Simulation system and method for vehicle charging behavior data of charging stations

Abstract

A simulation system and a simulation method of vehicle charging behavior data of a charging station. The simulation system comprises a vehicle group decision unit, an arrival time simulation unit, a duration stay simulation unit and a charging demand simulation unit. The vehicle group decision unit is used to determine a vehicle group of a vehicle. The arrival time simulation unit is used to obtain a simulated arrival time of the vehicle according to the vehicle group. The duration stay simulation unit is used to obtain a simulated duration stay of the vehicle according to the vehicle group. The charging demand simulation unit is used to obtain a simulated charging demand of the vehicle according to the vehicle group.

Description

Technical Field

[0001] This invention relates to a simulation system and method, and more particularly to a simulation system and method for vehicle charging behavior data at a charging station. Background Technology

[0002] To effectively manage energy and reduce operating costs, charging stations typically rely on models for inference and scheduling when planning charging and discharging activities. These models, based on predicted electricity demand, electricity price changes, and renewable energy generation, help determine when to perform charging or discharging operations to achieve load balancing and efficiency optimization. Therefore, intelligent model inference plays a crucial role in the operational planning of charging stations.

[0003] However, building accurate inferential models relies on a large amount of representative real-world data. This data typically covers factors such as traffic patterns, electricity consumption behavior, and weather changes, which are quite difficult to collect and often require long periods of observation and accumulation. As a result, the construction process of charging stations, from design to actual operation, often takes a year or even longer due to insufficient data and excessively long model validation times. Summary of the Invention

[0004] This invention relates to a simulation system and method for vehicle charging behavior data at charging stations. It utilizes appropriate clustering techniques to set the distribution of vehicle charging behavior data and then simulates various information based on this distribution. When collecting vehicle charging behavior data is difficult, this invention uses a simulation system to quickly generate multiple data points. In this way, the inference of charging and discharging schedules can be reduced from several months to a few days, significantly accelerating the establishment timeline of charging stations.

[0005] According to one aspect of the present invention, a method for simulating vehicle charging behavior data at a charging station is provided. The simulation method includes the following steps: determining a vehicle group for a vehicle; obtaining a simulated arrival time for the vehicle based on the vehicle group; obtaining a simulated duration stay for the vehicle based on the vehicle group; and obtaining a simulated charging demand for the vehicle based on the vehicle group.

[0006] According to another aspect of the present invention, a simulation system for vehicle charging behavior data at a charging station is provided. The simulation system includes a vehicle group determination unit, an arrival time simulation unit, a dwell time simulation unit, and a charging demand simulation unit. The vehicle group determination unit determines a vehicle group for a vehicle. The arrival time simulation unit obtains a simulated arrival time for a vehicle based on the vehicle group. The dwell time simulation unit obtains a simulated dwell time for a vehicle based on the vehicle group. The charging demand simulation unit obtains a simulated charging demand for a vehicle based on the vehicle group.

[0007] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the present invention. Attached Figure Description

[0008] Figure 1 This example illustrates the application of vehicle charging behavior data at charging stations.

[0009] Figure 2 A block diagram illustrating a simulation system for vehicle charging behavior data according to an embodiment of the present invention is shown.

[0010] Figure 3 A flowchart illustrating a method for simulating vehicle charging behavior data at a charging station according to an embodiment of the present invention is shown.

[0011] Figure 4 A detailed flowchart illustrating step S130 according to an embodiment of the present invention is shown.

[0012] Figure 5 An example illustrates a group of vehicles according to one embodiment.

[0013] Figure 6 Organize the behavioral patterns, charging methods, charging frequency, frequent arrival times, and frequent dwell times of each vehicle group.

[0014] Figure 7 This example illustrates a group of vehicles that you want to simulate.

[0015] Figure 8 A detailed flowchart illustrating step S132 according to an embodiment of the present invention is shown.

[0016] Figure 9A The historical arrival time probability density function and the simulated arrival time probability density function are illustrated according to an embodiment of the present invention.

[0017] Figure 9B The simulated arrival time probability density function with Gaussian variability added according to an embodiment of the present invention is illustrated.

[0018] Figure 9C An example illustrating the Monte Carlo algorithm.

[0019] Figure 10 A detailed flowchart illustrating step S133 according to an embodiment of the present invention is shown.

[0020] Figure 11A The cumulative distribution function of historical dwell time according to an embodiment of the present invention is illustrated.

[0021] Figure 11B The probability density function of historical dwell time according to an embodiment of the present invention is illustrated.

[0022] Figure 11C The probability density function of simulated dwell time according to an embodiment of the present invention is illustrated.

[0023] Figure 11D The simulation dwell time probability density function with Gaussian variation added is illustrated according to an embodiment of the present invention.

[0024] Figure 12 A detailed flowchart illustrating step S134 according to an embodiment of the present invention is shown.

[0025] Figure 13A The cumulative distribution function of historical charging demand according to an embodiment of the present invention is illustrated.

[0026] Figure 13B The probability density function of historical charging demand according to an embodiment of the present invention is illustrated.

[0027] Figure 13C The probability density function of simulated charging demand according to an embodiment of the present invention is illustrated.

[0028] Figure 13D The probability density function of simulated charging demand with Gaussian variation is illustrated according to an embodiment of the present invention.

[0029] In the attached figures, the following labels are used:

[0030] 131: Vehicle Group Decision Unit

[0031] 132: Arrival Time Simulation Unit

[0032] 1321, 1331, 1341: Historical data acquisition elements

[0033] 1322, 1332, 1342: Function decomposition elements

[0034] 1323, 1333, 1343: Mutant Enhancement Elements

[0035] 1324, 1334, 1344: Generating elements

[0036] 133: Dwell Time Simulation Unit

[0037] 134: Charging Demand Simulation Unit

[0038] 135: Departure Time Simulation Unit

[0039] 136: Charging Time Simulation Unit

[0040] 137: Initial Power Simulation Unit

[0041] 180: Storage Unit

[0042] 190: Control Unit

[0043] 1000: Simulation System

[0044] 9000: Charge / Discharge Scheduling System

[0045] AT: Simulated Arrival Time

[0046] BC: Battery Capacity

[0047] BV: Behavioral Fixedness

[0048] CD: Simulated charging demand

[0049] CDF3: Cumulative Distribution Function of Historical Dwell Time

[0050] CDF4: Cumulative Distribution Function of Historical Charging Demand

[0051] CDS: Charge / Discharge Schedule

[0052] CR: Charging speed

[0053] CT: Simulated charging time

[0054] DS: Simulated Dwell Time

[0055] FAT: Frequent Arrival Time

[0056] FDS: Frequent Stay Time

[0057] FQ: Charging frequency

[0058] GP: Vehicle Group

[0059] IF1: Chart Information

[0060] GS21, GS22, GS31, GS32, GS41, GS42: Gaussian mutation

[0061] LT: Simulated departure time

[0062] PB: Scale

[0063] PDF2: Historical Arrival Time Probability Density Function

[0064] PDF21, PDF22: Simulated Arrival Time Probability Density Function

[0065] PDF3: Historical Stay Time Probability Density Function

[0066] PDF31, PDF32: Probability density function for simulated dwell time

[0067] PDF4: Historical Charging Demand Probability Density Function

[0068] PDF41, PDF42: Probability density function of simulated charging demand

[0069] S110, S120, S130, S131, S132, S1321, S1322, S1323, S1324, S1325, S1326, S1327, S133, S1331, S1332, S1333, S1334, S1335, S1336, S1337, S134, S1341, S1342, S1343, S1344, S1345, S1346, S1347, S135, S136, S137, S138, S140, S150, S160, S170: Steps

[0070] SC: Simulated starting charge

[0071] SMD: Vehicle Charging Behavior Data

[0072] TG: Target Battery Level

[0073] TY: Charging method

[0074] VC: Vehicle Detailed Implementation

[0075] The structural and working principles of the present invention will be described in detail below with reference to the accompanying drawings:

[0076] The technical terms used in this specification are based on common terminology in the field. Where this specification provides explanations or definitions for certain terms, the interpretations of those terms shall be based on the explanations or definitions provided in this specification. Each embodiment of the present invention has one or more technical features. Where feasible, those skilled in the art may selectively implement some or all of the technical features in any embodiment, or selectively combine some or all of the technical features in these embodiments.

[0077] Please refer to Figure 1This example illustrates the application of vehicle charging behavior data (SMD) at a charging station. In this embodiment, the simulation system 1000 can simulate the vehicle charging behavior data (SMD) at the charging station. The vehicle charging behavior data (SMD) can be, for example, dozens to tens of thousands of different vehicle charging behaviors recorded at this charging station. Simulated charging behaviors may include, for example, simulated arrival time / start time (AT), simulated leaving time / end time (LT), simulated charging duration (DC), simulated stay time (DS), simulated charging demand (CD), simulated starting state of charge (SC), ... but are not limited to these.

[0078] These vehicle charging behavior data (SMD) can be data from a specific period, such as several years, a quarter, a year, or a single day. Taking a year's worth of data as an example, this vehicle charging behavior data SMD can be analyzed into various statistical charts. For example... Figure 1 As shown, the chart information IF1 is a statistical chart of simulated arrival time AT and simulated departure time LT, but it is not limited to this. This chart information IF1 or vehicle charging behavior data SMD can be input into a charge / discharge scheduling system 9000 to deduce the optimal charge / discharge schedule CDS.

[0079] In situations where collecting vehicle charging behavior data is difficult, this invention uses a simulation system 1000 to quickly generate several vehicle charging behavior data sets (SMD). This reduces the inference of charge / discharge schedules (CDS) from several months to a few days, significantly accelerating the establishment of charging stations.

[0080] Please refer to Figure 2The diagram illustrates a block diagram of a vehicle charging behavior data simulation system 1000 according to an embodiment of the present invention. The simulation system 1000 includes a vehicle group determination unit 131, an arrival time simulation unit 132, a dwell time simulation unit 133, a charging demand simulation unit 134, a departure time simulation unit 135, a charging time simulation unit 136, an initial charge level simulation unit 137, a storage unit 180, and a control unit 190. The vehicle group determination unit 131, arrival time simulation unit 132, dwell time simulation unit 133, charging demand simulation unit 134, departure time simulation unit 135, charging time simulation unit 136, and initial charge level simulation unit 137 are used to simulate various information. The control unit 190 is used to control the simulation method and integrate various information. Storage unit 180 is used to store data, such as any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk drive (HDD), solid state drive (SSD), or similar elements or combinations thereof, and is used to store multiple modules or various applications that can be executed by a processor.

[0081] The vehicle group determination unit 131, arrival time simulation unit 132, dwell time simulation unit 133, charging demand simulation unit 134, departure time simulation unit 135, charging time simulation unit 136, initial charge simulation unit 137, and / or control unit 190 are, for example, a circuit, a circuit board, a storage device storing program code, or a chip. The chip is, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose microcontrollers (MCUs), microprocessors, digital signal processors (DSPs), programmable controllers, application-specific integrated circuits (ASICs), graphics processing units (GPUs), image signal processors (ISPs), image processing units (IPUs), arithmetic logic units (ALUs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), or other similar elements or combinations thereof.

[0082] Arrival time simulation unit 132 includes a historical data acquisition element 1321, a function decomposition element 1322, a generation element 1324, and a variation enhancement element 1323. Dwell time simulation unit 133 includes a historical data acquisition element 1331, a function decomposition element 1332, a generation element 1334, and a variation enhancement element 1333. Charging demand simulation unit 134 includes a historical data acquisition element 1341, a function decomposition element 1342, a generation element 1344, and a variation enhancement element 1343.

[0083] In this embodiment, the simulation system 1000 can set the distribution of vehicle charging behavior data through appropriate clustering techniques, and then simulate various information accordingly. A flowchart is provided below to explain the operation of each component in detail.

[0084] Please refer to Figure 3The diagram illustrates a flowchart of a method for simulating vehicle charging behavior data at a charging station according to an embodiment of the present invention. The simulation method includes, for example, steps S110 to S170. The simulation method is executed, for example, by a computer, chip, firmware, or computer-readable recording medium loaded with program code.

[0085] In step S110, the control unit 190 sets a start date for a simulation period. The simulation period may be, for example, a year, a quarter, or a month. The control unit 190 may, for example, randomly select a date within the simulation period.

[0086] Next, in step S120, the control unit 190 sets the number of simulated vehicles for this date. The control unit 190, for example, randomly selects the number of vehicles within a predetermined range.

[0087] Then, in step S130, the control unit 190 controls the vehicle group decision unit 131, arrival time simulation unit 132, stay time simulation unit 133, charging demand simulation unit 134, departure time simulation unit 135, charging time simulation unit 136 and initial charge simulation unit 137 to simulate a vehicle charging behavior data SMD.

[0088] Next, in step S140, the control unit 190 determines whether the vehicle charging behavior data SMD passes verification. For example, the control unit 190 can verify whether the simulated dwell time DS is greater than the simulated charging time CT, whether the simulated initial charge SC is greater than 0, etc. If the vehicle charging behavior data SMD fails verification, the process must return to step S130; if the vehicle charging behavior data SMD passes verification, the process proceeds to step S150.

[0089] In step S150, the control unit 190 determines whether the accumulated vehicle charging behavior data SMD for the day has reached the number of simulated vehicles. If the accumulated vehicle charging behavior data SMD for the day has not reached the number of simulated vehicles, the process returns to step S130; if the accumulated vehicle charging behavior data SMD for the day has reached the number of simulated vehicles, the process proceeds to step S160.

[0090] In step S160, the control unit 190 determines whether the simulation has been completed for each day during the simulation period. If the simulation has been completed for each day during the simulation period, the process ends; if there is a day during the simulation period that has not been completed, the process proceeds to step S170.

[0091] In step S170, the control unit 190 determines the next date to be simulated. For example, the control unit 190 selects the next date sequentially in a cyclical manner.

[0092] After each day of the simulation is completed, vehicle charging behavior data SMD can be stored in storage unit 180 or output from storage unit 180. This vehicle charging behavior data SMD can be input to... Figure 1 The 9000 charge / discharge scheduling system is used to deduce the optimal charge / discharge scheduling CDS.

[0093] Please refer to Figure 4 The diagram illustrates a detailed flowchart of step S130 according to an embodiment of the present invention. Step S130 includes, for example, steps S131 to S138.

[0094] In step S131, the vehicle group determination unit 131 determines the vehicle group GP of vehicle VC.

[0095] Please refer to Figure 5 The example illustrates a vehicle group GP according to one embodiment. Figure 5 This is merely an example, and the invention is not limited to this. Figure 5 Example. All vehicle VCs to be simulated are classified according to their behavioral fixedness (BV) into, for example, 90% "with fixed behavior", 10% "without fixed behavior" and an additional "random new addition". Vehicle VCs with fixed behavior will charge at charging stations according to specific habits; vehicle VCs without fixed behavior will not charge at charging stations according to specific habits.

[0096] Furthermore, according to the charging method, TY is classified as 20% "unidirectional charging" and 80% "bidirectional charging with no fixed behavior". Unidirectional charging vehicle VC can only receive power from the grid or other vehicle VCs; bidirectional charging vehicle VC can not only receive power from the grid or other vehicle VCs, but also charge the grid or other vehicle VCs.

[0097] Furthermore, according to the charging frequency (FQ), vehicles are classified as "high-frequency charging" at 10% and "low-frequency charging" at 90%. Vehicles using high-frequency charging (VC) typically go to the charging station to charge when the battery level is high; vehicles using low-frequency charging (VC) typically go to the charging station to charge when the battery level is low.

[0098] Alternatively, based on the Frequent Arrival Time (FAT) and Frequent Stay Time (FDS), it can be categorized as 50% "7:00–10:00; 4–12hr" and 50% "Other Cases".

[0099] Based on behavioral consistency (BV), charging type (TY), charging frequency (FQ), frequent arrival time (FAT), and frequent dwell time (FDS), vehicle VC can be classified into several vehicle groups (GP) from "A" to "L". Each vehicle group (GP) corresponds to its respective percentage (PB).

[0100] Please refer to Figure 6 It compiles the behavioral fixedness (BV), charging type (TY), charging frequency (FQ), frequent arrival time (FAT), and frequent stay time (FDS) of each vehicle group GP. Taking vehicle group GP "A" as an example, the behavioral fixedness (BV) is "fixed behavior", the charging type (TY) is "one-way charging", the charging frequency (FQ) is "high-frequency charging", the frequent arrival time (FAT) is "7:00-10:00", and the frequent stay time (FDS) is "4-12 hours". Taking vehicle group GP "B" as an example, the behavioral fixedness (BV) is "fixed behavior", the charging type (TY) is "one-way charging", the charging frequency (FQ) is "high-frequency charging", the frequent arrival time (FAT) is "other cases", and the frequent stay time (FDS) is "other cases"; and so on.

[0101] Please refer to Figure 7 The example illustrates a vehicle group GP consisting of several vehicles (VCs) to be simulated. Figure 7 This is merely an example, and the invention is not limited to this. Figure 7 Example. Once the number of simulated vehicles is determined, vehicle groups (GPs) can be configured according to the above ratio (PB). For example, in 60 vehicle VCs, the first 50 vehicle VCs are assigned to vehicle groups "A" to "J" according to the above ratio (PB), and the last 10 "randomly added" vehicle VCs are assigned to vehicle groups "K" and "L" according to the ratio of 20% "one-way charging" and 80% "two-way charging with no fixed behavior".

[0102] Next, in Figure 4 In step S132, such as Figure 2 As shown, the arrival time simulation unit 132 obtains a simulated arrival time AT for the vehicle VC based on the vehicle group GP. For example, different vehicle groups GP will be simulated in different ways. According to the characteristics of the vehicle group GP, a simulated arrival time AT close to real data can be obtained.

[0103] Next, in Figure 4 In step S133, such as Figure 2 As shown, the dwell time simulation unit 133 obtains a simulated dwell time (DS) of vehicle VC based on the vehicle group GP. For example, different vehicle groups GP will be simulated in different ways. According to the characteristics of the vehicle group GP, a simulated dwell time DS close to real data can be obtained.

[0104] Then, in Figure 4 In step S134, such as Figure 2As shown, the charging demand simulation unit 134 obtains a simulated charging demand (CD) for each vehicle (VC) based on the vehicle group (GP). For example, different vehicle groups (GPs) will be simulated in different ways. According to the characteristics of the vehicle group (GP), a simulated charging demand (CD) close to real-world data can be obtained.

[0105] Next, in Figure 4 In step S135, the control unit 190 obtains other information such as charging type TY, charging speed CR, target capacity TG, and battery capacity BC. The charging type TY is set according to the vehicle group GP, for example. The charging speed CR, target capacity TG, and battery capacity BC are randomly set within a predetermined range, for example.

[0106] Then, in Figure 4 In step S136, the departure time simulation unit 135 obtains the simulated departure time LT based on the simulated arrival time AT and the simulated stay time DS. For example, the departure time simulation unit 135 obtains the simulated departure time LT according to the following formula (1).

[0107] Simulated departure time LT = Simulated arrival time AT + Simulated stay time DS...(1)

[0108] Next, in Figure 4 In step S137, the charging time simulation unit 136 obtains the simulated charging time CT based on the simulated charging demand CD and the charging rate CR. For example, the charging time simulation unit 136 obtains the simulated charging time CT according to the following formula (2).

[0109]

[0110] Then, in Figure 4 In step S138, the initial charge simulation unit 137 obtains the simulated initial charge SC based on the target charge TG, the simulated charging demand CD, and the battery capacity BC. For example, the initial charge simulation unit 137 obtains the simulated initial charge SC according to the following formula (3).

[0111]

[0112] A vehicle charging behavior data set (SMD) obtained using the above method includes simulated arrival time (AT), simulated dwell time (DS), simulated charging demand (CD), simulated leaving time (LT), simulated charging time (CT), simulated initial charge level (SC), charging type (TY), charging speed (CR), target charge level (TG), and battery capacity (BC). This vehicle charging behavior data set (SMD) will be stored in storage unit 180.

[0113] Please refer to Figure 8 The diagram illustrates a detailed flowchart of step S132 according to an embodiment of the present invention. Step S132 includes, for example, steps S1321 to S1327. In step S1321, the historical data acquisition element 1321 obtains the historical arrival time probability density function (PDF) PDF2.

[0114] Please refer to Figure 9A The diagram illustrates the historical arrival time probability density function PDF2 and the simulated arrival time probability density functions PDF21 and PDF22 according to an embodiment of the present invention. The historical arrival time probability density function PDF2 is a distribution function statistically derived from real historical data regarding arrival time.

[0115] Next, in Figure 8 In step S1322, such as Figure 2 and Figure 9A As shown, the function decomposition element 1322 decomposes a simulated arrival time probability density function PDF21 (or PDF22) for the corresponding vehicle group GP from the historical arrival time probability density function PDF2. The frequent arrival times (FAT) of vehicle groups "A", "C", "E", and "G" are "7:00–10:00", and these groups correspond to the simulated arrival time probability density function PDF21. The frequent arrival times (FAT) of vehicle groups "B", "D", "F", and "H" are "other cases", and these groups correspond to the simulated arrival time probability density function PDF22.

[0116] Then, in Figure 8 Step S1323, as Figure 2 As shown, the mutation enhancement element 1323 adds a Gaussian mutation GS21 (or GS22) to the simulated arrival time probability density function PDF21 (or PDF22). Please refer to... Figure 9B The diagram illustrates the simulated arrival time probability density function PDF21 (PDF22) with Gaussian variation GS21 (or GS22) added according to an embodiment of the present invention. After adding Gaussian variation GS21, all values ​​within a certain variation range are considered to conform to the distribution of the simulated arrival time probability density function PDF21. After adding Gaussian variation GS22, all values ​​within a certain variation range are considered to conform to the distribution of the simulated arrival time probability density function PDF22.

[0117] Next, in Figure 8In steps S1324 to S1327, the generating element 1324 generates a simulated arrival time AT that conforms to the simulated arrival time probability density function PDF21 (or PDF22) through a Monte Carlo algorithm.

[0118] For example, please refer to Figure 9C The example illustrates the Monte Carlo algorithm. Figure 8 In step S1324, the generating element 1324 generates a simulated arrival time AT.

[0119] Then, in step S1325, the generating element 1324 determines whether the generated simulated arrival time AT conforms to the simulated arrival time probability density function PDF21 (or PDF22). If the generated simulated arrival time AT does not conform to the simulated arrival time probability density function PDF21 (or PDF22), then proceed to step S1326; if the generated simulated arrival time AT conforms to the simulated arrival time probability density function PDF21, then proceed to step S1327.

[0120] In step S1326, as Figure 9C At the intersection shown, element 1324 discards this simulated arrival time AT.

[0121] In step S1327, as Figure 9C The circled point indicates that element 1324 records the simulated arrival time AT. In other words, the Monte Carlo algorithm can successfully generate simulated arrival times AT that conform to the simulated arrival time probability density function PDF21 (or PDF22).

[0122] Once the simulated arrival time AT is generated, the process is complete. Figure 4 Step S132.

[0123] Please refer to Figure 10 The diagram illustrates a detailed flowchart of step S133 according to an embodiment of the present invention. Step S133 includes, for example, steps S1331 to S1337. In step S1331, as... Figure 2 As shown, the historical data acquisition element 1331 obtains the historical dwell time probability density function (PDF) PDF3.

[0124] Please refer to Figure 11A The diagram illustrates the Cumulative Distribution Function (CDF) CDF3 of historical stay time according to an embodiment of the present invention. The Cumulative Distribution Function CDF3 is a cumulative distribution function statistically derived from historical real data regarding stay time.

[0125] Please refer to Figure 11B The graph illustrates the historical stay time probability density function PDF3 according to an embodiment of the present invention. The historical stay time probability density function PDF3 is a distribution function statistically derived from past real data regarding stay time. Figure 11B The historical dwell time probability density function PDF3 can be derived from... Figure 11A The cumulative distribution function of historical dwell time (CDF3) is obtained.

[0126] Next, in Figure 10 In step S1332, such as Figure 2 As shown, the function decomposition element 1332, based on the historical dwell time probability density function PDF3, decomposes the vehicle group GP into a simulated dwell time probability density function PDF31 (or PDF32) corresponding to the vehicle group GP. Please refer to... Figure 11C The diagram illustrates the simulated dwell time probability density functions PDF31 and PDF32 according to an embodiment of the present invention. The frequent dwell time (FDS) of vehicle groups GP such as "A", "C", "E", and "G" is "4-12 hours", and these vehicle groups correspond to the simulated dwell time probability density function PDF31. The frequent dwell time (FDS) of vehicle groups GP such as "B", "D", "F", and "H" is "other cases", and these groups correspond to the simulated dwell time probability density function PDF32.

[0127] Then, in Figure 10 Step S1333, as follows Figure 2 As shown, the variation enhancement element 1333 adds a Gaussian variation GS31 (or GS32) to the simulated dwell time probability density function PDF31 (or PDF32). Please refer to... Figure 11D The diagram illustrates the simulated dwell time probability density function PDF31 (PDF32) with Gaussian variation GS31 (or GS32) added according to an embodiment of the present invention. After adding Gaussian variation GS31, the distribution within a certain variation range is considered to conform to the simulated dwell time probability density function PDF31. After adding Gaussian variation GS32, the distribution within a certain variation range is considered to conform to the simulated dwell time probability density function PDF32.

[0128] Next, in Figure 10 In steps S1334 to S1337, the generating element 1334 generates a simulated dwell time DS that conforms to the simulated dwell time probability density function PDF31 (or PDF32) through a Monte Carlo algorithm.

[0129] For example, in Figure 10In step S1334, the generating element 1334 generates a certain simulated dwell time DS.

[0130] Then, in step S1335, the generating element 1334 determines whether the generated simulated dwell time DS conforms to the simulated dwell time probability density function PDF31 (or PDF32). If the generated simulated dwell time DS does not conform to the simulated dwell time probability density function PDF31 (or PDF32), then proceed to step S1336; if the generated simulated dwell time DS conforms to the simulated dwell time probability density function PDF31, then proceed to step S1337.

[0131] In step S1336, the generating element 1334 discards this simulated dwell time DS.

[0132] In step S1337, the generating element 1324 records this simulated dwell time DS. That is, through the Monte Carlo algorithm, the simulated dwell time DS that conforms to the simulated dwell time probability density function PDF31 (or PDF32) can be successfully generated.

[0133] Once the simulated dwell time DS is generated, the process is complete. Figure 4 Step S133.

[0134] Please refer to Figure 12 The diagram illustrates a detailed flowchart of step S134 according to an embodiment of the present invention. Step S134 includes, for example, steps S1341 to S1347. In step S1341, as... Figure 2 As shown, the historical data acquisition element 1341 obtains the historical charging demand probability density function (PDF) PDF4.

[0135] Please refer to Figure 13A The diagram illustrates the historical cumulative distribution function (CDF) CDF4 of charging demand according to an embodiment of the present invention. The historical cumulative distribution function CDF4 is a cumulative distribution function statistically derived from real historical data on charging demand.

[0136] Please refer to Figure 13B The graph illustrates the historical charging demand probability density function PDF4 according to an embodiment of the present invention. The historical charging demand probability density function PDF4 is a distribution function statistically derived from real historical data regarding charging demand. Figure 13B The historical charging demand probability density function PDF4 can be derived from... Figure 13A The historical cumulative distribution function of charging demand, CDF4, is obtained.

[0137] Next, in Figure 12 In step S1342, such as Figure 2 As shown, the function decomposition element 1342, based on the vehicle group GP, decomposes the historical charging demand probability density function PDF4 into a simulated charging demand probability density function PDF41 (or PDF42) for the corresponding vehicle group GP. Please refer to... Figure 13C The diagram illustrates the simulated charging demand probability density functions PDF41 and PDF42 according to an embodiment of the present invention. The charging demand frequency FQ of vehicle groups GP such as "A", "B", "E", and "F" is designated as "high charging frequency," and these vehicle groups correspond to the simulated charging demand probability density function PDF41. The charging demand frequency FQ of vehicle groups GP such as "C", "D", "G", and "H" is designated as "low charging frequency," and these groups correspond to the simulated charging demand probability density function PDF42.

[0138] Then, in Figure 12 Step S1343, as follows Figure 2 As shown, the variation enhancement element 1343 adds a Gaussian variation GS41 (or GS42) to the probability density function PDF41 (or PDF42) of the simulated charging demand. Please refer to... Figure 13D The diagram illustrates the probability density function PDF41 (PDF42) of simulated charging demand with added Gaussian variation GS41 (or GS42) according to an embodiment of the present invention. After adding Gaussian variation GS41, the distribution within a certain variation range is considered to conform to the probability density function PDF41 of simulated charging demand. After adding Gaussian variation GS42, the distribution within a certain variation range is considered to conform to the probability density function PDF42 of simulated charging demand.

[0139] Next, in Figure 12 In steps S1344 to S1347, the generating element 1344 generates a simulated charging demand CD that conforms to the simulated charging demand probability density function PDF41 (or PDF42) through a Monte Carlo algorithm.

[0140] For example, in Figure 12 In step S1344, the generating element 1344 generates a certain simulated charging demand CD.

[0141] Then, in step S1345, the generating element 1344 determines whether the generated simulated charging demand CD conforms to the simulated charging demand density function PDF41 (or PDF42). If the generated simulated charging demand CD does not conform to the simulated charging demand probability density function PDF41 (or PDF42), then proceed to step S1346; if the generated simulated charging demand CD conforms to the simulated charging demand probability density function PDF41, then proceed to step S1347.

[0142] In step S1346, the generating element 1334 discards this simulated charging demand CD.

[0143] In step S1347, the generating element 1324 records this simulated charging demand CD. That is, through the Monte Carlo algorithm, the simulated charging demand CD that conforms to the simulated charging demand probability density function PDF41 (or PDF42) can be successfully generated.

[0144] Once the simulated charging demand CD is generated, the process is complete. Figure 4 Step S134.

[0145] According to the above embodiments, the simulation system 1000 can set the distribution of vehicle charging behavior data through appropriate clustering techniques, and then simulate various information accordingly. When collecting vehicle charging behavior data is difficult, this invention uses the simulation system 1000 to quickly generate several vehicle charging behavior data sets (SMDs). In this way, the inference of charge / discharge schedule CDSs can be reduced from several months to a few days, significantly accelerating the establishment timeline of charging stations.

[0146] Of course, the present invention may have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A method for simulating vehicle charging behavior data at a charging station, characterized in that, include: To determine a vehicle group; Based on the vehicle group, obtain a simulated arrival time for the vehicle; Based on the vehicle group, obtain a simulated dwell time for the vehicle; as well as Based on the vehicle group, a simulated charging demand for the vehicle is obtained.

2. The method for simulating vehicle charging behavior data at charging stations as described in claim 1, characterized in that, The steps for obtaining the simulated arrival time of a vehicle based on the vehicle group include: Obtain a historical arrival time probability density function; Based on the vehicle group, a simulated arrival time probability density function corresponding to the vehicle group is derived from the historical arrival time probability density function; and The simulated arrival time is generated using a Monte Carlo algorithm, which conforms to the probability density function of the simulated arrival time.

3. The method for simulating vehicle charging behavior data at charging stations as described in claim 2, characterized in that, The step of obtaining the simulated arrival time of a vehicle based on the vehicle group further includes: A Gaussian variability is added to the simulated arrival time probability density function.

4. The method for simulating vehicle charging behavior data at charging stations as described in claim 1, characterized in that, The steps for obtaining the simulated dwell time of a vehicle based on the vehicle group include: Obtain a probability density function for a historical dwell time; Based on the vehicle group, a simulated dwell time probability density function corresponding to the vehicle group is derived from the historical dwell time probability density function; and The simulated dwell time is generated by using a Monte Carlo algorithm, which satisfies the probability density function of the simulated dwell time.

5. The method for simulating vehicle charging behavior data at charging stations as described in claim 4, characterized in that, The step of obtaining the simulated dwell time of a vehicle based on the vehicle group further includes: A Gaussian variogram is added to the probability density function of the simulated dwell time.

6. The method for simulating vehicle charging behavior data at a charging station as described in claim 1, characterized in that, The step of obtaining the simulated charging demand of a vehicle based on the vehicle group includes: Obtain a historical probability density function of charging demand; Based on the vehicle group, a simulated charging demand probability density function corresponding to the vehicle group is derived from the historical charging demand probability density function; and A Monte Carlo algorithm is used to generate the simulated charging demand that conforms to the probability density function of the simulated charging demand.

7. The method for simulating vehicle charging behavior data at a charging station as described in claim 6, characterized in that, The step of obtaining the simulated charging demand of a vehicle based on the vehicle group further includes: A Gaussian variability is added to the probability density function of the simulated charging demand.

8. The method for simulating vehicle charging behavior data at a charging station as described in claim 6, characterized in that, The probability density function of the historical charging demand is obtained from a cumulative distribution function of the historical charging demand.

9. The method for simulating vehicle charging behavior data at a charging station as described in claim 1, characterized in that, The vehicle group is categorized according to its fixed behavior.

10. The method for simulating vehicle charging behavior data at a charging station as described in claim 1, characterized in that, The vehicle group is categorized according to its charging method.

11. The method for simulating vehicle charging behavior data at a charging station as described in claim 1, characterized in that, The vehicle group is categorized according to charging frequency.

12. The method for simulating vehicle charging behavior data at a charging station as described in claim 1, characterized in that, The vehicle group is categorized by its frequent arrival time and frequent stay time.

13. A simulation system for vehicle charging behavior data at a charging station, characterized in that, include: A vehicle group determination unit is used to determine a vehicle group for a vehicle; An arrival time simulation unit is used to obtain a simulated arrival time of a vehicle based on the vehicle group; A dwell time simulation unit is used to obtain a simulated dwell time of a vehicle based on the vehicle group; as well as A charging demand simulation unit is used to obtain a simulated charging demand for the vehicle based on the vehicle group.

14. The simulation system for vehicle charging behavior data of a charging station as described in claim 13, characterized in that, The arrival time simulation unit includes: A historical data acquisition element is used to obtain a historical arrival time probability density function; A function decomposition element is used to decompose a simulated arrival time probability density function corresponding to the vehicle group from the historical arrival time probability density function; and A generating element is used to generate the simulated arrival time that conforms to the simulated arrival time probability density function through a Monte Carlo algorithm.

15. The simulation system for vehicle charging behavior data at a charging station as described in claim 14, characterized in that, The arrival time simulation unit further includes: A mutation enhancement element is used to add a Gaussian mutation to the simulated arrival time probability density function.

16. The simulation system for vehicle charging behavior data of a charging station as described in claim 13, characterized in that, The dwell time simulation unit includes: A historical data acquisition element is used to obtain a historical dwell time probability density function; A function decomposition element is used to decompose a simulated dwell time probability density function corresponding to the vehicle group from the historical dwell time probability density function; and A generating element is used to generate the simulated dwell time that conforms to the probability density function of the simulated dwell time through a Monte Carlo algorithm.

17. The simulation system for vehicle charging behavior data of a charging station as described in claim 16, characterized in that, The dwell time simulation unit further includes: A mutation enhancement element is used to add a Gaussian mutation to the probability density function of the simulated dwell time.

18. The simulation system for vehicle charging behavior data of a charging station as described in claim 13, characterized in that, The charging demand unit includes: A historical data acquisition element is used to obtain a historical charging demand probability density function. A function decomposition element is used to decompose a simulated charging demand probability density function corresponding to the vehicle group from the historical charging demand probability density function; and A generating element is used to generate the simulated charging demand that conforms to the probability density function of the simulated charging demand through a Monte Carlo algorithm.

19. The simulation system for vehicle charging behavior data of a charging station as described in claim 18, characterized in that, The charging demand unit further includes: A mutation enhancement element is used to add a Gaussian variability to the probability density function of the simulated charging demand.

20. The simulation system for vehicle charging behavior data of a charging station as described in claim 18, characterized in that, The historical data acquisition element obtains the probability density function of historical charging demand from a cumulative distribution function of historical charging demand.

Images