Operation strategy optimization scheduling method and system for electric vehicle battery swap station

Abstract

The invention discloses an operation strategy optimization scheduling method and system for an electric vehicle battery swap station, relates to the technical field of electric vehicle charging and battery swap, and solves the technical problems of low operation efficiency, inflexible scheduling strategy, insufficient resource utilization rate and poor user experience of a current electric vehicle battery swap station. The method comprises the steps of collecting demand data of a battery swap station; classifying the target batteries in the battery swap station, and constructing a data set of the classified target batteries; collecting battery use data in the battery swap station; establishing a battery swap station operation optimization model; solving the operation optimization model of the battery swap station to obtain an optimal solution; and charging the battery in the battery swap station based on the optimal solution. The technical problem is solved.

Description

Technical Field

[0001] The present invention belongs to the field of electric vehicle charging and swapping, and specifically relates to an operation strategy optimization and scheduling method and system for an electric vehicle swapping station. Background Art

[0002] With the urgent global demand for sustainable transportation, the popularization of electric vehicles has accelerated, and swapping stations, as an innovative charging method, have attracted much attention. However, there are obvious deficiencies in the current operation efficiency and scheduling strategies of swapping stations. Many swapping stations still adopt fixed swapping processes, resulting in long waiting times for vehicles during peak periods, damaged user experiences, and idle resources. The battery utilization rate and replacement efficiency have not been optimized due to the lack of flexible scheduling, and the traditional operation mode is difficult to cope with the rapid growth of the number of electric vehicles. In addition, problems such as insufficient battery state monitoring, inaccurate prediction of swapping demands, and poor vehicle type compatibility further restrict the swapping efficiency.

[0003] The optimization of the operation strategy of a swapping station mainly involves the comprehensive consideration of multiple factors such as battery usage, swapping demands, and vehicle flow. First of all, the health state and remaining power of the battery are important factors affecting the swapping efficiency. If the battery is in good condition and has sufficient power, the swapping demand of the vehicle will naturally decrease; otherwise, it may lead to a sharp increase in swapping demands during peak periods. Therefore, by real-time monitoring the battery state and predicting the swapping demands of users, more accurate scheduling can be achieved. In addition, the swapping station also needs to consider the compatibility problems of different vehicle types to improve the swapping efficiency and user satisfaction.

[0004] In this context, the introduction of intelligent scheduling algorithms is particularly crucial. Through big data analysis and machine learning technologies, the swapping station can obtain and analyze information such as users' swapping demands, vehicle flow, and battery state in real time, so as to formulate flexible operation strategies. For example, by establishing a vehicle flow prediction model, the swapping station can adjust the allocation of swapping resources in advance to ensure that there are sufficient batteries and swapping equipment available during peak periods. In addition, combined with artificial intelligence technologies, the swapping station can also optimize the swapping sequence and time, reduce the waiting time of users, and improve the overall service efficiency.

[0005] In addition, the optimization of the operation strategy of the swapping station also needs to consider the power usage efficiency. During the charging and swapping processes, how to balance the grid load and reasonably arrange the charging time and power distribution can effectively reduce the operation cost. By optimizing the charging period and electricity price strategy, the swapping station can conduct centralized charging when the electricity price is low, thus enhancing the economic benefits.

[0006] In summary, there are problems such as low operation efficiency, inflexible scheduling strategies, insufficient resource utilization, and poor user experiences in current electric vehicle swapping stations.

[0007] Therefore, the present invention provides an operation strategy optimization scheduling method and system for an electric vehicle swapping station to solve the above problems. Summary of the Invention

[0008] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention provides an operation strategy optimization scheduling method and system for an electric vehicle swapping station to solve the technical problems of low operation efficiency, inflexible scheduling strategy, insufficient resource utilization rate and poor user experience existing in the current electric vehicle swapping station.

[0009] To achieve the above object, the first aspect of the present invention provides an operation strategy optimization scheduling method for an electric vehicle swapping station, including: Step 1: Collect the demand data of the swapping station; Step 2: Classify the target batteries in the swapping station and construct a data set of the classified target batteries; Step 3: Collect the battery usage data in the swapping station; Step 4: Establish an operation optimization model for the swapping station; Step 5: Solve the operation optimization model of the swapping station to obtain the optimal solution; charge the batteries in the swapping station based on the optimal solution.

[0010] Preferably, the collection of the demand data of the swapping station includes: Obtain the demand data of the swapping station from the information system of the swapping station and upload the demand data to the cloud decision-making platform of the swapping station; wherein, the demand data includes: the battery swapping demand of the swapping station within the scheduling period, the arrival time of the electric vehicle, and the time-of-use electricity price of the local area.

[0011] It should be noted that the information system of the swapping station refers to the system inside the swapping station for monitoring, managing, scheduling and recording various data; The cloud decision-making platform of the swapping station refers to an intelligent management system deployed in the cloud, which is specifically used for processing and optimizing the operation scheduling of the electric vehicle swapping station; The battery swapping demand of the swapping station within the scheduling period refers to how many electric vehicles will come to swap batteries within a specific scheduling period (such as one day, one week or one month); the arrival time of the electric vehicle refers to the specific time when the electric vehicle arrives at the swapping station for battery swapping; the local time-of-use electricity price refers to the different prices charged by the power supplier for electricity at different time periods.

[0012] Preferably, the classification of the target batteries in the swapping station and the construction of the data set of the classified target batteries include: Classify the batteries according to the source of the target batteries, and represent the relevant information of all target batteries in the scheduling area in the form of a data set; The data set is specifically as follows: ; ; ; ; ; ; Among them, is the total number of target batteries, is the number of batteries initially in the station, is the number of batteries replaced at the electric vehicle swap station, , is the set of all batteries, is the set of batteries initially in the station, is the set of batteries swapped into the station during the scheduling period, is the set of discrete time windows, represents the total number of time windows, T represents the scheduling period, and ∆t represents the interval time, refers to the set of arrival times of electric vehicles corresponding to the batteries swapped into the station. k represents the battery number in the set of batteries swapped into the station during the scheduling period, is the time-of-use electricity price set.

[0013] It should be noted that the set of discrete time windows means dividing a continuous time period into multiple non-overlapping, finite-length time periods (i.e., time windows). Each time window represents a specific time interval. These time windows are usually discrete, meaning there are clear boundaries between them and they do not overlap with each other. For example, if a day is divided into 24 time windows (one per hour), then S T ={1, 2, 3, …, 24}.

[0014] Preferably, the collection of battery usage data in the swap station includes: Obtain the battery usage data in the swap station and define three groups of variables based on the battery usage data. Among them, the battery usage data includes: the state of the battery in the swap station, the state of the battery entering the swap station, and the state of the battery leaving the swap station; Upload the battery usage data to the cloud decision-making platform of the swap station.

[0015] It should be noted that the state of the battery in the swap station refers to whether the battery is in the swap station; the state of the battery entering the swap station refers to whether the battery enters the swap station; the state of the battery leaving the swap station refers to whether the battery leaves the swap station.

[0016] Preferably, the definition of three groups of variables based on the battery usage data is used to judge the specific state of the battery, including: ; Among them, is a variable indicating whether the battery is in the battery swapping station. When = 1, it means that the i-th battery is in the battery swapping station at the t-th time window. When = 0, it means that the i-th battery is not in the battery swapping station at the t-th time window; i represents the battery number in the set of all batteries; ; Among them, is a variable indicating whether the battery enters the battery swapping station. When = 1, it means that the i-th battery enters the battery swapping station at the t-th time window. When = 0, it means that the i-th battery does not enter the battery swapping station at the t-th time window; ; Among them, is a variable indicating whether the battery leaves the battery swapping station. When = 1, it means that the i-th battery leaves the battery swapping station at the t-th time window. When = 0, it means that the i-th battery does not leave the battery swapping station at the t-th time window.

[0017] Preferably, the establishment of the operation optimization model of the battery swapping station includes: the objective function and constraint conditions of the operation optimization model of the battery swapping station.

[0018] Preferably, the objective function of the operation optimization model of the battery swapping station includes: Taking the minimum total charging cost within the optimized time window as the goal, construct the objective function: ; Among them, is the charging power of the i-th battery of the battery swapping station at the t-th time window, is the total number of all batteries, T is the scheduling period, ∆t is the interval time, is the time-of-use electricity price at the t-th time window.

[0019] It should be noted that the refers to the minimum value of the objective function, indicating the minimum total charging cost.

[0020] Preferably, the constraint conditions include: Battery usage times constraint: ; Among them, j represents the battery number in the set of batteries initially in the station; ; ; Logical constraint: ; Battery swapping time constraint: ; ; Among them, is an auxiliary time parameter; represents the time when the k-th battery is swapped into the battery swapping station; Battery quantity balance constraint in the station: ; Battery Soc balance constraint in the station: ; Among them, and respectively represent the Soc of the i-th battery in the 1st and t-th time periods, and the value of t is consistent with the total number of time windows ; Charging power upper and lower limit constraint: ; Among them, represents the maximum charging power of a single battery; Transformer capacity constraint: ; Among them, represents the capacity of the transformer in the battery swapping station; Initial Soc constraint: ; ; Among them, represents the Soc of the j-th battery in the 1st time period, represents the Soc of the k-th battery swapped into the station in the t-th time period, represents the initial Soc of the k-th battery swapped into the station; Overcharging avoidance constraint: ; Real-time Soc constraint: ; Among them, represents the Soc of the i-th battery in the (t - 1)-th time period; represents the rated capacity of a single battery; Soc constraint of the battery to be swapped: .

[0021] It should be noted that the Soc (state of charge) of the battery represents the proportion of the current remaining battery power to its total capacity; specifically, Soc reflects how much energy the battery can still provide in the current state, usually expressed as a percentage, ranging from 0% to 100%. The battery usage times constraint ensures that each battery is only replaced once within a scheduling cycle, which avoids the same battery being replaced multiple times on the same day, thereby preventing unnecessary operations and potential safety risks. The logic constraint considers the dynamic change in the number of batteries in the swapping station during the battery swapping process, that is, the number of batteries in the station is dynamically the same at different discrete time sections, ensuring that when the in-station state of the battery changes, relevant variables (variables indicating whether the battery is in the swapping station , variables indicating whether the battery is in the station , variables indicating whether the battery leaves the swapping station ) can obtain correct values. The battery swapping time constraint ensures that the battery swapping behavior of the electric vehicle occurs after the electric vehicle arrives at the station. The in-station battery quantity balance constraint ensures that when an electric vehicle replaces a low-power battery, another available high-power battery will be swapped out at the same time. The in-station battery Soc balance constraint ensures that the Soc of the in-station batteries is maintained within a reasonable range to support the battery swapping demand and avoid over-discharging or over-charging of the batteries. The charging power upper and lower limit constraints ensure that the charging power of each battery must be within its allowed upper and lower limits. Due to the limited capacity of the transformer in the swapping station, the sum of the powers of the batteries charging simultaneously must be strictly less than or equal to the capacity of the transformer; the transformer capacity constraint ensures that the power supply of the swapping station can meet all charging demands while avoiding overload and potential safety hazards The initial Soc constraint ensures that the initial Soc of the battery is consistent with the actual situation. The over-charging avoidance constraint ensures that the battery will not be over-charged to avoid damaging the battery health. The real-time Soc constraint describes the real-time Soc state of the battery and is used to monitor the charging process and state changes of the battery. The Soc constraint of the battery to be swapped ensures that the batteries swapped by the electric vehicle are all in a fully charged state.

[0022] The present invention transforms complex operation scheduling problems into solvable optimization problems through mathematical modeling, thereby providing scientific and accurate decision-making support for battery swapping stations. With the objective function of minimizing the total charging cost, multiple constraints are comprehensively considered, such as the number of battery usage times, battery swapping time, the number of batteries in the station and Soc balance, charging power limit, transformer capacity, initial Soc, avoiding overcharging, real-time Soc, and the Soc of the battery to be swapped, ensuring that each variable operates within a reasonable range and avoiding the risks of resource waste and equipment damage. Preferably, solving the operation optimization model of the battery swapping station to obtain the optimal solution; charging the batteries in the battery swapping station based on the optimal solution, including: Solving the solution of the objective function of the operation optimization model of the battery swapping station through mixed-integer linear programming based on the constraint conditions to obtain the optimal solution; wherein, the optimal solution includes: the optimal charging power and charging time of each battery in the battery swapping station. Generating a charging instruction based on the optimal solution and sending the charging instruction to the charging equipment in the battery swapping station; the charging equipment in the battery swapping station charges each battery based on the charging instruction.

[0023] It should be noted that the mixed-integer linear programming (MILP) is applicable to dealing with combined problems of discrete decision variables (such as whether to charge) and continuous decision variables (such as charging power), and MILP can accurately solve complex scheduling problems.

[0024] By introducing the mixed-integer linear programming (MILP) algorithm, this model can accurately handle combined problems of discrete and continuous decision variables, realize the calculation of the optimal charging power and charging time of each battery, enable the battery swapping station to reasonably arrange charging tasks during different electricity price periods, and effectively reduce the electricity cost. The charging instruction refers to the specific operation instruction generated by the cloud decision-making platform of the battery swapping station according to the optimal solution, which is used to guide the charging equipment in the battery swapping station to charge each battery; wherein, the charging instruction includes the optimal charging power and charging time corresponding to each battery.

[0025] The second aspect of the present invention provides an operation strategy optimization and scheduling system for an electric vehicle battery swapping station, including: a data acquisition module, a data analysis module, and an operation strategy optimization module; The data acquisition module: used to acquire the demand data of the battery swapping station; The data analysis module: classifies the target batteries in the battery swapping station and constructs a data set of the classified target batteries; and acquires the battery usage data in the battery swapping station. The operation strategy optimization module: establishes an operation optimization model of the battery swapping station; and solves the operation optimization model of the battery swapping station to obtain the optimal solution; charges the batteries in the battery swapping station based on the optimal solution.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The optimization of the operation strategy of the battery swapping station needs to consider the power usage efficiency. During the charging and battery swapping processes, how to balance the grid load, reasonably arrange the charging time and power distribution can effectively reduce the operation cost; there are problems in the current electric vehicle battery swapping stations such as low operation efficiency, inflexible dispatching strategy, insufficient resource utilization rate, and poor user experience; the present invention collects the demand data of the battery swapping station, classifies the target batteries in the battery swapping station, and constructs a data set of the classified target batteries, collects the battery usage data in the battery swapping station, establishes an operation optimization model for the battery swapping station, solves the operation optimization model of the battery swapping station to obtain the optimal solution, and charges the batteries in the battery swapping station based on the optimal solution. By optimizing the charging period and electricity price strategy, the battery swapping station can conduct centralized charging when the electricity price is low, improving the economic benefits and solving the above problems.

[0027] 2. The present invention transforms the complex operation dispatching problem into an optimizable problem that can be solved through mathematical modeling, thereby providing scientific and accurate decision-making support for the battery swapping station; with the minimization of the total charging cost as the objective function, multiple constraint conditions such as the number of battery usage times, battery swapping time, the number of batteries in the station and Soc balance, charging power limit, transformer capacity, initial Soc, avoiding overcharging, real-time Soc, and the Soc of the replaced battery are comprehensively considered to ensure that each variable operates within a reasonable range, avoiding the risks of resource waste and equipment damage; in addition, by introducing the mixed integer linear programming (MILP) algorithm, the model can accurately handle the combination problem of discrete and continuous decision variables, realize the calculation of the optimal charging power and charging time for each battery, enable the battery swapping station to reasonably arrange the charging tasks during different electricity price periods, effectively reduce the power cost; at the same time, the model also ensures that there are enough fully charged batteries available for replacement during peak hours, reducing the waiting time of users and improving the service efficiency and user experience; in summary, establishing the operation optimization model not only improves the operation efficiency and service quality of the battery swapping station, but also enhances its ability to cope with the rapid growth of the number of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0029] Figure 1 It is a schematic diagram of the method steps of the embodiment of the present invention; Figure 2 It is a schematic diagram of the specific process of the embodiment of the present invention; Figure 3 Schematic diagram of the specific charging process of the embodiment of the present invention. Specific implementation manners

[0030] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0031] Please refer to Figure 1 - Figure 2 , an embodiment of the first aspect of the present invention provides an optimal scheduling method for the operation strategy of an electric vehicle battery swapping station, including: Step 1: Collect the demand data of the battery swapping station; Step 2: Classify the target batteries in the battery swapping station and construct a data set of the classified target batteries; Step 3: Collect the battery usage data in the battery swapping station; Step 4: Establish an optimization model for the operation of the battery swapping station; Step 5: Solve the optimization model for the operation of the battery swapping station to obtain the optimal solution; charge the batteries in the battery swapping station based on the optimal solution.

[0032] Collecting the demand data of the battery swapping station includes: Obtaining the demand data of the battery swapping station from the information system of the battery swapping station and uploading the demand data to the cloud decision-making platform of the battery swapping station; wherein, the demand data includes: the battery swapping demand of the battery swapping station within the scheduling period, the arrival time of the electric vehicle, and the time-of-use electricity price of the local area.

[0033] Classifying the target batteries in the battery swapping station and constructing a data set of the classified target batteries includes: Classifying the batteries according to the source of the target batteries and representing the relevant information of all the target batteries in the scheduling area as a data set; The specific data set is as follows: ; ; ; ; ; ; Wherein, is the total number of target batteries, is the number of batteries initially in the station, is the number of batteries at the swapping station for electric vehicles, , is the set of all batteries, is the set of batteries initially at the station, is the set of batteries swapped into the station during the scheduling period, is the set of discrete time windows, represents the total number of time windows, T represents the scheduling period, and ∆t represents the interval time, is the set of arrival times of electric vehicles corresponding to the batteries swapped into the station. k represents the battery number in the set of batteries swapped into the station during the scheduling period, is the set of time-of-use electricity prices.

[0034] Collect battery usage data in the swapping station, including: Obtain battery usage data in the swapping station and define three groups of variables based on the battery usage data; among them, the battery usage data includes: the state of the battery in the swapping station, the state of the battery entering the swapping station, and the state of the battery leaving the swapping station; Upload the battery usage data to the cloud decision-making platform of the swapping station.

[0035] Define three groups of variables based on the battery usage data to judge the specific state of the battery, including: ; Among them, is the variable indicating whether the battery is in the swapping station. When = 1, it means that the i-th battery is in the swapping station at the t-th time window. When = 0, it means that the i-th battery is not in the swapping station at the t-th time window; i represents the battery number in the set of all batteries; ; Among them, is the variable indicating whether the battery enters the swapping station. When = 1, it means that the i-th battery enters the swapping station at the t-th time window. When = 0, it means that the i-th battery does not enter the swapping station at the t-th time window; ; Among them, is the variable indicating whether the battery leaves the swapping station. When = 1, it means that the i-th battery leaves the swapping station at the t-th time window. When = 0, it means that the i-th battery does not leave the swapping station at the t-th time window.

[0036] Establish an operation optimization model for the swapping station, including: the objective function and constraint conditions of the operation optimization model for the swapping station.

[0037] The objective function of the operation optimization model for the battery swapping station includes: Taking the minimum total charging cost within the optimized time window as the objective, the objective function is constructed as: ; Among them, is the charging power of the i-th battery in the battery swapping station at the t-th time window, is the total number of batteries, T is the scheduling period, ∆t is the interval time, is the time-of-use electricity price at the t-th time window.

[0038] The constraint conditions include: Battery usage times constraint: ; Among them, j represents the battery number in the set of batteries initially in the station; ; ; Logical constraint: ; Battery swapping time constraint: ; ; Among them, is the auxiliary time parameter; represents the time when the k-th battery is swapped into the battery swapping station; Balance constraint of the number of batteries in the station: ; Balance constraint of the Soc of the batteries in the station: ; Among them, and respectively represent the Soc of the i-th battery at the 1st and t-th time periods, and the value of t is consistent with the total number of time windows; Upper and lower limits constraint of the charging power: ; Among them, represents the maximum charging power of a single battery; Transformer capacity constraint: ; Among them, represents the capacity of the transformer in the battery swapping station; Initial Soc constraint: ; ; Among them, represents the Soc of the j-th battery in the first time period, represents the Soc of the k-th battery switched into the station in the t-th time period, represents the initial Soc of the k-th battery switched into the station; Overcharge avoidance constraint: ; Real-time Soc constraint: ; Among them, represents the Soc of the i-th battery in the (t - 1)-th time period; represents the rated capacity of a single battery; Soc constraint of the battery to be replaced: .

[0039] Refer to Figure 3 , solve the operation optimization model of the battery swapping station to obtain the optimal solution; charge the batteries in the battery swapping station based on the optimal solution, including: Solve the solution of the objective function of the operation optimization model of the battery swapping station through mixed-integer linear programming based on the constraint conditions to obtain the optimal solution; among them, the optimal solution includes: the optimal charging power and charging time of each battery in the battery swapping station; Generate a charging instruction based on the optimal solution and send the charging instruction to the charging equipment in the battery swapping station; the charging equipment in the battery swapping station charges each battery based on the charging instruction.

[0040] For example: Suppose there is a small electric vehicle battery swapping station, and the station is scheduled within a day (24 hours). Now it is necessary to optimize the battery swapping station as follows: Step 1: Collect the demand data of the battery swapping station; The demand data includes: Battery swapping demand: It is expected that 5 electric vehicles will come for battery swapping within a day.

[0041] Arrival time of electric vehicles: According to the user's reservation system, the arrival times of the vehicles are: 6:00, 10:00, 12:00, 16:00, 18:00.

[0042] Time-of-use electricity price: The time-of-use electricity price provided by the power supplier is as follows: 00:00 - 08:00 is: 0.3 yuan / kWh (off-peak electricity price); 08:00 - 17:00 is: 0.6 yuan / kWh (flat-section electricity price); From 17:00 to 24:00: 0.9 yuan / kWh (peak electricity price); Operation: The information system of the battery swapping station will automatically collect these demand data and upload them to the cloud decision-making platform. The cloud platform will perform subsequent optimization calculations based on these data.

[0043] Step 2: Classify the target batteries in the battery swapping station and construct a dataset of the classified target batteries; Battery classification: Initial in-station batteries: When the battery swapping station starts operating, there are already 5 fully charged batteries in the station, numbered B1, B2, B3, B4, and B5 respectively.

[0044] Batteries swapped into the station: It is expected that 5 electric vehicles will come to swap batteries on the same day. Therefore, 5 low-power batteries will be swapped into the station, numbered B6, B7, B8, B9, and B10 respectively.

[0045] Data set: is the total number of target batteries, is the number of initial in-station batteries, is the number of batteries swapped into the station by electric vehicles, , is the set of all batteries, is the set of initial in-station batteries, is the set of batteries swapped into the station during the scheduling period, is the set of discrete time windows, represents the total number of time windows, T represents the scheduling period, and ∆t represents the interval time, refers to the set of arrival times of electric vehicles corresponding to the batteries swapped into the station. k represents the battery number in the set of batteries swapped into the station during the scheduling period, is the time-of-use electricity price set.

[0046] Total number of batteries: = 10; Number of initial in-station batteries: = 5; Number of batteries swapped into the station: = 5; Set of all batteries: = {B1, B2, B3, B4, B5, B6, B7, B8, B9, B10}; Set of initial in-station batteries: = {B1, B2, B3, B4, B5}; Set of batteries swapped into the station: = {B6, B7, B8, B9, B10}; Set of discrete time windows: A day is divided into 24 time windows, each time window is 1 hour, i.e., ST = {1, 2, 3, …, 24}; Set of time-of-use electricity prices: According to the time-of-use electricity prices, construct a set of time-of-use electricity prices , where t represents the time window number.

[0047] Step 3: Collect battery usage data in the battery swapping station; Battery usage data: Status of the battery in the battery swapping station: Record whether each battery is in the battery swapping station. For example, the initial 5 batteries (B1, B2, B3, B4, B5) in the station are all in the battery swapping station within the first time window (00:00 - 01:00), so ; while the batteries swapped in (B6, B7, B8, B9, B10) are not in the battery swapping station within the first time window, so QUOTE .

[0048] Status of the battery entering the battery swapping station: Record whether each battery enters the battery swapping station. For example, when the first vehicle arrives at 8:00, the battery (B6) enters the battery swapping station, so ; within other time windows, the battery (B6) does not enter the battery swapping station, so , and t is not equal to 8.

[0049] Status of the battery leaving the battery swapping station: Record whether each battery leaves the battery swapping station. For example, when the first vehicle completes battery swapping at 8:00, the battery (B1) leaves the battery swapping station, so ; within other time windows, the battery (B1) does not leave the battery swapping station, so , and t is not equal to 8.

[0050] Step 4: Establish an optimization model for the operation of the battery swapping station; Objective function: The operation strategy of the battery swapping station aims to minimize the total charging cost within the optimization time window. The objective function expression is as follows: ; where, is the charging power of the i-th battery within the t-th time window, is the time-of-use electricity price of the t-th time window, ∆t is the length of each time window (1 hour), and T = 24 is the total number of time windows within the scheduling period.

[0051] Constraint conditions: Battery usage times constraint: Ensure that each battery is only replaced once within the scheduling period. For example, after battery (B6) enters the battery swapping station at 8:00, it cannot enter or leave the battery swapping station again on the same day.

[0052] Logical constraint: Ensure that the in-station status change of the battery conforms to the actual logic. For example, if battery (B6) enters the battery swapping station at 8:00, it was not in the battery swapping station before 8:00 and is in the battery swapping station after 8:00.

[0053] Battery swapping time constraint: Ensure that the battery swapping behavior of the electric vehicle occurs at its arrival time. For example, the first vehicle arrives at 8:00, so battery (B6) must enter the battery swapping station at 8:00, and battery (B1) must leave the battery swapping station at 8:00.

[0054] In-station battery quantity balance constraint: Ensure that each time a battery is swapped, the low-power battery being removed is replaced by another fully-charged battery. For example, when the first vehicle swaps its battery at 8:00, battery (B6) is removed, and battery (B1) is taken away.

[0055] In-station battery Soc balance constraint: Ensure that the Soc of the batteries in the battery swapping station is maintained within a reasonable range to avoid over-discharging or over-charging.

[0056] Charging power upper and lower limit constraint: Ensure that the charging power of each battery is within its allowed range. For example, the maximum charging power of the battery is 120kW, and the minimum charging power is 0kW.

[0057] Transformer capacity constraint: Ensure that the sum of the charging powers of the batteries charging simultaneously does not exceed the transformer capacity of the battery swapping station. Assuming the transformer capacity of the battery swapping station is 200kW, the sum of the charging powers of all batteries cannot exceed 200kW.

[0058] Initial Soc constraint: Ensure that the initial Soc of the battery conforms to the actual situation. For example, the initial Soc of the 5 batteries initially in the station is 100%, and the initial Soc of the 5 batteries swapped into the station is 20%.

[0059] Avoid over-charging constraint: Ensure that the battery is not over-charged to avoid damaging the battery health. For example, the maximum Soc of the battery is 100%, and it cannot exceed this value.

[0060] Real-time Soc constraint: Describe the real-time Soc status of the battery, which is used to monitor the charging process and status change of the battery.

[0061] Soc constraint of the battery being swapped: Ensure that the batteries swapped by the electric vehicle are all in a fully-charged state. For example, when the first vehicle swaps its battery at 8:00, the Soc of battery (B1) must be 100%.

[0062] Step 5: Solve the operation optimization model of the battery swapping station to obtain the optimal solution; Solution process: Use the Mixed-Integer Linear Programming (MILP) algorithm to solve the above optimization model. MILP can handle the combined problems of discrete decision variables (such as whether to charge) and continuous decision variables (such as charging power), and accurately solve complex scheduling problems.

[0063] After solving, the system obtains the optimal charging power and charging time for each battery. For example, the system may obtain the following results: After battery (B6) enters the battery swapping station at 6:00, it starts charging at the maximum charging power from 6:00 to 8:00 and reaches full charge at 12:00.

[0064] After battery (B7) enters the battery swapping station at 10:00, it charges at the maximum charging power from 10:00 to 12:00 and then charges at a power of 100 kW after 12:00, assuming it reaches full charge at 14:00.

[0065] After battery (B8) enters the battery swapping station at 12:00, it charges at a power of 100 kW from 12:00 to 14:00 and then charges at the maximum charging power after 14:00, assuming it reaches full charge at 16:00.

[0066] It should be noted that since the transformer capacity of the battery swapping station is 200 kW, the sum of the charging powers of all batteries cannot exceed 200 kW; during the period from 10:00 to 12:00, battery B7 charges at the maximum power, and at this time, there is still a large amount of remaining capacity in the transformer of the battery swapping station. Therefore, the power resources can be fully utilized to avoid waste. Then, during the period from 12:00 to 14:00, batteries B7 and B8 charge simultaneously. B7 continues to charge at a power of 100 kW, and B8 also starts charging at a power of 100 kW. The total power of the two exactly reaches the maximum capacity of the transformer, 200 kW, ensuring the efficient utilization of power resources and avoiding the risk of overload. After 14:00, B7 finishes charging, and B8 continues to charge at the maximum power until it reaches full charge at 16:00. This way of dynamically adjusting the charging power not only ensures that each battery can complete charging on time but also maximally utilizes the capacity of the transformer and avoids waste of power resources.

[0067] After battery (B9) enters the battery swapping station at 16:00, it starts charging at the maximum charging power from 00:00 the next day.

[0068] After battery (B10) enters the battery swapping station at 18:00, it starts charging at the maximum charging power from 00:00 the next day.

[0069] Step 6: Charge the batteries in the battery swapping station based on the optimal solution; Generate a charging instruction: Based on the optimal solution, the cloud decision-making platform generates a specific charging instruction and sends it to the charging equipment in the battery swapping station. The charging instruction includes the optimal charging power and charging time for each battery.

[0070] By reasonably allocating the charging power, the system can make full use of the capacity of the transformer during periods with lower electricity prices (such as flat-rate electricity prices), reduce the high-power charging demand during peak electricity price periods, and thus effectively reduce the overall charging cost. In addition, avoiding transformer overload can also reduce the additional costs caused by equipment failures or maintenance, further saving the operating cost. Reasonable charging power allocation helps reduce the load pressure on the transformer and other charging equipment, avoids being in a high-load operating state for a long time, thus extending the service life of the equipment, reducing the maintenance frequency and cost. At the same time, the charging process of the battery is also more stable, reducing the aging problem of the battery caused by overcharging or fast charging, and extending the service life of the battery. By optimizing the charging plan, the system can ensure that each battery is charged within the time required by the user, and will not cause the user to wait too long due to transformer overload or insufficient charging equipment. This not only improves the user's satisfaction, but also enhances the service reliability of the battery swapping station and improves the overall operating efficiency.

[0071] The second aspect of the embodiments of the present invention provides an operation strategy optimization and scheduling system for an electric vehicle battery swapping station, including: a data acquisition module, a data analysis module, and an operation strategy optimization module; Data acquisition module: used to acquire the demand data of the battery swapping station; Data analysis module: classify the target batteries in the battery swapping station and construct a data set of the classified target batteries; and, acquire the battery usage data in the battery swapping station; Operation strategy optimization module: establish an operation optimization model for the battery swapping station; and, solve the operation optimization model of the battery swapping station to obtain the optimal solution; charge the batteries in the battery swapping station based on the optimal solution.

[0072] The above embodiments are only used to illustrate the technical methods of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical methods of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for optimizing the operation strategy of an electric vehicle battery swap station, characterized in that: include: Step 1: Collect demand data of battery swap stations; Step 2: Classify the target batteries in the battery swap station and build a data set of the classified target batteries; Step 3: Collect battery usage data in the battery swap station; Step 4: Establish an optimization model for battery swap station operation; Step 5: Solve the battery swap station operation optimization model to obtain the optimal solution; charge the batteries in the battery swap station based on the optimal solution.

2. The method for optimizing the operation strategy of an electric vehicle battery swap station according to claim 1 is characterized in that: The collecting of demand data of the battery swap station includes: The demand data of the battery swap station is obtained from the information system of the battery swap station and uploaded to the cloud decision-making platform of the battery swap station; the demand data includes: the battery swap demand of the battery swap station within the scheduling cycle, the arrival time of the electric vehicle and the local time-of-use electricity price.

3. The method for optimizing the operation strategy of an electric vehicle battery swap station according to claim 1 is characterized in that: The target batteries in the battery swap station are classified and a data set of the classified target batteries is constructed, including: Classify the batteries according to their sources, and represent the relevant information of all target batteries in the dispatching area as a data set; The data set is specifically as follows: ； ； ； ； ； ； in, is the total number of target batteries, is the number of batteries initially present at the station, is the number of batteries that electric vehicles have replaced at the station, , It is a collection of all batteries. is the initial set of batteries on site, It is the collection of batteries replaced in the station during the dispatch cycle. is a set of discrete time windows, represents the total number of time windows, T represents the scheduling period, ∆t represents the interval time, It refers to the set of arrival times of electric vehicles corresponding to the batteries replaced at the station, k represents the battery number in the set of batteries replaced at the station during the scheduling period, It is a collection of time-of-use electricity prices.

4. The method for optimizing the operation strategy of an electric vehicle battery swap station according to claim 1 is characterized in that: The collecting of battery usage data in the battery swap station includes: Obtain battery usage data in the battery swap station, and define three sets of variables based on the battery usage data; the battery usage data includes: the state of the battery in the battery swap station, the state of the battery entering the battery swap station, and the state of the battery leaving the battery swap station; Upload battery usage data to the battery swap station cloud decision-making platform.

5. The method for optimizing the operation strategy of an electric vehicle battery swap station according to claim 4 is characterized in that: The three groups of variables are defined based on the battery usage data to determine the specific status of the battery, including: ； in, is the variable of whether the battery is in the battery swap station. =1, indicating that the i-th battery is in the battery swap station in the t-th time window. =0, indicating that the i-th battery is not in the battery swap station in the t-th time window; i represents the battery number in the set of all batteries; ； in, is the variable indicating whether the battery enters the battery swap station. =1, indicating that the i-th battery enters the battery swap station in the t-th time window. =0, indicating that the i-th battery did not enter the battery swap station in the t-th time window; ； in, is the variable indicating whether the battery leaves the battery swap station. =1, indicating that the i-th battery leaves the battery swap station in the t-th time window. =0, indicating that the i-th battery did not leave the battery swap station in the t-th time window.

6. The method for optimizing the operation strategy of an electric vehicle battery swap station according to claim 1 is characterized in that: The establishment of the battery swap station operation optimization model includes: the objective function and constraint conditions of the battery swap station operation optimization model.

7. The method for optimizing the operation strategy of an electric vehicle battery swap station according to claim 6 is characterized in that: The objective function of the battery swap station operation optimization model includes: With the goal of minimizing the total charging cost within the optimization time window, the objective function is constructed: ； in, is the charging power of the i-th battery in the battery swap station in the t-th time window, is the total number of batteries, T is the scheduling period, ∆t is the interval time, is the time-of-use electricity price in the tth time window.

8. The method for optimizing the operation strategy of an electric vehicle battery swap station according to claim 6 is characterized in that: The constraints include: Battery usage limit: ； Wherein, j represents the battery number in the battery set initially at the station; ； ； Logical constraints: ； Battery replacement time constraints: ； ； in, is the auxiliary time parameter; Indicates the time when the kth battery is replaced at the battery swap station; On-site battery quantity balancing constraints: ； On-site battery Soc balance constraints: ； in, and Respectively represent the Soc,t value of the i-th battery in the 1st and t-th time periods and the total number of time windows Stay consistent; Charging power upper and lower limit constraints: ； in, Indicates the maximum charging power of a single battery; Transformer capacity constraints: ； in, Indicates the capacity of the transformer in the battery swap station; Initial Soc constraints: ； ； in, represents the Soc of the jth battery in the first period, represents the Soc of the kth battery replaced in the station in the tth period, represents the initial Soc of the kth battery replaced at the station; Avoid overcharging constraints: ； Real-time Soc constraints: ； in, represents the Soc of the i-th battery in the t-1th period; Indicates the rated capacity of a single battery; Soc constraints of the replaced battery: 。 9. The method for optimizing the operation strategy of an electric vehicle battery swap station according to claim 1 is characterized in that: The battery swap station operation optimization model is solved to obtain the optimal solution; Charging the batteries in the battery swap station based on the optimal solution, including: Based on the constraints, the objective function of the battery swap station operation optimization model is solved by mixed integer linear programming to obtain the optimal solution; wherein the optimal solution includes: the optimal charging power and charging time of each battery in the battery swap station; A charging instruction is generated based on the optimal solution and sent to the charging equipment in the battery swap station; the charging equipment in the battery swap station charges each battery based on the charging instruction.

10. An operation strategy optimization and scheduling system for an electric vehicle battery swap station, used to execute an operation strategy optimization and scheduling method for an electric vehicle battery swap station according to any one of claims 1 to 9, characterized in that: include: Data collection module, data analysis module and operation strategy optimization module; Data collection module: used to collect demand data of battery swap stations; Data analysis module: classifies the target batteries in the battery swap station and builds a data set of the classified target batteries; and collects battery usage data in the battery swap station; Operation strategy optimization module: establish an operation optimization model for battery swap stations; And, solving the optimization model of the battery swap station operation to obtain the optimal solution; The batteries in the battery swap station are charged based on the optimal solution.

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