specifically as follows: The batteries stored in the battery swapping stations are divided into multiple charging range states, as detailed below: ； In the formula, express Time period The state of charge level of the battery. For the first Block battery in State of charge over a period of time; This indicates the minimum state-of-charge (SOC) battery threshold required for a battery swapping station (BSS) to provide services. , , These represent the minimum acceptable charge swapping state thresholds for users of categories A, B, and C, respectively. , , , These represent the first, second, third, and fourth levels of charge-up status, respectively. Establish a service capability mapping relationship between charging status and user category. for: 。 4. The day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds according to claim 1, characterized in that, The objective function established in the optimal scheduling model for the operating cost of the battery swapping station, which aims to minimize the total operating cost, is as follows: ； In the formula, Indicates total operating costs. This indicates the cost of purchasing electricity from the power grid. This indicates the configuration cost of the inventory batteries. This indicates the service penalty cost for failing to meet users' battery swapping needs.

5. The day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds according to claim 1, characterized in that, The expression for the power grid purchase cost is as follows: ； In the formula, express Electricity price per unit time period; Indicates the duration of a single scheduling period; express The electrical power purchased by the battery swapping station from the power grid during its designated time period; The formula for the configuration cost of the stock batteries is: ； In the formula, Indicates the discount rate. Indicates the lifespan of a single battery cell. , These represent the purchase price of a new battery per unit capacity and the residual value of a retired battery, respectively. Indicates the battery's rated capacity. This indicates the total number of batteries configured within the scheduling period. This represents the total number of scheduling cycles over the battery's lifespan. The expression for the service penalty cost is: ； In the formula, Indicates the first Penalty factors for similar users Indicates the time period Unsatisfied first The number of users requiring battery swapping services; Represents the set of scheduling periods within a single scheduling cycle; This represents a set of user categories.

6. A day-ahead optimization scheduling system for battery swapping stations based on user battery swapping thresholds, characterized in that, The module includes the day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds as described in any one of claims 1-5.

7. A processor for performing operations, characterized in that, The operation includes the step of performing the day-ahead optimization scheduling method for battery swapping stations based on user battery swapping thresholds as described in any one of claims 1-5.

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