Method and system for planning battery distribution path of battery swap station

Abstract

The invention relates to a battery swap station battery distribution path planning method and system, and belongs to the technical field of path planning, and the method comprises the following steps: obtaining distribution related data of to-be-distributed path planning; target functions of an optimized path are constructed according to the distribution related data, the target functions comprise a battery swap station vehicle transportation cost target function, a battery swap station distribution vehicle penalty cost target function and a battery swap station distribution vehicle total driving distance and total carbon emission target function, and the smaller the target function values are, the better the target functions are; and performing optimization solution on the target function of the optimized path according to the path constraint function, and outputting an optimal distribution path for the battery swap station to distribute vehicles. The method and the device are used for solving the problem of high distribution cost of a battery distribution station caused by long driving distance of a distribution vehicle and high carbon emission in a distribution process in an existing battery distribution path.

Description

Technical Field

[0001] The present invention relates to a method and system for planning a battery distribution path of a battery swapping station, belonging to the technical field of path planning. Background Art

[0002] With the increasingly serious environmental problems caused by social industrialization, electric heavy trucks, as the most potential and developing means of transportation at present, have more obvious characteristics of energy conservation and emission reduction compared with traditional vehicles. However, the battery endurance problem has always been an important factor restricting the development of electric heavy trucks. An electric heavy truck takes about 4-6 hours to charge once, which to a certain extent hinders the development of electric vehicles. As a new power supply mode, the battery swapping mode can shorten the battery swapping time to 3-5 minutes, greatly improving the charging and swapping efficiency, and is expected to become a very important power supply mode for the future development of electric heavy trucks. At present, there are many problems in battery distribution, such as too many distribution vehicles, temporary batteries that cannot arrive quickly in a short time, and low loading rate resulting in waste of resources. Therefore, formulating a reasonable and perfect battery distribution strategy plan is helpful for the economic benefits and service quality of the battery swapping station.

[0003] The optimization of the battery distribution path of the battery swapping station is the core of the battery distribution strategy, which has the characteristics of high modeling difficulty, complex calculation constraints, and high real-time requirements for the algorithm with time windows. Most traditional research methods study from aspects such as slow charging, fast charging, orderly, and disorderly charging methods of heavy trucks, which ensure the charging speed of heavy trucks, but there are also problems such as long distribution paths, high costs caused by distribution overtime, and high carbon emissions during the actual distribution process, which increases the cost input. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for planning a battery distribution path of a battery swapping station to solve the problem of high distribution costs of the battery distribution station caused by long driving distances of distribution vehicles, distribution overtime, and high carbon emissions during the distribution process in the existing battery distribution path.

[0005] To achieve the above purpose, the solution of the present invention includes:

[0006] A method for planning a battery distribution path of a battery swapping station according to the present invention includes the following steps:

[0007] (1) Obtain distribution-related data for the distribution path planning to be performed;

[0008] (2) Construct an objective function for optimizing the path based on the distribution-related data. The objective function for optimizing the path includes the objective function of the vehicle transportation cost at the battery swapping station, the objective function of the penalty cost for the distribution vehicle at the battery swapping station calculated based on the arrival time of the distribution vehicle, and the objective function of the total driving distance and total carbon emissions of the distribution vehicle at the battery swapping station. The smaller the value of the objective function of the vehicle transportation cost at the battery swapping station, the better the objective function; the smaller the value of the objective function of the penalty cost for the distribution vehicle at the battery swapping station, the better the objective function; the smaller the value of the objective function of the total driving distance and total carbon emissions of the distribution vehicle at the battery swapping station, the better the objective function.

[0009] (3) Optimize and solve the objective function of the optimized path according to the path constraint function, and output the optimal distribution path for the battery swapping station to dispatch vehicles.

[0010] The beneficial effects of the above technical solution are as follows: A method for planning the battery distribution path of a battery swapping station according to the present invention sets an objective function related to transportation cost, arrival time of distribution vehicles, total driving distance, and total carbon emissions, then sets a rule for selecting the optimal transportation path where the smaller the value, the better, and then combines the constraint function to optimize and solve the objective function. Finally, the optimal distribution path is output, achieving cost control in terms of transportation cost, arrival time of distribution vehicles, total driving distance, and total carbon emissions, reducing transportation cost, shortening transportation distance, improving transportation efficiency, reducing carbon emissions, and reducing environmental pollution.

[0011] Further, the objective function of the vehicle transportation cost at the battery swapping station is as follows:

[0012]

[0013] In the formula: f 1 represents the objective function of the vehicle transportation cost at the battery swapping station, N represents the number of distribution vehicles required by the battery swapping station, n represents the number of electric heavy truck user demands, d jk represents the distance between demand point j and demand point k, x ijk takes a value of 0 or 1. When the value is 1, it means that the i-th vehicle travels from j to k, c 0 represents the fixed cost of the vehicle, c ik represents the cost generated during the transportation of the vehicle between demand point j and demand point k.

[0014] The beneficial effects of the above technical solution are as follows: From the aspects of the number of distribution vehicles required by the battery swapping station, the number of electric heavy truck user demands, the fixed cost of the vehicle, and the cost generated during the transportation of the vehicle from one demand point to another, an objective function related to the transportation cost of the battery swapping station is set, making the calculation of the cost simpler and more convenient, improving the calculation efficiency, and reducing the transportation cost.

[0015] Further, the objective function of the penalty cost for the distribution vehicle at the battery swapping station is as follows:

[0016]

[0017] In the formula: f 2 is the penalty cost objective function for the distribution vehicles of the battery swapping station. μ and v represent penalty factors, and et ijk represents the earliest arrival time for delivering the i-th vehicle from demand point j to k, and rt ijk represents the arrival time for delivering the i-th vehicle from demand point j to k, and lt ijk represents the latest arrival time for delivering the i-th vehicle from demand point j to k.

[0018] The beneficial effects of the above technical solution are as follows: According to the arrival time, earliest arrival time, and latest arrival time of the vehicle from one demand point to another, a target function related to the penalty cost is set, which makes the monitoring of the vehicle transportation time more convenient, improves the calculation efficiency, and reduces the transportation cost.

[0019] Furthermore, the total driving distance and total carbon emission target functions of the distribution vehicles of the battery swapping station are as follows:

[0020]

[0021] In the formula: f 3 is the total driving distance and total carbon emission target function of the distribution vehicles of the battery swapping station. α and β are elastic factors, and d ijk represents the distance from demand point j to demand point k of vehicle i, o represents the battery swapping station distribution center, and d i，o represents the distance for the i-th vehicle to return to the distribution center, and q represents the carbon emission per unit distance.

[0022] The beneficial effects of the above technical solution are as follows: According to the distance of the vehicle from one demand point to another, the distance of a certain vehicle returning to the distribution center, and the carbon emission per unit distance, a target function related to the total driving distance and total carbon emission of the distribution vehicles of the battery swapping station is set, which facilitates the calculation of the path with the least carbon emission while meeting the vehicle transportation requirements, improves the calculation efficiency, and reduces the transportation cost.

[0023] Furthermore, the path constraint function includes:

[0024] The constraint function for restricting the weight of the goods carried by the vehicle should be less than or equal to the maximum rated load:

[0025]

[0026] In the formula: m ijk represents the load weight of the i-th vehicle from demand point j to demand point k, and w max represents the rated load;

[0027] Constraint function for restricting the number of vehicles dispatched by the battery swapping station:

[0028]

[0029] In the formula: x ijk represents the assignment times of the i-th vehicle from demand point j to demand point k, and N 0 represents the maximum assignment number of vehicles at the battery swapping station;

[0030] Constraint function to ensure that the battery carried by the vehicle is delivered and the empty vehicle finally returns to the distribution center:

[0031]

[0032] In the formula: x iio represents the i-th vehicle from demand point j to the distribution center. When the result is 1, it means the i-th vehicle arrives at the distribution center from demand point j. When the result is 0, it means the i-th vehicle does not arrive at the distribution center from demand point j;

[0033] Constraint function to ensure that there is exactly one vehicle serving the heavy truck users at the demand point:

[0034]

[0035] In the formula: It means that there is only one vehicle serving when the i-th vehicle arrives at demand point k;

[0036] Constraint function to ensure that all demand heavy truck users should be delivered to avoid omission:

[0037]

[0038] In the formula: x ij represents the assignment times of the i-th vehicle dispatched from demand point j, indicating that n vehicles are dispatched from demand point j by the i-th vehicle;

[0039] Constraint function to ensure that the arrival time of the distribution vehicle should be within the specified reasonable range, otherwise corresponding penalties should be imposed:

[0040] et ijk ≤tt ijk ≤lt ijk

[0041] et ijk represents the earliest arrival time for delivering the i-th vehicle from demand point j to k, rt ijk represents the arrival time for delivering the i-th vehicle from demand point j to k, lt iik represents the latest arrival time for delivering the i-th vehicle from demand point j to k.

[0042] The beneficial effects of the above technical solution are as follows: By setting constraint functions in various aspects, it is simpler and faster to find a suitable optimal path, simplifying the calculation steps and improving the calculation efficiency.

[0043] Further, in step (3), the ant colony algorithm is used for optimization and solution.

[0044] The beneficial effects of the above technical solution are as follows: Using the ant colony algorithm for optimization and solution simplifies the calculation steps and improves the calculation efficiency.

[0045] Further, during the process of using the ant colony algorithm for optimization and solution, the pheromone concentration update formula is as follows:

[0046]

[0047]

[0048] In the formula: l ω represents the length of the ω-th ant crawling from a certain node to a certain node; l ω-1 represents the length of the (ω - 1)-th ant crawling from a certain node to a certain node; Δτ ij is the pheromone increment, and Q is a constant.

[0049] The beneficial effects of the above technical solution are as follows: Using the pheromone concentration update formula of the ant colony algorithm for optimization and solution simplifies the calculation steps and improves the calculation efficiency.

[0050] Further, the distribution-related data includes: the location of the distribution center, the demand quantity of electric heavy truck users, the fixed cost of the vehicle, the earliest arrival time of the distribution vehicle, the latest arrival time of the distribution vehicle, the carbon emission per unit distance, the rated load, the maximum number of vehicles assigned to the swapping station, and the distribution vehicles required by the swapping model.

[0051] The beneficial effects of the above technical solution are as follows: Collecting sufficient distribution-related data facilitates subsequent calculations and improves the operation efficiency.

[0052] Further, the distribution-related data is published by the swapping station through the MQTT publish-subscribe communication mode and then forwarded to the required heavy truck users by EMQX.

[0053] The beneficial effects of the above technical solution are as follows: Based on the MQTT communication mode, information interaction between electric heavy trucks and swapping stations is realized. Based on the swapping request, the swapping station dispatches vehicles in real time to meet the needs of electric heavy truck users, making the implementation of subsequent optimization algorithms more convenient and improving the reliability.

[0054] A battery distribution path planning system for a battery swapping station, comprising a processor and a memory, where the processor is configured to execute computer program instructions stored in the memory to implement a battery distribution path planning method for a battery swapping station introduced above. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a MQTT publish-subscribe communication model diagram in an embodiment of the present invention;

[0056] Figure 2 It is a schematic diagram of battery distribution for a battery swapping station in an embodiment of the present invention;

[0057] Figure 3 It is a flow chart of solving the optimization algorithm of the path optimization model for a battery swapping station in an embodiment of the present invention;

[0058] Figure 4 It is the optimal distribution path diagram of the traditional algorithm in an embodiment of the present invention;

[0059] Figure 5 It is the optimal distribution path diagram of the optimization algorithm in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments.

[0061] To save the transportation costs of enterprises, improve the utilization efficiency of vehicles, achieve reasonable allocation of resources, alleviate traffic congestion, and reduce the waiting time of users. The present invention proposes a battery distribution path planning method and system for a battery swapping station. Based on the real-time model of electric heavy truck reservation for battery swapping, it realizes the information interaction between heavy truck users and the battery swapping station. By introducing an optimization algorithm to solve the vehicle scheduling problem, it enables enterprises to efficiently complete the battery distribution task, achieving energy consumption reduction, significantly reducing the driving mileage, reducing the labor intensity of distribution personnel, improving customer satisfaction, and at the same time contributing to the development of the new energy industry, greatly improving the intelligent level of the logistics system distribution and enhancing the urban distribution efficiency. To achieve the above objectives, the present invention provides a path planning strategy method that reduces the distribution cost of the battery distribution station, shortens the total driving distance of distribution vehicles, and reduces the carbon emissions during the entire distribution process under the condition of meeting the battery swapping needs of electric heavy truck users. The specific solutions are as follows:

[0062] Method Embodiment:

[0063] A battery distribution path planning method of the present invention includes:

[0064] 1. Construct a real-time communication model between heavy truck users and the battery swapping station:

[0065] Based on the MQTT publish-subscribe communication mode, this invention constructs a communication model between heavy truck users and battery swapping stations to achieve communication and interaction between battery swapping stations and electric heavy trucks. The EMQX (message server) is responsible for all message routing and distribution. The battery swapping station, as the message publisher, sends messages with the battery swapping Topic to the EMQX, and the electric heavy truck receives message information by subscribing to the topic. In the publish-subscribe mode, the battery swapping station can act as both a message publisher and a subscriber. When the electric heavy truck publishes a Topic, the Topic is forwarded to the EMQX, and then the EMQX routes and forwards the message to all Subscribers (appointees) of that Topic. Based on the MQTT communication mode, information interaction between the electric heavy truck and the battery swapping station is achieved. Based on the battery swapping request Topic, the battery swapping station dispatches vehicles in real time to meet the needs of electric heavy truck users. The vehicles at the battery swapping station are dispatched through an optimized algorithm. For the specific implementation process, please refer to the appendix Figure 1 。

[0066] 2. Based on the communication interaction model, construct a battery path optimization strategy method:

[0067] 1) Construct the objective function of the transportation cost of vehicles at the battery swapping station:

[0068]

[0069] Among them, f 1 represents the objective function of the transportation cost of vehicles at the battery swapping station, N represents the number of distribution vehicles required by the battery swapping station, n represents the number of electric heavy truck user demands, d jk represents the distance between demand point j and demand point k, x ijk takes a value of 0 or 1. When the value is 1, it means that the i-th vehicle travels from j to k, c 0 represents the fixed cost of the vehicle, c jk represents the cost generated during the transportation of the vehicle between demand point j and demand point k.

[0070] 2) Construct the objective function of the penalty cost of the distribution vehicles at the battery swapping station:

[0071] f 2 = μ∑ i ∑ j ∑ k max[(et i - rt i ),0]+ v∑ i ∑ j ∑ k max[(rt i - lt i ),0] (2)

[0072] Among them, f 2is the penalty cost objective function for the distribution vehicles of the battery swapping station. μ and v represent the penalty factors, and et ijk represents the earliest arrival time for delivering the i-th vehicle from demand point j to k, and rt ijk represents the arrival time for delivering the i-th vehicle from demand point j to k, and lt ijk represents the latest arrival time for delivering the i-th vehicle from demand point j to k.

[0073] 3) Construct the objective functions for the total driving distance and total carbon emissions of the distribution vehicles of the battery swapping station:

[0074] f 3 = α(∑ i ∑ j ∑ k d ijk +∑ i d i，o ) + β(q(∑ i ∑ j ∑ k d ijk +∑ i d i，o )) (3)

[0075] where f 3 is the objective function for the total driving distance and total carbon emissions of the distribution vehicles of the battery swapping station. α and β are elastic factors, and d ijk represents the distance from demand point j to demand point k for vehicle i, o represents the battery swapping station distribution center, and d i，o represents the distance for the i-th vehicle to return to the distribution center, and q represents the carbon emissions per unit distance.

[0076] 4) Construct the overall objective function for optimizing the distribution path of the battery swapping path model:

[0077] F = min(f 1 + f 2 + f 3 ) (4)

[0078] 5) Construct the constraint conditions of the battery swapping path model:

[0079] ∑ i ∑ j ∑ k m ijk ≤ w max (5)

[0080] Equation (5) restricts that the weight of the goods carried by the vehicle should be less than or equal to the maximum rated load, where w max represents the rated load.

[0081] ∑ i ∑ j ∑ kx ijk ≤N 0 (6)

[0082] Equation (6) restricts that the vehicles dispatched by the battery swapping station should be less than the maximum number of vehicles, where N 0 represents the maximum number of vehicle assignments at the battery swapping station.

[0083] m i，o =0 (7)

[0084] Equation (7) ensures that the batteries carried by the vehicle when it returns empty to the distribution center are all delivered, where m i，o represents the weight of the batteries carried when returning to the distribution center.

[0085]

[0086] Equation (8) ensures that when the vehicle departs from the battery swapping station and the number of heavy truck users it serves is 0, it means this vehicle is not used.

[0087] ∑ i ∑ k z ik =1 (9)

[0088] Equation (9) ensures that there is exactly one vehicle serving the heavy truck users at the demand point, avoiding being delivered by multiple vehicles simultaneously.

[0089] ∑ i ∑ j x ij =n (10)

[0090] Equation (10) means that all heavy truck users in demand should be delivered, avoiding omissions.

[0091] et i ≤rt i ≤lt i (11)

[0092] Equation (11) means that the arrival time of the delivery vehicle should be within the specified reasonable range, otherwise corresponding penalties should be imposed.

[0093] 6) Based on the battery swapping path model provided above, the present invention uses the ant colony algorithm (ACO) to solve the problem. ACO has strong robustness and can search for solutions simultaneously at multiple points in the problem space, and is widely used in vehicle path optimization research. The traditional ACO algorithm has the problem of low search efficiency. To enable the algorithm to search for the shortest path more efficiently, the present invention optimizes the update rule of pheromone, and the specific expression is as follows:

[0094]

[0095]

[0096] Among them, Δτ ij (i) represents the pheromone increment, and Q represents a constant. If I ω-1 is greater than I ω , then η > 1, and at this time, the pheromone increment of I ω should be increased. On the contrary, if I ω-1 is less than I ω , then η < 1, and at this time, the pheromone increment of I ω should be decreased. The specific solution steps of the optimization algorithm are as Figure 3 .

[0097] 7) Based on an engineering example, input the coordinates of the distribution center and each demand point, the battery demand, as well as the left and right time windows and service time corresponding to each heavy truck demand point, and solve through the optimized ACO. The solution process steps of the ant colony algorithm for battery distribution paths are as shown in the appendix Figure 3 , and the distribution path of the battery swapping station is in the form of Figure 2 .

[0098] The distribution path before the optimization of the ant colony algorithm is as Figure 4 , and the optimal distribution path after the optimization algorithm of the ant colony algorithm is as Figure 5 . It can be seen by comparison that the path after the optimization algorithm of the ant colony algorithm is significantly shorter, and the cost is also correspondingly reduced. The following is a comparison table of the distribution costs before and after the algorithm optimization.

[0099] The comparison table of the distribution costs before and after the algorithm optimization is shown in Table 1 below:

[0100] Table 1 Comparison table of distribution costs before and after algorithm optimization

[0101]

[0102]

[0103] The comparison table of the path optimization distribution before and after the algorithm improvement is shown in Table 2 below:

[0104] Table 2 Path optimization distribution table before and after algorithm improvement

[0105]

[0106] System embodiment:

[0107] A battery distribution path planning system for a battery swapping station includes a processor and a memory. The processor is used to execute computer program instructions stored in the memory to implement a battery distribution path planning method for a battery swapping station. A battery distribution path planning method for a battery swapping station has been introduced clearly enough in the method embodiment and will not be elaborated here.

Claims

1. A method for planning the battery distribution path of a battery swapping station, characterized in that, it includes the following steps: (1) Obtain the distribution-related data for the distribution path planning to be carried out; (2) Construct an objective function for optimizing the path according to the distribution-related data. The objective function for optimizing the path includes the vehicle transportation cost objective function of the battery swapping station, the penalty cost objective function of the distribution vehicle of the battery swapping station calculated according to the arrival time of the distribution vehicle, and the total driving distance and total carbon emission objective function of the distribution vehicle of the battery swapping station. Moreover, the smaller the value of the vehicle transportation cost objective function of the battery swapping station, the better the objective function; the smaller the value of the penalty cost objective function of the distribution vehicle of the battery swapping station, the better the objective function; the smaller the value of the total driving distance and total carbon emission objective function of the distribution vehicle of the battery swapping station, the better the objective function; (3) Optimize and solve the objective function for the optimized path according to the path constraint function, and output the optimal distribution path for the battery swapping station to dispatch vehicles.

2. The method for planning the battery distribution path of a battery swapping station according to claim 1, characterized in that, the vehicle transportation cost objective function of the battery swapping station is as follows: Where: f 1 represents the objective function of the vehicle transportation cost of the battery swapping station, N represents the distribution vehicles required by the battery swapping station, n represents the demand quantity of electric heavy truck users, and d jk represents the distance between demand point j and demand point k, and x ijk takes a value of 0 or 1. When the value is 1, it means that the i-th vehicle travels from j to k, and c 0 represents the fixed cost of the vehicle, and c jk represents the cost generated during the transportation of the vehicle between demand point j and demand point k.

3. The method for planning the battery distribution path of a battery swapping station according to claim 1, characterized in that, the penalty cost objective function of the distribution vehicle of the battery swapping station is as follows: Where: f 2 is the penalty cost objective function of the distribution vehicle of the battery swapping station, μ and v represent penalty factors, and et ijk represents the earliest arrival time for the i-th vehicle to travel from demand point j to k, and rt ijk represents the arrival time for the i-th vehicle to travel from demand point j to k, and lt ijk represents the latest arrival time for the i-th vehicle to travel from demand point j to k.

4. The method for planning the battery distribution path of a battery swapping station according to claim 1, characterized in that, the total driving distance and total carbon emission objective function of the distribution vehicle of the battery swapping station are as follows: Where: f 3 is the total driving distance and total carbon emission target function of the distribution vehicles in the battery swapping station, α and β are elasticity factors, d ijk represents the distance from demand point j to demand point k by vehicle i, o represents the battery swapping station distribution center, d i,o represents the distance for the i-th vehicle to return to the distribution center, and q represents the carbon emission per unit distance.

5. The method for planning the battery distribution path of a battery swapping station according to claim 1, characterized in that, the path constraint function includes: The constraint function that the weight of the goods carried by the vehicle should be less than or equal to the maximum rated load: where: m ijk represents the load weight of the i-th vehicle from demand point j to demand point k, and w max represents the rated load; The constraint function that restricts the number of vehicles dispatched by the battery swapping station: Where: x ijk represents the assignment times of the i-th vehicle from demand point j to demand point k, and N 0 represents the maximum assignment number of vehicles at the battery swapping station; The constraint function that ensures that the battery carried by the vehicle is delivered and the vehicle finally returns to the distribution center empty: where: x ijo represents that the i-th vehicle travels from demand point j to the distribution center. When the result is 1, it means the i-th vehicle arrives at the distribution center from demand point j. When the result is 0, it means the i-th vehicle does not arrive at the distribution center from demand point j; The constraint function that ensures that there is exactly one vehicle serving the heavy truck users at the demand point: In the formula: represents that there is only one vehicle serving at the k-th demand point when the i-th vehicle arrives; The constraint function that represents that all demand heavy truck users should be distributed to avoid omission: where: x ij represents the assignment times of the i-th vehicle dispatched from the demand point j, indicating that n vehicles are dispatched from the demand point j by the i-th vehicle; The constraint function that represents that the arrival time of the distribution vehicle should be within the specified reasonable range, otherwise corresponding penalties should be imposed: and ijk ≤rt ijk ≤lt ijk et ijk represents the earliest arrival time when the $i$-th vehicle for distribution travels from demand point $j$ to $k$, $rt$ ijk represents the arrival time when the $i$-th vehicle for distribution travels from demand point $j$ to $k$, $lt$ ijk represents the latest arrival time when the $i$-th vehicle for distribution travels from demand point $j$ to $k$.

6. The method for planning the battery distribution path of a battery swapping station according to claim 1, characterized in that, in step (3), the ant colony algorithm is used for optimization and solution.

7. The method for planning the battery distribution path of a battery swapping station according to claim 6, characterized in that, during the process of using the ant colony algorithm for optimization and solution, the pheromone concentration update formula is as follows: where: l ω represents the length that the ω-th ant crawls from a certain node to a certain node; l ω-1 represents the length that the (ω - 1)-th ant crawls from a certain node to a certain node; Δτ ij is the pheromone increment, and Q is a constant.

8. The method for planning the battery distribution path of a battery swapping station according to claim 1, characterized in that, the distribution-related data includes: the location of the distribution center, the demand quantity of electric heavy truck users, the fixed cost of the vehicle, the earliest arrival time of the distribution vehicle, the latest arrival time of the distribution vehicle, the carbon emission per unit distance, the rated load, the maximum number of vehicles assigned by the battery swapping station, and the distribution vehicles required by the battery swapping model.

9. The method for planning the battery distribution path of a battery swapping station according to claim 8, characterized in that, the distribution-related data is published by the battery swapping station through the MQTT publish-subscribe communication mode and then forwarded by EMQX to the heavy truck users in need.

10. A battery distribution path planning system for a battery swapping station, characterized in that, it includes a processor and a memory, and the processor is used to execute computer program instructions stored in the memory to implement the battery distribution path planning method for a battery swapping station according to any one of claims 1-9.

Images