Electric vehicle charging load prediction method and device based on monte carlo simulation method
By constructing an electric vehicle charging simulation model using the Monte Carlo simulation method, setting charging constraints and calculating charging time, the prediction deviation problem when the number of charging piles is limited is solved, and more accurate charging load prediction is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUHAN HUAXIA INTELLIGENT TECH CO LTD
- Filing Date
- 2026-03-05
- Publication Date
- 2026-06-09
AI Technical Summary
When the number of charging piles in a region is limited, the applicability and accuracy of existing technologies for predicting electric vehicle charging load are low, making it difficult to meet the needs of refined power dispatching and charging facility planning.
A simulation model for electric vehicle charging was constructed using the Monte Carlo simulation method. Constraints such as charging date type, time period, start time and type were set to simulate the charging operation of electric vehicles. The charging duration and type were determined by the number of charging piles in use, and the total charging load was calculated.
It improves the accuracy and applicability of charging load forecasting, can adapt to the charging pile environment in different regions, reduces forecasting deviations caused by limited charging pile resources, and ensures that the forecast results are consistent with the actual situation.
Smart Images
Figure CN122178285A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electric vehicle load analysis technology, and specifically to a method and apparatus for predicting electric vehicle charging load based on Monte Carlo simulation. Background Technology
[0002] As electric vehicles (EVs) become a key component of the national development strategy, their industrial and market foundations have been initially established, and they have entered a rapid development phase driven by both policy and market forces. Driven by multiple factors such as low operating costs, diverse vehicle options, gradually improving charging infrastructure, and increased public awareness, the EV market is showing strong growth momentum. However, the diversified participation and user-driven nature of the EV charging operation market make charging station construction planning and charging strategy optimization crucial for improving operators' profitability. Therefore, accurate forecasting of EV charging load is particularly important.
[0003] Current research focuses primarily on electric vehicle load modeling and prediction based on probability statistics and data-driven approaches, emphasizing the analysis of the randomness of charging behavior, the correlation of key variables, and the practical validation of models. However, certain shortcomings remain: existing methods typically assume sufficient charging station resources, while in reality, many areas have a limited number of charging stations and uneven distribution, leading to charging resource shortages and impacting user strategies. Existing technologies fail to effectively incorporate factors such as charging station capacity limitations, queuing times, and user strategy adjustments, resulting in significant discrepancies between predicted charging loads and actual demand. Furthermore, prediction models relying on historical data struggle to reflect the dynamic characteristics of user behavior changes due to resource competition in areas with limited charging stations, such as off-peak charging or switching to other locations.
[0004] In summary, when the number of charging stations in a region is limited, these shortcomings make the applicability and accuracy of electric vehicle charging load forecasting low, making it difficult to meet the needs of refined power dispatching and charging facility planning. Summary of the Invention
[0005] In view of this, it is necessary to provide a method and device for predicting electric vehicle charging load based on Monte Carlo simulation, so as to solve the technical problem that the applicability and accuracy of electric vehicle charging load prediction are low when the number of charging piles in a region is limited.
[0006] To address the aforementioned problems, this invention provides a method for predicting electric vehicle charging load based on Monte Carlo simulation, comprising: Determine the target number of electric vehicles within the target area; A simulation model for electric vehicle charging is constructed based on the Monte Carlo simulation method. The simulation model is then used to simulate the charging operation of electric vehicles at charging stations in the target area under preset constraints. The constraints include the charging date type, charging time period, charging start time, and charging type set in the charging stations in the target area. The operating parameters for each electric vehicle to perform charging operations are extracted from the simulation results of the simulation model, and the charging time of the electric vehicle is determined based on the operating parameters and the number of charging piles already in use in the target area. Calculate the total charging load of electric vehicles in the target area based on the charging duration and the target number.
[0007] In one possible implementation, the constraints are constructed in the following manner: Set different charging time periods for each charging date type; Set the corresponding charging start time for each charging period; Set the corresponding charging type for each charging start time.
[0008] In one possible implementation, the operating parameters include charging date type, charging time period, and daily mileage. Determining the charging duration of the electric vehicle based on the operating parameters and the number of charging stations already in use within the target area includes: The battery state of charge at the end of the electric vehicle trip is determined based on the daily mileage. During the charging time period, the charging type when the electric vehicle performs a charging operation is determined based on the number of charging piles already in use in the target area. The charging time of the electric vehicle is calculated based on the charging power under the charging type and the battery state of charge.
[0009] In one possible implementation, the charging type includes a regular charging type and a fast charging type, wherein the charging power of the fast charging type is greater than that of the regular charging type, and determining the charging type when the electric vehicle performs a charging operation based on the number of charging piles already in use in the target area includes: When the number of charging piles in use within the target area is less than the preset proportion of the total number of charging piles, the charging type when the electric vehicle performs the charging operation is determined to be the regular charging type. When the number of charging piles already in use in the target area is not less than a preset proportion of the total number of charging piles, the charging type when the electric vehicle performs the charging operation is selected as the regular charging type based on a preset first probability, and the charging type when the electric vehicle performs the charging operation is selected as the fast charging type based on a second probability, where the second probability is the difference between 1 and the first probability.
[0010] In one possible implementation, calculating the total charging load of electric vehicles in the target area based on the charging duration and the target number includes: The start and end times of the charging operation are determined based on the charging start time and the charging duration, which are included in the operating parameters. Determine the time period for calculating the charging load within the specified start and end times; Within each time period, the total charging load of electric vehicles performing charging operations within the target area is calculated based on the target number.
[0011] In one possible implementation, calculating the total charging load of electric vehicles performing charging operations within the target area based on the target number includes: Determine the individual charging load of each electric vehicle within the target area that performs charging operations within the corresponding charging duration; The individual charging load of each electric vehicle is summed up according to the target number to obtain the total charging load of electric vehicles performing charging operations within the target area.
[0012] In one possible implementation, the method further includes: Determine the simulation time period corresponding to the total charging load of electric vehicles in the target area, wherein the simulation time period includes at least two consecutive time periods; During the simulation period, the simulation model is invoked to generate a charging load change curve of the total charging load over time, and the charging load change curve is visualized.
[0013] The present invention also provides an electric vehicle charging load prediction device based on Monte Carlo simulation, comprising: The statistics module is used to determine the target number of electric vehicles within the target area; The simulation module is used to construct a simulation model of electric vehicle charging based on the Monte Carlo simulation method, and to call the simulation model to simulate the charging operation of electric vehicles at charging piles in the target area under preset constraints. The constraints include the charging date type, charging time period, charging start time and charging type set in the charging piles in the target area. The calculation module is used to extract the operating parameters for each electric vehicle to perform a charging operation from the simulation results of the simulation model, and to determine the charging time of the electric vehicle based on the operating parameters and the number of charging piles already in use in the target area. The prediction module is used to calculate the total charging load of electric vehicles in the target area based on the charging duration and the target number.
[0014] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a program; the processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps of the electric vehicle charging load prediction method based on Monte Carlo simulation as described above.
[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for predicting electric vehicle charging load based on Monte Carlo simulation.
[0016] The beneficial effects of adopting the above implementation method are as follows: The electric vehicle charging load prediction method and device based on Monte Carlo simulation provided by this invention constructs the constraints of the simulation model by setting conditions in the charging piles in the target area. This incorporates realistic factors such as the limitations of the charging pile conditions in the target area into the simulation model, making the charging operation simulation process more closely resemble the real-world scenario of the target area. This approach allows the charging load prediction process to adapt to various charging pile environments in different target areas, demonstrating good applicability. Furthermore, by incorporating the number of used charging piles in the target area to calculate the charging time based on the charging operation simulation results, the prediction deviation caused by limited charging pile resources can be avoided, ensuring the consistency between the predicted charging load and the actual charging pile situation, and improving the accuracy of electric vehicle charging load prediction. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 A flowchart illustrating the electric vehicle charging load prediction method based on Monte Carlo simulation provided by this invention; Figure 2 A schematic diagram of the architecture of the simulation model provided by this invention; Figure 3A schematic diagram illustrating the effect of the charging load variation curve provided by the present invention; Figure 4 A schematic diagram of the electric vehicle charging load prediction device based on Monte Carlo simulation provided by the present invention; Figure 5 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0020] In the description of the embodiments of this application, unless otherwise stated, "a plurality of" means two or more.
[0021] In this embodiment of the invention, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, apparatus, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product or device.
[0022] The naming or numbering of steps in the embodiments of the present invention does not mean that the steps in the method flow must be executed in the time / logical order indicated by the naming or numbering. The execution order of the named or numbered process steps can be changed according to the technical purpose to be achieved, as long as the same or similar technical effect can be achieved.
[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0024] The electric vehicle charging load prediction method based on Monte Carlo simulation provided by this invention can be applied to scenarios involving charging statistics of electric vehicles in a residential area. The executing entity can be a charging pile management system, a maintenance system, or a terminal, server, or cloud device connected to these systems. These systems collect and upload the target number of electric vehicles in the corresponding area in real time, and then call the electric vehicle charging load prediction method based on Monte Carlo simulation provided by this invention to ultimately estimate the total charging load of electric vehicles in the target area. This estimate serves as a reference for power dispatching and facility planning in the area.
[0025] The electric vehicle charging load prediction method based on Monte Carlo simulation provided by this invention is described in detail below.
[0026] Figure 1 This is a flowchart illustrating the electric vehicle charging load prediction method based on Monte Carlo simulation provided by the present invention.
[0027] like Figure 1 As shown, the electric vehicle charging load prediction method based on Monte Carlo simulation can be implemented through the following steps 101 to 104, which are explained in detail below.
[0028] Step 101: Determine the target number of electric vehicles within the target area.
[0029] Here, we first define a target area for prediction, such as a residential community or urban area. Based on the market size of electric vehicles in the target area, we can determine the number of electric vehicles in that area. Specifically, we can use electric vehicle registration data based on the market size of the car market for prediction, or we can query and obtain data on electric vehicle ownership in the target area to determine the target number. Let's denote the target number as N.
[0030] Step 102: Construct a simulation model for electric vehicle charging based on the Monte Carlo simulation method, and call the simulation model under preset constraints to simulate the charging operation of electric vehicles at charging piles in the target area.
[0031] Here, the embodiment of the present invention uses the Monte Carlo simulation method for modeling, and constructs a simulation model that can simulate the charging operation of electric vehicles in the target area at the charging piles in the target area. The model can simulate the uncertain charging behavior of electric vehicles in the target area under set constraints, simulate the electric vehicle selecting a charging pile to perform the charging operation, and then output the model simulation data after the electric vehicle performs the charging operation.
[0032] The simulation is conducted under preset constraints. These constraints include the charging date type, charging time period, charging start time, and charging type set for the charging stations in the target area. The charging date type is divided into weekdays and rest days. The charging time period is from 6 PM to 7 AM the following day, or from 7 AM to 6 PM. The charging start time is set according to a distribution at a given time point, including uniform and normal distributions. For example, a uniform distribution is set as U(420, 1080) or U(1080, 1860), and a normal distribution is set as N(1150, 100...). 2 ) and N(1150, 150) 2 Charging types are divided into fast charging and regular charging.
[0033] In one possible implementation, the constraints are constructed in the following manner, as detailed below.
[0034] First, set different charging time periods for each charging date type; that is, set different charging time periods for different charging date types.
[0035] Furthermore, a corresponding charging start time is set for each charging period, that is, different charging start times are set for different charging periods. Finally, a corresponding charging type is set for each charging start time, that is, a corresponding charging type is set for different charging start times.
[0036] Therefore, by setting these three steps sequentially, the simulation model is limited to meet certain conditions when simulating electric vehicle charging operations, including charging date type, charging time period, charging start time, and charging type. These conditions restrict the charging operation mode of electric vehicles at charging stations in the target area, and the results of these constraint settings can be represented by Table 1 below: Table 1:
[0037] The settings in Table 1 ensure that the simulation model must execute the charging operation simulation process according to the aforementioned constraints. Furthermore, based on Table 1, it is assumed that all charging types are constant current charging to facilitate the calculation of charging power, and the influence of other random current factors is ignored. Charging load outside the time period specified in Table 1 is ignored, and the total charging load is the result of summing the individual charging loads of each electric vehicle performing its charging operation.
[0038] In this embodiment of the invention, by incorporating realistic factors such as the limitations of charging pile conditions in the target area into the simulation model, the charging operation simulation process of the simulation model is made closer to the real scene of the target area. This approach makes the charging load prediction process adaptable to various charging pile environments in different target areas, and has good applicability.
[0039] Step 103: Extract the operating parameters for each electric vehicle to perform charging operations from the simulation results of the simulation model, and determine the charging time of the electric vehicle based on the operating parameters and the number of charging piles already in use in the target area.
[0040] When performing the simulation, the simulation time and the target number of electric vehicles in the target area are set. These data and the contents of Table 1 are input into the simulation model, and then the model is started to simulate the charging operation of electric vehicles in the target area. The simulation results are various operational data of electric vehicles in the target area, divided into travel data and charging data. Travel data includes the battery state of charge (SOC) of the battery vehicle during travel, daily mileage, and battery SOC before charging. Charging data includes charging date type, charging time period, charging type, and charging start time.
[0041] Therefore, after the simulation process is completed, the operating parameters of each electric vehicle performing the charging operation can be extracted from the simulation results of the simulation model. These parameters can then be used to calculate the charging time in combination with the number of charging piles already in use in the target area, thereby enabling the prediction and estimation of the total charging load of electric vehicles.
[0042] In one possible implementation, the charging time of electric vehicles can be determined based on operating parameters and the number of charging piles already in use within the target area. This can be achieved in the following ways, which will be explained in detail below.
[0043] First, determine the battery state of charge at the end of the electric vehicle trip based on the daily mileage.
[0044] Here, the operating parameters include charging date type, charging time period, and daily mileage. In this embodiment of the invention, it is assumed that the battery power consumption of the electric vehicle is proportional to the driving distance, and that the power consumption per kilometer of travel is the same for each electric vehicle. The state of charge of the battery after the electric vehicle's trip is completed is calculated using the following formula: (1) in, This indicates the state of charge of the battery after the electric vehicle trip ends. To correspond to the battery state of charge of the electric vehicle at the time of its last complete charge, d represents the daily driving mileage, and This indicates the maximum daily driving distance of an electric vehicle, used to characterize the maximum driving range of an electric vehicle after its last complete charge.
[0045] Next, within the charging time period set in the charging station, the charging type when the electric vehicle performs the charging operation is determined based on the number of charging stations already in use in the target area.
[0046] Here, the state of charge of the battery after the electric vehicle trip can be used to determine whether there is a charging need for the electric vehicle, and at the same time, the charging method that the electric vehicle may choose can be determined. First, within the charging time period in the operating parameters, the charging type when the electric vehicle performs a charging operation is determined based on the number of charging piles already in use in the target area.
[0047] In one possible implementation, the charging type of an electric vehicle is determined based on the number of charging piles already in use within the target area. This can be achieved in the following ways, which are explained in detail below.
[0048] When the number of charging piles in use within the target area is less than the preset proportion of the total number of charging piles, the charging type selected when the electric vehicle performs the charging operation is the regular charging type.
[0049] Here, the total number of charging piles and the number of charging piles in use can be determined directly in the target area by counting the number of charging piles. Based on this, we can judge the occupancy of charging pile resources in the target area during the model simulation, and thus determine the charging type of electric vehicles using charging piles to perform charging operations. The charging type includes regular charging type and fast charging type. The charging power under the fast charging type is greater than the charging power under the regular charging type.
[0050] If the number of charging piles in use within the target area is less than the total number of charging piles at a preset ratio, it indicates that the charging pile resources in the target area are relatively sufficient during the simulation model's execution. Therefore, it can be determined that the charging type when the electric vehicle performs the charging operation is the conventional charging type. The preset ratio is denoted as... The value can generally be taken as 50%.
[0051] When the number of charging piles already in use in the target area is not less than the preset proportion of the total number of charging piles, the charging type when the electric vehicle performs the charging operation is selected as the regular charging type based on the preset first probability, and the charging type when the electric vehicle performs the charging operation is selected as the fast charging type based on the second probability.
[0052] If the number of used charging piles in the target area is not less than a preset proportion of the total number of charging piles, it indicates that the charging pile resources in the target area are relatively scarce during the current simulation model. To ensure rapid charging completion, the charging type for the electric vehicle to perform the charging operation can be selected as the conventional charging type based on a preset first probability. Here, the first probability is denoted as... For example, 50% can be used, but this can be adjusted according to the actual situation. If the number of used charging stations in the target area is close to the preset proportion of the total number of charging stations... ,but The smaller the value, the larger the value. In other cases, based on the second probability, the charging type for the electric vehicle is selected as fast charging. Here, the second probability is 1 and the first probability... The difference, i.e. In other words, if the regular charging type is not selected, it can be determined that the fast charging type has been selected.
[0053] In this embodiment of the invention, the charging type used in the simulation model is determined by referencing the number of charging piles already in use within the target area. This enables the rational selection of the charging operation mode under conditions of scarce charging resources, ensuring that the charging operation of electric vehicles is completed as much as possible with limited charging pile resources, and reducing the statistical error of the total charging load.
[0054] Finally, the charging time of the electric vehicle is calculated based on the charging power under the charging type and the battery state of charge.
[0055] Once the charging type for each electric vehicle during the simulation is determined, the charging power of the corresponding charging type and the battery state of charge at the end of the electric vehicle's trip can be utilized. The charging time of the electric vehicle is calculated and denoted as T, as shown in the following formula: (2) Where C represents the battery capacity of the electric vehicle, and P represents the charging power of the corresponding charging type, which can be determined in advance.
[0056] Therefore, based on the battery capacity of each electric vehicle and the state of charge of the battery after the trip, the charging time for each electric vehicle to perform the charging operation during the simulation model can be calculated one by one.
[0057] In this embodiment of the invention, based on the operating parameters of the model simulation results and the number of charging piles already in use within the target area, the charging type of each electric vehicle is determined, and then the charging time is calculated. This takes into account the impact of choosing between fast charging and slow charging modes on the charging load distribution, making the subsequent load prediction results more comprehensive and accurate.
[0058] Step 104: Calculate the total charging load of electric vehicles in the target area based on the charging time and the target number.
[0059] After determining the charging time for each electric vehicle in the simulation model, the individual charging load for each electric vehicle is further calculated. Then, combined with the target number of battery-powered vehicles within the target area, the total charging load within the target area is estimated.
[0060] In one possible implementation, the total charging load of electric vehicles in the target area is calculated based on the charging time and the target number. This can be achieved in the following way, which is explained in detail below.
[0061] First, the start and end times of the charging operation are determined based on the operating parameters, including the charging start time and the charging duration.
[0062] The operating parameters extracted from the simulation results include the charging start time, denoted as... Therefore, based on the charging start time The charging duration T determines the charging end time, which in turn determines the start and end times of the charging operation. The charging end time is denoted as... The formula is as follows: (3) Next, determine the time period for calculating the charging load within the start and end times.
[0063] In this embodiment of the invention, the charging load at the start and end times of charging is calculated according to a preset time cycle. Assuming a day is 1440 minutes, and each minute is considered a time cycle, then 1440 charging load calculations are needed per day. Since the start and end times of each electric vehicle's charging operation, i.e., the charging duration, have already been determined, the number of time cycles within the time period of the start and end times can be further determined.
[0064] Finally, within each time period, the total charging load of electric vehicles performing charging operations within the target area is calculated based on the target number.
[0065] In one possible implementation, the total charging load of electric vehicles performing charging operations within the target area is calculated based on the target number. This can be achieved in the following ways, which are explained in detail below.
[0066] First, determine the individual charging load of each electric vehicle in the target area that performs charging operations within the corresponding charging duration. Then, accumulate the individual charging load of each electric vehicle according to the target number to obtain the total charging load of electric vehicles performing charging operations in the target area.
[0067] Specifically, for each electric vehicle, in each time period Within this timeframe, the charging load of an electric vehicle (EV) can be determined based on the charging power P corresponding to the charging type. Here, without considering charge loss during charging, the independent charging load of the EV can be taken as a value within a given time period. The total charging power within the range is denoted as , where i represents the i-th time period for calculating the charging load within the start and end times. Therefore, using the same method, an independent charging load can be calculated for each time period within the charging duration for the electric vehicle.
[0068] Finally, the independent charging load for each electric vehicle is determined based on the target number N of electric vehicles. By summing the results, the total charging load of electric vehicles performing charging operations within the target area can be determined, denoted as . The formula is expressed as follows: (4) Where n represents the nth electric vehicle in the target area, and N represents the target number of electric vehicles in the target area.
[0069] Similarly, using the same method, in each time period Within the target area, the total charging load of electric vehicles performing charging operations can be calculated. This serves as a prediction of the charging load for electric vehicles.
[0070] In this embodiment of the invention, the total charging load of electric vehicles in a target area is assessed by using each electric vehicle as the unit of calculation. The individual charging load of each electric vehicle is calculated independently based on its charging duration and then summed. This avoids planning the charging time sequence of electric vehicles, reduces computational load, and ensures the accuracy of the total charging load. Furthermore, by strictly dividing the calculation into fixed time periods to obtain the total charging load, the changes in the total charging load over time are more clearly determined. This allows for the acquisition of electric vehicle charging behavior strategies within the target area, even with limited charging pile resources, providing a targeted basis for subsequent power dispatching and charging facility planning.
[0071] In one possible implementation, after calculating the total charging load of electric vehicles in the target area, the embodiments of the present invention also realize the visualization of the total charging load.
[0072] First, determine the simulation time period corresponding to the total charging load of electric vehicles in the target area. This can be achieved by setting a specific simulation time period to visualize the total charging load of electric vehicles within the target area. The simulation time period includes at least two consecutive time cycles, but it must be ensured that the simulation time period meets the constraints of the simulation model, i.e., the simulation time period falls within the charging time period.
[0073] Furthermore, during the simulation period, the simulation model is invoked to generate a charging load change curve showing the total charging load changing over time, and the charging load change curve is visualized.
[0074] like Figure 2 As shown, after setting the simulation time period, it can be input into the simulation model. The simulation model then filters the data based on the simulation time period, determines the time cycles included within that period, and outputs the total charging load for each time cycle within the simulation time period. This is visualized through the formation of a charging load change curve. The visualized curve is shown in the figure below. Figure 3 As shown, the horizontal axis of the coordinate system in the effect diagram represents the time periods included in the simulation period, with six time periods displayed in the figure. The vertical axis represents the total charging load within the corresponding time period. Thus, this visualization clearly shows the changes in the total charging load during the simulation period, providing a basis for subsequent power dispatching and charging facility planning.
[0075] In summary, this embodiment of the invention constructs the constraints of the simulation model by setting conditions in the charging piles within the target area. By incorporating realistic factors such as the limitations of the charging pile conditions in the target area into the simulation model, the charging operation simulation process more closely resembles the actual scenario of the target area. This approach allows the charging load prediction process to adapt to various charging pile environments in different target areas, demonstrating good applicability. Furthermore, by incorporating the number of already used charging piles in the target area into the calculation of charging time based on the charging operation simulation results, prediction deviations caused by limited charging pile resources can be avoided, ensuring consistency between the predicted charging load and the actual charging pile situation, and improving the accuracy of electric vehicle charging load prediction.
[0076] The electric vehicle charging load prediction device based on Monte Carlo simulation method provided by this invention is described in detail below.
[0077] like Figure 4 As shown, the electric vehicle charging load prediction device based on Monte Carlo simulation method specifically includes a statistics module 401, a simulation module 402, a calculation module 403, and a prediction module 404.
[0078] The system includes the following modules: a statistics module 401, used to determine the target number of electric vehicles within the target area; a simulation module 402, used to construct a simulation model for electric vehicle charging based on the Monte Carlo simulation method, and to simulate the charging operation of electric vehicles at charging stations in the target area under preset constraints, including the charging date type, charging time period, charging start time, and charging type set in the charging stations in the target area; a calculation module 403, used to extract the operating parameters for each electric vehicle's charging operation from the simulation results of the simulation model, and to determine the charging duration of the electric vehicles based on the operating parameters and the number of charging stations already in use in the target area; and a prediction module 404, used to calculate the total charging load of electric vehicles in the target area based on the charging duration and the target number.
[0079] In one possible implementation, the electric vehicle charging load prediction device based on Monte Carlo simulation further includes a visualization module 405. The visualization module 405 is used to determine the simulation time period corresponding to the total charging load of electric vehicles in the target area, wherein the simulation time period includes at least two consecutive time periods; within the simulation time period, the simulation model is invoked to generate a charging load change curve of the total charging load over time, and the charging load change curve is visualized.
[0080] The electric vehicle charging load prediction device based on Monte Carlo simulation provided in the above embodiments can realize the technical solutions described in the above embodiments of the electric vehicle charging load prediction method based on Monte Carlo simulation. The specific implementation principles of each module or unit can be found in the corresponding content in the above embodiments of the electric vehicle charging load prediction method based on Monte Carlo simulation, and their technical effects can also be referred to each other, which will not be repeated here.
[0081] like Figure 5 As shown, the present invention also provides an electronic device 500. The electronic device 500 includes a processor 501, a memory 502, and a display 503. Figure 5 Only some components of the electronic device 500 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.
[0082] In some embodiments, memory 502 may be an internal storage unit of electronic device 500, such as a hard disk or memory of electronic device 500. In other embodiments, memory 502 may also be an external storage device of electronic device 500, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 500.
[0083] Furthermore, the memory 502 may include both internal storage units of the electronic device 500 and external storage devices. The memory 502 is used to store application software and various types of data installed on the electronic device 500.
[0084] In some embodiments, processor 501 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 502 or process data, such as the electric vehicle charging load prediction method based on Monte Carlo simulation in this invention.
[0085] In some embodiments, display 503 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 503 is used to display information from electronic device 500 and to display a visual user interface. Components 501-503 of electronic device 500 communicate with each other via a system bus.
[0086] In some embodiments of the present invention, when the processor 501 executes the charging load prediction program in the memory 502, the following steps can be implemented: determining the target number of electric vehicles in the target area; constructing a simulation model for electric vehicle charging based on the Monte Carlo simulation method, and calling the simulation model under preset constraints to simulate the charging operation of electric vehicles at charging piles in the target area, wherein the constraints include the charging date type, charging time period, charging start time, and charging type set in the charging piles in the target area; extracting the operating parameters for each electric vehicle to perform charging operations from the simulation results of the simulation model, and determining the charging duration of electric vehicles based on the operating parameters and the number of charging piles already in use in the target area; calculating the total charging load of electric vehicles in the target area based on the charging duration and the target number.
[0087] It should be understood that when the processor 501 executes the charging load prediction program in the memory 502, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.
[0088] Furthermore, the embodiments of the present invention do not specifically limit the type of electronic device 500 mentioned. Electronic device 500 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, electronic device 500 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).
[0089] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program is implemented to perform the electric vehicle charging load prediction method based on Monte Carlo simulation provided by the above methods. The method includes: determining the target number of electric vehicles in a target area; constructing a simulation model of electric vehicle charging based on Monte Carlo simulation, and simulating the charging operation of electric vehicles at charging piles in the target area under preset constraints, wherein the constraints include the charging date type, charging time period, charging start time, and charging type set in the charging piles in the target area; extracting the operating parameters of each electric vehicle performing the charging operation from the simulation results of the simulation model, and determining the charging duration of the electric vehicles based on the operating parameters and the number of used charging piles in the target area; and calculating the total charging load of electric vehicles in the target area based on the charging duration and the target number.
[0090] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.
[0091] The above provides a detailed description of the electric vehicle charging load prediction method and device based on Monte Carlo simulation provided by the present invention. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for predicting electric vehicle charging load based on Monte Carlo simulation, characterized in that, include: Determine the target number of electric vehicles within the target area; A simulation model for electric vehicle charging is constructed based on the Monte Carlo simulation method. The simulation model is then used to simulate the charging operation of electric vehicles at charging stations in the target area under preset constraints. The constraints include the charging date type, charging time period, charging start time, and charging type set in the charging stations in the target area. The operating parameters for each electric vehicle to perform charging operations are extracted from the simulation results of the simulation model, and the charging time of the electric vehicle is determined based on the operating parameters and the number of charging piles already in use in the target area. Calculate the total charging load of electric vehicles in the target area based on the charging duration and the target number.
2. The electric vehicle charging load prediction method based on Monte Carlo simulation as described in claim 1, characterized in that, The constraints are constructed in the following manner: Set different charging time periods for each charging date type; Set the corresponding charging start time for each charging period; Set the corresponding charging type for each charging start time.
3. The electric vehicle charging load prediction method based on Monte Carlo simulation according to claim 1, characterized in that, The operating parameters include charging date type, charging time period, and daily mileage. Determining the charging time of the electric vehicle based on the operating parameters and the number of charging stations already in use within the target area includes: The battery state of charge at the end of the electric vehicle trip is determined based on the daily mileage. During the charging time period, the charging type when the electric vehicle performs a charging operation is determined based on the number of charging piles already in use in the target area. The charging time of the electric vehicle is calculated based on the charging power under the charging type and the battery state of charge.
4. The electric vehicle charging load prediction method based on Monte Carlo simulation according to claim 3, characterized in that, The charging type includes conventional charging and fast charging, wherein the charging power of fast charging is greater than that of conventional charging. Determining the charging type for the electric vehicle based on the number of charging piles already in use within the target area includes: When the number of charging piles in use within the target area is less than the preset proportion of the total number of charging piles, the charging type when the electric vehicle performs the charging operation is determined to be the regular charging type. When the number of charging piles already in use in the target area is not less than a preset proportion of the total number of charging piles, the charging type when the electric vehicle performs the charging operation is selected as the regular charging type based on a preset first probability, and the charging type when the electric vehicle performs the charging operation is selected as the fast charging type based on a second probability, where the second probability is the difference between 1 and the first probability.
5. The electric vehicle charging load prediction method based on Monte Carlo simulation according to claim 1, characterized in that, The step of calculating the total charging load of electric vehicles in the target area based on the charging duration and the target quantity includes: The start and end times of the charging operation are determined based on the charging start time and the charging duration, which are included in the operating parameters. Determine the time period for calculating the charging load within the specified start and end times; Within each time period, the total charging load of electric vehicles performing charging operations within the target area is calculated based on the target number.
6. The electric vehicle charging load prediction method based on Monte Carlo simulation according to claim 5, characterized in that, The step of calculating the total charging load of electric vehicles performing charging operations within the target area based on the target number includes: Determine the individual charging load of each electric vehicle within the target area that performs charging operations within the corresponding charging duration; The individual charging load of each electric vehicle is summed up according to the target number to obtain the total charging load of electric vehicles performing charging operations within the target area.
7. The electric vehicle charging load prediction method based on Monte Carlo simulation according to claim 1, characterized in that, The method further includes: Determine the simulation time period corresponding to the total charging load of electric vehicles in the target area, wherein the simulation time period includes at least two consecutive time periods; During the simulation period, the simulation model is invoked to generate a charging load change curve of the total charging load over time, and the charging load change curve is visualized.
8. A device for predicting electric vehicle charging load based on Monte Carlo simulation, characterized in that, include: The statistics module is used to determine the target number of electric vehicles within the target area; The simulation module is used to construct a simulation model of electric vehicle charging based on the Monte Carlo simulation method, and to call the simulation model to simulate the charging operation of electric vehicles at charging piles in the target area under preset constraints. The constraints include the charging date type, charging time period, charging start time and charging type set in the charging piles in the target area. The calculation module is used to extract the operating parameters for each electric vehicle to perform a charging operation from the simulation results of the simulation model, and to determine the charging time of the electric vehicle based on the operating parameters and the number of charging piles already in use in the target area. The prediction module is used to calculate the total charging load of electric vehicles in the target area based on the charging duration and the target number.
9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps of the electric vehicle charging load prediction method based on Monte Carlo simulation as described in any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the electric vehicle charging load prediction method based on Monte Carlo simulation as described in any one of claims 1 to 7.