Mobile energy storage vehicle power supply service extension method and system based on replaceable battery

Abstract

A mobile energy storage vehicle power supply service prolonging method and system based on a replaceable battery, the method comprises the steps of firstly determining power consumption demands of power consumption areas and power supply service duration of energy storage vehicles, constructing a power supply demand prediction table, then distributing the energy storage vehicles for each power consumption area, and based on the current residual electric quantity of the energy storage vehicles and the power supply demand table, determining the power supply service duration of the energy storage vehicles; judging whether the energy storage vehicles need to be supplemented with energy or not, then planning a shortest path by the control center based on an improved Dijkstra algorithm, and dispatching maintenance vehicles to sequentially go to the mobile energy storage vehicles needing to be supplemented with energy according to the shortest path to supplement electric energy; through the design of secondary power supply, under the condition that the cost of the standby energy storage vehicle is greatly reduced, the possibility of power supply interruption caused by limited electric energy of the mobile energy storage vehicle is reduced, the power supply service time of the mobile energy storage vehicle is prolonged, and the power supply efficiency is improved through an improved path planning algorithm. The defect that the traditional Dijkstra algorithm cannot process the path point problem is overcome, and the optimal energy complementing path is accurately provided for the mobile energy storage vehicle.

Description

Technical Field

[0001] The present invention relates to a power supply replenishment means, belonging to the field of energy supply of mobile energy storage devices, and particularly relates to a method and system for extending the power supply service of a mobile energy storage vehicle based on replaceable batteries. Background Art

[0002] With the continuous optimization of the energy power generation structure in recent years, the proportion of non-fossil energy in the total installed capacity has been continuously increasing; however, new energy is prone to being interfered by natural conditions during the power generation process, resulting in problems such as power outages or fluctuations. In order to ensure the stable operation of the system, it is necessary to use an energy storage system to control the system and solve technical defects.

[0003] Existing energy storage devices have been widely used in the power system. As a short-term energy storage device, a mobile energy storage vehicle can relieve line transmission congestion by transmitting electric energy across nodes, and the mobile energy storage vehicle can move across different systems within the power system to adapt to seasonal or long-term changes in renewable resources. Through the charging and discharging of the mobile energy storage vehicle, the ability to cut peaks and fill valleys can be increased, bringing flexibility to the service of the power system.

[0004] Since the energy that a single mobile energy storage vehicle can carry is limited, in actual work, there may be a situation where the electric energy cannot meet the user's needs and power supply interruption occurs. Although a supplementary mobile energy storage vehicle can be used to replace the energy-deficient energy storage vehicle to provide continuous power supply service, in the case of multiple mobile energy storage vehicles working simultaneously, the cost of the standby energy storage vehicle is too high. Therefore, compared with the supplementary scheme, a mobile energy storage vehicle with a replaceable battery can ensure sufficient electric energy by replacing the battery, thereby extending its working time and providing stable and sufficient electric energy; however, this involves the path planning problem of the mobile energy storage vehicle. The traditional Dijkstra algorithm can only find the shortest path from the initial node to the termination node, and cannot ensure that the found shortest path contains several waypoints that we are interested in. There is a defect that the path planning cannot handle waypoints, and the path planning accuracy is not high. Summary of the Invention

[0005] The purpose of the present invention is to overcome the above-mentioned defects and problems in the prior art, and provide a method and system for extending the power supply service of a mobile energy storage vehicle based on replaceable batteries with relatively high accuracy.

[0006] To achieve the above purpose, the technical solution of the present invention is: A method for extending the power supply service of a mobile energy storage vehicle based on replaceable batteries, comprising:

[0007] S1. Determine the power consumption demand within the power consumption area and the power supply service duration of the mobile energy storage vehicle, and construct a power supply demand prediction table;

[0008] S2. Allocate at least one mobile energy storage vehicle to each power consumption area, and based on the current remaining power of the mobile energy storage vehicle and the power supply demand table, sequentially determine whether all mobile energy storage vehicles need to be recharged;

[0009] S3. When the maintenance vehicle is not performing a recharging task and there are mobile energy storage vehicles that need to be recharged, the control center plans the shortest path based on the improved Dijkstra algorithm and sends out the maintenance vehicle to go to the mobile energy storage vehicles that need to be recharged in sequence according to the shortest path for power replenishment.

[0010] The specific steps of step S1 include:

[0011] S11. Based on the historical power consumption records of the power consumption area, determine the power consumption demand value of the power consumption area and divide it into M demand mobile energy storage vehicle power supply areas, each area is represented by m = 1,..., M;

[0012] S12. Determine the power supply service duration T total , and divide it into T time periods at equal time intervals Δ t , each time period is represented by t = 1,..., T;

[0013] S13. Quantify the specific power consumption demand of each demand power supply area within the power supply service duration to obtain the power supply demand prediction table.

[0014] The specific steps of step S2 include:

[0015] S21. At the end of the t time periods, each mobile energy storage vehicle predicts the predicted remaining power E rest (m) after two maintenance vehicle recharging cycles according to the current remaining power E(t, m) and the power supply demand table d(t, m), and its expression is as follows:

[0016]

[0017] Among them: E(t, m) is the current power of the mth mobile energy storage vehicle at the end of the tth time period; C is the one-time maintenance cycle of the maintenance vehicle;

[0018] S22. Compare the predicted remaining power E rest (m) with the set minimum power threshold V; if the predicted remaining power E rest (m) is lower than the minimum power threshold V, then the mobile energy storage vehicle needs to be recharged.

[0019] The steps of planning the shortest path based on the improved Dijkstra algorithm in step S3 are as follows:

[0020] S31. Establish a weighted undirected graph based on the map of the power consumption area. Set the control center and all demand power supply areas as the nodes of the graph, and set the weights between every two nodes.

[0021] When there is a path between two nodes, the weight between the two nodes is recorded as the actual distance of the path between the two nodes; when there is no path between two nodes, the weight is recorded as infinity.

[0022] S32. Record the nodes corresponding to all mobile energy storage vehicles that need to be recharged as waypoints, and record the node where the control center is located as the initial node. Based on Dijkstra's algorithm, find the shortest path and the corresponding weight between any two nodes among the waypoints and the initial node.

[0023] S33. Find all path sequences that all waypoints may form, add the initial node to the head and tail of each path sequence respectively, and calculate the total weight required for each path sequence.

[0024] S34. Based on the path sequence with the lowest total weight among all path sequences, output the specific path corresponding to the path sequence as the shortest path search result; if there are multiple path sequences with the lowest total weight, arbitrarily select one of them as the output result.

[0025] A power supply service extension system for mobile energy storage vehicles based on replaceable batteries, the system includes:

[0026] A power supply demand confirmation module, used to determine the power supply demand and the power supply service duration of the mobile energy storage vehicle in the power consumption area, and construct a power supply demand prediction table.

[0027] A recharge demand confirmation module, used to allocate at least one mobile energy storage vehicle to each power consumption area, and based on the current remaining power of the mobile energy storage vehicle and the power supply demand table, sequentially determine whether all mobile energy storage vehicles need to be recharged.

[0028] A path planning module, used when the maintenance vehicle does not perform a recharge task and there are mobile energy storage vehicles that need to be recharged, the control center plans the shortest path based on the improved Dijkstra's algorithm, and sends the maintenance vehicle to the mobile energy storage vehicles that need to be recharged in turn according to the shortest path for power replenishment.

[0029] The power supply demand confirmation module is used to confirm the power supply demand according to the following steps:

[0030] S11. Based on the historical power consumption records of the power consumption area, determine the power supply demand value of the power consumption area, and divide it into M demand mobile energy storage vehicle power supply areas, each area is represented by m = 1,..., M.

[0031] S12. Determine the power supply service duration T of the mobile energy storage vehicletotal and divide it into T time periods at equal time intervals Δ t denoted as t = 1, ..., T for each time period;

[0032] S13. Quantify the specific power consumption demand of each power supply demand area within the power supply service duration to obtain a power supply demand prediction table.

[0033] The energy replenishment demand confirmation module is used to confirm the energy replenishment demand according to the following steps:

[0034] S21. At the end of the t-th time period, each mobile energy storage vehicle predicts the predicted remaining power E rest (m) after two maintenance vehicle energy replenishment cycles based on the current remaining power E(t, m) and the power supply demand table d(t, m), and its expression is as follows:

[0035]

[0036] where: E(t, m) is the current power of the m-th mobile energy storage vehicle at the end of the t-th time period; C is the one-time maintenance cycle of the maintenance vehicle;

[0037] S22. Compare the predicted remaining power E rest (m) with the set minimum power threshold V; if the predicted remaining power E rest (m) is lower than the minimum power threshold V, then the mobile energy storage vehicle needs to be replenished with energy.

[0038] The path planning module is used to plan the shortest path according to the following steps:

[0039] S31. Based on the map of the power consumption area, establish a weighted undirected graph, establish the control center and all power supply demand areas as the nodes of the graph, and set the weight value between every two nodes;

[0040] When there is a path between two nodes, the weight value between the two nodes is recorded as the actual distance of the actual road between the two nodes; when there is no path between two nodes, the weight value is recorded as infinity;

[0041] S32. Record the nodes corresponding to all mobile energy storage vehicles that need to be replenished with energy as waypoints, and record the node where the control center is located as the initial node; based on Dijkstra's algorithm, find the shortest path and the corresponding weight value between any two nodes among the waypoints and the initial node;

[0042] S33. Find all path sequences that all waypoints may form, add the initial node to the head and tail of each path sequence respectively, and calculate the total weight value required for each path sequence;

[0043] S34. Based on the path sequence with the lowest total weight among all path sequences, output the corresponding specific path as the shortest path search result; if there are multiple path sequences with the lowest total weight, arbitrarily select one of them as the output result.

[0044] A power supply service extension device for a mobile energy storage vehicle with replaceable batteries, the device includes a processor and a memory;

[0045] The memory is used to store computer program code and transmit the computer program code to the processor;

[0046] The processor is used to execute the above-mentioned power supply service extension method for a mobile energy storage vehicle with replaceable batteries according to the instructions in the computer program code.

[0047] A computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed on a computer, the above-mentioned power supply service extension method for a mobile energy storage vehicle with replaceable batteries is implemented.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] A power supply service extension method and system for a mobile energy storage vehicle with replaceable batteries according to the present invention. The method first determines the power consumption demand in the power consumption area and the power supply service duration of the energy storage vehicle, constructs a power supply demand prediction table, then allocates energy storage vehicles to each power consumption area, and based on the current remaining power of the energy storage vehicle and the power supply demand table, determines whether all energy storage vehicles need to be replenished. Then, the control center plans the shortest path based on the improved Dijkstra algorithm and sends out maintenance vehicles to go to the mobile energy storage vehicles that need to be replenished in turn according to the shortest path for power replenishment; in the application of this design, through the design of secondary power supply, while greatly reducing the cost of standby energy storage vehicles, the possibility of power supply interruption due to limited power of mobile energy storage vehicles is reduced, the power supply service time of mobile energy storage vehicles is extended, and through the improved path planning algorithm, the defect that the traditional Dijkstra algorithm cannot handle the problem of passing points is solved, and the best replenishment path for mobile energy storage vehicles is provided accurately and efficiently, ensuring the stability of the power supply replenishment system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is the flowchart of the method of the present invention.

[0051] Figure 2 is the weighted undirected graph and shortest path schematic diagram in Embodiment 1 of the present invention.

[0052] Figure 3 is the weighted undirected graph of the standard Dijkstra algorithm in Embodiment 1 of the present invention.

[0053] Figure 4 It is the system structure diagram of the present invention.

[0054] Figure 5 It is the device structure diagram of the present invention.

[0055] In the figure: power supply demand confirmation module 1, energy replenishment demand confirmation module 2, path planning module 3, processor 4, memory 5, computer program code 51. Detailed implementation manners

[0056] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0057] Embodiment 1:

[0058] In this solution, a two - level power supply mechanism is mainly designed. The primary power supply is that the mobile energy storage vehicle provides uninterrupted power supply services in the working area. Since the electric energy that a single mobile energy storage vehicle can store is limited, it may lead to the consequence of power interruption. This is unacceptable in some scenarios. Therefore, to avoid the phenomenon of power interruption, a secondary power supply is designed: a maintenance vehicle carrying batteries periodically replenishes the mobile energy storage vehicles lacking electric energy. In this way, the working hours of all mobile energy storage vehicles can be effectively extended to meet the power demand during the entire working time. Since the maintenance vehicle needs to replace the batteries of each mobile energy storage vehicle for energy replenishment as soon as possible, it is very important to find the shortest path starting from the control center, replenishing each mobile energy storage vehicle lacking electric energy in turn, and finally returning to the control center. Such a path planning problem can be summarized as the shortest path planning problem with waypoints. Since the standard Dijkstra algorithm cannot handle waypoints, this solution improves the algorithm to obtain the solution of the shortest path planning problem with waypoints. This solution only needs to introduce an additional maintenance vehicle, which carries batteries to periodically replace the batteries of the mobile energy storage vehicles with insufficient electric energy, and can effectively improve the working hours of multiple mobile energy storage vehicles at a relatively low cost.

[0059] The general energy replenishment logic of this solution is as follows: First, based on past power demand data and users' electricity usage applications, determine the areas that need to be powered by mobile energy storage vehicles, and draw them into a power supply demand prediction table; then allocate a mobile energy storage vehicle to each area with power supply demand, reach the designated location before the power supply service starts and provide power supply services. All energy storage vehicles will periodically judge whether they need to replace the battery according to the remaining power and the power supply demand prediction table, and feedback to the control center; finally, the control center will also periodically detect whether there are mobile energy storage vehicles that need to replace the battery. When there are energy storage vehicles with insufficient power, the maintenance vehicle will be activated to replace the batteries of these energy storage vehicles in turn according to the principle of the shortest path to increase the working duration of the energy storage vehicle power supply system; based on the above energy replenishment logic, this solution adopts the following technical means to achieve:

[0060] See Figure 1 , a method for extending the power supply service of a mobile energy storage vehicle based on replaceable batteries, including:

[0061] S1. Determine the power demand within the power consumption area and the power supply service duration of the mobile energy storage vehicle, and construct a power supply demand prediction table;

[0062] In this embodiment, first determine the specific power demands within the blocks that the mobile energy storage vehicle needs to serve. These demands usually concentrate in some areas, such as squares where performances are needed, parking lots with insufficient charging facilities, and communities with power demands but temporary power outages, etc. Within these areas, the power demand is expressed as a function of time, and this function is usually predictable. The normalized power demand can be obtained by analyzing past demand data; for example, the charging demand of a certain parking lot generally does not change much; the sudden power demand, such as a square where a performance is needed, can be obtained through the independent reporting of the demand side and scientific evaluation by the power supply side, and its power demand can be obtained by comparing with performances of similar scales. Assume that there are a total of M area locations in the served block that need to be powered by energy storage vehicles, and the power supply service time of the energy storage vehicle is T hours. Then it can be divided into T time periods at one-hour intervals, and the power supply demand of the energy storage vehicle in each area within each time period is quantified, and a power supply demand table can be obtained. The specific steps are as follows:

[0063] S11. Based on the historical power consumption records of the power consumption area, determine the power demand value of the power consumption area, and divide it into M areas that need mobile energy storage vehicle power supply, and each area is represented by m = 1,..., M;

[0064] S12. Determine the power supply service duration T total , and divide it into T time periods at equal time intervals Δ t , and each time period is represented by t = 1,..., T;

[0065] S13. Quantify the specific electricity demands of each demand power supply area within the power supply service duration to obtain a power supply demand prediction table, as shown in the following table:

[0066]

[0067] Among them, table(T, M) represents the specific electricity demand value of the Mth area in the tth time period.

[0068] S2. Allocate at least one mobile energy storage vehicle to each power consumption area, and based on the current remaining power of the mobile energy storage vehicle and the power supply demand table, sequentially determine whether all mobile energy storage vehicles need to be replenished with energy;

[0069] In this embodiment, it is assumed that there are a total of m mobile energy storage vehicles that can provide power supply services for these areas, and each area in need of power supply corresponds to one energy storage vehicle; before the start of each power supply service, that is, before t = 1, each mobile energy storage vehicle will depart from the control center and arrive at the designated location in the area in need of power supply to start providing charging and power supply services. It is assumed that each energy storage vehicle maintains a full charge state before providing charging services, that is, the initial energy of the mth mobile energy storage vehicle is E(0, m) = E total , m = 1,..., M, where E total , m is the remaining energy of the mth mobile energy storage vehicle at the end of the tth time period.

[0070] Since the electric energy that a single energy storage vehicle can carry is limited, it may not be able to meet all the demands of the served area, resulting in a power supply interruption problem. Therefore, this solution adopts a mobile energy storage vehicle with a quickly replaceable battery. When its remaining electric energy is insufficient, the maintenance vehicle can replace the battery for it to extend the working duration and ensure the sustainability of the charging service. Therefore, at the end of each time period, each energy storage vehicle will predict the remaining energy E rest (m) after two maintenance vehicle operation cycles based on the current remaining electric energy E(t, m) and the power supply demand table table. The specific steps are as follows:

[0071] S21. At the end of the tth time period, each mobile energy storage vehicle predicts the predicted remaining power E rest (m) after two maintenance vehicle replenishment cycles according to the current remaining power E(t, m) and the power supply demand table d(t, m). Its expression is as follows:

[0072]

[0073] Among them: E(t, m) is the current power of the mth mobile energy storage vehicle at the end of the tth time period; C is a maintenance cycle of the maintenance vehicle, preferably an integer multiple of the time period t;

[0074] S22. Compare the predicted remaining power E rest(m) is compared with the set minimum power threshold V; if the predicted remaining power E rest (m) is lower than the minimum power threshold V, then the mobile energy storage vehicle needs to be replenished with energy.

[0075] When the predicted remaining electric energy E rest (m) is lower than the threshold V, the energy storage vehicle will send a request to the control center to replace the battery. The value of V should be greater than 0 to cope with the situation where the actual power supply demand fluctuates. The first prediction of the remaining energy occurs at t = 1, and the last prediction of the remaining energy will occur at t = T - 2C. This is because at t = T - 2C, if the predicted remaining energy of the energy storage vehicle is still greater than the threshold V, it means that its remaining energy is sufficient to support the remaining power supply demand in the current area.

[0076] S3. When the maintenance vehicle is not performing the energy replenishment task and there are mobile energy storage vehicles that need to be replenished with energy, the control center plans the shortest path based on the improved Dijkstra algorithm and sends the maintenance vehicle to the mobile energy storage vehicles that need to be replenished with energy in turn according to the shortest path to replenish the electric energy.

[0077] In this embodiment, during these time periods of t ∈ [2, T - 2C], the control center will count the requests to replace the battery at the beginning of each time period. If there are no energy storage vehicles with insufficient electric energy, the control center will not take any measures and wait for the next time period. If there are energy storage vehicles with insufficient electric energy, the control center will record the locations of these energy storage vehicles in the waypoint list, and find a shortest path from the control center for the maintenance vehicle through the improved Dijkstra algorithm, which passes through these waypoints one by one and then returns to the control center, and then sends the maintenance vehicle to perform the task of replacing the battery. During the period when the maintenance vehicle is performing the task, the control center will not process the request to replace the battery until the maintenance vehicle completes a round of battery replacement tasks and then will reprocess the requests from the mobile energy storage vehicles. The specific steps of the improved Dijkstra algorithm are as follows:

[0078] S31. Establish a weighted undirected graph based on the map of the power consumption area, establish the control center and all the required power supply areas as the nodes of the graph, and set the weights between every two nodes;

[0079] When there is a path between two nodes, the weight between the two nodes is recorded as the actual distance of the path between the two nodes; when there is no path between two nodes, the weight is recorded as infinity;

[0080] S32. Record the nodes corresponding to all the mobile energy storage vehicles that need to be replenished with energy as waypoints, and record the node where the control center is located as the initial node; based on the Dijkstra algorithm, find the shortest path and the corresponding weights between any two nodes among the waypoints and the initial node, and record them in a table;

[0081] S33. Find all path sequences that all waypoints may form, with a total of , where n is the number of waypoints; and add the initial node to the head and tail of each path sequence respectively, because the path sequence starts and ends with the initial node. Then, according to the table in step S32, calculate the total weight required for each path sequence and record it;

[0082] S34. Based on the path sequence with the lowest total weight among all path sequences, and using the table in step S32, find the specific path corresponding to this path sequence and output it as the shortest path search result; if there are multiple path sequences with the lowest total weight, the optimal solution to this problem is not unique, then arbitrarily select one of them as the output result.

[0083] Based on the above method, an example simulation was carried out in this embodiment, as follows:

[0084] First, assume that there are a total of 9 areas in the served block that require mobile energy storage vehicles to supply power, and the power supply demands in each time period are shown in the following table:

[0085]

[0086] Assume that the upper limit of the battery capacity of each mobile energy storage vehicle is E total = 100 kw·h, the maintenance period of the maintenance vehicle is C = Δ t , and the lowest power threshold is V = 20 kw·h; at the end of the t = 1 time period, each mobile energy storage vehicle will detect whether the current remaining power can meet the power demand in the next two maintenance vehicle cycles. Assume that the actual remaining power is the same as the value obtained by subtracting the power demand in the first time period from the upper limit of the mobile energy storage vehicle's power (in fact, it may be different), then the predicted results are shown in the following table:

[0087] Region 1 2 3 4 5 6 7 8 9 <![CDATA[E rest (m)]]> 25 15 35 40 15 15 35 30 60

[0088] Among them, the predicted remaining electric energy in areas 2, 5, and 6 is lower than the threshold V. Therefore, the maintenance vehicle needs to replenish the corresponding mobile energy storage vehicle; convert the map of the served block (i.e., the power consumption area) into a weighted undirected graph, as Figure 2 shown. Areas 2, 5, and 6 are used as waypoints, and the 0 node where the control center is located is set as the initial node and input into the improved Dijkstra algorithm. The processing steps are as follows:

[0089] First, find the shortest path between any two nodes among the four nodes {0, 2, 5, 6}, and the corresponding weights, and record them in the shortest path table, as shown in the following table:

[0090] Node Shortest path Weight value {0,2} 0→2 3.5 {0,5} 0→4→5 6 {0,6} 0→4→6 7 ... ... ...

[0091] Next, perform a full permutation on the waypoints {2, 5, 6}, and add the initial node 0 to the beginning and end of each permutation, as shown in the following table:

[0092] Path sequence Total weight value {0,2,5,6,0} ... {0,2,6,5,0} ... {0,5,2,6,0} ... ... ...

[0093] Taking the first path sequence as an example to calculate the total weight. First, split the path sequence into several segments: {0, 2}, {2, 5}, {5, 6}, {6, 0}. Then, look up the weight corresponding to each segment in the shortest path table, and add them up as the total weight of this path sequence. The calculation result is 25. After calculating the total weights of all path sequences, find the path sequence with the lowest total weight. In this embodiment, the path sequence with the lowest total weight is not unique, so one of them is selected as the final shortest path sequence and output as {0, 5, 6, 2, 0}. By looking up the shortest path table, the final actual path can be determined as {0, 4, 5, 4, 6, 2, 0}, that is Figure 2 the path shown by the dotted arrow in

[0094] In this embodiment, the differences between the algorithm steps of this solution and the standard Dijkstra algorithm are further illustrated:

[0095] The standard Dijkstra algorithm can only find the shortest path from the initial node to the termination node, and cannot guarantee that the found shortest path contains several waypoints we are interested in. Therefore, this solution improves the standard Dijkstra algorithm. Using the idea of exhaustive search, find all possible path sequences starting from the initial node, passing through all waypoints, and finally returning to the initial node, and then find the path with the smallest weight among all possible path sequences as the optimal path for output.

[0096] The standard Dijkstra algorithm finds the shortest path from one node to another in a weighted directed graph or undirected graph (but the weight cannot be negative). The main feature is to use a greedy strategy starting from the starting point. Each time, traverse the adjacent nodes of the vertex that is closest to the starting point and has not been visited until it reaches the end point. However, the Dijkstra algorithm cannot handle the shortest path with waypoints. This is because the algorithm itself can only search for the shortest path from the starting point to the end point, and the searched path cannot guarantee to contain the waypoints we are interested in.

[0097] As Figure 3 shown Figure 3The standard Dijkstra's algorithm is adopted. According to the characteristics of the algorithm, it actually cannot handle the problem of traversing all nodes. Using the Dijkstra's algorithm, the shortest path from node 0 to node 5 can be found as 0→2→5, with a cost value of 6; and the shortest path from node 0 to node 4 is 0→2→1→3→4, with a cost of 7. Obviously, to reach node 4, it is impossible to pass through node 5, and to reach node 5, it is impossible to pass through nodes 1, 3, and 4; there is a problem of being unable to reach all nodes, so it is impossible to complete the shortest path planning task passing through all nodes.

[0098] This solution is to solve the shortest path planning problem with waypoints, and specifically analyze the shortest path planning problem with waypoints:

[0099] For the case of only containing a single waypoint, such as the case of A→B→C, it can be split into two paths A→B and B→C, and the Dijkstra's algorithm is used to find the shortest paths of the two paths respectively, and the two paths can be connected end to end to obtain the total shortest path. For the case of containing multiple waypoints, such as the shortest path planning starting from the initial node A, passing through nodes B and C, and reaching the terminal node D. This patent is based on the idea of exhaustive search to obtain the shortest path planning. First, find all possible permutations of the waypoints {B, C}: {B, C} and {C, B}, and add the initial node and the terminal node to the beginning and end of each permutation possibility to obtain all possible path sequences: {A, B, C, D} and {A, C, B, D}. Taking the path sequence {A, B, C, D} as an example, using the Dijkstra's algorithm to find the shortest paths and weights of A→B, B→C, and C→D respectively, the corresponding total shortest path and total weight of this path sequence can be obtained. Perform the above processing on all path sequences, and find the path sequence with the lowest total weight (if not unique, any one can be selected), which is the shortest path we need.

[0100] The above method is also applicable to the shortest path planning problem with any number of waypoints; however, calculating the shortest path and weight for each path sequence using the Dijkstra's algorithm will generate a large amount of repeated calculations. If the shortest paths and weights between any two nodes among all nodes (initial nodes and waypoint nodes) are calculated and stored in a table first, then when calculating the total shortest path and total weight of each path sequence, they can be directly obtained by looking up the table; in this way, the time complexity of the proposed improved Dijkstra's algorithm can be reduced from o(S*m*n 2 ) to o(m*n 2 +S). Where m is the number of nodes including the waypoint and the starting point, n is the number of nodes in the weighted undirected graph, is the number of full permutation results.

[0101] Embodiment 2:

[0102] See Figure 4 , a power supply service extension system for a mobile energy storage vehicle based on a replaceable battery, the system comprising:

[0103] A power supply demand confirmation module 1, configured to determine the power supply demand within the power consumption area and the power supply service duration of the mobile energy storage vehicle, and construct a power supply demand prediction table;

[0104] Further, the power supply demand confirmation module 1 is configured to confirm the power supply demand according to the following steps:

[0105] S11. Based on the historical power consumption records of the power consumption area, determine the power supply demand value of the power consumption area, and divide it into m demand mobile energy storage vehicle power supply areas, each area represented by m = 1,..., M;

[0106] S12. Determine the power supply service duration T total , and divide it into T time periods at equal time intervals Δ t , each time period represented by t = 1,..., T;

[0107] S13. Quantify the specific power supply demand of each demand power supply area within the power supply service duration to obtain a power supply demand prediction table.

[0108] A charging demand confirmation module 2, configured to allocate at least one mobile energy storage vehicle to each power consumption area, and sequentially determine whether all mobile energy storage vehicles need to be charged based on the current remaining power of the mobile energy storage vehicle and the power supply demand table;

[0109] Further, the charging demand confirmation module 2 is configured to confirm the charging demand according to the following steps:

[0110] S21. At the end of the t time periods, each mobile energy storage vehicle predicts the predicted remaining power E rest (m) after two maintenance vehicle charging cycles based on the current remaining power E(t, m) and the power supply demand table d(t, m), and its expression is as follows:

[0111]

[0112] Where: E(t, m) is the current power of the m-th mobile energy storage vehicle at the end of the t-th time period; C is the primary maintenance cycle of the maintenance vehicle;

[0113] S22. Compare the predicted remaining power E rest (m) with the set minimum power threshold V; if the predicted remaining power E rest (m) is lower than the minimum power threshold V, then the mobile energy storage vehicle needs to be charged.

[0114] The path planning module 3 is used to, when the maintenance vehicle does not perform the energy replenishment task and there are mobile energy storage vehicles that need energy replenishment, the control center plans the shortest path based on the improved Dijkstra algorithm and sends the maintenance vehicle to the mobile energy storage vehicles that need energy replenishment in turn according to the shortest path for power replenishment;

[0115] Further, the path planning module 3 is used to plan the shortest path according to the following steps:

[0116] S31. Establish a weighted undirected graph based on the map of the power consumption area, establish the control center and all demand power supply areas as the nodes of the graph, and set the weights between every two nodes;

[0117] When there is a path between two nodes, the weight between the two nodes is recorded as the actual distance of the journey between the two nodes; when there is no path between two nodes, the weight is recorded as infinity;

[0118] S32. Record the nodes corresponding to all mobile energy storage vehicles that need energy replenishment as waypoints, and record the node where the control center is located as the initial node; based on the Dijkstra algorithm, find the shortest path and the corresponding weight between any two nodes among the waypoints and the initial node;

[0119] S33. Find all path sequences that all waypoints may form, add the initial node to the head and tail of each path sequence respectively, and calculate the total weight required for each path sequence;

[0120] S34. Based on the one with the lowest total weight among all path sequences, output the specific path corresponding to the path sequence as the shortest path search result; if there are multiple path sequences with the lowest total weight, any one of them can be selected as the output result.

[0121] Embodiment 3:

[0122] See Figure 5 , a power supply service extension device for a mobile energy storage vehicle based on a replaceable battery, the device includes a processor 5 and a memory 6;

[0123] The memory 6 is used to store the computer program code 61 and transmit the computer program code 61 to the processor 5;

[0124] The processor 5 is used to execute the power supply service extension method for the mobile energy storage vehicle based on the replaceable battery described in Embodiment 1 according to the instructions in the computer program code 61.

[0125] In this embodiment, there is also provided a computer-readable storage medium storing computer-executable instructions, which, when executed on a computer, implement the method for extending the power supply service of a mobile energy storage vehicle based on replaceable batteries described in Embodiment 1.

[0126] Generally speaking, the computer instructions for implementing the method of the present invention can be carried by any combination of one or more computer-readable storage media. A non-transitory computer-readable storage medium may include any computer-readable medium except for the signals propagating temporarily by themselves.

[0127] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0128] The computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. In particular, the Python language suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0129] For the above-mentioned device and non-transitory computer-readable storage medium, reference can be made to the specific description of a method for extending the power supply service of a mobile energy storage vehicle based on replaceable batteries and its beneficial effects, which will not be elaborated here.

[0130] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for extending the power supply service of a mobile energy storage vehicle based on replaceable batteries, characterized in that: include: S1. Determine the power demand in the power consumption area and the power supply service duration of the mobile energy storage vehicle, and construct a power supply demand forecast table; S2. Allocate at least one mobile energy storage vehicle to each power consumption area, and determine in turn whether all mobile energy storage vehicles need to be recharged based on the current remaining power and power supply demand table of the mobile energy storage vehicles; S3. When the maintenance vehicle does not perform the energy replenishment task and there is a mobile energy storage vehicle that needs energy replenishment, the control center plans the shortest path based on the improved Dijkstra algorithm, and sends the maintenance vehicle to the mobile energy storage vehicle that needs energy replenishment in turn along the shortest path to replenish electricity.

2. The method for extending the power supply service of a mobile energy storage vehicle based on replaceable batteries according to claim 1 is characterized in that: The step S1 specifically includes: S11, based on the historical electricity consumption records of the electricity consumption area, determine the electricity demand value of the electricity consumption area, and divide it into M demand mobile energy storage vehicle power supply areas, each area is represented by m=1, ..., M; S12. Determine the power supply service duration T of the mobile energy storage vehicle total , and divide it at equal time intervals Δ t Divided into T time periods, each time period is represented by t=1, ..., T; S13. Quantify the specific electricity demand of each power supply demand area within the power supply service time to obtain a power supply demand forecast table.

3. The method for extending the power supply service of a mobile energy storage vehicle based on replaceable batteries according to claim 1 is characterized in that: The step S2 specifically includes: S21. At the end of r time periods, each mobile energy storage vehicle predicts the predicted remaining power E after two maintenance vehicle energy replenishment cycles based on the current remaining power E(t, m) and the power supply demand table d(t, m). rest (m), which is expressed as follows: Where: E(t, m) is the current power of the mth mobile energy storage vehicle at the end of the tth period; C is a maintenance cycle of the maintenance vehicle; S22, predict the remaining power E rest (m) Compare with the set minimum power threshold V; if the predicted remaining power E rest (m) When the power level is lower than the minimum threshold V, the mobile energy storage vehicle needs to be recharged.

4. The method for extending the power supply service of a mobile energy storage vehicle based on replaceable batteries according to claim 1 is characterized in that: The steps of planning the shortest path based on the improved Dijkstra algorithm in step S3 are as follows: S31, establishing a weighted undirected graph based on the map of the power consumption area, establishing the control center and all the power demand areas as nodes of the graph, and setting the weight between every two nodes; When there is a path between two nodes, the weight between the two nodes is recorded as the actual distance between the two nodes; when there is no path between the two nodes, the weight is recorded as infinity; S32, recording the nodes corresponding to all mobile energy storage vehicles that need to be recharged as waypoints, and recording the node where the control center is located as the initial node; Based on Dijkstra's algorithm, find the shortest path between any two nodes including the waypoint and the initial node and the corresponding weights; S33, finding all possible path sequences composed of all waypoints, and adding the initial node to the head and tail of each path sequence respectively, and calculating the total weight required for each path sequence; S34. Based on the path with the lowest total weight among all path sequences, the specific path corresponding to the path sequence is output as the shortest path search result; if there are multiple path sequences with the lowest total weight, one of them is arbitrarily selected as the output result.

5. A mobile energy storage vehicle power supply service extension system based on replaceable batteries, characterized in that: The system comprises: A power supply demand confirmation module (1) is used to determine the power demand in the power consumption area and the power supply service duration of the mobile energy storage vehicle, and to construct a power supply demand prediction table; An energy replenishment demand confirmation module (2) is used to allocate at least one mobile energy storage vehicle to each power consumption area, and based on the current remaining power of the mobile energy storage vehicle and the power supply demand table, determine in sequence whether all the mobile energy storage vehicles need energy replenishment; The path planning module (3) is used for, when the maintenance vehicle is not performing the energy replenishment task and there is a mobile energy storage vehicle that needs energy replenishment, the control center plans the shortest path based on the improved Dijkstra algorithm, and dispatches the maintenance vehicle to the mobile energy storage vehicle that needs energy replenishment in sequence along the shortest path to replenish electric energy.

6. The mobile energy storage vehicle power supply service extension system based on replaceable batteries according to claim 5 is characterized in that: The power supply demand confirmation module (1) is used to confirm the power supply demand according to the following steps: S11, based on the historical electricity consumption records of the electricity consumption area, determine the electricity demand value of the electricity consumption area, and divide it into M demand mobile energy storage vehicle power supply areas, each area is represented by m=1, ..., M; S12. Determine the power supply service duration T of the mobile energy storage vehicle total , and divide it at equal time intervals Δ t Divided into T time periods, each time period is represented by t=1, ..., T; S13. Quantify the specific electricity demand of each power supply demand area within the power supply service time to obtain a power supply demand forecast table.

7. The mobile energy storage vehicle power supply service extension system based on replaceable batteries according to claim 5 is characterized in that: The energy replenishment demand confirmation module (2) is used to confirm the energy replenishment demand according to the following steps: S21. At the end of t time periods, each mobile energy storage vehicle predicts the predicted remaining power E after two maintenance vehicle energy replenishment cycles based on the current remaining power E(t, m) and the power supply demand table d(t, m). rest (m), which is expressed as follows: Where: E(t, m) is the current power of the mth mobile energy storage vehicle at the end of the tth period; C is a maintenance cycle of the maintenance vehicle; S22, predict the remaining power E rest (m) Compare with the set minimum power threshold V; if the predicted remaining power E rest (m) When the power level is lower than the minimum threshold V, the mobile energy storage vehicle needs to be recharged.

8. The mobile energy storage vehicle power supply service extension system based on replaceable batteries according to claim 5 is characterized in that: The path planning module (3) is used to plan the shortest path according to the following steps: S31, establishing a weighted undirected graph based on the map of the power consumption area, establishing the control center and all the power demand areas as nodes of the graph, and setting the weight between every two nodes; When there is a path between two nodes, the weight between the two nodes is recorded as the actual distance between the two nodes; when there is no path between the two nodes, the weight is recorded as infinity; S32, recording the nodes corresponding to all mobile energy storage vehicles that need to be recharged as waypoints, and recording the node where the control center is located as the initial node; Based on Dijkstra's algorithm, find the shortest path between any two nodes including the waypoint and the initial node and the corresponding weights; S33, finding all possible path sequences composed of all waypoints, and adding the initial node to the head and tail of each path sequence respectively, and calculating the total weight required for each path sequence; S34. Based on the path with the lowest total weight among all path sequences, the specific path corresponding to the path sequence is output as the shortest path search result; if there are multiple path sequences with the lowest total weight, one of them is arbitrarily selected as the output result.

9. A mobile energy storage vehicle power supply service extension device based on replaceable batteries, characterized in that: The device comprises a processor (4) and a memory (5); The memory (5) is used to store computer program code (51) and transmit the computer program code (51) to the processor (5); The processor (5) is used to execute the method for extending the power supply service of a mobile energy storage vehicle based on a replaceable battery according to any one of claims 1 to 4 according to the instructions in the computer program code (51).

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed on a computer, the method for extending the power supply service of a mobile energy storage vehicle based on a replaceable battery as described in any one of claims 1 to 4 is implemented.

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