Scheduling method, system and equipment of mobile energy storage charging pile and storage medium

Abstract

The invention provides a mobile energy storage charging pile scheduling method, system and device and a storage medium. The method comprises the steps that historical charging demand data and charging demand data of all areas are acquired, and historical data are checked to obtain check data; and training the prediction model by using the verification data to obtain a prediction value containing the charging demand quantity of each time period. And selecting a time period with the maximum charging demand as a working section of the mobile energy storage charging pile according to the predicted value, and determining a starting time point and a strategy. Voltage or current is adjusted according to a strategy to completely discharge the charging pile, and residual electric energy is obtained. Unconsumed electric energy is reversely scheduled in combination with the regional demand data and the residual electric energy, regional charging load distribution is adjusted, and a target scheduling ratio is obtained. And according to the target ratio and the user charging request, a scheduling instruction is sent to the charging pile controller meeting the requirement, and a charging service is provided for the electric vehicle. The method has the advantages that the mobile charging piles are reasonably and efficiently dispatched, and the situation that the existing charging piles are saturated in proportion and insufficient in regional proportion balance in the peak demand period is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile charging pile scheduling, and in particular to a scheduling method, system, device and storage medium for a mobile energy storage charging pile. Background Art

[0002] Against the backdrop of the rapid development of electric vehicles and their charging facilities, the scheduling of mobile charging piles has become a technical problem that needs to be solved urgently. With the significant increase in the number of electric vehicles, the demand for charging has risen sharply, and existing fixed charging facilities can no longer meet this demand. Although the current car-to-pile ratio has reached 1:1 and has improved, the distribution of charging facilities in high-demand areas is still uneven, especially in key locations. The construction of charging facilities still faces challenges. In addition, electric vehicle users often have to spend a long time looking for charging piles, which further exacerbates the charging problem. In order to cope with the shortage of charging piles, the application of mobile charging piles has become an important solution.

[0003] At present, in one existing technology, based on the existing charging pile, an energy storage device is used to realize self-supply charging of energy, and realize the storage and two-way flow of electric energy. However, none of them really solves the problem of how the mobile charging pile can cope with demand. During the peak demand stage, the energy storage or power of the mobile charging pile is limited.

[0004] In summary, when facing peak demand, the ratio of charging piles in the prior art is close to saturation, and it is impossible to reasonably and efficiently dispatch mobile charging piles, resulting in insufficient ratio in the balance area. Summary of the invention

[0005] The present invention provides a scheduling method, system, device and storage medium for mobile energy storage charging piles to solve the technical problem that when facing peak demand, the charging pile ratio in the prior art is close to saturation, and the mobile charging piles cannot be reasonably and efficiently scheduled, resulting in insufficient balance area ratio.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a scheduling method for a mobile energy storage charging pile, comprising: Obtain historical charging demand data and charging demand data for each region; Verifying the historical charging demand data to obtain verified charging demand data; According to the verified charging demand data, a charging demand prediction model is trained to obtain a predicted value of the charging demand; wherein the predicted value includes the charging demand amount in each time period; According to the predicted value, the time period with the largest charging demand is selected as the working period of the mobile energy storage charging pile, and the charging start time point and charging strategy are determined; Adjust the charging voltage or current according to the charging strategy to put the mobile energy storage charging pile in a fully discharged state, and obtain the remaining power data of the mobile energy storage charging pile; According to the charging demand data of each area and the remaining electric energy data, the unconsumed electric energy is reversed, the charging load distribution between the areas is adjusted, and the target scheduling ratio of the mobile energy storage charging pile resources is obtained; According to the target scheduling ratio and the charging request information of the electric vehicle user end, a scheduling instruction is sent to the mobile energy storage charging pile controller that meets the target scheduling ratio requirements, so that the mobile energy storage charging pile performs scheduling operations and provides charging services for the electric vehicle; the scheduling instruction includes the location information of the electric vehicle.

[0007] In an optional implementation, the predicted value selects the time period with the largest charging demand as the working period of the mobile energy storage charging pile, and determines the charging start time point and the charging strategy, including: Determine the time period with the maximum charging demand according to the predicted value; The period with the maximum charging demand is used as the working period of the mobile energy storage charging pile; According to the working period, the start time of the working period is selected as the charging start time point, and a high-power fast charging mode is selected to determine the charging strategy.

[0008] In an optional implementation, the charging strategy adjusts the charging voltage or current so that the mobile energy storage charging pile is in a fully discharged state, and obtains the remaining power data of the mobile energy storage charging pile, including: According to the charging strategy, a target charging voltage and a target charging current are calculated; Adjusting the actual charging voltage and the actual charging current to the target charging voltage and the target charging current to obtain a discharge condition; According to the discharge condition, the mobile energy storage charging pile discharge process is started, and the discharge is continued until the mobile energy storage charging pile is in a fully discharged state, wherein the fully discharged state includes the minimum safe power threshold of the mobile energy storage charging pile; According to the fully discharged state, the discharge operation is stopped, and the remaining power data of the mobile energy storage charging pile is obtained using the power measurement module preset in the mobile energy storage charging pile.

[0009] In an optional implementation, the charging demand data of each region and the remaining electric energy data are used to reverse the unconsumed electric energy, adjust the charging load distribution between regions, and obtain the target scheduling ratio of mobile energy storage charging pile resources, including: Calculating the difference in electric energy demand of each area according to the charging demand data of each area and the remaining electric energy data; Perform reverse adjustment processing according to the power demand difference to determine the power adjustment amount of each area; According to the electric energy adjustment amount, the charging load distribution between regions is adjusted, and the optimization target is determined. The scheduling ratio scheme of each mobile energy storage charging pile is calculated by the optimization algorithm, and the target scheduling ratio of the mobile energy storage charging pile resources is obtained by simulation test; The reverse regulation process includes transferring unconsumed electric energy from an area with low electric energy demand to an area with high electric energy demand.

[0010] In an optional implementation, the scheduling ratio scheme of each mobile energy storage charging pile is calculated by an optimization algorithm, and the target scheduling ratio of mobile energy storage charging pile resources is obtained by simulation test, including: According to the optimization algorithm, combined with the adjusted load and the number of mobile energy storage charging piles, a scheduling ratio scheme for each mobile energy storage charging pile is calculated; Apply the scheduling and matching scheme in a simulation environment and collect test data; The test data is analyzed, the scheduling ratio scheme is adjusted, and the target scheduling ratio of mobile energy storage charging pile resources is obtained.

[0011] In an optional implementation, the target scheduling ratio and the charging request information of the electric vehicle user end send a scheduling instruction to the mobile energy storage charging pile controller that meets the target scheduling ratio requirements, so that the mobile energy storage charging pile performs a scheduling operation and provides charging services for the electric vehicle, including: According to the target scheduling ratio and the charging request information of the electric vehicle user end, a mobile energy storage charging pile that meets the target scheduling ratio requirement is selected, and a scheduling instruction is generated; Sending the dispatch instruction to the controller of the charging pile through a communication protocol; The controller performs scheduling operations according to the scheduling instructions, including starting a charging program to provide charging services for the electric vehicle.

[0012] In an optional implementation, verifying the charging demand data, training the charging demand prediction model, and obtaining the predicted value of the charging demand includes: An initial prediction model is constructed according to the verification charging demand data, and a loss function is configured for the initial prediction model; wherein the verification charging demand data includes the idle time of the mobile energy storage charging pile, the idle time period, and the remaining power of the vehicle that was not fully charged in the previous time period; The initial prediction model is trained until the loss function converges, and the model training is determined to be complete to obtain a charging demand prediction model; The output result of the charging demand prediction model is used as the predicted value of the charging demand.

[0013] In a second aspect, the present invention provides a dispatching system for a mobile energy storage charging pile, comprising: A data acquisition module is used to acquire historical charging demand data and charging demand data for each region; A verification module, used to verify the historical charging demand data to obtain verified charging demand data; A training module, used to train a charging demand prediction model according to the verified charging demand data to obtain a predicted value of the charging demand; wherein the predicted value includes the charging demand amount in each time period; A strategy determination module is used to select the time period with the largest charging demand as the working period of the mobile energy storage charging pile according to the predicted value, and determine the charging start time point and charging strategy; An adjustment module, used to adjust the charging voltage or current according to the charging strategy so that the charging pile is in a fully discharged state and obtain the remaining power data of the charging pile; An allocation module, configured to reverse the unconsumed electric energy according to the charging demand data of each area and the remaining electric energy data, adjust the charging load distribution between areas, and obtain a target scheduling ratio of charging pile resources; The instruction scheduling module is used to send a scheduling instruction to the charging pile controller that meets the target scheduling ratio requirements according to the target scheduling ratio and the charging request information of the electric vehicle user end, so that the charging pile performs scheduling operations and provides charging services for the electric vehicle; the scheduling instruction includes the location information of the electric vehicle.

[0014] In a third aspect, the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the scheduling method for a mobile energy storage charging pile described in any one of the above is implemented.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for scheduling mobile energy storage charging piles.

[0016] Compared with the prior art, the present invention has the following beneficial effects: obtaining historical charging demand data and charging demand data of each region; verifying the historical charging demand data to obtain verification charging demand data; training the charging demand prediction model according to the verification charging demand data to obtain the predicted value of the charging demand; wherein the predicted value includes the charging demand in each time period; according to the predicted value, selecting the time period with the largest charging demand as the working time period of the mobile energy storage charging pile, and determining the charging start time point and the charging strategy; adjusting the charging voltage or current according to the charging strategy so that the mobile energy storage charging pile is in a fully discharged state, and obtaining the remaining power data of the mobile energy storage charging pile; according to the charging demand data of each region and the remaining power data, reversely adjusting the unconsumed power, adjusting the charging load distribution between regions, and obtaining the target scheduling ratio of the mobile energy storage charging pile resources; according to the target scheduling ratio and the charging request information of the electric vehicle user end, sending a scheduling instruction to the mobile energy storage charging pile controller that meets the target scheduling ratio requirement, so that the mobile energy storage charging pile performs a scheduling operation and provides charging services for the electric vehicle; the scheduling instruction includes the location information of the electric vehicle.

[0017] Through the above technical scheme, historical charging demand data and charging demand data of each region are first obtained. Then, the historical charging demand data is checked to obtain the verification charging demand data, and the charging demand prediction model is trained with it, so as to obtain the predicted value of the charging demand in each time period. Then, the time period with the largest charging demand is selected as the working period of the mobile energy storage charging pile according to the predicted value, and the charging start time point and charging strategy are determined. According to the charging strategy, the charging voltage or current is adjusted to make the mobile energy storage charging pile in a fully discharged state, and its remaining power data is obtained at the same time. Then, according to the charging demand data and the remaining power data of each region, the unconsumed electric energy is reversed to adjust the charging load distribution between regions, and the target scheduling ratio of the mobile energy storage charging pile resources is obtained. Finally, according to the target scheduling ratio and the charging request information of the electric vehicle user end, a scheduling instruction containing the electric vehicle location information is sent to the mobile energy storage charging pile controller that meets the target scheduling ratio requirements, so that the mobile energy storage charging pile performs scheduling operations and provides charging services for the electric vehicle. The present invention reasonably and efficiently schedules mobile charging piles through data verification and simulation training, avoiding the saturation of existing charging pile ratios and insufficient regional ratio balance during peak demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a method for scheduling a mobile energy storage charging pile provided by the first embodiment of the present invention; Figure 2 It is a structural diagram of a scheduling system for a mobile energy storage charging pile provided in a second embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Reference Figure 1 The first embodiment of the present invention provides a method for scheduling a mobile energy storage charging pile, comprising the following steps: S1, obtaining historical charging demand data and charging demand data for each region; S2, verifying the historical charging demand data to obtain verified charging demand data; S3, training a charging demand prediction model according to the verified charging demand data to obtain a predicted value of the charging demand; wherein the predicted value includes the charging demand amount in each time period; S4, according to the predicted value, selecting the time period with the largest charging demand as the working period of the mobile energy storage charging pile, and determining the charging start time point and charging strategy; S5, adjusting the charging voltage or current according to the charging strategy to put the mobile energy storage charging pile in a fully discharged state, and obtaining the remaining power data of the mobile energy storage charging pile; S6, according to the charging demand data of each area and the remaining power data, reverse the unconsumed power, adjust the charging load distribution between areas, and obtain the target scheduling ratio of mobile energy storage charging pile resources; S7, according to the target scheduling ratio and the charging request information of the electric vehicle user end, sending a scheduling instruction to the mobile energy storage charging pile controller that meets the target scheduling ratio requirements, so that the mobile energy storage charging pile performs scheduling operations and provides charging services for the electric vehicle; the scheduling instruction includes the location information of the electric vehicle.

[0021] In step S1, historical charging demand data and charging demand data for each region are obtained.

[0022] It should be noted that historical charging demand data is data reflecting charging conditions in different time periods in the past, and regional charging demand data is data on the differences in charging demand in different geographical regions. Among them, historical charging demand data is obtained from charging pile management systems, intelligent vehicle data platforms, etc., and charging demand data in each region is divided into regional statistics using geographic information systems combined with charging pile location information, or obtained with the assistance of mobile device positioning. Historical charging demand data can be used to train prediction models to determine the working hours and strategies of mobile energy storage charging piles. Charging demand data in each region can be used to reasonably allocate charging resources, optimize the layout of charging piles, and carry out targeted services.

[0023] In step S2, the historical charging demand data is verified to obtain verified charging demand data.

[0024] It should be noted that the verification charging demand data includes the idle time of the mobile energy storage charging pile, the idle time period, and the remaining power of the vehicle that was not fully charged in the previous time period.

[0025] In one embodiment, the historical charging demand data is first cleaned. Statistical methods are used to remove outliers that are obviously deviated from the normal range. For example, if the charging demand in a certain time period is very different from that in most other time periods and exceeds the reasonable range, it is identified as an outlier and removed. At the same time, the integrity of the data is checked to see if there are any missing records. For missing values, reasonable methods such as interpolation or mean filling can be used to supplement them. Secondly, a data consistency check is performed. This includes a time consistency check to ensure that the changes in charging demand in adjacent time periods are within a reasonable range to avoid unreasonable large fluctuations; it also includes a logical consistency check, which combines other relevant data such as power grid load, traffic flow, etc. to check whether the charging demand data is logically reasonable. The verification charging demand data is obtained by the above method. Among them, the verification charging demand data is used to train the charging demand prediction model to improve accuracy, assist in analyzing charging demand to formulate reasonable strategies, and provide a basis for decisions such as charging facility planning.

[0026] In step S3, the charging demand prediction model is trained according to the verified charging demand data to obtain a predicted value of the charging demand; wherein the predicted value includes the charging demand amount in each time period.

[0027] In one embodiment, an initial prediction model is constructed based on the verified charging demand data, and a loss function is configured for the initial prediction model; wherein the verified charging demand data includes the idle time of the mobile energy storage charging pile, the idle time period, and the remaining power of the vehicle that was not fully charged in the previous time period; the initial prediction model is trained until the loss function converges, and the model training is determined to be completed to obtain a charging demand prediction model; and the output result of the charging demand prediction model is used as the predicted value of the charging demand.

[0028] Among them, the loss function is configured as: The loss function value L represents the average error between the predicted value and the actual charging demand value, N is the total number of electric vehicles, is the predicted value, is the actual charging demand value.

[0029] It should be noted that the process of obtaining the charging demand prediction model includes inputting the verified charging demand data into the initial prediction model for iterative calculation. When the loss function value of the initial prediction model is greater than the preset loss threshold, the initial prediction parameters are updated to make the initial prediction parameters converge. The loss function value is calculated again until the loss function value is less than the preset loss threshold. It is then determined that the training is completed and the charging demand prediction model is obtained.

[0030] In step S4, according to the predicted value, the time period with the largest charging demand is selected as the working period of the mobile energy storage charging pile, and the charging start time point and charging strategy are determined.

[0031] It should be noted that the charging start time is a specific time determined based on the predicted period with the largest charging demand and the actual situation. The charging strategy is a series of methods and measures formulated to meet different charging needs and achieve efficient charging, including power allocation strategy, price adjustment strategy and service optimization strategy.

[0032] In one embodiment, the method for determining the charging start time point and the charging strategy in step S4 specifically includes steps S41 to S43, as follows: S41, determining the time period with the maximum charging demand according to the predicted value; In one embodiment, the predicted value is obtained by analyzing historical charging data, predicting statistical models, and collecting data from intelligent monitoring equipment. Then, data analysis is performed, and time series analysis tools are used to identify trends and periodicity, observe the changing trend to determine the growth or decline over time, and analyze the periodic characteristics. At the same time, statistical analysis is performed to calculate the average, peak, and valley charging demand in different time periods, and variance analysis and correlation analysis are performed. The data is then presented in a visual manner, such as a line chart, a bar chart, a heat map, etc., to more intuitively observe the changes in charging demand in different time periods, and quickly determine the time period with the largest charging demand. The determination method is then verified using historical data, and if there is a deviation, the cause is analyzed and the method and parameters are adjusted. Finally, since the charging demand will change over time and various factors, it is necessary to monitor the charging demand in real time, and when new data appears, the predicted value is updated in a timely manner and the time period with the largest charging demand is re-determined.

[0033] S42, using the period with the maximum charging demand as the working period of the mobile energy storage charging pile; According to the period with the highest charging demand, this period is set as the working period of the mobile energy storage charging pile.

[0034] S43, according to the working period, selecting the start time of the working period as the charging start time point, selecting a high-power fast charging mode, and determining a charging strategy.

[0035] Based on the work period, first extract the work start time from the calendar application or work scheduling system. Next, determine the charging start time to be one hour before the start of the work period. Then, configure the charging strategy on the charging station or smart charging device, select the high-power fast charging mode, and set the charging start time. After starting charging, monitor the charging status in real time to ensure that charging is proceeding as expected and completed before the start of the work period. Finally, record the data and results of the charging process to optimize the charging strategy.

[0036] In step S5, the charging voltage or current is adjusted according to the charging strategy to put the mobile energy storage charging pile in a fully discharged state, and the remaining power data of the mobile energy storage charging pile is obtained.

[0037] It should be noted that the remaining power data refers to the quantitative information of the remaining available power of the mobile energy storage charging pile at a specific moment, and the fully discharged state includes the minimum safe power threshold of the mobile energy storage charging pile.

[0038] In one embodiment, according to the charging strategy, the target charging voltage and the target charging current are calculated; the actual charging voltage and the actual charging current are adjusted to the target charging voltage and the target charging current to obtain the discharge condition; according to the discharge condition, the mobile energy storage charging pile discharge process is started, and the discharge is continued until the mobile energy storage charging pile is in a fully discharged state; according to the fully discharged state, the discharge operation is stopped, and the power measurement module pre-installed in the mobile energy storage charging pile is used to obtain the remaining power data of the mobile energy storage charging pile. The remaining power data is used to optimize the charging scheduling, and the charging piles with less remaining power are arranged to be charged first; it can predict the service capacity and provide a basis for resource allocation; and it provides a reference for energy management decisions and user services.

[0039] In step S6, according to the charging demand data of each area and the remaining electric energy data, the unconsumed electric energy is reversed, the charging load distribution between areas is adjusted, and the target scheduling ratio of the mobile energy storage charging pile resources is obtained.

[0040] In one embodiment, the method for obtaining the target scheduling ratio of mobile energy storage charging pile resources in the above step S6 specifically includes steps S61 to S63 as follows: S61, calculating the difference in electric energy demand of each area according to the charging demand data of each area and the remaining electric energy data; In this embodiment, the total charging demand of each region is first obtained based on the number of charging times and the average amount of electricity charged each time in the charging demand data of each region. Then, the remaining power data of each region is combined, that is, the total remaining power of the mobile energy storage charging piles in each region. Finally, the total charging demand of each region is subtracted from the total remaining power of the region, and the difference obtained is the difference in power demand of each region.

[0041] S62, performing reverse adjustment processing according to the power demand difference to determine the power adjustment amount of each area; In this embodiment, the calculated power demand differences of each region are first carefully analyzed. If the power demand difference of a region is positive, it means that the charging demand of the region exceeds the existing remaining power and there is a power shortage; if the power demand difference is negative, it means that the remaining power in the region is relatively sufficient.

[0042] For areas with positive differences in power demand, a reasonable power adjustment amount needs to be determined to increase the power supply in the area. This can be determined in the following ways: On the one hand, consider allocating some power from areas with negative differences in power demand, and determine the amount of power that can be allocated based on factors such as the distance between different areas, power transmission costs, and feasibility. On the other hand, it is also possible to consider arranging mobile energy storage charging piles to be concentrated in the area, or speeding up the charging speed of charging piles in the area to improve power supply capacity. Taking these factors into consideration, determine an adjustment amount that can effectively alleviate the power shortage in the area.

[0043] For areas where the difference in power demand is negative, the power supply in the area can be appropriately reduced, or the excess power can be allocated to other areas with demand. For example, the charging speed of the charging piles in the area can be reduced, or some mobile energy storage charging piles can be allocated to areas with greater power demand. The determined power adjustment amount should be able to fully utilize the excess power in the area without causing too much impact on the future charging demand of the area. Through the above reverse adjustment process, the power adjustment amount of each area is finally determined.

[0044] It should be noted that the reverse regulation process includes transferring unconsumed electric energy from an area with low electric energy demand to an area with high electric energy demand.

[0045] S63, adjusting the charging load distribution between regions according to the electric energy adjustment amount, and determining the optimization target, calculating the scheduling ratio scheme of each mobile energy storage charging pile through the optimization algorithm, and obtaining the target scheduling ratio of mobile energy storage charging pile resources through simulation test; It should be noted that the target scheduling ratio refers to the distribution ratio plan of mobile energy storage charging piles among different areas, which is calculated through an optimization algorithm and determined after simulation testing.

[0046] In this embodiment, according to the optimization algorithm, combined with the adjusted load and the number of mobile energy storage charging piles, a scheduling ratio plan for each mobile energy storage charging pile is calculated; the scheduling ratio plan is applied in a simulation environment and test data is collected; the test data is analyzed, the scheduling ratio plan is adjusted, and the target scheduling ratio of mobile energy storage charging pile resources is obtained.

[0047] In step S7, according to the target scheduling ratio and the charging request information of the electric vehicle user end, a scheduling instruction is sent to the mobile energy storage charging pile controller that meets the target scheduling ratio requirements, so that the mobile energy storage charging pile performs scheduling operations and provides charging services for the electric vehicle; the scheduling instruction includes the location information of the electric vehicle.

[0048] It should be noted that the dispatching operation refers to a series of actions performed by the mobile energy storage charging pile according to the dispatching instructions received.

[0049] In one embodiment, according to the target scheduling ratio and the charging request information of the electric vehicle user end, a mobile energy storage charging pile that meets the target scheduling ratio requirements is selected, and a scheduling instruction is generated; the scheduling instruction is sent to the controller of the charging pile through a communication protocol; the controller performs scheduling operations according to the scheduling instruction, including starting a charging program to provide charging services for the electric vehicle.

[0050] In summary, the present invention obtains historical charging demand data and charging demand data of each region; verifies the historical charging demand data to obtain verification charging demand data; trains the charging demand prediction model according to the verification charging demand data to obtain the predicted value of charging demand; wherein the predicted value includes the charging demand in each time period; according to the predicted value, selects the time period with the largest charging demand as the working time period of the mobile energy storage charging pile, and determines the charging start time point and the charging strategy; adjusts the charging voltage or current according to the charging strategy so that the mobile energy storage charging pile is in a fully discharged state, and obtains the remaining power data of the mobile energy storage charging pile; according to the charging demand data of each region and the remaining power data, reverses the unconsumed power, adjusts the charging load distribution between regions, and obtains the target scheduling ratio of mobile energy storage charging pile resources; according to the target scheduling ratio and the charging request information of the electric vehicle user end, sends a scheduling instruction to the mobile energy storage charging pile controller that meets the target scheduling ratio requirement, so that the mobile energy storage charging pile performs a scheduling operation and provides charging services for the electric vehicle; the scheduling instruction includes the location information of the electric vehicle.

[0051] Through the above technical scheme, historical charging demand data and charging demand data of each region are first obtained. Then, the historical charging demand data is checked to obtain the verification charging demand data, and the charging demand prediction model is trained with it, so as to obtain the predicted value of the charging demand in each time period. Then, the time period with the largest charging demand is selected as the working period of the mobile energy storage charging pile according to the predicted value, and the charging start time point and charging strategy are determined. According to the charging strategy, the charging voltage or current is adjusted to make the mobile energy storage charging pile in a fully discharged state, and its remaining power data is obtained at the same time. Then, according to the charging demand data and the remaining power data of each region, the unconsumed electric energy is reversed to adjust the charging load distribution between regions, and the target scheduling ratio of the mobile energy storage charging pile resources is obtained. Finally, according to the target scheduling ratio and the charging request information of the electric vehicle user end, a scheduling instruction containing the electric vehicle location information is sent to the mobile energy storage charging pile controller that meets the target scheduling ratio requirements, so that the mobile energy storage charging pile performs scheduling operations and provides charging services for the electric vehicle. The present invention reasonably and efficiently schedules mobile charging piles through data verification and simulation training, avoiding the saturation of existing charging pile ratios and insufficient regional ratio balance during peak demand.

[0052] The following describes the working process of the present invention by taking a relatively common scenario as an example. Please also refer to the working scenario schematic diagram.

[0053] In the management of a certain electric vehicle charging network, mobile energy storage charging piles are equipped with an intelligent scheduling system to achieve efficient service. The core of this system is a scheduling method based on data analysis, and its workflow is as follows: First, the system collects historical charging demand data and charging demand data for each region. These data are input into the system together with the performance parameters of the mobile energy storage charging pile. The performance parameters include key information such as the maximum output power and remaining power of the charging pile, which is crucial for subsequent analysis and decision-making.

[0054] Next, the system verifies the historical charging demand data to obtain verified charging demand data. The verified charging demand data is used to train the charging demand prediction model, which can accurately predict the charging demand in each time period. The model evaluates the actual charging demand and compares it with historical data to determine whether it meets the expected trend.

[0055] Based on the predicted value, the system selects the time period with the highest charging demand as the working period of the mobile energy storage charging pile, and determines the charging start time and charging strategy. If the predicted value shows that the demand is high in a certain period, the system will set a corresponding high-power output strategy; if the demand is low, the system will adjust the strategy to optimize resource utilization.

[0056] Afterwards, the system adjusts the charging voltage or current according to the charging strategy, so that the mobile energy storage charging pile is in a fully discharged state and obtains the remaining power data. Combining the charging demand data and remaining power data of each region, the system reverses the unconsumed power, analyzes the charging load distribution between regions, and generates the target scheduling ratio.

[0057] Based on the target dispatch ratio, the system generates dispatch instruction data. This data is used to instruct the mobile energy storage charging pile to perform dispatch operations so that it can accurately provide charging services for electric vehicles in need.

[0058] Finally, the system generates control instructions based on the charging strategy and dispatch instruction data to control the mobile energy storage charging pile in real time. These instructions ensure that the strategy adjustment and dispatch operation during the charging process meet the actual needs, thereby improving the quality and efficiency of charging services.

[0059] Reference Figure 2 The second embodiment of the present invention provides a scheduling system for a mobile energy storage charging pile, including: The data acquisition module 101 is used to acquire historical charging demand data and charging demand data of each region; A verification module 102, configured to verify the historical charging demand data to obtain verified charging demand data; A training module 103 is used to train a charging demand prediction model according to the verified charging demand data to obtain a predicted value of the charging demand; wherein the predicted value includes the charging demand amount in each time period; A strategy determination module 104 is used to select the time period with the largest charging demand as the working period of the mobile energy storage charging pile according to the predicted value, and determine the charging start time point and charging strategy; The adjustment module 105 is used to adjust the charging voltage or current according to the charging strategy so that the charging pile is in a fully discharged state and obtain the remaining power data of the charging pile; The allocation module 106 is used to reverse the unconsumed electric energy according to the charging demand data of each area and the remaining electric energy data, adjust the charging load distribution between areas, and obtain the target scheduling ratio of charging pile resources; The instruction scheduling module 107 is used to send a scheduling instruction to the charging pile controller that meets the target scheduling ratio requirements according to the target scheduling ratio and the charging request information of the electric vehicle user end, so that the charging pile performs scheduling operations and provides charging services for the electric vehicle; the scheduling instruction includes the location information of the electric vehicle.

[0060] Preferably, the adjustment module includes: A strategy calculation module, used to calculate a target charging voltage and a target charging current according to the charging strategy; A voltage adjustment module, used for adjusting the actual charging voltage and the actual charging current to the target charging voltage and the target charging current to obtain a discharge condition; A discharge module, used to start the discharge process of the mobile energy storage charging pile according to the discharge condition, and continue discharging until the mobile energy storage charging pile is in a fully discharged state, wherein the fully discharged state includes the minimum safe power threshold of the mobile energy storage charging pile; The remaining electric energy module is used to stop the discharge operation according to the complete discharge state, and use the electric energy measurement module preset in the mobile energy storage charging pile to obtain the remaining electric energy data of the mobile energy storage charging pile.

[0061] Preferably, the allocation module comprises: A calculation module, used for calculating the difference in electric energy demand of each area according to the charging demand data of each area and the remaining electric energy data; A reverse adjustment processing module, used to perform reverse adjustment processing according to the power demand difference and determine the power adjustment amount of each area; The optimization calculation module is used to adjust the charging load distribution between regions according to the electric energy adjustment amount, and determine the optimization target, calculate the scheduling ratio plan of each mobile energy storage charging pile through the optimization algorithm, and obtain the target scheduling ratio of mobile energy storage charging pile resources through simulation test.

[0062] Preferably, the instruction scheduling module includes: A selection generation module is used to select a mobile energy storage charging pile that meets the target scheduling ratio requirements according to the target scheduling ratio and the charging request information of the electric vehicle user end, and generate a scheduling instruction; A sending module, used to send the scheduling instruction to the controller of the charging pile through a communication protocol; The charging service module is used for the controller to perform scheduling operations according to the scheduling instructions, including starting the charging program to provide charging services for electric vehicles.

[0063] It should be noted that the scheduling system for a mobile energy storage charging pile provided in an embodiment of the present invention is used to execute all the process steps of the scheduling method for a mobile energy storage charging pile in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0064] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a scheduling program for a mobile energy storage charging pile. When the processor executes the computer program, the steps in the above-mentioned scheduling method embodiments of each mobile energy storage charging pile are implemented, such as Figure 1Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the instruction scheduling module.

[0065] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0066] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0067] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0068] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0069] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0070] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0071] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for scheduling a mobile energy storage charging pile, characterized in that: Executed by the dispatch system, including: Obtain historical charging demand data and charging demand data for each region; Verifying the historical charging demand data to obtain verified charging demand data; According to the verified charging demand data, a charging demand prediction model is trained to obtain a predicted value of the charging demand; wherein the predicted value includes the charging demand amount in each time period; According to the predicted value, the time period with the largest charging demand is selected as the working period of the mobile energy storage charging pile, and the charging start time point and charging strategy are determined; Adjust the charging voltage or current according to the charging strategy to put the mobile energy storage charging pile in a fully discharged state, and obtain the remaining power data of the mobile energy storage charging pile; According to the charging demand data of each area and the remaining electric energy data, the unconsumed electric energy is reversed, the charging load distribution between the areas is adjusted, and the target scheduling ratio of the mobile energy storage charging pile resources is obtained; According to the target scheduling ratio and the charging request information of the electric vehicle user end, a scheduling instruction is sent to the mobile energy storage charging pile controller that meets the target scheduling ratio requirements, so that the mobile energy storage charging pile performs scheduling operations and provides charging services for the electric vehicle; the scheduling instruction includes the location information of the electric vehicle.

2. The method for dispatching a mobile energy storage charging pile according to claim 1, characterized in that: According to the predicted value, the time period with the largest charging demand is selected as the working period of the mobile energy storage charging pile, and the charging start time point and charging strategy are determined, including: Determine the time period with the maximum charging demand according to the predicted value; The period with the maximum charging demand is used as the working period of the mobile energy storage charging pile; According to the working period, the start time of the working period is selected as the charging start time point, and a high-power fast charging mode is selected to determine the charging strategy.

3. The method for dispatching a mobile energy storage charging pile according to claim 1, characterized in that: The charging voltage or current is adjusted according to the charging strategy to put the mobile energy storage charging pile in a fully discharged state, and the remaining power data of the mobile energy storage charging pile is obtained, including: According to the charging strategy, a target charging voltage and a target charging current are calculated; Adjusting the actual charging voltage and the actual charging current to the target charging voltage and the target charging current to obtain a discharge condition; According to the discharge condition, the mobile energy storage charging pile discharge process is started, and the discharge is continued until the mobile energy storage charging pile is in a fully discharged state, wherein the fully discharged state includes the minimum safe power threshold of the mobile energy storage charging pile; According to the fully discharged state, the discharge operation is stopped, and the remaining power data of the mobile energy storage charging pile is obtained using the power measurement module preset in the mobile energy storage charging pile.

4. The method for dispatching a mobile energy storage charging pile according to claim 1, characterized in that: According to the charging demand data of each area and the remaining power data, the unconsumed power is reversed, the charging load distribution between the areas is adjusted, and the target scheduling ratio of mobile energy storage charging pile resources is obtained, including: Calculating the difference in electric energy demand of each area according to the charging demand data of each area and the remaining electric energy data; Perform reverse adjustment processing according to the power demand difference to determine the power adjustment amount of each area; According to the electric energy adjustment amount, the charging load distribution between regions is adjusted, and the optimization target is determined. The scheduling ratio scheme of each mobile energy storage charging pile is calculated by the optimization algorithm, and the target scheduling ratio of the mobile energy storage charging pile resources is obtained by simulation test; The reverse regulation process includes transferring unconsumed electric energy from an area with low electric energy demand to an area with high electric energy demand.

5. The method for dispatching a mobile energy storage charging pile according to claim 4, characterized in that: According to the scheduling ratio scheme of each mobile energy storage charging pile calculated by the optimization algorithm, the target scheduling ratio of the mobile energy storage charging pile resources is obtained by simulation test, including: According to the optimization algorithm, combined with the adjusted load and the number of mobile energy storage charging piles, a scheduling ratio scheme for each mobile energy storage charging pile is calculated; Apply the scheduling and matching scheme in a simulation environment and collect test data; The test data is analyzed, the scheduling ratio scheme is adjusted, and the target scheduling ratio of mobile energy storage charging pile resources is obtained.

6. The method for dispatching a mobile energy storage charging pile according to claim 1, characterized in that: According to the target scheduling ratio and the charging request information of the electric vehicle user end, a scheduling instruction is sent to the mobile energy storage charging pile controller that meets the target scheduling ratio requirements, so that the mobile energy storage charging pile performs a scheduling operation and provides charging services for the electric vehicle, including: According to the target scheduling ratio and the charging request information of the electric vehicle user end, a mobile energy storage charging pile that meets the target scheduling ratio requirement is selected, and a scheduling instruction is generated; Sending the dispatch instruction to the controller of the charging pile through a communication protocol; The controller performs scheduling operations according to the scheduling instructions, including starting a charging program to provide charging services for the electric vehicle.

7. The method for dispatching a mobile energy storage charging pile according to claim 1, characterized in that: According to the verified charging demand data, a charging demand prediction model is trained to obtain a predicted value of the charging demand, including: An initial prediction model is constructed according to the verification charging demand data, and a loss function is configured for the initial prediction model; wherein the verification charging demand data includes the idle time of the mobile energy storage charging pile, the idle time period, and the remaining power of the vehicle that was not fully charged in the previous time period; The initial prediction model is trained until the loss function converges, and the model training is determined to be complete to obtain a charging demand prediction model; The output result of the charging demand prediction model is used as the predicted value of the charging demand.

8. A dispatching system for a mobile energy storage charging pile, characterized in that: include: A data acquisition module is used to acquire historical charging demand data and charging demand data for each region; A verification module, used to verify the historical charging demand data to obtain verified charging demand data; A training module, used to train a charging demand prediction model according to the verified charging demand data to obtain a predicted value of the charging demand; wherein the predicted value includes the charging demand amount in each time period; A strategy determination module is used to select the time period with the largest charging demand as the working period of the mobile energy storage charging pile according to the predicted value, and determine the charging start time point and charging strategy; An adjustment module, used to adjust the charging voltage or current according to the charging strategy so that the charging pile is in a fully discharged state and obtain the remaining power data of the charging pile; An allocation module, configured to reverse the unconsumed electric energy according to the charging demand data of each area and the remaining electric energy data, adjust the charging load distribution between areas, and obtain a target scheduling ratio of charging pile resources; The instruction scheduling module is used to send a scheduling instruction to the charging pile controller that meets the target scheduling ratio requirements according to the target scheduling ratio and the charging request information of the electric vehicle user end, so that the charging pile performs scheduling operations and provides charging services for the electric vehicle; the scheduling instruction includes the location information of the electric vehicle.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for scheduling a mobile energy storage charging pile as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the scheduling method for the mobile energy storage charging pile according to any one of claims 1 to 7.

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