A method, device and equipment for analyzing vehicle charging demand based on probability flow

Abstract

The present invention discloses a method, device and equipment for analyzing automobile charging demand based on probability flow, which relates to the field of plug-in hybrid electric vehicle charging demand analysis. The method includes establishing a single PHEV charging demand model based on a probability model and considering the uncertainty factors of PHEV charging behavior; describing the behavior of multiple PHEVs according to queuing theory, establishing a discrete distribution of PHEV market share and randomly selecting PHEV categories; randomly simulating PHEV charging demand, obtaining PHEV overall charging power demand samples, and fitting the distribution of PHEV overall charging power demand using statistical analysis methods. The present application can effectively realize the analysis of the impact of PHEV charging on the power grid.

Description

Technical Field

[0001] The present application relates to the field of plug-in hybrid electric vehicle charging demand analysis, and specifically to a method, device and equipment for analyzing vehicle charging demand based on probabilistic power flow. Background Art

[0002] As environmental pressure and energy depletion become increasingly severe, electric vehicles (EVs) have attracted more and more attention due to their higher energy utilization and lower tail gas emissions compared to traditional internal combustion engine vehicles. At present, due to the technical limitations of power batteries, the most promising electric vehicles may be plug-in hybrid electric vehicles (PHEVs) that compromise between cost and performance. Currently, major automobile manufacturers are also committed to the development of PHEV technology.

[0003] PHEVs are typically equipped with a transmission that includes at least an internal combustion engine, an electric motor, a battery storage system, and a means to charge the battery system from an external power source. The battery capacity is usually several kilowatt-hours or more to power the vehicle in all-electric driving mode. When the battery is insufficient, the internal combustion engine can be used to extend its driving range.

[0004] In recent years, PHEVs are rapidly being accepted by more and more consumers. With the rapid increase in the number of PHEVs, their charging demand will become an important part of the overall load in the power system. Therefore, how to analyze the impact of a large number of PHEV charging on the power grid has become an urgent problem to be solved. Summary of the invention

[0005] The present application provides a method, device and equipment for analyzing automobile charging demand based on probabilistic power flow, which can effectively analyze the impact of PHEV charging on the power grid.

[0006] In a first aspect, an embodiment of the present application provides a method for analyzing vehicle charging demand based on probabilistic power flow, and the method for analyzing vehicle charging demand based on probabilistic power flow includes:

[0007] Based on the probability model and considering the uncertainty of PHEV charging behavior, a single PHEV charging demand model is established;

[0008] The behaviors of multiple PHEVs are described based on queuing theory, a discrete distribution of PHEV market share is established, and the categories of PHEVs are randomly selected;

[0009] The charging demand of PHEV is randomly simulated to obtain the sample of the overall charging power demand of PHEV, and the distribution of the overall charging power demand of PHEV is fitted by using statistical analysis method.

[0010] In combination with the first aspect, in one implementation, the probability model is based on the uncertainty factors of the PHEV charging behavior to establish a single PHEV charging demand model, specifically:

[0011] Define a key parameter that represents the percentage of the total energy input provided by the battery to describe the operating state of the PHEV.

[0012]

[0013] Among them, E Eng Represents the total energy input into the vehicle engine and motor drive controller during a set time period, E Bat Indicates the energy delivered by the vehicle battery to the motor drive controller within a set time period, k EV Indicates the proportion of total energy input provided by the battery;

[0014] Define the total battery capacity of a PHEV.

[0015]

[0016] Among them, C Bat Represents the total battery capacity of the PHEV, μk EV Indicates k EV The mean value, μC Bat Represents C Bat , L represents the Cholesky decomposition of the covariance matrix, N1 and N2 represent two independent standard normal variables;

[0017] The performance of PHEV is evaluated based on the energy consumption per mile, denoted as E m :

[0018]

[0019] Among them, A E and B E represents the coefficient constant;

[0020] According to the statistics of PHEV driving mode, the daily mileage of a PHEV tends to obey the log-normal distribution, denoted as M d :

[0021]

[0022] Among them, e represents a natural constant, N represents a standard normal variable, and μ m and σ m represents the lognormal distribution parameters;

[0023] Calculate the daily charging energy of a PHEV, denoted as D E :

[0024]

[0025] Among them, M E Indicates the maximum driving range of a PHEV in all-electric driving mode.

[0026] In combination with the first aspect, in one implementation,

[0027] For the lognormal distribution parameter μ m and σ m , specifically:

[0028]

[0029]

[0030] in, Indicates M d The mean of Indicates M d The standard deviation of

[0031] For the maximum driving range of PHEV in all-electric mode M E , specifically:

[0032]

[0033] In combination with the first aspect, in one implementation, the behaviors of multiple PHEVs are described according to queuing theory, specifically:

[0034] A queuing model is built under the charging scenario of an electric vehicle charging station, in which the PHEVs charging at the electric vehicle charging station can be regarded as customers waiting to be served in a queue of M, M, c. The first M indicates that the customer arrival interval follows the mean T λ The second M indicates that the time to serve a customer has a mean of T μ exponential distribution, c represents the maximum number of customers served at the same time;

[0035] The arrival and charging patterns of PHEVs are described by Poisson distribution. The number of customers waiting at the electric vehicle charging station is assumed to be infinite. The number of PHEVs charging simultaneously in a queue M, M, c is recorded as n. At this time, n obeys the discrete distribution:

[0036]

[0037] Among them, p nrepresents the discrete distribution that n vehicles satisfy when charging at the same time, i represents the intermediate quantity, ρ represents the occupancy rate of each charging pile,

[0038] A queuing model is established for charging scenarios in residential areas, where PHEVs charging in residential areas can be regarded as a queue in M, M, c, k, N max The number of customers waiting to be served in the queue, k represents the maximum number of customers being served or waiting in the queue, N max Indicates the maximum number of customers that may be served;

[0039] The arrival and charging patterns of PHEVs are described by Poisson distribution. The number of customers that can be served in a residential community is assumed to be limited. In a M, M, c, k, N max The number of PHEVs charging simultaneously in the queue is recorded as n, and the discrete distribution of n is:

[0040]

[0041] The calculated charging service time T of PHEV in the queuing model follows the mean value T μ The exponential distribution is:

[0042] T=-T μ ·ln(U)

[0043] Wherein, U represents a variable uniformly distributed in (0,1), and the queuing model includes a queuing model in a charging scenario at an electric vehicle charging station and a queuing model in a charging scenario in a residential community;

[0044] By restricting T, we get:

[0045]

[0046] Among them, T min represents the minimum value of T, T max Indicates the maximum value of T;

[0047] Select and determine the PHEV charging power level;

[0048] Calculate the average charging current I of a PHEV:

[0049]

[0050] Among them, D E represents the daily charging energy of PHEV, V represents the charging voltage, I max Indicates the maximum charging current;

[0051] The total charging power demand P of all n PHEVs charging at electric vehicle charging stations or residential areas is calculated as:

[0052]

[0053] Among them, I i represents the average charging current of the i-th PHEV.

[0054] In combination with the first aspect, in one implementation, the discrete distribution of PHEV market share is established and the category of PHEV is randomly selected, specifically:

[0055] The market share of PHEV is set as a discrete distribution, and the category of PHEV is randomly selected according to the market share of PHEV.

[0056] In combination with the first aspect, in one implementation,

[0057] The random simulation of the charging demand of the PHEV to obtain the overall charging power demand sample of the PHEV is specifically as follows: the sample selection process is repeated in sequence until a set number of samples are generated, thereby obtaining the overall charging power demand sample of the PHEV;

[0058] The sample selection process is specifically as follows:

[0059] The number of PHEVs charged simultaneously is randomly generated, and the category of PHEV is randomly selected according to the market share of PHEVs;

[0060] Randomly generate k for PHEV EV and C Bat Parameters and calculate the energy consumption per mile E of PHEV m , k EV Indicates the proportion of total energy input provided by the battery, C Bat Indicates the total battery capacity of the PHEV;

[0061] Randomly generate the mileage M of the PHEV d , calculate the daily charging energy D of PHEV E ;

[0062] The charging time T of the PHEV is randomly generated, the charging current I of the PHEV is calculated, and the total charging power demand P is accumulated.

[0063] In combination with the first aspect, in one implementation, the distribution of the overall charging power demand of the PHEV is obtained by fitting using a statistical analysis method, specifically:

[0064] Setting coefficient constant A for different types of PHEV E and B E ;

[0065] Calculate and determine k EV ,

[0066]

[0067] Among them, M E Indicates the maximum driving range of a PHEV in all-electric driving mode;

[0068] Describing k based on bivariate normal distribution EV and C Bat ,

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] Among them, mink EV Indicates k EV The minimum value of maxk EV Indicates k EV The maximum value, minC Bat Represents C Bat The minimum value, maxC Bat Represents C Bat The maximum value of Indicates k EV The standard deviation of Represents C Bat The standard deviation of , S represents the coefficient, and its value is 0.8;

[0075] Based on the queuing model in the charging scenario of electric vehicle charging stations, the queuing model in the charging scenario in residential communities, and the selected PHEV charging power level, the distribution of the overall charging power demand of PHEV is fitted.

[0076] In a second aspect, an embodiment of the present application provides a vehicle charging demand analysis device based on probability power flow, and the vehicle charging demand analysis device based on probability power flow includes:

[0077] Creating a module for modeling a single PHEV charging demand based on a probabilistic model and taking into account uncertainties in PHEV charging behavior;

[0078] A description module is used to describe the behavior of multiple PHEVs based on queuing theory, establish a discrete distribution of PHEV market share and randomly select the category of PHEVs;

[0079] The execution module is used to randomly simulate the charging demand of PHEV, obtain the overall charging power demand sample of PHEV, and use statistical analysis to fit the distribution of the overall charging power demand of PHEV.

[0080] In combination with the second aspect, in one implementation, the probability model is based on the uncertainty factors of the PHEV charging behavior to establish a single PHEV charging demand model, specifically:

[0081] Define a key parameter that represents the percentage of the total energy input provided by the battery to describe the operating state of the PHEV.

[0082]

[0083] Among them, E Eng Represents the total energy input into the vehicle engine and motor drive controller during a set time period, E Bat Indicates the energy delivered by the vehicle battery to the motor drive controller within a set time period, k EV Indicates the proportion of total energy input provided by the battery;

[0084] Define the total battery capacity of a PHEV.

[0085]

[0086] Among them, C Bat Represents the total battery capacity of the PHEV, μk EV Indicates k EV The mean value, μC Bat Represents C Bat , L represents the Cholesky decomposition of the covariance matrix, N1 and N2 represent two independent standard normal variables;

[0087] The performance of PHEV is evaluated based on the energy consumption per mile, denoted as E m :

[0088]

[0089] Among them, A E and B E represents the coefficient constant;

[0090] According to the statistics of PHEV driving mode, the daily mileage of a PHEV tends to obey the log-normal distribution, denoted as M d :

[0091]

[0092] Among them, e represents a natural constant, N represents a standard normal variable, and μ m and σ m represents the lognormal distribution parameters;

[0093] Calculate the daily charging energy of a PHEV, denoted as D E :

[0094]

[0095] Among them, M E Indicates the maximum driving range of a PHEV in all-electric driving mode.

[0096] In a third aspect, an embodiment of the present application provides a vehicle charging demand analysis device based on probabilistic power flow, wherein the vehicle charging demand analysis device based on probabilistic power flow comprises a processor, a memory, and a vehicle charging demand analysis program based on probabilistic power flow stored in the memory and executable by the processor, wherein when the vehicle charging demand analysis program based on probabilistic power flow is executed by the processor, the steps of the vehicle charging demand analysis method based on probabilistic power flow described above are implemented.

[0097] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0098] (1) This application considers more uncertain factors that affect PHEV charging, uses queuing theory to describe the behavior of multiple PHEVs, uses random simulation and statistical analysis to fit the appropriate distribution of PHEV overall charging demand, models the PHEV overall charging demand under different scenarios, and analyzes the impact of PHEV charging on the power grid;

[0099] (2) This application can consider more uncertain factors that affect PHEV charging, effectively simplify the calculation of the numerical characteristics of the overall charging power demand of PHEV, and provide strong evidence for applying probabilistic power flow analysis to the impact of PHEV charging on the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 A flowchart of a vehicle charging demand analysis method based on probability flow for this application;

[0101] Figure 2 This is a structural schematic diagram of a vehicle charging demand analysis device based on probability flow in this application;

[0102] Figure 3 This is a schematic diagram of the hardware structure of the vehicle charging demand analysis device based on probabilistic flow in this application. DETAILED DESCRIPTION

[0103] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0104] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0105] On the first aspect, the embodiment of the present application provides a method for analyzing automobile charging demand based on probabilistic power flow, combines queuing theory to model and analyze the daily charging energy of electric vehicles in a certain area, uses random simulation and statistical analysis to fit the appropriate distribution of PHEV's overall charging power demand, and constructs a PHEV's overall charging power demand model under different scenarios. It can consider more uncertain factors that affect PHEV charging, effectively simplify the calculation of the numerical characteristics of PHEV's overall charging power demand, and provide strong evidence for applying probabilistic power flow to analyze the impact of PHEV charging on the power grid.

[0106] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the vehicle charging demand analysis method based on probability flow in this application. Figure 1 As shown in the figure, the vehicle charging demand analysis method based on probability flow includes:

[0107] S1: Based on the probability model and considering the uncertainty of PHEV charging behavior, a single PHEV charging demand model is established;

[0108] Furthermore, in one embodiment, a single PHEV charging demand model is established based on the probability model and taking into account the uncertainty factors of PHEV charging behavior, specifically:

[0109] S101: defining a key parameter for representing a proportion of the total energy input provided by the battery to describe the operating state of the PHEV,

[0110]

[0111] Among them, E Eng Represents the total energy input into the vehicle engine and motor drive controller during a set time period, E Bat Indicates the energy delivered by the vehicle battery to the motor drive controller within a set time period, k EV Indicates the proportion of total energy input provided by the battery;

[0112] S102: Define the total battery capacity of the PHEV.

[0113]

[0114] Among them, C Bat Represents the total battery capacity of the PHEV, μk EV Indicates k EV The mean value, μC Bat Represents C Bat , L represents the Cholesky decomposition of the covariance matrix, N1 and N2 represent two independent standard normal variables;

[0115] S103: Evaluate the performance of the PHEV based on energy consumption per mile, denoted as E m :

[0116]

[0117] Among them, A E and B E represents the coefficient constant;

[0118] S104: According to the statistics of PHEV driving modes, it is found that the daily driving mileage of a PHEV tends to obey the log-normal distribution, which is recorded as M d :

[0119]

[0120] Among them, e represents a natural constant, N represents a standard normal variable, and μ m and σ m represents the lognormal distribution parameters;

[0121] S105: Calculate the daily charging energy of a PHEV, denoted as D E :

[0122]

[0123] Among them, M E Indicates the maximum driving range of a PHEV in all-electric driving mode.

[0124] Furthermore, for the lognormal distribution parameter μ m and σ m , specifically:

[0125]

[0126]

[0127] in, Indicates M d The mean of Indicates M d The standard deviation of

[0128] For the maximum driving range of PHEV in all-electric mode M E , specifically:

[0129]

[0130] S2: Describe the behavior of multiple PHEVs based on queuing theory, establish a discrete distribution of PHEV market share and randomly select PHEV categories;

[0131] Furthermore, in one embodiment, the behaviors of multiple PHEVs are described according to queuing theory, specifically:

[0132] S201: A queuing model is established in the charging scenario of an electric vehicle charging station, where the PHEVs charging at the electric vehicle charging station can be regarded as customers waiting to be served in a queue of M, M, c, where the first M indicates that the customer arrival interval follows the mean T λ The second M indicates that the time to serve a customer has a mean of T μ exponential distribution, c represents the maximum number of customers served at the same time;

[0133] S202: The arrival and charging pattern of PHEVs is described by Poisson distribution. The number of customers waiting at the electric vehicle charging station is assumed to be infinite. The number of PHEVs charging simultaneously in a queue M, M, c is recorded as n. At this time, the discrete distribution of n is:

[0134]

[0135] Among them, p n represents the discrete distribution that n vehicles satisfy when charging at the same time, i represents the intermediate quantity, ρ represents the occupancy rate of each charging pile,

[0136] S203: Establish a queuing model for charging in a residential area, where the PHEVs charging in the residential area can be regarded as a queue in M, M, c, k, N max The number of customers waiting to be served in the queue, k represents the maximum number of customers being served or waiting in the queue, N max Indicates the maximum number of customers that may be served;

[0137] S204: The arrival and charging patterns of PHEVs are described by Poisson distribution. The number of customers that can be served in a residential community is assumed to be limited. In a M, M, c, k, N max The number of PHEVs charging simultaneously in the queue is recorded as n, and the discrete distribution of n is:

[0138]

[0139] It should be noted that c≤k≤N max ;

[0140] S205: Calculate that the charging service time T of PHEV in the queuing model is subject to the mean value T μ The exponential distribution is:

[0141] T=-T μ ·ln(U)

[0142] Wherein, U represents a variable uniformly distributed in (0,1), and the queuing model includes a queuing model in a charging scenario at an electric vehicle charging station and a queuing model in a charging scenario in a residential community;

[0143] S206: restrict T to obtain:

[0144]

[0145] Among them, T min represents the minimum value of T, T max represents the maximum value of T; that is, considering that it is unreasonable for PHEV to have a very short charging time, and the charging time also has an upper limit due to its battery capacity or service limitations, T is limited to the interval [T min ,T max ]Inside;

[0146] S207: Select and determine the PHEV charging power level. Specifically, considering that the service time for charging PHEVs can be reduced by providing a higher charging power, the charging power level of the electric vehicle charging station is selected to be level 3 (400V / 63A); the charging slots in the residential area are limited by the power distribution capacity, and their charging power level is usually limited to level 1 (230V / 16A);

[0147] S208: Calculate the average charging current I of the PHEV:

[0148]

[0149] Among them, D E represents the daily charging energy of PHEV, V represents the charging voltage, I max Indicates the maximum charging current;

[0150] S209: Calculate the total charging power demand P of all n PHEVs charging at the electric vehicle charging station or residential area:

[0151]

[0152] Among them, I i represents the average charging current of the i-th PHEV.

[0153] Furthermore, in one embodiment, a discrete distribution of PHEV market shares is established and the category of PHEV is randomly selected, specifically: the market share of PHEV is set to a discrete distribution, and the category of PHEV is randomly selected according to the market share of PHEV.

[0154] In the actual application process, in order to consider the differences in PHEV parameters, this application is based on the possible k EV and C Bat , PHEVs are divided into 4 categories as shown in Table 1 below, and the assumed market shares are shown in Table 2 below.

[0155] Table 1

[0156] type <![CDATA[Minimum C Bat (kWh)]]> <![CDATA[Maximum k EV (kWh)]]> Small car 8 12 Economy car 10 14 Mid-size sedan 14 18 Light Truck / SUV 19 23

[0157] Table 2

[0158] type Percent (100%) Small car 0.2 Economy car 0.3 Mid-size sedan 0.3 Light Truck / SUV 0.2

[0159] S3: Randomly simulate the charging demand of PHEV to obtain the sample of the overall charging power demand of PHEV, and use statistical analysis to fit the distribution of the overall charging power demand of PHEV.

[0160] Further, in one embodiment, the charging demand of the PHEV is randomly simulated to obtain the overall charging power demand sample of the PHEV, specifically: the sample selection process is repeated in sequence until a set number of samples are generated, thereby obtaining the overall charging power demand sample of the PHEV;

[0161] The specific sample selection process is as follows:

[0162] S301: According to step S202 or S204, randomly generate the number of PHEVs to be charged simultaneously, and randomly select the category of PHEV according to the market share of PHEV, that is, randomly select the category of PHEV according to the market share of PHEV in Table 2;

[0163] S302: According to step S102, randomly generate k of the PHEV EV and C Bat Parameters, according to step S103, calculate the energy consumption per mile E of the PHEV m , k EV Indicates the proportion of total energy input provided by the battery, C Bat Indicates the total battery capacity of the PHEV;

[0164] S303: According to step S104, randomly generate the mileage M of the PHEVd According to step S105, the daily charging energy D of the PHEV is calculated E ;

[0165] S304: According to step S206, randomly generate the charging time T of the PHEV, according to step S208, calculate the charging current I of the PHEV, and according to step S209, accumulate the total charging power demand P.

[0166] Furthermore, in one embodiment, the distribution of the overall charging power demand of PHEV is fitted by using a statistical analysis method, specifically:

[0167] S311: Setting coefficient constant A for different types of PHEVs E and B E , as shown in Table 3 below, the coefficient constant A for different types of PHEVs is given E and B E ;

[0168] Table 3

[0169] type <![CDATA[A E (kWh / mile)]]> <![CDATA[B E (kWh / mile)]]> Small car 0.3790 0.4541 Economy car 0.4288 0.4179 Mid-size sedan 0.5740 0.4040 Light Truck / SUV 0.8180 0.4802

[0170] S312: Calculate and determine k EV ,

[0171]

[0172] Among them, M E Indicates the maximum driving range of a PHEV in all-electric driving mode;

[0173] That is, considering the M of different types of PHEV E Wide range, choice Approximately, so k EV The range of is determined by the above formula. In actual application, k EV The possible ranges are shown in Table 4 below.

[0174] Table 4

[0175] type <![CDATA[Minimum k EV > <![CDATA[Maximum k EV > Small car 0.2447 0.5976 Economy car 0.2750 0.6151 Mid-size sedan 0.2939 0.5475 Light Truck / SUV 0.3224 0.4800

[0176] S313: Describing k based on bivariate normal distribution EV and C Bat ,

[0177]

[0178]

[0179]

[0180]

[0181]

[0182] Among them, mink EV Indicates k EV The minimum value of maxk EV Indicates k EV The maximum value, minC Bat Represents C Bat The minimum value, maxC Bat Represents C Bat The maximum value of Indicates k EV The standard deviation of Represents C Bat The standard deviation of , S represents the coefficient, and its value is 0.8;

[0183] S314: Based on the queuing model in the charging scenario of the electric vehicle charging station, the queuing model in the charging scenario in the residential community, and the selected PHEV charging power level, the distribution of the overall charging power demand of the PHEV is fitted.

[0184] That is, the relevant parameters of the queuing model in the charging scenario of the electric vehicle charging station, the relevant parameters of the queuing model in the charging scenario in the residential area, and the charging level parameters are selected to fit the distribution of the overall charging power demand of PHEV in the electric vehicle charging station scenario. The selected queuing model parameters and charging level parameters are shown in Table 5 below.

[0185] Table 5

[0186]

[0187] Furthermore, it is assumed that the charging power follows the Weibull distribution:

[0188]

[0189] Through the maximum likelihood estimation, the fitting results show that a is 0.192329, b is 1.87103, and the confidence level 1-α is 95%. The Kolmogorov-Smirnov goodness of fit test (KS test) is performed with a significance level of α of 5% to verify the above hypothesis.

[0190] In a second aspect, an embodiment of the present application also provides a vehicle charging demand analysis device based on probabilistic power flow.

[0191] In one embodiment, referring to Figure 2 , Figure 2 This is a functional module diagram of the vehicle charging demand analysis device based on probability flow in this application. Figure 2As shown, the vehicle charging demand analysis device based on probability flow includes a creation module, a description module and an execution module.

[0192] The creation module is used to establish a single PHEV charging demand model based on the probability model and taking into account the uncertainty of PHEV charging behavior; the description module is used to describe the behavior of multiple PHEVs according to queuing theory, establish a discrete distribution of PHEV market share and randomly select PHEV categories; the execution module is used to randomly simulate the charging demand of PHEVs, obtain a sample of the overall charging power demand of PHEVs, and use statistical analysis to fit the distribution of the overall charging power demand of PHEVs.

[0193] In this application, a single PHEV charging demand model is established based on the probability model and considering the uncertainty factors of PHEV charging behavior, specifically:

[0194] Define a key parameter that represents the percentage of the total energy input provided by the battery to describe the operating state of the PHEV.

[0195]

[0196] Among them, E Eng Represents the total energy input into the vehicle engine and motor drive controller during a set time period, E Bat Indicates the energy delivered by the vehicle battery to the motor drive controller within a set time period, k EV Indicates the proportion of total energy input provided by the battery;

[0197] Define the total battery capacity of a PHEV.

[0198]

[0199] Among them, C Bat Represents the total battery capacity of the PHEV, μk EV Indicates k EV The mean value, μC Bat Represents C Bat , L represents the Cholesky decomposition of the covariance matrix, N1 and N2 represent two independent standard normal variables;

[0200] The performance of PHEV is evaluated based on the energy consumption per mile, denoted as E m :

[0201]

[0202] Among them, A E and B E represents the coefficient constant;

[0203] According to the statistics of PHEV driving mode, the daily mileage of a PHEV tends to obey the log-normal distribution, denoted as M d :

[0204]

[0205] Among them, e represents a natural constant, N represents a standard normal variable, and μ m and σ m represents the lognormal distribution parameters;

[0206] Calculate the daily charging energy of a PHEV, denoted as D E :

[0207]

[0208] Among them, M E Indicates the maximum driving range of a PHEV in all-electric driving mode.

[0209] Among them, the functional implementation of each module in the above-mentioned vehicle charging demand analysis device based on probability flow corresponds to the steps in the above-mentioned vehicle charging demand analysis method embodiment based on probability flow, and its functions and implementation processes are no longer repeated here one by one.

[0210] In a third aspect, an embodiment of the present application provides a vehicle charging demand analysis device based on probabilistic current flow. The vehicle charging demand analysis device based on probabilistic current flow may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0211] Reference Figure 3 , Figure 3 The hardware structure diagram of the vehicle charging demand analysis device based on probability flow involved in the embodiment of the present application is shown in FIG. In the embodiment of the present application, the vehicle charging demand analysis device based on probability flow may include a processor, a memory, a communication interface, and a communication bus.

[0212] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0213] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, etc., which are used to realize the interconnection of devices inside the vehicle charging demand analysis device based on probability flow, and an interface for realizing the interconnection between the vehicle charging demand analysis device based on probability flow and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0214] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0215] The processor may be a general-purpose processor, and the general-purpose processor may call the automobile charging demand analysis program based on probability flow stored in the memory, and execute the automobile charging demand analysis method based on probability flow provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). Among them, the method executed when the automobile charging demand analysis program based on probability flow is called may refer to the various embodiments of the automobile charging demand analysis method based on probability flow of the present application, which will not be repeated here.

[0216] Those skilled in the art will understand that Figure 3 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0217] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0218] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0219] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0220] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0221] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0222] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for analyzing automobile charging demand based on probability flow, characterized in that: The vehicle charging demand analysis method based on probability power flow includes: Based on the probability model and considering the uncertainty of PHEV charging behavior, a single PHEV charging demand model is established; The behaviors of multiple PHEVs are described based on queuing theory, a discrete distribution of PHEV market share is established, and the categories of PHEVs are randomly selected; Randomly simulate the charging demand of PHEV to obtain the sample of the overall charging power demand of PHEV, and use statistical analysis to fit the distribution of the overall charging power demand of PHEV; The above-mentioned single PHEV charging demand model is established based on the probability model and taking into account the uncertainty factors of PHEV charging behavior, specifically: Define a key parameter that represents the percentage of the total energy input provided by the battery to describe the operating state of the PHEV. Among them, E Eng Represents the total energy input into the vehicle engine and motor drive controller during a set time period, E Bat Indicates the energy delivered by the vehicle battery to the motor drive controller within a set time period, k EV Indicates the proportion of total energy input provided by the battery; Define the total battery capacity of a PHEV. Among them, C Bat Represents the total battery capacity of the PHEV, μk EV Indicates k EV The mean value, μC Bat Represents C Bat , L represents the Cholesky decomposition of the covariance matrix, N1 and N2 represent two independent standard normal variables; The performance of PHEV is evaluated based on the energy consumption per mile, denoted as E m : Among them, A E and B E represents the coefficient constant; According to the statistics of PHEV driving mode, the daily mileage of a PHEV tends to obey the log-normal distribution, denoted as M d : Among them, e represents a natural constant, N represents a standard normal variable, and μ m and σ m represents the lognormal distribution parameters; Calculate the daily charging energy of a PHEV, denoted as D E : Among them, M E Indicates the maximum driving range of a PHEV in all-electric driving mode.

2. The method for analyzing vehicle charging demand based on probabilistic power flow according to claim 1, characterized in that: For the lognormal distribution parameter μ m and σ m , specifically: in, Indicates M d The mean of Indicates M d The standard deviation of For the maximum driving range of PHEV in all-electric mode M E , specifically:

3. The method for analyzing automobile charging demand based on probability power flow according to claim 1, characterized in that: The behavior of multiple PHEVs is described according to queuing theory, specifically: A queuing model is built under the charging scenario of an electric vehicle charging station, in which the PHEVs charging at the electric vehicle charging station can be regarded as customers waiting to be served in a queue of M, M, c. The first M indicates that the customer arrival interval follows the mean T λ The second M indicates that the time to serve a customer has a mean of T μ exponential distribution, c represents the maximum number of customers served at the same time; The arrival and charging patterns of PHEVs are described by Poisson distribution. The number of customers waiting at the electric vehicle charging station is assumed to be infinite. The number of PHEVs charging simultaneously in a queue M, M, c is recorded as n. At this time, n obeys the discrete distribution: Among them, p n represents the discrete distribution that n vehicles satisfy when charging at the same time, i represents the intermediate quantity, ρ represents the occupancy rate of each charging pile, A queuing model is established for charging scenarios in residential areas, where PHEVs charging in residential areas can be regarded as a queue in M, M, c, k, N max The number of customers waiting to be served in the queue, k represents the maximum number of customers being served or waiting in the queue, N max Indicates the maximum number of customers that may be served; The arrival and charging patterns of PHEVs are described by Poisson distribution. The number of customers that can be served in a residential community is assumed to be limited. In a M, M, c, k, N max The number of PHEVs charging simultaneously in the queue is recorded as n, and the discrete distribution of n is: The calculated charging service time T of PHEV in the queuing model follows the mean value T μ The exponential distribution is: T=-T μ ·ln(U) Wherein, U represents a variable uniformly distributed in (0,1), and the queuing model includes a queuing model in a charging scenario at an electric vehicle charging station and a queuing model in a charging scenario in a residential community; By restricting T, we get: Among them, T min represents the minimum value of T, T max Indicates the maximum value of T; Select and determine the PHEV charging power level; Calculate the average charging current I of a PHEV: Among them, D E represents the daily charging energy of PHEV, V represents the charging voltage, I max Indicates the maximum charging current; The total charging power demand P of all n PHEVs charging at electric vehicle charging stations or residential areas is calculated as: Among them, I i represents the average charging current of the i-th PHEV.

4. The method for analyzing automobile charging demand based on probability power flow according to claim 1, characterized in that: The discrete distribution of PHEV market share is established and the category of PHEV is randomly selected, specifically: The market share of PHEV is set as a discrete distribution, and the category of PHEV is randomly selected according to the market share of PHEV.

5. The method for analyzing automobile charging demand based on probabilistic power flow according to claim 1, characterized in that: The random simulation of the charging demand of the PHEV to obtain the overall charging power demand sample of the PHEV is specifically as follows: the sample selection process is repeated in sequence until a set number of samples are generated, thereby obtaining the overall charging power demand sample of the PHEV; The sample selection process is specifically as follows: The number of PHEVs charged simultaneously is randomly generated, and the category of PHEV is randomly selected according to the market share of PHEVs; Randomly generate k for PHEV EV and C Bat Parameters and calculate the energy consumption per mile E of PHEV m , k EV Indicates the proportion of total energy input provided by the battery, C Bat Indicates the total battery capacity of the PHEV; Randomly generate the mileage M of the PHEV d , calculate the daily charging energy D of PHEV E ; The charging time T of the PHEV is randomly generated, the charging current I of the PHEV is calculated, and the total charging power demand P is accumulated.

6. A method for analyzing automobile charging demand based on probabilistic power flow as claimed in claim 5, characterized in that: The distribution of the overall charging power demand of PHEV is obtained by fitting using the statistical analysis method, specifically: Setting coefficient constant A for different types of PHEV E and B E ; Calculate and determine k EV , Among them, M E Indicates the maximum driving range of a PHEV in all-electric driving mode; Describing k based on bivariate normal distribution EV and C Bat , Among them, mink EV Indicates k EV The minimum value of maxk EV Indicates k EV The maximum value, minC Bat Represents C Bat The minimum value, maxC Bat Represents C Bat The maximum value of Indicates k EV The standard deviation of Represents C Bat The standard deviation of , S represents the coefficient, and its value is 0.8; Based on the queuing model in the charging scenario of electric vehicle charging stations, the queuing model in the charging scenario in residential communities, and the selected PHEV charging power level, the distribution of the overall charging power demand of PHEV is fitted.

7. A vehicle charging demand analysis device based on probability flow, characterized in that: The vehicle charging demand analysis device based on probability flow includes: Creating a module for modeling a single PHEV charging demand based on a probabilistic model and taking into account uncertainties in PHEV charging behavior; A description module is used to describe the behavior of multiple PHEVs based on queuing theory, establish a discrete distribution of PHEV market share and randomly select the category of PHEVs; An execution module is used to randomly simulate the charging demand of PHEV, obtain the overall charging power demand sample of PHEV, and use statistical analysis to fit the distribution of the overall charging power demand of PHEV; The above-mentioned single PHEV charging demand model is established based on the probability model and taking into account the uncertainty factors of PHEV charging behavior, specifically: Define a key parameter that represents the percentage of the total energy input provided by the battery to describe the operating state of the PHEV. Among them, E Eng Represents the total energy input into the vehicle engine and motor drive controller during a set time period, E Bat Indicates the energy delivered by the vehicle battery to the motor drive controller within a set time period, k EV Indicates the proportion of total energy input provided by the battery; Define the total battery capacity of a PHEV. Among them, C Bat Represents the total battery capacity of the PHEV, μk EV Indicates k EV The mean value, μC Bat Represents C Bat , L represents the Cholesky decomposition of the covariance matrix, N1 and N2 represent two independent standard normal variables; The performance of PHEV is evaluated based on the energy consumption per mile, denoted as E m : Among them, A E and B E represents the coefficient constant; According to the statistics of PHEV driving mode, the daily mileage of a PHEV tends to obey the log-normal distribution, denoted as M d : Among them, e represents a natural constant, N represents a standard normal variable, and μ m and σ m represents the lognormal distribution parameters; Calculate the daily charging energy of a PHEV, denoted as D E : Among them, M E Indicates the maximum driving range of a PHEV in all-electric driving mode.

8. A vehicle charging demand analysis device based on probability flow, characterized in that: The vehicle charging demand analysis device based on probabilistic power flow includes a processor, a memory, and a vehicle charging demand analysis program based on probabilistic power flow stored in the memory and executable by the processor, wherein when the vehicle charging demand analysis program based on probabilistic power flow is executed by the processor, the steps of the vehicle charging demand analysis method based on probabilistic power flow as described in any one of claims 1 to 6 are implemented.

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