Two-stage electric vehicle battery replacement optimization scheduling method based on cloud battery replacement scheduling system

Abstract

The invention discloses a two-stage electric vehicle battery replacement optimization scheduling method based on a cloud battery replacement scheduling system. According to the scheduling method, a cloud EV two-stage power conversion scheduling system model is constructed by analyzing the mechanism of an EV power conversion scheduling system; secondly, optimization scheduling is carried out on the EV through two-stage optimization, in the first stage, a day-ahead power conversion scheduling plan is output with the purpose of maximizing the power conversion request response rate, and bearing capacity evaluation is carried out; in the second stage, on the basis of the first stage, the time-of-use electricity price is considered, the charging cost is optimized, and an ordered charging model with the purpose of minimizing the charging cost is established, so that the purpose of reducing the operation cost is achieved. Compared with a traditional nearby guiding strategy, the method can more effectively improve the power conversion success rate and reduce the BCSS charging cost in a large-scale power conversion scene, and provides a solution for large-scale EV power conversion scheduling in a smart city background.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle battery swapping scheduling in smart cities, specifically a two-stage electric vehicle battery swapping optimization scheduling method based on a cloud-based battery swapping scheduling system. Background Technology

[0002] Driven by both "dual carbon" goals and the construction of smart cities, the intelligent and low-carbon transformation of transportation energy systems has become a key development objective. Electric vehicles (EVs), as the core carrier of clean energy transportation, have become an important technological path to address EV range anxiety and extend battery life due to their high-efficiency energy replenishment characteristics and centralized battery management advantages, compared to charging. However, with the surge in demand for battery swapping, the low dynamic demand response rate, high operating costs of battery charging and swapping stations (BCSS), and grid load imbalance have become increasingly prominent issues exposed in large-scale battery swapping scenarios. Therefore, how to achieve coordinated optimization of "user demand response, operator cost control, and grid load balancing" through intelligent scheduling strategies has become one of the core problems that smart city EV battery swapping systems urgently need to solve. Summary of the Invention

[0003] To improve the large-scale EV battery swapping scenarios in smart cities, this invention proposes a two-stage electric vehicle battery swapping optimization scheduling method based on a cloud-based battery swapping scheduling system.

[0004] This invention is achieved through the following technical solution:

[0005] A two-stage battery swapping optimization scheduling method for electric vehicles based on a cloud-based battery swapping scheduling system is proposed. First, a cloud-based EV battery swapping scheduling system model is established. Then, a two-stage optimization method is used to optimize the scheduling of EVs. The first stage aims to maximize the battery swapping request response rate and provides a battery swapping scheduling plan. The second stage, based on the first stage, optimizes charging costs by taking into account time-of-use pricing. Specifically, the method includes the following steps:

[0006] S1 establishes a platform model encompassing battery swapping users, transportation networks, BCSS charging and swapping stations, battery charging bays, and a cloud dispatch center.

[0007] The first phase of S2 optimization, based on the cloud scheduling center platform, aims to maximize the response rate of battery swapping requests in the context of disordered charging and outputs a day-ahead battery swapping scheduling plan.

[0008] The S3 two-stage optimization is based on the one-stage battery swapping scheduling plan. With the goal of minimizing the total charging cost, it extracts the battery swapping demand, calculates the state of charge, charging amount and number of time periods of the swapped-out batteries, determines the charging power for each time period, considers the rated power limit of the charging compartment and battery inventory constraints, and finally outputs an orderly charging plan.

[0009] Furthermore, the specific process for establishing the models of battery swapping users, transportation network, battery swapping stations (BCSS), battery charging bays, and cloud dispatch center platform in step S1 is as follows:

[0010] 1) Battery swapping user model

[0011] User's remaining mileage The cutoff interval is Discrete exponential distribution:

[0012] ;

[0013] In the formula: For the number of EVs; For the first Battery swapping users The remaining mileage; These are parameters of the exponential distribution; It is a set of integers; for The probability density function that follows a discrete exponential distribution;

[0014] User initial location node The cutoff interval is Cut-off normal distribution:

[0015] ;

[0016] In the formula: d EVi For EV i The initial position; The location parameters are those of a normal distribution; is the scale parameter of the normal distribution; for The probability density function that follows a truncated normal distribution; The cumulative distribution function representing the standard normal distribution;

[0017] Constructing a reservation time slot matrix :

[0018] ;

[0019] EV i Reservation time slot Follows a mixed distribution:

[0020] ;

[0021] In the formula: and It is a discrete uniform distribution. For peak hours, For off-peak hours, This represents the probability of peak preference. For the i-th user vehicle EV i Scheduled battery swapping time slots;

[0022] EV i Appointment Time The probability P of battery swapping is:

[0023] ;

[0024] In the formula: and These represent the number of peak and off-peak periods, respectively. and All are indicator functions, when and The value is 1 if it is true, and 0 otherwise.

[0025] 2) Traffic network model

[0026] Using adjacency matrix Describe the characteristics of the internal road network of the system. Represents a node With nodes The distance;

[0027] ;

[0028] 3) BCSS model

[0029] During battery swapping, the internal batteries of the BCSS are charged simultaneously. The number of fully charged batteries in the BCSS changes at different times as the EV arrives at the station for battery swapping and the charging compartment completes charging, but the total number of batteries in the station remains constant.

[0030] ;

[0031] In the formula: Let be a binary variable, representing EV i Is it within a time period? In BCSS j Battery swapping is performed if This indicates that EV i During the period At BCSS j Battery swapping; This indicates that EV i Not during the time period At BCSS j Battery swapping; Let be a binary variable, representing EV i Are the replaced batteries within the specified time period? In BCSS j While charging, if This indicates that the battery is in a certain time period. It is in charging state. This indicates that the battery is in a certain time period. Not charging; For BCSS j Total number of internal batteries; BCSS j The number of fully charged batteries in stock during time period t; This represents the total number of EVs participating in the battery swapping program.

[0032] BCSS after battery swapping j The battery inventory update model is as follows:

[0033] ;

[0034] in:

[0035] ;

[0036] In the formula: The initial number of full batteries in BCSS; BCSS j The quantity of fully charged batteries in stock during period t-1. For EV i A flag indicating whether the replaced battery has finished charging; a value of 1 indicates that the battery has completed charging within a certain time period. full; For EV i The period during which the replaced battery begins to charge. For EV i The number of time intervals required to fully charge the replaced battery;

[0037] After determining the scheduling plan, the charging range is obtained:

[0038] ;

[0039] ;

[0040] In the formula: and Corresponding to BCSS j Upper limit for charging resource utilization and lower limit for ensuring the feasibility of battery swapping; This refers to the cumulative charging capacity after the second phase of optimization. For BCSS j In time period The amount of electricity that can be charged in a disorderly manner can be replaced and recharged immediately. For BCSS j During the period The amount of electricity charged in a delayed charging sequence; and BCSS j In time period The charging capacity and average charging power; This refers to the charging time.

[0041] The cumulative charging amount is the same at the beginning and end of the operation period, that is, when Sometimes:

[0042] ;

[0043] 4) Battery charging case model

[0044] The internal BCSS system processes the batteries removed from vehicles and charges them according to the unordered charging schedule as soon as the vehicle arrives at the station, ensuring the charging compartment is fully charged. Number of time periods for replaced batteries awaiting charging and charging capacity for:

[0045] ;

[0046] In the formula: for In time period t The average charging power of the replaced batteries per unit time period. for The state of charge of the replaced batteries; and These represent the full-charge capacity of the EV battery and the energy consumption coefficient per kilometer, respectively. for The remaining driving distance.

[0047] 5) Dispatch Center Model

[0048] Based on Dijkstra's algorithm, using the adjacency matrix Initial position of vehicle d EVi BCSS position P BCSSj Calculate EV i With each BCSS j The shortest distance between them is denoted by a matrix. Based on the vehicle's remaining mileage Determine whether it can reach BCSS j ,like EVi Reachable BCSS j Otherwise, EV i Unreachable BCSS j Construct an accessibility matrix between vehicles and BCSS , among which, if Then EV i Reachable BCSS j ,like Then EV i Unreachable BCSS j C represents the set of reachable BCSSs for vehicles within the system;

[0049] Next, conduct a load-bearing capacity assessment, BCSS j During the period The load-bearing capacity is:

[0050] ;

[0051] In the formula: As a bearing capacity assessment index, it characterizes The number of EVs that can continue to be supported during time period t, if ,but The battery swapping demand is met during time period t; if ,but The battery swapping demand is not met during time period t; for The number of fully charged batteries in stock during time period t-1; for The number of EVs swapped during time period t.

[0052] Furthermore, the first phase of optimization specifically includes:

[0053] Using the maximum battery swapping request response rate as the objective function:

[0054] ;

[0055] The constraints include:

[0056] Battery swapping behavior constraints:

[0057] ;

[0058] Reachability constraints:

[0059] ;

[0060] User-reserved battery swapping time slot constraints:

[0061] ;

[0062] Charging process constraints:

[0063] ;

[0064] Let be a binary variable, representing electric vehicles. Is it within a time period? exist Battery swapping is performed if , then it means During the period At Battery swapping; otherwise, ; The total number of BCSS;

[0065] Battery inventory constraints:

[0066] ;

[0067] ;

[0068] BCSS j In time period Full battery inventory quantity;

[0069] Charging behavior constraints:

[0070] ;

[0071] Charging case quantity constraints:

[0072] ;

[0073] In the formula, Number of charging compartments for each BCSS.

[0074] Furthermore, the second-stage optimization specifically includes:

[0075] The objective function for the second-stage optimization is to minimize the charging cost within the BCSS scheduling cycle.

[0076] ;

[0077] In the formula, For time period Electricity price, ;

[0078] The constraints are:

[0079] Charging power constraint: BCSS j Charging power does not exceed the limit at any time.

[0080] ;

[0081] In the formula: The rated charging power of the charging case; for Maximum charging power limit; For EV i In time period t in BCSS j The charging power;

[0082] Charging compartment quantity constraint: At any given time, the number of rechargeable batteries inside the BCSS shall not exceed the number of charging compartments.

[0083] ;

[0084] Charging capacity constraints: Ensure that batteries removed from the battery swapping schedule can be fully charged within a specified time, for EVs. i In BCSS j During the time period Battery swapping involves replacing the batteries that are not yet ready to be charged, which then need to be used in the next... Charging should be completed within a specified time period, and the total charging amount should meet the following requirements.

[0085] ;

[0086] ;

[0087] In the formula, This represents the battery charging state variable obtained in the first stage of optimization. BCSS j Internal battery The amount of electricity that needs to be charged, due to Indicates battery In time period Whether it is in a charging state, therefore BCSS j In time period The number of batteries currently charging; the fractional part represents the number of batteries. In time period The allocated charging power; This refers to the charging time.

[0088] The beneficial effects of this invention are:

[0089] This invention is based on a cloud-based battery swapping scheduling system and constructs a two-stage optimization model. The first-stage optimization model takes into account inventory constraints and is supplemented by carrying capacity assessment under the condition of disordered charging, and outputs an executable day-ahead battery swapping scheduling plan. The second-stage optimization model is based on the first-stage battery swapping scheduling plan, combined with time-of-use pricing, and solves the charging optimization model for each BCSS. It determines the charging power for each time period with the goal of minimizing the total charging cost, takes into account constraints such as the rated power limit of the charging bay and battery inventory, and finally outputs an ordered charging plan.

[0090] The first phase optimized the battery swapping scheduling plan, improving the response rate to battery swapping demands. The second phase optimized charging power allocation, reducing charging costs and improving the service efficiency and economic benefits of the Battery Service Center (BCSS). By assessing the carrying capacity of each BCSS and optimizing resource allocation, the effectiveness and reliability of scheduling results were ensured. Utilizing user pre-booking information effectively improved scheduling accuracy and resource utilization efficiency, reducing waste of flexible resources. Attached Figure Description

[0091] Figure 1 This is a diagram illustrating the overall architecture of the cloud-based battery swapping scheduling system in this embodiment of the invention.

[0092] Figure 2 This is a schematic diagram of the urban road network structure in an embodiment of the present invention;

[0093] Figure 3 This is an example diagram of the road network topology in an embodiment of the present invention;

[0094] Figure 4 This is a flowchart of the two-stage optimized scheduling process in an embodiment of the present invention;

[0095] Figure 5 The optimized battery swapping schedule for 500 EVs in this embodiment of the invention;

[0096] Figure 6 The results of the optimized charging plan for 500 EVs in this embodiment of the invention. Detailed Implementation

[0097] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0098] This embodiment provides a two-stage optimized scheduling method for electric vehicle battery swapping based on a cloud-based battery swapping scheduling system, which specifically includes the following steps:

[0099] S1 establishes a platform model integrating battery swapping users, transportation network, BCSS, battery charging warehouse, and cloud dispatch center, forming a five-dimensional interactive architecture of "vehicle-road-station-warehouse-dispatch". Based on the cloud-based battery swapping dispatch system under this architecture, a two-stage optimization model is constructed.

[0100] like Figure 1 As shown, this embodiment constructs a collaborative management system that deeply integrates information flow, energy flow, and traffic flow. By integrating five core entities—battery swapping users, traffic network, BCSS, battery charging warehouse, and cloud dispatch platform—it forms a five-dimensional interactive architecture of "vehicle-road-station-warehouse-dispatch." Through multi-source information fusion and dynamic optimization decision-making, it achieves a synergistic improvement in the efficiency of battery swapping demand response and operational economy.

[0101] Battery swapping users are modeled as follows:

[0102] The system's user-side modeling includes three parts: the user's remaining mileage, the user's initial location, and the user's scheduled time slot. (User's remaining mileage...) The cutoff interval is Discrete exponential distribution:

[0103] (1)

[0104] In the formula: For the number of EVs; For the i-th battery swapping user EV i The remaining mileage; These are parameters of the exponential distribution; It is a set of integers.

[0105] User initial location node The cutoff interval is Cut-off normal distribution:

[0106] (2)

[0107] In the formula: d EVi For EV i The initial position; The location parameters are those of a normal distribution; is the scale parameter of the normal distribution.

[0108] Considering users' high sensitivity to time management and scheduling, the system prioritizes user-booked battery swapping time slots during optimized scheduling to improve user satisfaction. To accurately characterize the temporal distribution of user battery swapping behavior, this embodiment simulates a 24-hour self-service BCSS system, dividing the day into 48 time slots, each with a duration of... The time periods 17-20 and 37-40, corresponding to the commuting peak, are defined as the peak times for user-reserved battery swapping, and a reservation time period matrix is ​​constructed. :

[0109] (3)

[0110] Peak Hour Gathering for:

[0111] (4)

[0112] Off-peak hours gathering for:

[0113] (5)

[0114] EV i Reservation time slot Follows a mixed distribution:

[0115] (6)

[0116] In the formula: and It is a discrete uniform distribution. For peak hours, For off-peak hours, This represents the probability of peak preference. For the i-th user vehicle EV i Scheduled battery swapping times.

[0117] EV i Appointment Time The probability of battery swapping is:

[0118] (7)

[0119] In the formula: and These represent the number of peak and off-peak periods, respectively. and All are indicator functions, when and It is 1 if it is true, otherwise it is 0.

[0120] The road network is modeled as follows:

[0121] Figure 2 This is a schematic diagram of the urban road network structure in an embodiment of the present invention, such as... Figure 2 As shown, the internal road network of the system utilizes an adjacency matrix. Describes road characteristics, which include 24 nodes, 76 road segments, and 5 BCSSs (located at nodes 11, 15, 17, 18, and 24, respectively, denoted as...). ). Represents a node With nodes The distance.

[0122] Figure 3 The road network topology example diagram in this embodiment of the invention is shown below, and the constructed adjacency matrix is ​​as follows:

[0123] (8)

[0124] BCSS modeling is as follows:

[0125] During battery swapping, the internal batteries of the BCSS are charged simultaneously. The number of fully charged batteries in the BCSS changes at different times as the EV arrives at the station for battery swapping and the charging compartment completes charging, but the total number of batteries in the station remains constant.

[0126] (9)

[0127] In the formula: Let be a binary variable, representing electric vehicles. Is it within a time period? In BCSS j Battery swapping is performed if This indicates that EV During the period At BCSS j Battery swapping; This indicates that EV Not during the time period At BCSS j Battery swapping; Let be a binary variable, representing EV i Are the replaced batteries ready for recharge within the specified time period? In BCSS j While charging, if This indicates that the battery is in a certain time period. It is in charging state. This indicates that the battery is in a certain time period. Not charging; For BCSS j Total number of internal batteries; For BCSS j The number of fully charged batteries in stock during time period t; This represents the total number of EVs participating in the battery swapping program.

[0128] BCSS after battery swapping j The battery inventory update model is as follows:

[0129] (10)

[0130] in:

[0131] (11)

[0132] In the formula: The initial number of full batteries in BCSS; For BCSS j The quantity of fully charged batteries in stock during period t-1. For EV i A flag indicating whether the replaced battery has finished charging; a value of 1 indicates that the battery has completed charging within a certain time period. full; For EV i The period during which the replaced battery begins to charge. For EV i The number of time intervals required to fully charge the replaced battery.

[0133] Once the scheduling plan is determined, the charging range can be obtained:

[0134] (12)

[0135] (13)

[0136] In the formula: and Corresponding to BCSS j Upper limit for charging resource utilization and lower limit for ensuring the feasibility of battery swapping; This refers to the cumulative charging capacity after the second phase of optimization. For BCSS j In time period The amount of electricity that can be charged in a disorderly manner can be replaced and recharged immediately. For BCSS j During the period The amount of electricity charged in a delayed charging sequence; and BCSS j In time period The charging capacity and average charging power; This refers to the charging time.

[0137] The cumulative charging amount is the same at the beginning and end of the operation period, that is, when Sometimes:

[0138] (14)

[0139] The charging case is modeled as follows:

[0140] The internal BCSS system charges the batteries removed from the vehicle. Assuming the battery state of charge is equal at the EV's origin and destination points and the vehicle's energy consumption per unit mile is fixed, the battery is swapped immediately upon arrival at the station, and charging follows a non-sequential charging schedule. j Internal Number of time periods for replaced batteries awaiting charging and charging capacity for:

[0141] (15)

[0142] In the formula: for In time period t The average charging power of the replaced batteries per unit time period. The state of charge of the battery to be charged; and These represent the full-charge capacity of the EV battery and the energy consumption coefficient per kilometer, respectively. for The remaining driving distance.

[0143] The dispatch center is modeled as follows:

[0144] The scheduling process uses user reservation information and road network information as a basis to perform user reachability analysis and assess the carrying capacity of BCSSs within the system. The reachability analysis provides an EVi-reachable set of BCSSs. BCSS load-bearing capacity assessment combined with BCSS j The feasibility of the scheduling plan was assessed using internal data and user reservation data.

[0145] Specifically, based on Dijkstra's algorithm, using the adjacency matrix... Initial position of vehicle d EVi BCSS position P BCSSj Calculate EV i With each BCSS j The shortest distance between them is denoted by a matrix. Then, based on the vehicle's remaining driving mileage... Determine whether it can reach BCSS j .like EV i Reachable BCSS j Otherwise, EV i Unreachable BCSS j Construct the reachability matrix between vehicles and BCSS. , among which, if Then EV i Reachable BCSS j ,like Then EV i Unreachable BCSS j C represents the set of reachable BCSSs for vehicles within the system.

[0146] Further, load-bearing capacity assessment: BCSS j During the period The load-bearing capacity is:

[0147] (16)

[0148] In the formula: As a bearing capacity assessment index, it characterizes The number of EVs that can continue to be supported during time period t, if ,but The battery swapping demand is met during time period t; if ,but The battery swapping demand is not met during time period t; for The quantity of fully charged batteries in stock during time period t-1; for Number of EVs swapped during time period t

[0149] S2 uses a two-stage optimization approach to optimize the scheduling of EVs, such as... Figure 4 As shown, the first stage aims to maximize the response rate of battery swapping requests. Based on the cloud scheduling center platform, an executable day-ahead battery swapping scheduling plan is output in the case of disordered charging.

[0150] Using the maximum battery swapping request response rate as the objective function:

[0151] (17)

[0152] The constraints are:

[0153] Battery swapping behavior constraints:

[0154] (18)

[0155] Reachability constraints:

[0156] (19)

[0157] User-reserved battery swapping time slot constraints:

[0158] (20)

[0159] Charging process constraints:

[0160] (twenty one)

[0161] Let be a binary variable, representing electric vehicles. Is it within a time period? exist Battery swapping is performed if , then it means During the period At Battery swapping; otherwise, ; This represents the total number of BCSS.

[0162] Battery inventory constraints:

[0163] (twenty two)

[0164] (twenty three)

[0165] BCSS j In time period Full battery inventory quantity.

[0166] Charging behavior constraints:

[0167] (twenty four)

[0168] Charging case quantity constraints:

[0169] (25)

[0170] In the formula, Number of charging compartments for each BCSS.

[0171] S3. The second-stage optimization is based on the first-stage battery swapping scheduling plan. With the goal of minimizing the total charging cost, it extracts the battery swapping demand, calculates the state of charge (SOC) of the swapped-out batteries, the amount of electricity required for charging, and the number of time periods, determines the charging power for each time period, considers constraints such as the rated power limit of the charging compartment and battery inventory, and finally outputs an orderly charging plan.

[0172] The goal is to minimize the charging cost within the BCSS scheduling cycle.

[0173] (26)

[0174] In the formula, For time period Electricity price (unit: yuan / kWh, ¥ / kWh) .

[0175] The constraints are:

[0176] Charging power constraint: BCSS j Charging power does not exceed the limit at any time.

[0177] (27)

[0178] In the formula: The rated charging power of the charging case; for Maximum charging power limit For EV i In time period t in BCSS j The charging power.

[0179] Charging compartment quantity constraint: At any given time, the number of rechargeable batteries inside the BCSS shall not exceed the number of charging compartments.

[0180] (28)

[0181] Charging capacity constraint: Ensure that batteries removed from the battery swapping schedule can be fully charged within the specified time. For EVs i In BCSS j During the time period Battery swapping involves replacing the batteries that are not yet ready to be charged, which then need to be used in the next... Charging should be completed within a specified time period, and the total charging amount should meet the following requirements.

[0182] (29)

[0183] (30)

[0184] In the formula, This represents the battery charging state variable obtained in the first stage of optimization. BCSS j Internal battery The amount of electricity required to charge (unit: kilowatt-hours, kWh). Because... Indicates battery In time period Whether it is in a charging state, therefore BCSS j In time period The number of batteries currently charging; the fractional part represents the number of batteries. In time period The allocated charging power; This refers to the charging time.

[0185] S4. Finally, the two-stage optimization scheduling is used to achieve efficient and accurate scheduling of battery swapping demand and reasonable optimization of charging plans, and the optimization scheduling ends.

[0186] Figure 5 As shown in the figure, the optimized battery swapping scheduling results for 500 EVs in this embodiment of the invention are as follows: the carrying capacity assessment index of each station remains non-negative, verifying the feasibility of the proposed method; the battery swapping load between stations tends to be balanced, effectively suppressing single-station overload and resource squeeze; the demand shows a bimodal distribution that is highly consistent with user reservation preferences, maintaining the inventory safety boundary while ensuring the expected time window, realizing the feasibility of the scheduling plan and ensuring the user battery swapping response rate.

[0187] Figure 6 The figure shows the results of the optimized charging plan for 500 EVs in this embodiment of the invention. As the scale of battery-swapping EVs expands, the battery swapping demand assigned to BCSS in the first phase of optimization increases accordingly. While ensuring the feasibility of the first phase scheduling, time-of-use pricing is used to further reduce charging costs.

[0188] By adopting the aforementioned scheme and steps, this invention effectively solves the above problems and achieves the following benefits: (1) The first stage optimizes the battery swapping scheduling plan, improving the response rate of battery swapping demand; the second stage optimizes the charging power allocation, reducing charging costs and improving the service efficiency and economic benefits of BCSS. (2) By evaluating the carrying capacity of each BCSS, resource allocation is optimized to ensure the effectiveness and reliability of scheduling results. (3) By utilizing the information of users' advance reservations, the accuracy of scheduling and resource utilization efficiency are effectively improved, and the waste of flexible resources is reduced.

[0189] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention in detail. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A two-stage battery swapping optimization scheduling method for electric vehicles based on a cloud-based battery swapping scheduling system, characterized in that: First, a cloud-based EV battery swapping scheduling system model is established. Then, a two-stage optimization is used to optimize EV scheduling. The first stage aims to maximize the battery swapping request response rate and provides a battery swapping scheduling plan. The second stage, based on the first stage, takes into account time-of-use pricing to optimize charging costs. The specific steps include the following: S1. Establish a platform model for battery swapping users, transportation network, BCSS charging and swapping stations, battery charging warehouses, and cloud dispatch center. S2. Phase 1 optimization: Based on the cloud scheduling center platform, in the scenario of disordered charging, with the goal of maximizing the response rate of battery swapping requests, a day-ahead battery swapping scheduling plan is output. S3. The second-stage optimization is based on the first-stage battery swapping scheduling plan. With the goal of minimizing the total charging cost, it extracts the battery swapping demand, calculates the state of charge, charging amount and number of time periods of the swapped-out batteries, determines the charging power for each time period, considers the rated power limit of the charging compartment and battery inventory constraints, and finally outputs an orderly charging plan.

2. The two-stage battery swapping optimization scheduling method for electric vehicles based on a cloud-based battery swapping scheduling system according to claim 1, characterized in that, The specific process for establishing the models of battery swapping users, transportation network, battery swapping station BCSS, battery charging warehouse, and cloud dispatch center platform in step S1 is as follows: 1) Battery swapping user model User's remaining mileage The cutoff interval is Discrete exponential distribution: ； In the formula: For the number of EVs; For the first Battery swapping users The remaining mileage; These are parameters of the exponential distribution; It is a set of integers; for The probability density function that follows a discrete exponential distribution; User initial location node The cutoff interval is Cut-off normal distribution: ； In the formula: for The initial position; The location parameters are those of a normal distribution; is the scale parameter of the normal distribution; for The probability density function that follows a truncated normal distribution; The cumulative distribution function representing the standard normal distribution; Constructing a reservation time slot matrix : ； Reservation time slot Follows a mixed distribution: ； In the formula: and It is a discrete uniform distribution. For peak hours, For off-peak hours, This represents the probability of peak preference. For the first individual user vehicles Scheduled battery swapping time slots; Appointment Time Probability of battery swapping for: ； In the formula: and These represent the number of peak and off-peak periods, respectively. and All are indicator functions, when and The value is 1 if it is true, and 0 otherwise. 2) Traffic network model Using adjacency matrix Describe the characteristics of the internal road network of the system. Represents a node With nodes The distance; ； 3) BCSS model During battery swapping, the internal batteries of the BCSS are charged simultaneously. The number of fully charged batteries in the BCSS changes at different times as the EV arrives at the station for battery swapping and the charging compartment completes charging, but the total number of batteries in the station remains constant. ； In the formula: For binary variables, representing Is it within a time period? exist Battery swapping is performed if , then it means During the period At Battery swapping; , then it means Not during the time period At Battery swapping; For binary variables, representing Are the replaced batteries within the specified time period? exist While charging, if This indicates that the battery is in a certain time period. It is in charging state. This indicates that the battery is in a certain time period. Not charging; for Total number of internal batteries; express Time period The quantity of fully charged batteries in stock; This represents the total number of EVs participating in the battery swapping program. After battery swapping occurs The battery inventory update model is as follows: ； in: ； In the formula: The initial number of full batteries in BCSS; for Time period -1 is the number of fully charged batteries in stock. for A flag indicating whether the replaced battery has finished charging; a value of 1 indicates that the battery has completed charging within a certain time period. full; for The period when the replaced battery begins to charge. for The number of time intervals required to fully charge the replaced battery; After determining the scheduling plan, the charging range is obtained: ； ； In the formula: and Corresponding to Upper limit for charging resource utilization and lower limit for ensuring the feasibility of battery swapping; This refers to the cumulative charging capacity after the second phase of optimization. for In time period The amount of electricity that can be charged in a disorderly manner can be replaced and recharged immediately. for During the period The amount of electricity charged in a delayed charging sequence; and They are respectively In time period The charging capacity and average charging power; This refers to the charging time. The cumulative charging amount is the same at the beginning and end of the operation period, that is, when Sometimes: ； 4) Battery charging case model The internal BCSS system processes the batteries removed from vehicles and charges them according to the unordered charging schedule as soon as the vehicle arrives at the station, ensuring the charging compartment is fully charged. Number of time periods for replaced batteries awaiting charging and charging capacity for: ； In the formula: for During the period right The average charging power of the replaced batteries per unit time period. The state of charge of the replaced battery; and These represent the full-charge capacity of the EV battery and the energy consumption coefficient per kilometer, respectively. for The remaining driving distance; 5) Dispatch Center Model Based on Dijkstra's algorithm, using the adjacency matrix Initial position of the vehicle BCSS location calculate With each The shortest distance between them is denoted by a matrix. Based on the vehicle's remaining mileage Determine whether it can reach. ,like , Reachable ;otherwise, Unreachable Construct an accessibility matrix between vehicles and BCSS , among which, if Then EV i Reachable ,like but Unreachable C represents the set of reachable BCSSs for vehicles within the system; Then conduct a load-bearing capacity assessment. During the period The load-bearing capacity is: ； In the formula: As a bearing capacity assessment index, it characterizes During the period The number of EVs that can continue to be supported by battery swapping, if ,but During the period To meet the needs of battery swapping; if ,but During the period The battery swapping requirement is not met. for During the period -1 is the inventory quantity of fully charged batteries; for During the period The number of EVs requiring battery swapping.

3. The two-stage battery swapping optimization scheduling method for electric vehicles based on a cloud-based battery swapping scheduling system according to claim 2, characterized in that, The first-stage optimization specifically includes: Using the maximum battery swapping request response rate as the objective function: ； The constraints include: Battery swapping behavior constraints: ； Reachability constraints: ； User-reserved battery swapping time slot constraints: ； Charging process constraints: ； in, Let be a binary variable, representing electric vehicles. Is it within a time period? exist Battery swapping is performed if , then it means During the period At Battery swapping; otherwise, ; The total number of BCSS; Battery inventory constraints: ； ； in, express In time period Full battery inventory quantity; Charging behavior constraints: ； Charging case quantity constraints: ； In the formula, Number of charging compartments for each BCSS.

4. The two-stage battery swapping optimization scheduling method for electric vehicles based on a cloud-based battery swapping scheduling system according to claim 2, characterized in that, The two-stage optimization is specifically as follows: The objective function for the second-stage optimization is to minimize the charging cost within the BCSS scheduling cycle. ； In the formula, For time period Electricity price, ; The constraints are: Charging power constraints: Charging power does not exceed the limit at any time. ； In the formula: The rated charging power of the charging case; for Maximum charging power limit; for In time period exist The charging power; Charging compartment quantity constraint: At any given time, the number of rechargeable batteries inside the BCSS shall not exceed the number of charging compartments. ； Charging capacity constraint: Ensure that batteries removed from the battery swapping schedule can be fully charged within the specified time. exist During the time period Battery swapping involves replacing the batteries that are not yet ready to be charged, which then need to be used in the next... Charging should be completed within a specified time period, and the total charging amount should meet the following requirements. ； ； In the formula, This represents the battery charging state variable obtained in the first stage of optimization. express Internal battery The amount of electricity that needs to be charged, due to Indicates battery In time period Whether it is in a charging state, therefore express In time period The number of batteries currently charging; This refers to the charging time.

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