A method and device for optimizing the layout of battery swapping stations for pure electric autonomous taxis

Abstract

This invention relates to the field of autonomous driving technology, and more particularly to a method and apparatus for optimizing the layout of battery swapping stations for pure electric autonomous taxis. The method acquires battery swapping demand data for battery swapping stations, obtains the demand distribution, determines the budget uncertainty set, acquires information on alternative battery swapping station locations, the maximum battery capacity of the battery swapping station, and the system cost of the battery swapping station, and inputs this information into a facility location layout optimization model that considers the uncertainty of battery swapping demand. Based on the robust equivalence transformation method, the method uses a solution algorithm based on the Benders decomposition method to solve the facility location layout optimization model, obtaining the battery swapping station layout and operation plan. The construction process of the facility location layout optimization model includes: using conditions based on the budget uncertainty set, alternative battery swapping station locations, the maximum battery capacity of the battery swapping station, and the system cost information of the battery swapping station as constraints, and minimizing the system cost of the battery swapping station as the objective. Compared with the prior art, this invention has the advantages of meeting battery swapping demand while reducing the total cost of the battery swapping system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, in particular to a pure electric automatic driving taxi battery swap station layout optimization method and device. BACKGROUND

[0002] With the increasing severity of global energy crisis and environmental problems, the development of electric and autonomous driving technologies has rapidly become an important direction in the future transportation field. Pure electric autonomous driving taxis, as a combination of these two technologies, not only represent the trend of future transportation tools, but also show strong development prospects. First, pure electric autonomous driving taxis can effectively alleviate traffic congestion problems. In traditional transportation systems, traffic accidents and unreasonable traffic flow caused by human driving factors are the main causes of congestion. Autonomous driving technology, through efficient algorithms and intelligent traffic management systems, can significantly reduce traffic accidents and optimize traffic flow, thereby alleviating congestion problems. In addition, autonomous driving technology can also improve vehicle utilization and road resource utilization efficiency through fleet scheduling and path planning. Second, with the increasing aging of the population, the demand for convenient and safe travel methods in society has increased. Pure electric autonomous driving taxis can provide barrier-free and low-cost travel services, providing better travel options for the elderly and other people with limited mobility. At the same time, autonomous driving technology can also reduce traffic accidents caused by human driving and improve travel safety.

[0003] In the operation of pure electric autonomous driving taxis, efficient energy replenishment is a key link. Compared with traditional charging mode, the battery swap mode has significant advantages: first, the battery swap mode can complete energy replenishment in a few minutes through quick battery replacement, greatly improving vehicle operation efficiency and increasing operator revenue; second, the battery swap mode can realize centralized management and maintenance of batteries, improving battery life and performance. In addition, the battery swap mode can also reduce power grid load fluctuations through optimized scheduling, achieving efficient use of energy.

[0004] However, the battery swap demand has great randomness and uncertainty, which puts higher requirements on the layout of the battery swap station. In the existing research and practice, the layout of the battery swap station often lacks sufficient consideration of demand uncertainty, resulting in low utilization of the battery swap station or failure to meet actual demand. For example, Chinese patent CN106682759A discloses a battery supply system for electric taxis and a network optimization method. The method is suitable for a battery supply system including battery swap stations and charging centers. By analyzing the GPS historical data of taxis, the geographical position distribution of taxis during charging period is obtained, a selection scheme model based on a multinomial Logit model is established, the selection scheme of taxis for battery swap stations is determined, and the cost of taxis reaching the target swap station is determined. Then, a battery swap station site selection model including the cost is established, and the optimal battery swap station site selection scheme meeting various constraints is selected from multiple alternative battery swap station site selection schemes through the site selection model. The method can design and optimize the overall city battery supply network from the perspective of the taxi company and find the optimal charging center and battery swap station site selection scheme. However, the method does not consider the uncertainty of battery swap demand, and there is still certain limitation in the layout of battery swap stations. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide a pure electric automatic driving taxi battery swap station layout optimization method and device. By analyzing historical battery swap data, a robust optimization site selection model considering battery swap demand uncertainty is constructed, and a corresponding solution algorithm is proposed to optimize the location and number of battery swap stations, the number of stored batteries, and the selection of battery swap sites and the proportion of vehicles.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] According to a first aspect of the present application, a pure electric automatic driving taxi battery swap station layout optimization method is provided, comprising the following steps: obtaining battery swap demand data of a battery swap station to obtain demand distribution, determining a budget uncertainty set, obtaining battery swap station candidate points, maximum battery capacity of the battery swap station and battery swap station system cost information, and inputting a pre-constructed facility site selection and layout optimization model considering battery swap demand uncertainty; based on a robust equality transformation method, a solution algorithm based on Benders decomposition method is used to solve the facility site selection and layout optimization model to obtain a battery swap station layout and operation scheme; wherein the construction process of the facility site selection and layout optimization model includes: taking the conditions based on the budget uncertainty set, the battery swap station candidate points, the maximum battery capacity of the battery swap station and the battery swap station system cost information as constraints, and taking the minimization of the battery swap station system cost as the target.

[0008] As a preferred technical solution, the battery swap station system cost information includes battery swap station construction cost, battery purchase and operation cost, and unit distance travel time cost.

[0009] As a preferred technical solution, the layout and operation plan of the battery swapping station includes the location and number of battery swapping stations, the number of batteries to be stored, and the selection of stations and proportions for vehicle battery replacement.

[0010] As a preferred technical solution, the objective function of the facility site selection and layout optimization model is expressed as:

[0011]

[0012] In the formula, Q represents the cost of the battery swapping station system, and a j For the fixed construction cost of the battery swapping station, h j The battery purchase and operation costs for each battery swapping station are given by q, where q is the cost per unit distance traveled per hour; I and J represent the locations of battery swapping demand points and the set of candidate locations for battery swapping stations; y j This is a 0-1 variable; it takes the value 1 when a battery swapping station is established at the alternative site j, and 0 otherwise. j d is an integer variable representing the number of batteries that need to be stored in the established battery swapping station; ij The driving distance between the battery swapping demand point and the battery swapping station; For uncertain parameters related to battery swapping demand; x ij As a continuous variable, it represents the proportion of battery swapping demand generated at point i that is served by the alternative battery swapping station point j.

[0013] As a preferred technical solution, the constraints of the facility site selection and layout optimization model include:

[0014]

[0015]

[0016] In the formula, M j Let j be the maximum number of batteries that can be accommodated at the alternative battery swapping station. Represents a set of integers.

[0017] As a preferred technical solution, the facility location and layout optimization model is solved using a solution algorithm based on the Benders decomposition method, specifically including: redefining the uncertain parameters based on the robust equivalence and introducing corresponding control parameters for each battery swapping demand point, transforming the facility location and layout optimization model into a first facility location and layout optimization model based on the redefined uncertain parameters and control parameters; introducing dual variables and reconstructing the first facility location and layout optimization model into a deterministic facility location and layout optimization model to be solved based on duality theory; and solving the deterministic facility location and layout optimization model using a solution algorithm based on the Benders decomposition method.

[0018] As a preferred technical solution, the deterministic facility location and layout optimization model is solved using a solution algorithm based on the Benders decomposition method. Specifically, the deterministic facility location and layout optimization model is decomposed into a main problem and sub-problems, while introducing feasible cuts and optimal cuts.

[0019] As a preferred technical solution, the first facility site selection and layout optimization model is expressed as follows:

[0020]

[0021] In the formula, Q r Let I and J be the objective function value, where I and J are the points where battery swapping demand arises and the set of candidate locations for battery swapping stations; q is the cost per unit distance traveled; and d is the cost per unit distance traveled. ij k is the driving distance between the battery swapping demand point and the battery swapping station. i x represents the average demand for battery swapping. ij As a continuous variable, it represents the proportion of battery swapping demand generated at point i that is served by alternative battery swapping station point j. This represents the maximum fluctuation in battery swapping demand; n j This is an integer variable representing the number of batteries that need to be stored at the established battery swapping station; Let S be the non-integer part of the variable corresponding to the battery swapping demand; K is the set of all fluctuating points in the battery swapping demand point i; and S is defined as:

[0022]

[0023] In the formula, K i Indicating uncertain parameters for battery swapping demand The set of demand points where fluctuations occur, P i Indicating uncertain parameters for battery swapping demand The set, Γ i For the control parameters corresponding to battery swapping demand point i, Indicates not exceeding Γ i The largest integer, t i For control parameter Γ i The decimal part of the value.

[0024] As a preferred technical solution, the deterministic facility site selection and layout optimization model is expressed as follows:

[0025]

[0026] In the formula, Q represents the cost of the battery swapping station system, and a j For the fixed construction cost of the battery swapping station, h j The battery purchase and operation costs for each battery swapping station are given by q, where q is the cost per unit distance traveled per hour; I and J represent the locations of battery swapping demand points and the set of candidate locations for battery swapping stations; y jn is a 0-1 variable, taking the value 1 when a battery swapping station is established at candidate point j, and 0 otherwise; j d is an integer variable representing the number of batteries that need to be stored in the established battery swapping station; ij k is the driving distance between the battery swapping demand point and the battery swapping station. i x represents the average demand for battery swapping. ij p is a continuous variable, representing the proportion of battery swapping demand generated at point i that is served by battery swapping station j; i μ i q i v i For different dual variables, Γ i M represents the control parameters corresponding to battery swapping demand point i; j Let j be the maximum number of batteries that the battery swapping station can accommodate. Represents a set of integers; This represents the maximum fluctuation in battery swapping demand.

[0027] According to a second aspect of the present invention, a device for optimizing the layout of battery swapping stations for pure electric autonomous taxis is provided, comprising a memory, a processor, and a program stored in the memory, wherein the processor executes the program to implement the method described herein.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The battery swapping station layout optimization method provided by the present invention takes into account the uncertainty of battery swapping demand and the service level of battery swapping stations (including alternative battery swapping stations, maximum battery capacity of battery swapping stations, and system cost of battery swapping stations). It can reduce the construction cost of battery swapping stations, battery purchase and operation costs, and unit distance driving time cost while meeting battery swapping demand, which helps to improve the operating income of the fleet.

[0030] 2. This invention introduces a budget uncertainty set to characterize battery swapping demand, which can effectively balance the solution efficiency and conservatism of the facility site selection and layout optimization model. Furthermore, based on the robust equivalence formula, the uncertain model is transformed into a deterministic model for solution, thereby improving the applicability of the proposed layout method.

[0031] 3. Based on the Benders decomposition method, this invention breaks down the facility site selection and layout optimization problem considering uncertain battery swapping demand into a main problem and sub-problems, and proposes a corresponding Benders solution algorithm, which can effectively improve the quality and efficiency of layout and operation schemes. Attached Figure Description

[0032] Figure 1 A schematic diagram of the process framework for the method of this invention;

[0033] Figure 2This is a schematic diagram of the research scenario in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the layout and operation scheme of the battery swapping station in an embodiment of the present invention;

[0035] Figure 4 This is a convergence graph of the algorithm provided in the embodiments of the present invention. Detailed Implementation

[0036] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0037] Example

[0038] like Figure 1 As shown in the figure, this embodiment provides a method for optimizing the layout of battery swapping stations for pure electric autonomous taxis. The method first acquires battery swapping demand data for the stations, obtains the demand distribution, determines the budget uncertainty set, and acquires information on alternative battery swapping stations, the maximum battery capacity of the stations, and the system cost of the stations. Next, the aforementioned data are input into a pre-constructed facility location optimization model considering uncertain battery swapping demand. Finally, based on the robust equivalence transformation method, the facility location optimization model is solved using a Benders decomposition-based algorithm, outputting the battery swapping station layout and operation plan. The system cost information for the battery swapping stations includes the construction cost, battery purchase and operation cost, and unit distance travel time cost. The layout and operation plan includes the location and number of battery swapping stations, the number of batteries to be stored, and the selection of stations and their proportions for vehicle battery replacement. Furthermore, the construction process of the facility location optimization model considering uncertain battery swapping demand includes: using constraints based on the budget uncertainty set, alternative battery swapping stations, the maximum battery capacity of the stations, and cost information as constraints, and aiming to minimize the system cost of the battery swapping stations. Figure 2 This illustrates one research scenario of the method provided in this embodiment.

[0039] The specific steps for implementing the aforementioned method are as follows:

[0040] The first step is to collect operational data from the battery swapping station.

[0041] The battery swapping station operation data includes battery swapping demand data, alternative swapping station locations, maximum battery capacity of swapping stations, and the system cost information for swapping stations, including construction costs, battery purchase and operation costs. Specifically: A survey is conducted with the swapping station operating company to determine the average daily battery swapping demand for each station. For stations with large fluctuations, the demand distribution is obtained (in this embodiment, uncertain battery swapping demand is represented by a budget uncertainty set, requiring the acquisition of average battery swapping demand, maximum fluctuating demand, and minimum fluctuating demand). Based on this, the construction costs, battery purchase and operation costs of swapping stations are further obtained, and alternative swapping station locations and the maximum allowable number of batteries at a swapping station (i.e., the maximum battery capacity of the swapping station) are surveyed.

[0042] The second step is to collect taxi fleet operation data.

[0043] Taxi fleet operation data mainly includes unit distance travel time cost information from the battery swapping station system cost information. Specifically, it is necessary to conduct research with the fleet manager to obtain information such as unit distance travel time cost.

[0044] The third step is model parameter calibration. Based on the research conducted in the first and second steps, the parameter thresholds for the construction and operation of battery swapping stations are obtained, and the required parameters are set to appropriate values.

[0045] The fourth step is to construct an optimization model for facility site selection and layout that considers the uncertainty of battery swapping demand. This model is constructed with constraints such as uncertain battery swapping demand, alternative battery swapping station locations, and the number of batteries, and aims to minimize the construction cost of battery swapping stations, the purchase and operation cost of batteries, and the cost per unit distance traveled. This model contains uncertain parameters related to battery swapping demand. The mixed-integer linear problem requires that any feasible value must satisfy the uncertain parameter constraint, and the uncertain demand can be obtained from the corresponding distribution through actual operational data.

[0046] The objective function of the facility site selection and layout optimization model is expressed as:

[0047]

[0048] In the formula, Q represents the system cost of the battery swapping station, which is the operating cost of pure electric autonomous taxi operators under the battery swapping mode. This cost includes three components: the construction cost of the battery swapping station, the battery purchase and operating cost, and the unit distance travel time cost; a j For the fixed construction cost of the battery swapping station, h j The battery purchase and operation costs for each battery swapping station are given, where q is the cost per unit distance traveled per hour; I and J are the sets of points where battery swapping demand arises and candidate locations for battery swapping stations (i = {1, ..., l}, j = {1, ..., m}); y j This is a 0-1 variable; it takes the value 1 when a battery swapping station is established at the alternative site j, and 0 otherwise.j d is an integer variable representing the number of batteries that need to be stored in the established battery swapping station; ij The driving distance between the battery swapping demand point and the battery swapping station (all distances in this embodiment are Euclidean distances); For uncertain parameters related to battery swapping demand; x ij Let y be a continuous variable, representing the proportion of battery swapping demand generated at point i that is served by the alternative battery swapping station at point j. j n j x ij All of them are decision variables.

[0049] The constraints of the facility site selection and layout optimization model include:

[0050]

[0051]

[0052] In the formula, M j Let j be the maximum number of batteries that the battery swapping station can accommodate. Represents a set of integers.

[0053] Constraint (2) ensures that batteries can only be purchased and swapped when the battery swapping station is built, and the number of batteries cannot exceed the maximum capacity of the battery swapping station; Constraint (3) requires that the demand for battery swapping at the alternative point j of the battery swapping station cannot exceed the number of batteries at that station; Constraint (4) requires that all battery swapping demands be met; Constraint (5) indicates that battery swapping can only be carried out at the battery swapping station that has already been built; Constraint (6) is a continuous decision variable, representing the proportion of demand point i served by the alternative point j of the battery swapping station; Constraints (7) and (8) are 0-1 variable constraints and integer variable constraints.

[0054] Fifth, based on the robust equivalence formula, the model constructed in step four contains uncertain parameters. The aforementioned facility site selection and layout optimization model is transformed into a first facility site selection and layout optimization model based on redefined uncertain parameters and control parameters. Specifically:

[0055] Based on robust equivalence, uncertain parameters of battery swapping demand. It can be redefined For each battery swapping demand point i, a control parameter Γ is introduced. i Its value range is [0, |P] i |], by setting the control parameter Γ i Different values ​​were used to analyze the conservatism of the robust optimization model. For the entire battery swapping network system, the demand disturbance range should satisfy:

[0056]

[0057] The robustness of the battery swapping system is adjusted by introducing a control parameter Γ, the value of which reflects the operator's risk appetite. If the operator is aggressive or risk-taking, Γ will be set relatively small; conversely, Γ will take a relatively large value.

[0058] Based on this, the initial facility location layout optimization model can be transformed into a first facility location layout optimization model based on redefined uncertain parameters and control parameters. Compared with the initial facility location layout optimization model, the specific changes in the first facility location layout optimization model are reflected in: incorporating uncertain parameters related to battery swapping demand. Formulas (1) and (3) can be transformed into the following form:

[0059]

[0060] In the formula, Q r The objective function value; k i This represents the average demand for battery swapping. This represents the maximum fluctuation in battery swapping demand. Let K be the non-integer part of the variable corresponding to the battery swapping demand; K is the set of all points in the battery swapping demand point i that fluctuate; the subset S is defined as:

[0061]

[0062] In the formula, t i For control parameter Γ i The decimal part of the value. Define P. i Uncertain parameters for battery swapping demand The set K; for the entire battery swapping network system, the fluctuations in some demand points are small and negligible, but the fluctuations in other demand points are large and will affect the overall layout plan. Therefore, a set K is defined. i Uncertain parameters for battery swapping demand Points where demand fluctuates. This represents the largest integer not exceeding this number. In a battery swapping system, there can be at most... The demand fluctuates at each point, and the coefficient k i,t The change can be made by To express.

[0063] Step 6: Introduce the dual variable μ i ,p i ,ν i ,q i Based on duality theory, the first facility site selection and layout optimization model obtained in step five is reconstructed into a deterministic facility site selection and layout optimization model. Specifically:

[0064] Based on duality theory, the parts of formulas (10) and (11) containing uncertain parameters can be transformed into the following form:

[0065]

[0066] Under the premise that the initial facility location layout optimization model considering the uncertainty of battery swapping demand can improve the rationality of the layout scheme, in order to solve the problem of the difficulty in solving the uncertain parameters of battery swapping demand, a deterministic facility location layout optimization model is obtained based on formulas (13) to (16) and the robust equivalence. The final model is summarized as formulas (17) to (18).

[0067]

[0068]

[0069] Step 7: Solve the deterministic facility location and layout optimization model obtained in Step 6 using a solution algorithm based on the Benders decomposition method. Specifically:

[0070] Since the facility location problem has been proven to be an NP-hard problem, and since the model contains integer variables and 0-1 variables, the model becomes increasingly difficult to solve as the scale of the facility layout problem increases. Therefore, this embodiment uses the Benders decomposition method to decompose formulas (17) to (18) into the main problem and sub-problems, and introduces feasible cuts and optimal cuts to propose a solution algorithm based on the Benders decomposition method to improve computational efficiency.

[0071] The main problem is represented as:

[0072]

[0073] The subproblem is represented as:

[0074]

[0075]

[0076] Feasible cut and optimal cut are represented as follows:

[0077]

[0078] Next, a case study is used to verify the effectiveness of the method proposed in this embodiment and to obtain an optimal layout scheme for battery swapping stations.

[0079] (1) This case study uses the operating area of ​​a battery-swapping taxi company in a certain region as the research scenario. Based on communication with the battery-swapping taxi company, a research scenario with 4 battery-swapping demand points and 3 alternative battery-swapping station locations (mainly large shopping malls and vacant parking lots) was selected, such as... Figure 3 As shown in section (a), the cost of a vehicle traveling to a battery swapping station to replace its battery is 1.1 yuan / km (including power consumption, customer loss, reduced operating time, etc.); since the battery model and configuration are uniform, the battery price is set at 80,000 yuan; the operating cost of the battery swapping station (battery charging, personnel expenses, etc.) is 245 yuan / day; the construction cost of the battery swapping station is 1.2 million yuan / station; the number of batteries that a battery swapping station can accommodate is 45 per station.

[0080] (2) Figure 3 Part (b) is the layout and operation plan of the battery swapping stations. Figure 4 This section describes the convergence of the algorithm in this case. Figure 3 In the diagram, blue dots represent the locations of battery swapping demand points, red stars represent the locations of alternative battery swapping station locations, and points marked with red circles represent established battery swapping stations. The thickness of the red circles represents the number of batteries within each station, and the blue line represents the demand matching result (the line thickness represents the demand allocation ratio). The results show that battery swapping stations should be built at all three alternative locations, with battery configurations of 35, 30, and 11 batteries respectively. For battery swapping demand point 1, 38% of the demand was allocated to battery swapping station 1, and 62% to battery swapping station 2. For battery swapping demand point 2, all demand was allocated to battery swapping station 1. For battery swapping demand point 3, 25% of the demand was allocated to battery swapping station 3, and 75% to battery swapping station 1. For battery swapping demand point 4, all demand was allocated to battery swapping station 2.

[0081] (3) Table 1 shows the results of the general deterministic location model and the facility location layout optimization model proposed in this embodiment. In the facility location layout optimization model proposed in this embodiment, it is assumed that Γ = 4 (Γ1 = Γ2 = Γ3 = Γ4 = 1), that is, all four battery swapping demand points are disturbed, and the disturbance size is set to 10%. It can be clearly seen that the result of the model proposed in this embodiment is that all three candidate points are selected, while in the general deterministic location model (i.e., Γ = 0, at which point all points will not need to be changed), only 1 and 2 are selected. The model proposed in this embodiment requires 89 batteries, while the general deterministic location model requires 65. The construction cost will be significantly increased in this case. However, in the early stage of development, it is necessary to increase the number of batteries to meet all the needs of the vehicles, which is consistent with the results obtained in the survey of battery swapping taxi companies.

[0082] Table 1 compares the results of a general deterministic location model and the model proposed in this embodiment.

[0083]

[0084] (4) Table 2 shows the results of the analysis of the impact of the control parameter Γ on the objective function, number of iterations, and running time. It can be clearly seen that when the control parameter Γ remains unchanged, the objective function, number of iterations, and running time will all increase with the increase of disturbance. This is because the increase of disturbance will lead to greater demand fluctuations. In order to meet the service level, operators need to increase investment, and the solution of the model will also become more complicated. When the disturbance is fixed, the running time and number of iterations when all four demand points are uncertain parameters are smaller than those when only one demand point is uncertain. This is because when only one demand point is uncertain, the model must first calculate which point is uncertain, which brings a certain degree of complexity to the calculation.

[0085] Table 2. Results Analysis under Different Scenario Settings

[0086]

[0087]

[0088] (5) It can be observed that the solution obtained by the facility site selection and layout optimization model proposed in this embodiment is higher than that of the general deterministic model in terms of cost and battery quantity configuration. This is because the model proposed in this embodiment takes battery swapping demand as an uncertain parameter and considers the worst case (the maximum possible battery swapping demand) in the entire service process, thereby meeting the battery swapping demand of the fleet by increasing costs and promoting the development of battery swapping taxis.

[0089] Furthermore, this embodiment also provides a layout optimization device for battery swapping stations for pure electric autonomous taxis, including a memory, a processor, and a program stored in the memory. When the processor executes the program, it implements one or more steps of the aforementioned method, which will not be described in detail here.

[0090] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for optimizing the layout of a pure electric automatic driving taxi battery swap station, characterized in that, The method comprises the following steps: obtaining battery swap station battery swap demand data to obtain a demand distribution, determining a budget uncertainty set, obtaining battery swap station candidate points, maximum battery swap station battery capacity and battery swap station system cost information, and inputting a pre-constructed facility location layout optimization model considering battery swap demand uncertainty; solving the facility location layout optimization model by using a Benders decomposition method-based solving algorithm based on a robust equality transformation method to obtain a battery swap station layout and operation scheme; The facility location layout optimization model is constructed by taking conditions based on the budget uncertainty set, the battery swap station candidate points, the maximum battery swap station battery capacity and the battery swap station system cost information as constraints and taking minimization of battery swap station system cost as an objective; The facility location layout optimization model is solved by using a Benders decomposition method-based solving algorithm based on a robust equality transformation method, and specifically includes: Based on the robust equality, the uncertain parameters are redefined, and for each battery swap demand point, a corresponding control parameter is introduced, and the facility location layout optimization model is transformed into a first facility location layout optimization model based on the redefined uncertain parameters and control parameters; Based on the duality theory, the first facility location layout optimization model is reconstructed into a to-be-solved deterministic facility location layout optimization model by introducing a dual variable; The deterministic facility location layout optimization model is solved by using a Benders decomposition method-based solving algorithm; The deterministic facility location layout optimization model is solved by using a Benders decomposition method-based solving algorithm, and specifically includes: the deterministic facility location layout optimization model is decomposed into a master problem and a sub-problem, and a feasible cut and an optimal cut are introduced; The first facility location layout optimization model is represented as: In the formula, Q r The objective function value, This is a set of locations where battery swapping demand arises and potential locations for battery swapping stations. Cost per unit distance travel time; The driving distance between the battery swapping demand point and the battery swapping station; This represents the average demand for battery swapping. For continuous variables, it means that in The battery swapping demand generated at the point is selected by the alternative battery swapping station. The proportion of services provided This represents the maximum fluctuation in battery swapping demand. This is an integer variable representing the number of batteries that need to be stored at the established battery swapping station; The non-integer part of the variable corresponding to the battery swapping demand; K For battery swapping demand points The set of all points in the system where fluctuations occur. S Defined as: In the formula, denotes the uncertain parameter of battery swap demand a set of demand points that fluctuate, denotes the uncertain parameter of battery swap demand a set of demand points that fluctuate, is a battery swap demand point i a corresponding control parameter, denotes the maximum integer not exceeding the decimal part value of the control parameter t i is the decimal part value of the control parameter . The deterministic facility location layout optimization model is represented as: The feasible cut and the optimal cut are represented as: wherein, is the cost of the battery swap station system, is the fixed construction cost of the battery swap station, is the cost of the battery acquisition and operation of each battery swap station; is a 0-1 variable, which is 1 when a battery swap station is established at the alternative point , and 0 otherwise; is the driving distance between the battery swap demand point and the battery swap station; is the average value of the battery swap demand; p i , μ i 、q i 、v i is a different dual variable, is the battery swap demand point i corresponding to the control parameter; is the maximum number of batteries accommodated by the battery swap station, represents a set of integers; is the maximum fluctuation value of the battery swap demand.

2. The method of claim 1, wherein, The battery swap station system cost information includes battery swap station construction cost, battery purchase and operation cost, and unit distance driving time cost.

3. The method of claim 1, wherein, The battery swap station layout and operation scheme includes battery swap station construction location and quantity, stored battery quantity, and vehicle battery replacement site selection and proportion.

4. The method of claim 1, wherein, The objective function of the facility location layout optimization model is represented as: In the formula, is the cost of the battery swap station system, is the fixed construction cost of the battery swap station, is the cost of purchasing and operating the battery of each battery swap station, is the driving distance between the battery swap demand point and the battery swap station; is the battery swap demand uncertainty parameter.

5. The method of claim 4, wherein, The constraints of the facility location layout optimization model include: 。 6. A pure electric automatic driving taxi battery swap station layout optimization device, comprising a memory, a processor, and a program stored in the memory, characterized in that, The processor implements the method of any one of claims 1-5 when executing the program.

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