An electric vehicle charging power calculation method

Abstract

The present application relates to a kind of electric vehicle charging power calculation method, the method includes the following steps: using the database data of Internet of Vehicles to the space-time characteristic parameter in trip chain is statistically analyzed and is synthesized and establishes mathematical model to simulate user travel regularity;Different probability distribution functions are constructed to simulate the single charging behavior characteristics of electric vehicle user in real time;Determine the power size of charging and the duration length of charging;The charging power of single electric vehicle in daily is estimated and calculated;Obtain the space-time distribution curve of electric vehicle charging power in single day.The present application considers the charging behavior habit of user, the state of electric vehicle battery and the anxiety psychology of user to potential trip, establishes more real user charging decision model, can more practically and accurately estimate the space-time distribution of electric vehicle charging power in single day, has strong practicability.

Description

Technical Field

[0001] This invention relates to a method for calculating the charging power of electric vehicles, belonging to the field of electric vehicles, and involving energy infrastructure planning and operation, particularly a method for calculating the charging power of electric vehicles that takes into account both user travel patterns and the battery's own state. Background Technology

[0002] Electric vehicles (EVs) have experienced rapid development in recent years as an important means of alleviating the energy crisis and environmental pollution. The widespread adoption of EVs will generate substantial charging demand. However, the diverse travel characteristics and daily charging habits of EV users mean that the large-scale EV charging power will exhibit high randomness and dispersion in its spatiotemporal distribution, inevitably posing a more severe challenge to the safe and stable operation of the power distribution network. Accurately predicting the spatiotemporal distribution of EV charging power is not only fundamental to studying the impact of EVs on the power distribution network but also a necessary prerequisite for promoting the optimized planning of charging facilities and the orderly scheduling of EV charging and discharging power.

[0003] Numerous domestic and international scholars have conducted research on electric vehicle (EV) charging power prediction, primarily using statistical models to analyze the spatiotemporal characteristics of EV charging load, simulate EV user travel patterns, and consider differences in EV user charging behavior under various influencing factors to predict EV charging power. However, most studies neglect the impact of battery state and user anxiety about potential range on the spatiotemporal distribution of charging power. As EVs age, the capacity of their power batteries gradually decreases, while internal resistance increases, resulting in less charge for the same battery state of charge (SOC). This insufficient charge may trigger range anxiety in EV users, leading them to maintain a higher SOC level. Therefore, it is necessary to consider battery state and user anxiety about potential range as key factors influencing EV users' charging decisions and propose more practical and reasonable methods for EV charging power prediction. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings and deficiencies of existing technologies and propose a method for calculating the charging power of electric vehicles that takes into account both user travel patterns and battery status. This method considers the battery status of the electric vehicle and the user's anxiety about potential trips as key factors influencing the user's charging decision. A charging power decision model is established to achieve accurate prediction and calculation of the daily charging power of electric vehicles.

[0005] The present invention solves its technical problem by adopting the following technical solution:

[0006] A method for calculating the charging power of an electric vehicle, the method comprising the following steps:

[0007] S1. Based on the travel characteristics of electric vehicle users, statistical analysis and synthesis of the spatiotemporal characteristic parameters in the travel chain are carried out using vehicle network database data, and a mathematical model is established to simulate user travel patterns.

[0008] S2. To simulate the individual charging behavior characteristics of electric vehicle users based on their different charging habits, different probability distribution functions are constructed.

[0009] S3. The battery status of electric vehicles and the user's anxiety about potential trips are taken as key factors influencing the charging power decision of electric vehicle users, and the charging power and charging duration are determined.

[0010] S4. Use computer sampling simulation and real-world simulation methods to simulate the travel and charging habits of each electric vehicle, and then make a preliminary estimate of the daily charging power of a single electric vehicle.

[0011] S5. By superimposing the estimated charging power of each electric vehicle after considering its own state correction, the spatiotemporal distribution curve of electric vehicle charging power within a single day is obtained.

[0012] In the above-mentioned electric vehicle charging power calculation method, the spatiotemporal characteristic parameters in the travel chain based on the vehicle network database in step S1 include the number of daily trips, the first departure time from home, the duration of a single trip, the length of stay, the departure and destination of a single trip, and the distance of a single trip.

[0013] In the above-mentioned electric vehicle charging power calculation method, the method of statistical analysis and synthesis of spatiotemporal characteristic parameters in the travel chain based on vehicle network database data in step S1 and the establishment of a mathematical model is as follows: statistical analysis and synthesis of the number of daily trips, the duration of a single trip, and the distance of a single trip are performed using the log-normal probability density function to establish a mathematical model; statistical analysis and synthesis of the first departure time from home is performed using the normal probability density function to establish a mathematical model; the departure and destination types of electric vehicle users' single trips are divided into four categories: residential areas, office areas, business areas, and cultural and entertainment areas, and the transition characteristics of the departure and destination of a single trip are modeled using a spatial state transition matrix; based on the departure and destination types of a single trip, the length of stay is divided into three categories: residential areas, office areas, and other areas, and the length of stay in residential areas, office areas, and other areas is statistically analyzed and synthesized using Weibull distribution and generalized extreme value distribution to establish a mathematical model.

[0014] In the above-described electric vehicle charging power calculation method, the user charging habit-related characteristics considered in step S2 include the ideal state of charge (SOC) of the battery during the electric vehicle user's daily commutes. eBattery State of Charge (SOC) during the user's first trip away from home on a daily schedule f and the state of charge (SOC) value of the battery, which characterizes the initial charging of electric vehicles at low power. l ;

[0015] In the above-mentioned method for calculating the charging power of electric vehicles that takes into account both user travel patterns and battery status, step S2 uses a uniform probability density function to calculate the ideal state of charge (SOC) value of the charging battery during the daily trip of the electric vehicle user. e Statistical analysis and synthesis were performed, and a mathematical model was established, including the SOC during the sampling process. exp Assign to SOC1;

[0016] In the above-described electric vehicle charging power calculation method, step S2 uses a normal distribution probability density function to calculate the battery state of charge (SOC) value at the start of low-power charging for electric vehicle users. l Perform statistical analysis and synthesis and establish a mathematical model.

[0017] In the above-mentioned electric vehicle charging power calculation method, the battery state in step S3 is used to characterize the ratio of the actual capacity to the rated capacity of the electric vehicle battery, which follows a uniform distribution U(0.8,1).

[0018] In the above-mentioned electric vehicle charging power calculation method, the user's anxiety about the potential journey in step S3 is characterized by the user's anxiety coefficient η about the mileage.

[0019] In the above-described electric vehicle charging power calculation method, the battery state in step S3 is related to the user's anxiety about the potential journey, as shown in the following formula:

[0020]

[0021] In the formula, η is the range anxiety coefficient that characterizes the user's range anxiety, and SOB is the battery state.

[0022] In the above-described electric vehicle charging power calculation method, the electric vehicle user's choice of whether to charge and the magnitude of the charging power in step S3 satisfies the following formula:

[0023]

[0024] In the formula, 0 represents zero charging power, i.e., no charging; SOC r，i For electric vehicles to reach the starting destination D of a single trip i The remaining state of charge of the battery; Q is the actual capacity of the electric vehicle battery, in kWh; ω is the power consumption of the electric vehicle per kilometer, in kWh / km; d i+1P represents the distance of a single trip during the (i+1)th journey, in km. f For high-power charging, P l This refers to low-power charging power, measured in kW; t p，i For electric vehicles, the starting point and destination D on a single trip i The dwell time is measured in seconds (s).

[0025] In the above-mentioned method for calculating the charging power of electric vehicles that takes into account both user travel patterns and battery status, the charging duration in step S3 satisfies the following formula:

[0026]

[0027] In the formula, P i For electric vehicles, the starting point and destination D on a single trip i The charging power selected at that time.

[0028] The advantages and positive effects of this invention compared to the prior art are:

[0029] This invention takes into account the number of trips a user takes each day and constructs a more detailed electric vehicle trip prediction model. By taking into account the user's charging tendency, the electric vehicle battery status, and the user's anxiety about potential trips, a more realistic user charging decision model is established, which can more realistically and accurately predict the spatiotemporal distribution of electric vehicle charging power within a single day. Attached Figure Description

[0030] Figure 1 This is a flowchart of the present invention;

[0031] Figure 2 This is a schematic diagram of the travel chain of the present invention;

[0032] Figure 3 This is a flowchart of the daily charging power estimation calculation for electric vehicles according to the present invention. Detailed Implementation

[0033] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments:

[0034] like Figure 1 As shown, Figure 1 This is a flowchart illustrating the overall process of the method described in this invention. This invention is a method for calculating the charging power of electric vehicles that takes into account both user travel patterns and the battery's own state. Its innovation lies in including the following steps:

[0035] S1. Based on the travel characteristics of electric vehicle users, statistical analysis and synthesis of the spatiotemporal characteristic parameters in the travel chain are carried out using vehicle network database data, and a mathematical model is established to simulate user travel patterns.

[0036] In this invention, the spatiotemporal characteristic parameters of electric vehicle users in the travel chain based on the vehicle network database are as follows: Figure 2 As shown in the figure, the dashed lines represent the driving process of the electric vehicle, the solid lines represent the stopping process, the black solid circles represent the start and end times of the electric vehicle's journey, and the hollow circles represent the arrival and departure times of the electric vehicle users' respective destinations. The spatiotemporal characteristic parameters of electric vehicle users include independent characteristic parameters: the number of trips per day M, and the first departure time from home T. s，1 Duration of a single trip (t) r，i Length of stay t p，i Single trip departure and destination D i And the distance d of a single trip on the way i And non-independent characteristic parameters: arrival time T a，i and the start time T of the next journey s，i+1 The non-independent parameter is calculated by the following formula:

[0037]

[0038] In this invention, the log-normal distribution probability density function is used to represent the number of daily trips M and the duration of a single trip t. r The statistical analysis and synthesis of the single trip distance d were conducted to establish a mathematical model; the normal distribution probability density function was used to analyze the time T of the first departure from home. s，1 Statistical analysis and synthesis were conducted, and a mathematical model was established; the origin and destination D of a single trip for an electric vehicle user were analyzed. i The types are divided into four categories: residential areas, office areas, business areas, and cultural and entertainment areas. A spatial state transition matrix is ​​used to represent the departure and destination D of a single trip. i The transfer characteristics are modeled; the residence time t is used as the model. p Based on the characteristics of the origin and destination of a single trip, the system is divided into three categories: residential areas, office areas, and other areas for fitting. The Weibull distribution and the generalized extreme value distribution are used to conduct statistical analysis and synthesis of the dwell time in residential areas, office areas, and other areas, respectively, and a mathematical model is established. It is also assumed that the first trip origin of electric vehicle users is always a residential area.

[0039] In this invention, the number of daily trips M is statistically analyzed and synthesized using a log-normal distribution to establish a mathematical model, the probability density function of which is as follows:

[0040]

[0041] In the formula, μ L and σ L It is determined by specific vehicle-to-everything (V2X) database data;

[0042] In this invention, the time T of the first departure from home is... s，1 The normal distribution is used for statistical analysis and synthesis, and a mathematical model is established. Its probability density function is as follows:

[0043]

[0044] In the formula, μ T and σ T Determined by specific vehicle-to-everything (V2X) database data;

[0045] In this invention, the duration t of a single trip during transit is... r The log-normal distribution is used for statistical analysis and synthesis to establish a mathematical model, whose probability density function is as follows:

[0046]

[0047] In the formula, μ t and σ t Determined by specific vehicle-to-everything (V2X) database data;

[0048] In this invention, the length of stay is determined based on the departure and destination types of a single trip. During the statistical analysis and synthesis process to establish a mathematical model, different probability distribution functions can be selected for different departure and destination types of a single trip, including Weibull distribution and generalized extreme value distribution. The specific distribution function selection is determined based on the statistical analysis and synthesis of specific vehicle network database data and the results of establishing the mathematical model.

[0049] In this invention, it is assumed that there is a certain correlation between the current trip's departure and destination, the current location, and the trip's departure time. Considering a 15-minute time interval, a day is discretized into 96 sub-time periods. The user's movement between different regions within each sub-time period is statistically analyzed. Since there are four possible departure points and four possible destinations for a single trip, the spatial movement probability matrix established within sub-time period T has a size of 4×4, as shown in the following formula:

[0050]

[0051] In the formula, D represents the origin of a user's single trip within the Tth sub-period. i The departure and destination for a single trip is D. j The probability, and should satisfy Considering the wide coverage area in reality, and the possibility of users driving and moving within the same area, the diagonal elements in the matrix are not necessarily zero, i.e., p T,k,k ≠0;

[0052] In this invention, the distance d of a single trip is considered to be subordinate to the duration t of a single trip.r The normal distribution under certain conditions has a specific conditional probability density function as shown in the following formula:

[0053]

[0054] In the formula, μ d (t r ) and σ d (t r It is determined by specific vehicle network database data.

[0055] S2. To simulate the individual charging behavior characteristics of electric vehicle users based on their different charging habits, different probability distribution functions are constructed.

[0056] In this invention, the user charging habit-related characteristics considered include the ideal state of charge (SOC) of the battery during the daily commutes of electric vehicle users. e Battery State of Charge (SOC) during the user's first trip away from home on a daily schedule f and the state of charge (SOC) value of the battery, which characterizes the initial charging of electric vehicles at low power. l ;

[0057] In this invention, the ideal state of charge (SOC) of a rechargeable battery during a daily trip for an electric vehicle user is considered to be... e Following a uniform distribution U(0.8,1), the SOC during the sampling process is... e Assign to SOC f ;

[0058] In this invention, the State of Charge (SOC) of the battery at the start of low-power charging for electric vehicle users is considered to be... l It follows a normal distribution N(0.5,0.1).

[0059] S3. The battery status of electric vehicles and the user's anxiety about potential trips are taken as key factors influencing the charging power decision of electric vehicle users, and the charging power and charging duration are determined.

[0060] In this invention, the State of Battery (SOB) is used to characterize the ratio of the actual capacity to the rated capacity of the electric vehicle battery, following a uniform distribution U(0.8,1). The user's anxiety about potential travel distance is characterized by the user's anxiety coefficient η regarding mileage. The relationship between the SOB and the user's anxiety about potential travel distance satisfies the following equation:

[0061]

[0062] In the formula, η is the range anxiety coefficient that characterizes the user's range anxiety, and SOB is the battery state.

[0063] In this invention, the choice of whether to charge and the amount of charging power made by electric vehicle users satisfies the following formula:

[0064]

[0065] In the formula, 0 represents zero charging power, i.e., no charging; SOC r，i For electric vehicles to reach the starting destination D of a single trip i The remaining state of charge of the battery; Q is the actual capacity of the electric vehicle battery, in kWh; ω is the power consumption of the electric vehicle per kilometer, in kWh / km; d i+1 P represents the distance of a single trip during the (i+1)th journey, in km. f For high-power charging, P l This refers to low-power charging power, measured in kW; t p，i For electric vehicles, the starting point and destination D on a single trip i The dwell time is measured in seconds (s).

[0066] In this invention, the duration of charging selected by the user satisfies the following formula:

[0067]

[0068] In the formula, P i For electric vehicles, the starting point and destination D on a single trip i The charging power selected at that time.

[0069] S4. Use computer sampling simulation and real-world simulation methods to simulate the travel and charging habits of each electric vehicle, and then make a preliminary estimate of the daily charging power of a single electric vehicle.

[0070] In this invention, the process for estimating the daily charging power of a single electric vehicle is as follows: Figure 3 As shown, the steps are as follows:

[0071] S401. Extract the state of battery (SOB) of the electric vehicle based on the probability density function;

[0072] S402. Calculate the actual capacity of the electric vehicle battery based on the state of charge (SOB) of the electric vehicle battery.

[0073] S403. Calculate the user's anxiety coefficient regarding range based on the electric vehicle battery state of charge (SOB).

[0074] S404. Considering the charging habits of electric vehicle users, extract the ideal state of charge (SOC) value of the charging battery and the SOC value of the battery when the user starts charging at low power, and calculate the SOC value of the battery when leaving home for the first time.

[0075] S405. The first trip will start from a residential community, and the number of trips per day will be randomly selected.

[0076] S406. Extract the departure and destination of the journey based on the spatial state transition matrix;

[0077] S407. Extract the duration of a single trip during the journey based on the origin and destination of this trip;

[0078] S408. Extract the distance of a single trip during the journey based on the duration of that single trip.

[0079] S409. Extract the length of stay based on the departure point and destination type of the itinerary;

[0080] S4010, Make a decision on charging power;

[0081] S411. Calculate the actual charging duration based on the charging power, and record the electric vehicle charging power, location, and time period;

[0082] S412. Determine if the electric vehicle is the last trip. If so, end the day's trip. Otherwise, proceed to step S406 and repeat steps S406-S411.

[0083] S5. By superimposing the estimated charging power of each electric vehicle after considering its own state correction, the spatiotemporal distribution curve of electric vehicle charging power within a single day is obtained.

[0084] This invention takes into account users' charging habits, electric vehicle battery status, and users' anxiety about potential trips, and establishes a more realistic user charging decision model. It can more realistically and accurately predict the spatiotemporal distribution of electric vehicle charging power within a single day, and has strong practicality.

[0085] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Various substitutions, changes and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the scope of the present invention is not limited to the contents disclosed in the embodiments and drawings.

Claims

1. A method for calculating the charging power of an electric vehicle, characterized in that, The method includes the following steps: S1. Based on the travel characteristics of electric vehicle users, statistical analysis and synthesis of the spatiotemporal characteristic parameters in the travel chain are carried out using vehicle network database data, and mathematical models are established to reveal the travel patterns of users. S2. To simulate the individual charging behavior characteristics of electric vehicle users based on their different charging habits, different probability distribution functions are constructed. S3. The battery status of electric vehicles and users' anxiety about potential trips are taken as key factors influencing electric vehicle users' decisions on whether to charge and the amount of charging power, and the amount of charging power, the access location, and the duration of the same charging behavior are determined. S4. Use computer sampling simulation and real-world simulation methods to quantify the travel and charging habits of each electric vehicle, and estimate the daily charging power of a single electric vehicle based on the load integration method. S5. Superimpose the estimated charging power of each electric vehicle after considering its own state correction to obtain the distribution curve of the charging power of all electric vehicles connected to a specific location within a single day. The spatiotemporal characteristic parameters in the travel chain based on the vehicle network database in step S1 include the number of daily trips, the first departure time of the electric vehicle from home, the duration of a single trip, the length of stay, the departure and destination of a single trip, and the distance of a single trip. A mathematical model was established by statistically analyzing and synthesizing the number of daily trips, the duration of each trip, and the distance of each trip using the log-normal probability density function; a mathematical model was also established by statistically analyzing and synthesizing the time of first departure from home using the normal probability density function. The origin and destination of a single trip are divided into four categories according to their different travel characteristics: residential area, office area, business area and cultural and entertainment area. The transition characteristics of the origin and destination of a single trip are mathematically modeled by a spatial state transition matrix. The dwell time was divided into three categories: residential area, office area and other areas. The Weibull distribution and the generalized extreme value distribution were used to conduct statistical analysis and synthesis of the dwell time in residential area, office area and other areas respectively, and a mathematical model was established. The user charging habit-related characteristics considered in step S2 include the ideal state-of-charge value of the battery during the daily commutes of electric vehicle users. Battery state of charge value during the user's first trip away from home on a daily schedule. and the battery state-of-charge value characterizing the initial charging of electric vehicle users at low power. The ideal state-of-charge (SOC) value of a rechargeable battery during a user's daily commut is determined using a uniform probability density function. Perform statistical analysis and synthesis, establish a mathematical model, and incorporate the sampling process. Assigned The normal distribution probability density function is used to analyze the battery state-of-charge value of electric vehicle users at the start of low-power charging. Perform statistical analysis and synthesis and establish a mathematical model.

2. The method for calculating the charging power of an electric vehicle according to claim 1, characterized in that, In step S3, the battery state follows a uniform distribution. .

3. The method for calculating the charging power of an electric vehicle according to claim 1, characterized in that, In step S3, the user's anxiety about the potential trip is assessed through the user's anxiety coefficient regarding mileage. To express, It satisfies the following formula: ， In the formula, To characterize the user's anxiety level regarding potential trips, SOB represents the battery status.

4. The method for calculating the charging power of an electric vehicle according to claim 1, characterized in that, In step S3, the electric vehicle user's choice regarding whether to charge and the amount of charging power satisfies the following formula: ， In the formula, 0 represents zero charging power, that is, no charging; For electric vehicles to reach the starting destination of a single trip The remaining state of charge of the battery; This refers to the actual capacity of the electric vehicle battery, expressed in kW h. Electricity consumption per kilometer for electric vehicles, expressed in kW h / km; For the first The distance of a single trip during the journey, in km; For high-power charging, Low-power charging power, measured in kW; For electric vehicles, the departure and destination on a single trip The dwell time is measured in seconds (s).

5. The method for calculating the charging power of an electric vehicle according to claim 4, characterized in that, The duration of charging in step S3 satisfies the following formula: ， In the formula, For electric vehicles, the departure and destination on a single trip The charging power selected at that time.

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