Idle mobile charging station scheduling method based on meeting records in electric vehicle network

Abstract

The invention discloses an idle mobile charging station scheduling method based on meeting records in an electric vehicle network, and the method comprises the following steps: S1, collecting the historical meeting records of all coordinate points in a region through mobile charging stations, and calculating the future weighted charging priority of the nearby coordinate points; s2, the cloud server calculates a regional weighted mobile charging station demand value based on the historical charging demand of each region; s3, the mobile charging station selects a target coordinate of the next time slot according to the future weighted charging priority; and the cloud server macroscopically deploys the mobile charging stations to carry out cross-regional movement in a period according to the weighted mobile charging station demand value of each region. According to the invention, through the distributed scheduling of predicting the future trajectory based on the encounter record, the temporary charging demand of the electric vehicle is met, the burden of the cloud server is significantly reduced, meanwhile, regional supply and demand imbalance is avoided through cross-regional scheduling, the overall universality of the scheme is high, and the method has extremely high use and popularization values.

Description

Technical Field

[0001] The present invention belongs to the technical field of the Internet of Things for electric vehicles, and particularly relates to a scheduling method for idle mobile charging stations based on encounter records in an electric vehicle network. Background Art

[0002] In the Internet of Things for electric vehicles, fixed charging stations are the most common charging method for electric vehicles. However, during peak charging hours, and in cases where there is a shortage of charging facilities such as on highways or in remote areas, fixed charging stations often cannot meet the charging demands of electric vehicles. To address this issue, mobile charging stations have emerged as a supplementary form to fixed charging stations. The focus of the present invention is on how to reasonably schedule idle mobile charging stations so that they can be pre-deployed in the areas where electric vehicles with insufficient battery power are located but have not received charging requests, thereby quickly meeting their charging needs.

[0003] Normally, before an electric vehicle issues a charging request, idle mobile charging stations do not actively move in its direction. However, by pre-scheduling mobile charging stations according to potential charging demands through a cloud server and actively tracking electric vehicles that may issue demands, the timely charging ratio of electric vehicles can be significantly increased, thereby enhancing the overall efficiency and revenue of the charging stations.

[0004] However, when scheduling mobile charging stations, it is inevitable to encounter uncertain factors in the environment. These factors may cause the optimal solution obtained from the original deterministic model to lose its meaning. In the Internet of Things for electric vehicles, a relatively prominent uncertain factor is the charging demand of electric vehicles, which is affected by fluctuations in multiple variables such as weather, road traffic flow, and driver travel patterns. In addition, the magnitude of the charging demand is also closely related to life factors such as peak commuting hours, holidays, and the flow of people. These dynamic changes in demand play an important role in the scheduling results of mobile charging stations. Most existing mobile charging station scheduling algorithms ignore the historical encounter data of mobile charging stations and complete the scheduling of each charging station through a cloud server, which places a great pressure on the server. Therefore, how to reduce the server pressure while effectively using historical data to predict the future charging demand distribution has become one of the common research goals of those skilled in the art. Summary of the Invention

[0005] Through the analysis of the historical encounter records of mobile charging stations, the present invention proposes a scheduling method for idle mobile charging stations based on encounter records in an electric vehicle network.

[0006] To achieve the above object, the technical solution of the present invention is as follows. A scheduling method for idle mobile charging stations based on encounter records in an electric vehicle network includes the following steps:

[0007] S1. The mobile charging station collects historical encounter records of each coordinate point in the area and calculates the future weighted charging priority of nearby coordinate points, including the following steps:

[0008] S11. Divide a city into z regions and use the set R = {R1, R2, ..., R k ,R z} indicates that R k represents region k, let R k There are (m+1)×(n+1) road intersections in total. In the tth time slot, the coordinate R k The encounter data of (i,j),i∈[0,m],j∈[0,n] is Among them ON k (i,j,t), VN k (i,j,t) and CN k (i, j, t) respectively represent the time slot t in R k The number of electric vehicles with charging needs at (i, j), the number of electric vehicles, and the number of mobile charging stations. Mobile charging stations exchange encounter data with each other when encountering other mobile charging stations. Each mobile charging station only stores the encounter data of the past τ time slots.

[0009] S12, Yizhi mobile charging stations need to go to places with high demand for charging and few mobile charging stations to maximize their profits. k The charging priority score of (i, j) in the t+1th time slot can be expressed as:

[0010]

[0011] R k The weighted charging priority prediction value of (i, j) at the t+1th time slot is expressed as:

[0012]

[0013] S2. The cloud server predicts the regional weighted mobile charging station demand based on the historical charging demand of each region. Specifically, the steps include:

[0014] S21, the cross-region scheduling period T contains α time slots, R k The weighted mobile charging station demand value of the region in the lth period is expressed as:

[0015]

[0016] Among them, CR kl R k The number of mobile charging stations in the region in the α-1th time slot of the lth cycle, OR kl R kThe number of electric vehicles in the area that generate charging demand in the l-th cycle. Predict the weighted mobile charging station demand value of the area R in the l+1-th cycle based on the past n cycles k Weighted mobile charging station demand value:

[0017]

[0018] BPW kl+1 >0 indicates that in the next cycle, area R k requires more mobile charging stations. S3. The mobile charging station selects the target coordinates for the next time slot according to the future weighted charging priority; the cloud server macroscopically coordinates the mobile charging stations to move across regions within the cycle according to the weighted mobile charging station demand values of each region, including the following steps:

[0019] S31. The mobile charging station preferentially responds to charging requests during the journey

[0020] S31. At the last time slot of each cross-region movement cycle, each region distributes the redundant charging stations in this cycle to adjacent regions according to the proportion of the weighted demand prediction value in the next cycle

[0021] S32. When a mobile charging station that does not participate in cross-region scheduling moves each time, it will select the target coordinate point with the highest future weighted charging priority for movement.

[0022] Among them, S11 classifies and quantifies the encounter data between mobile charging stations and electric vehicles in the grid road network model, and uses this as the basis for determining the future movement trajectory of the mobile charging station.

[0023] Among them, in S12, each mobile charging station needs to calculate the future weighted charging priority of nearby coordinate points at each different time slot, and the calculation process of each time slot is independent.

[0024] Among them, in S21, the cloud server will predict the weighted mobile charging station demand values of future regions according to the data of each region in the past n cycles.

[0025] Among them, in S3, the cloud server macroscopically regulates the number of mobile charging stations in each region, and the mobile charging station independently calculates its own future movement trajectory.

[0026] An electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method for scheduling idle mobile charging stations in an electric vehicle network based on encounter records.

[0027] A computer-readable storage medium, on which computer instructions are stored. When the computer instructions are executed by a processor, they implement the method for scheduling idle mobile charging stations in an electric vehicle network based on encounter records.

[0028] Compared with the prior art, on the one hand, the present invention makes full use of historical encounter records, proposes a method for calculating the future weighted charging priority of each coordinate point within a region, has a low deployment threshold, and as time goes by, the collected encounter records become more perfect, can continuously optimize the moving trajectory of mobile charging stations within the region, significantly improve the completion rate of charging orders, and at the same time, due to the distributed scheduling within the region, significantly reduce the burden on the cloud server. On the other hand, the present invention proposes a method for predicting the weighted demand of mobile charging stations in each region, and avoids the imbalance between charging supply and demand in the region through cross-regional scheduling. The calculation scheme of the predicted value of the weighted charging priority of the coordinate points given in the present invention can adapt to various complex scenarios, the overall scheme is easy to deploy, has strong universality, and can continuously optimize the moving trajectory of mobile charging stations by continuously collecting encounter records after deployment, improve the completion rate of charging orders, not only facilitate electric vehicle users but also enhance the interests of mobile charging station merchants, and has high use and promotion value. Description of the Drawings

[0029] Figure 1 Flow chart of cross-regional scheduling of mobile charging stations

[0030] Figure 2 Flow chart of independent scheduling of mobile charging stations within a region Detailed Embodiment

[0031] Embodiment: Figure 1 It is the flow chart of cross-regional scheduling of mobile charging stations. At the last time slot of the l cycle, the cloud server summarizes the number of charging orders in each region during this cycle and the number of mobile charging stations at the current time slot. Predict the weighted demand value of mobile charging stations in each region for the next cycle according to historical data, and allocate the redundant charging stations during this cycle to adjacent regions according to the proportion of the demand prediction value for the next cycle in each region.

[0032] Figure 2 It is the flow chart of independent scheduling of mobile charging stations within a region. At the t time slot, the mobile charging station collects the encounter records of the current coordinate point and exchanges historical encounter records with other mobile charging stations. Based on these records, calculate the predicted value of the weighted charging priority of the coordinate points near the next time slot, and select the target coordinate point for movement at the next time slot accordingly.

[0033] The present invention discloses a method for scheduling idle mobile charging stations based on encounter records in an electric vehicle network, including the following steps:

[0034] S1. The mobile charging station collects historical encounter records of each coordinate point within the region and calculates the future weighted charging priority of the nearby coordinate points, including the following steps:

[0035] S11. Divide a city into z regions and use the set R = {R1, R2,..., Rk , R z}, where R k represents area k. Let R k have (m + 1)×(n + 1) road intersections. In the t-th time slot, the encounter data at coordinate R k (i, j), where i ∈ [0, m] and j ∈ [0, n], is where ON k (i, j, t), VN k (i, j, t), and CN k (i, j, t) respectively represent the number of electric vehicles with charging requirements, the number of electric vehicles, and the number of mobile charging stations at R k (i, j) in the t-th time slot. Mobile charging stations exchange encounter data with each other when they meet other mobile charging stations. Each mobile charging station only stores the encounter data of the past τ time slots.

[0036] S12. It is easy to know that mobile charging stations need to go to places with high charging demand and few mobile charging stations as much as possible to maximize their own profits. The charging priority score of coordinate point R k (i, j) in the (t + 1)-th time slot can be expressed as:

[0037]

[0038] R k The predicted value of the weighted charging priority of R

[0039]

[0040] S2. Based on the historical charging demand of each area, the cloud server predicts the weighted mobile charging station demand in the area, which specifically includes the following steps:

[0041] S21. The cross-regional scheduling period T contains α time slots. R k The weighted mobile charging station demand value in the l-th cycle of the area is expressed as:

[0042]

[0043] where CR kl is the number of mobile charging stations owned by R k area in the (α - 1)-th time slot of the l-th cycle, and OR kl is the number of electric vehicles with charging requirements generated by R k area in the l-th cycle. Predict the weighted mobile charging station demand value of area R k in the (l + 1)-th cycle based on the weighted mobile charging station demand values of the past n cycles:

[0044]

[0045] BPW kl+1 >0 indicates the next cycle area R k More mobile charging stations are needed,

[0046] S3. The mobile charging station selects the target coordinates for the next time slot according to the future weighted charging priority; the cloud server macroscopically coordinates the mobile charging stations to perform cross-region movement within the cycle according to the weighted mobile charging station demand values of each region, including the following steps:

[0047] S31. The mobile charging station preferentially responds to charging requests during travel,

[0048] S31. At the last time slot of each cross-region movement cycle, each region distributes the redundant charging stations in this cycle to adjacent regions according to the proportion of the weighted demand prediction value for the next cycle,

[0049] S32. Each time a mobile charging station that does not participate in cross-region scheduling moves, it will select the target coordinate point with the highest future weighted charging priority for movement.

[0050] It should be noted that the above embodiments are not used to limit the protection scope of the present invention, and equivalent transformations or substitutions made on the basis of the above technical solutions all fall within the protection scope of the claims of the present invention.

Claims

1. A scheduling method for idle mobile charging stations based on encounter records in an electric vehicle network, characterized in that It includes the following steps: S1. The mobile charging station collects the historical encounter records of each coordinate point in the area and calculates the future weighted charging priorities of nearby coordinate points, including the following steps: S2. The cloud server predicts the regional weighted mobile charging station demand based on the historical charging demands of each area. S3. The mobile charging station selects the target coordinates for the next time slot according to the future weighted charging priorities; the cloud server macroscopically coordinates the mobile charging stations to perform cross-regional movement within the cycle according to the regional weighted mobile charging station demand values.

2. The method for scheduling idle mobile charging stations based on encounter records in an electric vehicle network according to claim 1, wherein: S1. The mobile charging station collects the historical encounter records of each coordinate point in the area and calculates the future weighted charging priorities of nearby coordinate points, including the following steps: S11. Divide a city into z regions and use the set R = {R1, R2, …, R k , R z} to represent, where R k represents region k. Suppose there are (m + 1)×(n + 1) road intersections in R k . In the t-th time slot, the encounter data at the coordinate R k (i, j), i ∈ [0, m], j ∈ [0, n] is where ON k (i, j, t), VN k (i, j, t) and CN k (i, j, t) respectively represent the number of electric vehicles with charging requirements, the number of electric vehicles, and the number of mobile charging stations at R k (i, j) in the t-th time slot. Mobile charging stations exchange encounter data with each other when they meet other mobile charging stations. Each mobile charging station only stores the encounter data of the past τ time slots. S12. It is easy to know that the mobile charging station needs to go to the places with high charging demand and few mobile charging stations as much as possible to maximize its own profit, and the coordinate point R k (i, j)'s charging priority score at the (t + 1)-th time slot can be expressed as: R k (i, j)'s predicted weighted charging priority value at the (t + 1)-th time slot is expressed as:

3. The method for scheduling idle mobile charging stations based on encounter records in an electric vehicle network according to claim 1, wherein: S2. The cloud server predicts the regional weighted mobile charging station demand based on the historical charging demands of each area, specifically including the following steps: S21. The cross-regional scheduling period T contains α time slots. The weighted demand value of mobile charging stations in region R in the l-th period is expressed as: k in the l-th period Among them, CR kl is the number of mobile charging stations owned by the R k area in the (α - 1)-th time slot of the l-th cycle, and OR kl is the number of electric vehicles with charging demands generated in the R k area in the l-th cycle. The weighted mobile charging station demand value of the R area in the (l + 1)-th cycle is predicted based on the weighted mobile charging station demand values of the past n cycles k Weighted mobile charging station demand value: BPW kl+1 > 0 indicates the next cycle area R k More mobile charging stations are needed.

4. The method for scheduling idle mobile charging stations based on encounter records in an electric vehicle network according to claim 1, wherein: S3. The mobile charging station selects the target coordinates for the next time slot according to the future weighted charging priorities; the cloud server macroscopically coordinates the mobile charging stations to perform cross-regional movement within the cycle according to the regional weighted mobile charging station demand values, including the following steps: S31. The mobile charging station preferentially responds to charging requests during travel. S31. At the last time slot of each cross-regional movement cycle, each area distributes the redundant charging stations in this cycle to adjacent areas according to the proportion of the predicted values of the weighted demands in the next cycle. S32. Each time a mobile charging station that does not participate in cross-regional scheduling moves, it will select the target coordinate point with the highest future weighted charging priority for movement.

5. The method for scheduling idle mobile charging stations based on encounter records in an electric vehicle network according to claim 2, wherein: S11 classifies and quantifies the encounter data between the mobile charging station and the electric vehicle in the grid road network model, and uses this as the basis for determining the future movement trajectory of the mobile charging station. In S12, each mobile charging station needs to calculate the future weighted charging priorities of nearby coordinate points at each different time slot, and the calculation process for each time slot is independent.

6. The method for scheduling idle mobile charging stations based on encounter records in an electric vehicle network according to claim 3, wherein: In S21, the cloud server predicts the future regional weighted mobile charging station demand values according to the data of each area in the past n cycles.

7. The method for scheduling idle mobile charging stations based on encounter records in an electric vehicle network according to claim 1, characterized in that: In S3, the cloud server macroscopically controls the number of mobile charging stations in each area, and the mobile charging stations independently calculate their own future movement trajectories.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for scheduling idle mobile charging stations based on encounter records in an electric vehicle network as described in any one of claims 1 to 7 above.

9. A computer-readable storage medium having computer instructions stored thereon, characterized in that, When the computer instruction is executed by the processor, it implements the method for scheduling idle mobile charging stations based on encounter records in an electric vehicle network as described in any one of claims 1-7.

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