Charging pile use scheduling method

Abstract

The invention provides a charging pile use scheduling method, and the method comprises the steps: collecting the use data of a charging pile, and uploading the use data to a corresponding block chain for storage; receiving a charging request of a user, confirming a waiting parking lot, and obtaining an idle charging pile according to the working state of the charging pile in the waiting parking lot; obtaining use data of the idle charging piles from the block chain, and evaluating self loss of the idle charging piles based on the use data; acquiring environmental parameters of the first N idle charging piles with less self-loss, and constructing a virtual loss evolution path based on the charging time period, the self-loss of the idle charging piles and the environmental parameter change in the charging process; a grey wolf optimization algorithm is adopted to process the virtual loss evolution path based on the minimum final loss, and an optimal charging pile is obtained; navigating the user to the optimal charging pile for charging; and the charging piles are dispatched and used based on the final loss, so that the use states of the charging piles tend to be uniform, and simultaneous maintenance is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and in particular to a method for scheduling the use of charging piles. Background Art

[0002] With the continuous improvement of living standards, the number of cars owned is also increasing. At the same time, with the improvement of environmental awareness, new energy vehicles have developed rapidly. Traditional fuel vehicles are driven by gasoline, which will produce harmful gases and pollute the atmospheric environment. New energy vehicles are driven by electricity and do not produce harmful gases during driving. Therefore, they have been vigorously promoted. Regardless of whether it is a fuel vehicle or a new energy vehicle, neither has unlimited endurance. Fuel vehicles maintain endurance by replenishing gasoline, while new energy vehicles maintain endurance by charging.

[0003] At present, in addition to home charging piles, parking lots with charging piles are set up in some shopping malls or roadsides for charging new energy vehicles. When choosing charging piles for charging, new energy vehicle owners are somewhat random. Some charging piles are used more times, resulting in different usage and wear of charging piles in the same parking lot. If all charging piles are repaired and maintained uniformly, it will cause a waste of maintenance resources. Since the degree of wear and tear of charging piles varies, if only the charging piles with heavier wear and tear are repaired and maintained each time, maintenance personnel will have to perform repairs multiple times, which will also cause a waste of maintenance resources. Summary of the invention

[0004] In view of this, the present invention proposes a charging pile usage scheduling method, which can evaluate the usage status of the charging piles and then schedule their use, so as to unify the use of the charging piles to facilitate simultaneous repair and maintenance, thereby reducing the waste of maintenance resources.

[0005] The technical solution of the present invention is achieved in this way:

[0006] A charging pile usage scheduling method comprises the following steps:

[0007] Step S1: Collect the usage data of the charging pile and upload it to the corresponding blockchain for storage;

[0008] Step S2: receiving a charging request from a user, confirming the parking lot to be visited, and obtaining an idle charging pile according to the working status of the charging pile in the parking lot to be visited;

[0009] Step S3: Obtain usage data of idle charging piles from the blockchain, and evaluate the self-loss of the idle charging piles based on the usage data;

[0010] Step S4, obtaining the environmental parameters of the first N idle charging piles with the least self-loss, and constructing a virtual loss evolution path based on the charging period, the self-loss of the idle charging piles, and the changes in environmental parameters during the charging process;

[0011] Step S5: Using the Grey Wolf Optimization Algorithm to process the virtual loss evolution path based on the minimum final loss, and obtain the optimal charging pile;

[0012] Step S6: Navigate the user to the optimal charging station for charging.

[0013] Preferably, the specific steps of step S1 are:

[0014] Step S11, collecting the charging start and end time, charging duration, charging efficiency and charging power during a single charging process of the charging pile;

[0015] Step S12, packing the charging start and end time, charging duration, charging efficiency and charging power into a data packet;

[0016] Step S13: upload the data packet as a block of the blockchain.

[0017] Preferably, the specific steps of step S2 are:

[0018] Step S21: The user actively initiates a charging request or automatically initiates a charging request when checking that the user's vehicle battery is low;

[0019] Step S22: query nearby parking lots with charging piles according to the location of the user when the request is initiated, and the user confirms the parking lot to be visited;

[0020] Step S23: query the working status of all charging piles in the parking lot to be visited, and obtain unused idle charging piles.

[0021] Preferably, the specific steps of step S3 are:

[0022] Step S31, reading the usage data stored in all blocks in the blockchain;

[0023] Step S32: Obtain the total charging time, average charging efficiency and average charging power of the charging pile according to the usage data;

[0024] Step S33: assign different weights to the total charging time, average charging efficiency and average charging power respectively, and sum the assigned data to obtain the quantified self-loss.

[0025] Preferably, the specific steps of step S4 are:

[0026] Step S41, sort the idle charging piles from small to large according to their own losses, and select the first N idle charging piles;

[0027] Step S42: Obtain environmental parameters of the first N idle charging piles, where the environmental parameters include specific locations of the idle charging piles, locations of surrounding buildings and trees, and weather data of the parking lot to be visited;

[0028] Step S43, estimating the vehicle charging time according to the user's charging request, and estimating the charging time period according to the vehicle charging time and the location of the user's vehicle;

[0029] Step S44: Map the charging period into a virtual loss evolution path, add the idle charging pile's own loss at the front end of the virtual loss evolution path, and add environmental parameters to the virtual loss evolution path based on the passage of time.

[0030] Preferably, the number N is determined based on the number of charging piles in the parking lot to be visited.

[0031] Preferably, the specific steps of step S5 are:

[0032] Step S51, initializing the gray wolf population and the number of iterations;

[0033] Step S52: randomly select a virtual loss evolution path, and calculate the fitness value of the gray wolf based on the minimum final loss;

[0034] Step S53, selecting another virtual loss evolution path, calculating the fitness value, comparing it with the fitness value calculated last time, and retaining the virtual loss evolution path with the smaller fitness value;

[0035] Step S54: perform iterative calculations and output the idle charging pile corresponding to the virtual loss evolution path with the smallest fitness value as the optimal charging pile.

[0036] Preferably, the number of iterations is the number of virtual loss evolution paths.

[0037] Preferably, the specific steps of step S6 are:

[0038] Step S61, obtaining the location of the user's vehicle and the internal road planning map of the parking lot to be reached;

[0039] Step S62: planning a driving route for the user's vehicle to the optimal charging pile in the parking lot to be visited;

[0040] Step S63: The user navigates to the optimal charging station for charging based on the driving route.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] A charging pile usage scheduling method of the present invention stores the usage data of all charging piles on a chain. The characteristics of the blockchain can ensure the accuracy of the data information, so that when the self-loss of the charging pile is evaluated based on the usage data, the accuracy of the evaluation can be guaranteed. After the user has a charging request, the parking lot where the user is going to charge is first determined, and the idle charging piles in the parking lot to be visited are queried. Then, after the self-loss of the idle charging piles is evaluated, the first N idle charging piles can be selected as alternative charging piles for the user to charge. The alternative charging piles need to be further screened. By obtaining the changes in the environmental parameters of the idle charging piles, a virtual loss evolution path can be constructed. The virtual loss evolution path can be processed and optimized through the gray wolf optimization algorithm, and finally an optimal charging pile is output. The optimal charging pile is the charging pile with the least final loss. The user can drive the vehicle to the optimal charging pile for charging according to the navigation information, so that the use of the charging pile with the least loss can be realized, and the loss of the charging piles is unified, so as to facilitate simultaneous repair and maintenance and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 A flowchart of a charging pile usage scheduling method of the present invention;

[0045] Figure 2 This is a flow chart of step S1 of a charging pile usage scheduling method of the present invention;

[0046] Figure 3 This is a flow chart of step S2 of a charging pile usage scheduling method of the present invention;

[0047] Figure 4 This is a flow chart of step S3 of a charging pile usage scheduling method of the present invention;

[0048] Figure 5 This is a flow chart of step S4 of a charging pile usage scheduling method of the present invention;

[0049] Figure 6 This is a flow chart of step S5 of a charging pile usage scheduling method of the present invention;

[0050] Figure 7 This is a flow chart of step S6 of a charging pile usage scheduling method of the present invention; DETAILED DESCRIPTION

[0051] In order to better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0052] At present, with the continuous increase in the number of new energy vehicles, the supporting charging pile facilities are also constantly increasing and improving. Several charging piles are installed in some parking lots and on the side of the road for new energy vehicles to charge for a fee. When car owners choose charging piles for charging, there is a certain randomness. Under long-term use, the charging piles will have different losses. If the charging piles are not scheduled for use, frequent and multiple repairs and maintenance are required, resulting in increased operating costs. A charging pile use scheduling method of the present invention can schedule the use of charging piles based on the loss of charging piles, guide users to drive their vehicles to charging piles with less loss for charging, thereby unifying the use status of the charging piles, so as to facilitate repair and maintenance at the same time, save maintenance resources, and reduce operating costs.

[0053] See also Figures 1 to 7 The present invention provides a charging pile use scheduling method, comprising the following steps:

[0054] Step S1: Collect the usage data of the charging pile and upload it to the corresponding blockchain for storage. The specific steps are as follows:

[0055] Step S11, collecting the charging start and end time, charging duration, charging efficiency and charging power during a single charging process of the charging pile;

[0056] Step S12, packing the charging start and end time, charging duration, charging efficiency and charging power into a data packet;

[0057] Step S13: upload the data packet as a block of the blockchain.

[0058] In order to ensure that the selection of charging piles is objective and notarized, the present invention adopts the form of blockchain to store data. After collecting the usage data of each charging pile during each charging process, the collected usage data is packaged into a data packet and uploaded to the blockchain. Each block on the blockchain represents the charging data of each charging pile, and the blocks on the blockchain are connected in chronological order. The accuracy of data collection can be guaranteed through the characteristics of blockchain, and then when the usage data of the charging pile is obtained for loss assessment, the accuracy of the assessment can be guaranteed.

[0059] Step S2: receiving the user's charging request, confirming the parking lot to be visited, and obtaining an idle charging pile according to the working status of the charging pile in the parking lot to be visited; the specific steps are:

[0060] Step S21: The user actively initiates a charging request or automatically initiates a charging request when checking that the user's vehicle battery is low;

[0061] Step S22: query nearby parking lots with charging piles according to the location of the user when the request is initiated, and the user confirms the parking lot to be visited;

[0062] Step S23: query the working status of all charging piles in the parking lot to be visited, and obtain unused idle charging piles.

[0063] The scheduling of charging piles requires the existence of vehicle charging requests. Charging requests can be actively initiated or automatically initiated. Active initiation refers to requests initiated by users after planning based on their own situation and subsequent use of the vehicle, while automatic initiation refers to automatically initiating a charging request to the user side by monitoring the vehicle's power status when the power is insufficient. The user determines whether charging is needed and initiates a charging request. After obtaining the user's charging request, the user searches for nearby parking lots with charging piles based on the vehicle's location. The user selects a parking lot by himself, and then the parking lot can be output as a parking lot to be visited. The parking lot to be visited will contain several charging piles, and some charging piles may be occupied, including being charged or occupied by other vehicles. Therefore, it is necessary to query the working status of the charging piles in the parking lot to be visited, including querying whether there are vehicles charging or occupying spaces. Finally, the unused charging piles can be recorded as idle charging piles. At this time, the idle charging piles are charging piles that users can drive to charge.

[0064] After obtaining the idle charging pile, it is necessary to determine which charging pile is the charging pile where the user will eventually charge, and the basis for determination is the loss of the charging pile. Therefore, in step S3, the usage data of the idle charging pile is obtained from the blockchain, and the loss of the idle charging pile itself is evaluated based on the usage data; the specific steps are:

[0065] Step S31, reading the usage data stored in all blocks in the blockchain;

[0066] Step S32: Obtain the total charging time, average charging efficiency and average charging power of the charging pile according to the usage data;

[0067] Step S33: assign different weights to the total charging time, average charging efficiency and average charging power respectively, and sum the assigned data to obtain the quantified self-loss.

[0068] Each charging pile corresponds to a blockchain, which stores the historical usage data of the charging piles. All usage data of the charging piles can be read from the blockchain. The total charging time can be obtained by adding up the charging time contained in each block, and the average charging efficiency and average charging power can be obtained by adding up the charging efficiency and charging power of each block and averaging them. When measuring the self-loss of the charging pile, the total charging time, average charging efficiency and average charging power are mainly used. Different weights are assigned to each of them to unify the data, and finally the loss value is obtained by adding them up. The loss value is the quantified self-loss of the charging pile. The larger the loss value, the higher the self-loss. When selecting an idle charging pile for charging, the one with small self-loss should be selected to unify the loss.

[0069] Step S4: Obtain the environmental parameters of the first N idle charging piles with the least self-loss, and construct a virtual loss evolution path based on the charging period, the self-loss of the idle charging piles, and the changes in environmental parameters during the charging process; the specific steps are:

[0070] Step S41, sort the idle charging piles from small to large according to their own losses, and select the first N idle charging piles;

[0071] Step S42: Obtain environmental parameters of the first N idle charging piles, where the number N is determined based on the number of charging piles in the parking lot to be visited, and the environmental parameters include specific locations of the idle charging piles, locations of surrounding buildings and trees, and weather data of the parking lot to be visited;

[0072] Step S43, estimating the vehicle charging time according to the user's charging request, and estimating the charging time period according to the vehicle charging time and the location of the user's vehicle;

[0073] Step S44: Map the charging period into a virtual loss evolution path, add the idle charging pile's own loss at the front end of the virtual loss evolution path, and add environmental parameters to the virtual loss evolution path based on the passage of time.

[0074] The self-loss of the charging pile is the loss before the user charges, and the idle charging pile will also have a certain loss during the charging process of the user's vehicle. When selecting an idle charging pile, in addition to considering the self-loss before charging, the overall loss of the idle charging pile after charging is also considered. The single loss is generally not much different. In order to reduce the amount of calculation, before calculating the overall loss, only the first N idle charging piles are selected according to the self-loss for calculation. The value of N is related to the total number of charging piles to be sent to the parking lot. The more charging piles there are, the larger the N value is. Therefore, N idle charging piles can be determined, and then the overall loss of the N idle charging piles can be evaluated.

[0075] For charging piles, their losses during the charging process are not only related to their usage data, but also to environmental changes during the charging process. Therefore, the environmental parameters of the first N charging piles are obtained, where the environmental parameters include the specific location of the idle charging piles, the location of surrounding buildings and trees, and the weather data of the parking lot to be visited. The environmental parameters will change during the charging process of the vehicle. For example, as time changes, the shadows of buildings and trees will block some idle charging piles, while the idle charging piles that are not blocked by shadows will have a relatively high temperature during the charging process due to exposure to the sun, thereby affecting the charging efficiency and increasing the loss of the charging piles. Similarly, if the weather in the parking lot to be visited changes over time, it will also affect the normal use of the idle charging piles, and then cause different degrees of loss to the idle charging piles. That is, according to the changes in environmental factors, different idle charging piles will produce different losses during the charging process.

[0076] Based on this, after estimating the charging time of the vehicle according to the user's charging request, the distance to the parking lot to be visited is determined according to the location of the vehicle, and then the time for the vehicle to arrive at the parking lot to be visited can be calculated, and the charging period of the vehicle can be estimated. According to historical data, the usage data of the idle charging piles in the charging period can be obtained as the basis for calculating the loss of the idle charging piles. Then, after mapping the charging period into a virtual loss evolution path, environmental parameters are added to the virtual loss evolution path based on the passage of time, and the self-loss of the idle charging piles is added to the front end of the virtual loss evolution path, that is, following the time sequence change of the virtual loss evolution path, the idle charging piles will increase the partial loss during the charging process in addition to their own losses. Each idle charging pile will construct a virtual loss evolution path, and different virtual loss evolution paths will obtain different final losses. The calculation of the final loss is realized by step S5. In step S5, the gray wolf optimization algorithm is used to process the virtual loss evolution path based on the minimum final loss, and the optimal charging pile is obtained; the specific steps are:

[0077] Step S51, initializing the gray wolf population and the number of iterations, where the number of iterations is the number of virtual loss evolution paths;

[0078] Step S52: randomly select a virtual loss evolution path, and calculate the fitness value of the gray wolf based on the minimum final loss;

[0079] Step S53, selecting another virtual loss evolution path, calculating the fitness value, comparing it with the fitness value calculated last time, and retaining the virtual loss evolution path with the smaller fitness value;

[0080] Step S54: perform iterative calculations and output the idle charging pile corresponding to the virtual loss evolution path with the smallest fitness value as the optimal charging pile.

[0081] The Grey Wolf Optimization Algorithm calculates the overall loss of each virtual loss evolution path and uses it as the fitness value. The smaller the fitness value, the lower the overall loss. After calculating the fitness values ​​of all virtual loss evolution paths, the idle charging pile corresponding to the virtual loss evolution path with the smallest fitness value is output as the optimal charging pile. The optimal charging pile is the charging pile that can be used by users for charging, and it is the charging pile with the least loss after comprehensive evaluation, so as to unify the usage status of the charging piles.

[0082] Step S6: Navigate the user to the optimal charging station for charging. The specific steps are as follows:

[0083] Step S61, obtaining the location of the user's vehicle and the internal road planning map of the parking lot to be reached;

[0084] Step S62: planning a driving route for the user's vehicle to the optimal charging pile in the parking lot to be visited;

[0085] Step S63: The user navigates to the optimal charging station for charging based on the driving route.

[0086] After obtaining the optimal charging pile, the user needs to be navigated to the optimal charging pile. The navigation route includes the external route from the user's vehicle to the parking lot to be visited and the internal route from the parking lot to the optimal charging pile. The final user can move according to the driving route so as to move to the optimal charging pile for charging, so that the charging pile with the least loss can be used. By scheduling the use of the charging piles, the use status of the charging piles can be unified, so that they can be repaired and maintained at the same time, reducing the use of maintenance resources and reducing operating costs.

[0087] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A charging pile usage scheduling method, characterized in that: The following steps are involved: Step S1: Collect usage data of charging piles and upload them to the corresponding blockchain for storage; Step S2: receiving a charging request from a user, confirming the parking lot to be visited, and obtaining an idle charging pile according to the working status of the charging pile in the parking lot to be visited; Step S3: Obtain usage data of idle charging piles from the blockchain, and evaluate the self-loss of the idle charging piles based on the usage data; Step S4, obtaining the environmental parameters of the first N idle charging piles with the least self-loss, and constructing a virtual loss evolution path based on the charging period, the self-loss of the idle charging piles, and the changes in environmental parameters during the charging process; Step S5: Using the Grey Wolf Optimization Algorithm to process the virtual loss evolution path based on the minimum final loss, and obtain the optimal charging pile; Step S6: Navigate the user to the optimal charging station for charging.

2. A charging pile use scheduling method according to claim 1, characterized in that: The specific steps of step S1 are: Step S11, collecting the charging start and end time, charging duration, charging efficiency and charging power during a single charging process of the charging pile; Step S12, packing the charging start and end time, charging duration, charging efficiency and charging power into a data packet; Step S13: upload the data packet as a block of the blockchain.

3. A charging pile use scheduling method according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: The user actively initiates a charging request or automatically initiates a charging request when checking that the user's vehicle battery is low; Step S22: query nearby parking lots with charging piles according to the location of the user when the request is initiated, and the user confirms the parking lot to be visited; Step S23: query the working status of all charging piles in the parking lot to be visited, and obtain unused idle charging piles.

4. A charging pile use scheduling method according to claim 2, characterized in that: The specific steps of step S3 are: Step S31, reading the usage data stored in all blocks in the blockchain; Step S32: Obtain the total charging time, average charging efficiency and average charging power of the charging pile according to the usage data; Step S33: assign different weights to the total charging time, average charging efficiency and average charging power respectively, and sum the assigned data to obtain the quantified self-loss.

5. A charging pile use scheduling method according to claim 1, characterized in that: The specific steps of step S4 are: Step S41, sort the idle charging piles from small to large according to their own losses, and select the first N idle charging piles; Step S42: Obtain environmental parameters of the first N idle charging piles, where the environmental parameters include specific locations of the idle charging piles, locations of surrounding buildings and trees, and weather data of the parking lot to be visited; Step S43, estimating the vehicle charging time according to the user's charging request, and estimating the charging time period according to the vehicle charging time and the location of the user's vehicle; Step S44: Map the charging period into a virtual loss evolution path, add the idle charging pile's own loss at the front end of the virtual loss evolution path, and add environmental parameters to the virtual loss evolution path based on the passage of time.

6. A charging pile use scheduling method according to claim 5, characterized in that: The number of N is determined based on the number of charging piles in the parking lot to be visited.

7. A charging pile use scheduling method according to claim 1, characterized in that: The specific steps of step S5 are: Step S51, initializing the gray wolf population and the number of iterations; Step S52: randomly select a virtual loss evolution path, and calculate the fitness value of the gray wolf based on the minimum final loss; Step S53, selecting another virtual loss evolution path, calculating the fitness value, comparing it with the fitness value calculated last time, and retaining the virtual loss evolution path with the smaller fitness value; Step S54: perform iterative calculations and output the idle charging pile corresponding to the virtual loss evolution path with the smallest fitness value as the optimal charging pile.

8. A charging pile use scheduling method according to claim 7, characterized in that: The number of iterations is the number of virtual loss evolution paths.

9. A charging pile use scheduling method according to claim 1, characterized in that: The specific steps of step S6 are: Step S61, obtaining the location of the user's vehicle and the internal road planning map of the parking lot to be reached; Step S62: planning a driving route for the user's vehicle to the optimal charging pile in the parking lot to be visited; Step S63: The user navigates to the optimal charging station for charging based on the driving route.

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