Electric vehicle charging load prediction model construction method considering multiple power consumption factors

Abstract

The invention provides an electric vehicle charging load prediction model construction method considering multiple power consumption factors. The method comprises the steps of determining the number of active electric vehicles every day; analyzing multiple power consumption factors of the electric vehicle, establishing a total power consumption model per hundred kilometers, and obtaining an expected power consumption value per hundred kilometers of the electric vehicle under the condition of considering all the factors; and predicting and generating a load curve representing the sum of the time-varying charging demands of all the electric vehicles in the target area. By comprehensively considering multiple power consumption factors of the electric vehicle and utilizing a Monte Carlo simulation method and a probability distribution model, accurate prediction of the charging load of the electric vehicle is firstly realized; according to the method, the number of active electric automobiles every day is determined, the probability distribution of the first charging time and the travelled distance is simulated more accurately, and finally, the power grid resource distribution is optimized, the peak load is reduced, and the stability and reliability of the power grid are improved by analyzing the air conditioner use probability and the power consumption proportion of the air conditioner in the on-off state.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging energy consumption identification and analysis, and specifically to a method for constructing an electric vehicle charging load prediction model considering multiple power-consuming factors. Background Art

[0002] At present, the technology of new energy electric vehicles has developed rapidly, and the market share has been continuously increasing. However, the charging of electric vehicles has great randomness in time and space. After being connected to the power grid on a large scale, it will inevitably have an adverse impact on the operation of the power grid, such as affecting the stable operation of the power grid and generating a large number of harmonics causing voltage distortion, etc. Modeling the charging load of a large number of electric vehicles is the basis for studying its impact on the power grid. At present, some researchers have studied various factors that may affect the electric vehicle load and established a load prediction model, but the factors considered in the prediction are still not comprehensive.

[0003] In real life, the power consumption per 100 kilometers of electric vehicles is affected by various factors, such as the brand of the electric vehicle, the degree of battery aging, the degree of motor aging, the power consumption of in-vehicle equipment, and road conditions, etc. In addition, the existing research does not consider the interference of idle electric vehicles on model establishment.

[0004] Therefore, it is very necessary to propose a method for constructing an electric vehicle charging load prediction model considering multiple power-consuming factors. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a method for constructing an electric vehicle charging load prediction model considering multiple power-consuming factors. To achieve the above purpose, the present invention is realized through the following technical solutions: A method for constructing an electric vehicle charging load prediction model considering multiple power-consuming factors, including:

[0006] By obtaining and cleaning the electric vehicle ownership and travel statistics data in the target area, excluding vehicles that have not been used for a long time, and distinguishing the travel patterns on holidays and non-holidays, the number of daily active electric vehicles is determined;

[0007] Analyze multiple power-consuming factors including the brand, driving road conditions, aging degree, and in-vehicle equipment usage of the electric vehicle, evaluate the impact of the factors on power consumption, divide the power consumption per 100 kilometers of electric vehicles of different brands into multiple intervals, calculate the proportion of each interval brand, and then establish a total power consumption model per 100 kilometers to obtain the expected value of the power consumption per 100 kilometers of electric vehicles considering all factors;

[0008] Based on the total power consumption model, using the Monte Carlo simulation method, set the input parameters affecting the charging load prediction, simulate the probability distributions of the first charging time and the driving mileage through the probability distribution model, and finally predict and generate a load curve representing the total charging demand of all electric vehicles over time in the target area.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0010] By comprehensively considering multiple power consumption factors of electric vehicles, such as brand, driving road conditions, aging degree, and in-vehicle equipment usage, the present invention uses the Monte Carlo simulation method and the probability distribution model to first achieve accurate prediction of the charging load of electric vehicles; by obtaining and cleaning the ownership and travel statistical data of electric vehicles in the target area, excluding vehicles that have not been used for a long time, and distinguishing the travel patterns on holidays and non-holidays, the number of daily active electric vehicles can be determined, thereby more accurately simulating the probability distributions of the first charging time and the driving mileage.

[0011] In addition, by analyzing the air conditioner usage probability and its power consumption ratio in the on and off states, the present invention can evaluate the impact of air conditioner usage on power consumption, further improving the prediction accuracy of the model. It can not only provide a scientific basis for power grid load management and the formulation of electric vehicle charging strategies, but also optimize the allocation of power grid resources, reduce peak loads, and improve the stability and reliability of the power grid, thus providing important support for the construction of smart grids and the orderly charging of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The disclosure of the present invention will be described with reference to the accompanying drawings. It should be understood that the drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In the drawings, the same reference numerals are used to refer to the same components. Among them:

[0013] Figure 1 is a schematic flow chart of the method for constructing a charging load prediction model proposed in an embodiment of the present invention;

[0014] Figure 2 is a schematic flow chart of the calculation of the charging load of electric vehicles based on the Monte Carlo method proposed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] It is easy to understand that according to the technical solution of the present invention, without changing the essence of the present invention, those of ordinary skill in the art can propose various structural ways and implementation ways that can be mutually replaced. Therefore, the following detailed embodiments and the accompanying drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as all of the present invention or as a limitation or restriction on the technical solution of the present invention.

[0016] The present invention will be further described in detail below with reference to the accompanying drawings, but it is not intended to limit the present invention.

[0017] As an understanding of the technical concept and implementation principle of the present invention, the main purpose of the present invention is to solve the problems that the existing electric vehicle charging load prediction model does not consider comprehensive factors and has insufficient prediction accuracy.

[0018] As Figure 1 - Figure 2 shown, as an embodiment of the present invention, a method for constructing an electric vehicle charging load prediction model considering multiple power consumption factors is proposed. By comprehensively analyzing multiple power consumption factors of electric vehicles, including vehicle brand, driving road conditions, aging degree, and in-vehicle equipment usage, a new prediction model is constructed. Using the Monte Carlo simulation method and probability distribution model, combined with actual travel statistical data and environmental temperature changes, the charging behavior and driving mileage of electric vehicles are accurately simulated, providing a scientific basis for grid load management, optimizing the charging strategy, and reducing the grid peak load.

[0019] It includes the following specific steps:

[0020] S1. By obtaining and cleaning the electric vehicle ownership and travel statistical data in the target area, excluding vehicles that have not been used for a long time, and distinguishing the travel patterns on holidays and non-holidays, the number of daily active electric vehicles is determined.

[0021] S2. Analyze multiple power consumption factors including the brand, driving road conditions, aging degree, and in-vehicle equipment usage of electric vehicles, evaluate the impact of these factors on power consumption, divide the power consumption per 100 kilometers of electric vehicles of different brands into multiple intervals, and calculate the proportion of each interval brand.

[0022] S3. Establish a total power consumption model per 100 kilometers, and obtain the expected value of the power consumption per 100 kilometers of electric vehicles considering all factors.

[0023] S4. Based on the total power consumption model, using the Monte Carlo simulation method, set the input parameters affecting the charging load prediction, simulate the probability distributions of the first charging time and driving mileage through the probability distribution model, and finally predict and generate a load curve representing the total charging demand of all electric vehicles in the target area changing with time.

[0024] In an embodiment of the present invention, the specific implementation steps of step S1 include:

[0025] S1-1. Obtain the actual household electric vehicle ownership and travel statistics data of the target area through methods such as questionnaires, contacting relevant institutions or platforms. Example: A detailed questionnaire can be designed according to actual needs, covering key information of electric vehicles, and distributed through multiple online and offline channels to collect extensive data. At the same time, actively contact relevant institutions such as traffic management departments, vehicle registration institutions, and electric vehicle charging service providers, obtain official statistical data through cooperation and data sharing, and then use existing electric vehicle management platforms or applications to collect users' travel and charging data to further enrich the data sources. Finally, set the database to be updated regularly to reflect the latest changes in electric vehicle ownership and travel patterns.

[0026] S1-2. Perform data cleaning. It can be understood that the purpose of data cleaning is to eliminate duplicate, incorrect, and incomplete data, exclude the interference of idle electric vehicles on charge prediction data, and obtain the total number of locally active electric vehicles per day. The process is as follows: When excluding vehicles that have not been used for a long time, define vehicles used within two weeks as active electric vehicles. Based on the data obtained from the survey or relevant institutions, calculate the total number of active electric vehicles using the following formula:

[0027] N active =N total -N nonuse =α a N total (1)

[0028] In the formula, N active is the total number of active electric vehicles; N nonuse is the total number of electric vehicles that have not been used for a long time; N total is the electric vehicle ownership in the target area; α a is the electric vehicle activity factor. At the same time, since the number of daily travel vehicles on holidays and non-holidays is different, therefore, taking 14 days as a cycle, calculate the expected value N Dr-exp N Dunr-exp of the daily travel vehicles on holidays and non-holidays to distinguish the travel patterns on holidays and non-holidays:

[0029]

[0030] In the formula, D r is the number of holiday days, and 14 - D r is the number of non-holiday days.

[0031] In an embodiment of the present invention, in step S2, when evaluating the influence of multiple power consumption factors on power consumption, multiple power consumption factors need to be considered, including different electric vehicle brands, driving road conditions, the aging degree of electric vehicles, and the usage situation of in-vehicle equipment, so as to analyze the influence of each power consumption factor during the driving process of electric vehicles on power consumption. It should be noted that the power consumption per 100 kilometers of electric vehicles of different brands is different. Currently, the power consumption per 100 kilometers of mainstream electric vehicles on the market is between 9 and 21 kWh. Therefore, the power consumption intervals of different brands of electric vehicles are divided into [9 kWh, 12 kWh], (12 kWh, 15 kWh], (15 kWh, 18 kWh], and (18 kWh, 21 kWh]. The analysis and / or evaluation process is as follows:

[0032] Let the proportion of electric vehicle brands in each interval be denoted as β1, β2, β3, and β4 respectively, and take the average value of the power consumption in each interval. Then, by statistically analyzing the proportions β1, β2, β3, and β4 of different brands of electric vehicles in each power consumption interval, the power consumption per 100 kilometers EC1 can be calculated, with the unit of kWh, to evaluate the influence of vehicle type on power consumption:

[0033] EC1 = 10.5β1 + 13.5β2 + 16.5β3 + 19.5β4 (4)

[0034] It should be noted that in practice, the driving road conditions are closely related to the local road construction level and vehicle ownership. Generally, the more roads there are, the higher the road quality, the better the road conditions, the more vehicle ownership, the more vehicles on the road, and the worse the road conditions. Especially during the commuting peak period, when electric vehicles are in poor road conditions or traffic jams, they need to frequently start, accelerate, or decelerate, and the power consumption will increase at this time. Therefore, by introducing the average congestion factor γ, the power consumption can be adjusted based on the traffic conditions to evaluate the influence of traffic conditions on power consumption EC2, EC2 = γEC1 (5), where, is the average congestion distance per 100 kilometers of electric vehicles, and H is the average power increase rate during congestion.

[0035] Furthermore, the aging degree of electric vehicles includes the aging degree of the battery and the aging degree of the motor. Battery aging and motor aging will increase the power consumption per 100 kilometers of electric vehicles. Therefore, by considering the aging coefficients η1 and η2 of the battery and motor of electric vehicles, the influence of the aging degree on power consumption EC3 can be quantified, with the unit of kWh, EC3 = (η1 + η2)EC1 (6). Specifically, when implementing, the aging coefficients η1 and η2 are both local statistical data.

[0036] Furthermore, since the usage of in-vehicle equipment is mainly related to the ambient temperature, the ambient temperature affects the utilization rate of the electric vehicle air conditioner. The ambient temperature is obtained through the meteorological temperature data of the day, and the ambient temperature varying with time is denoted as the function T(t). T(t) is fitted using trigonometric functions, and the probability of the in-vehicle air conditioner being turned on at different ambient temperatures is fitted using the Cauchy distribution. Based on this, the impact of air conditioner usage on power consumption EC4 can also be evaluated by analyzing the air conditioner usage probability and the power consumption ratio in the on and off states.

[0037]

[0038] In the formula, K is the ratio of the power consumption when the air conditioner is on to the power consumption when it is off, and p i is the probability of turning on the air conditioner at different times, and is the probability of turning on the air conditioner throughout the day.

[0039] Based on the above technical concept, it should be noted that when calculating the ratio K of the power consumption when the air conditioner is on to the power consumption when it is off, it needs to be determined according to the statistical law based on the relationship between the ambient temperature T(t) at different times of the day and the air conditioner turning-on probability f T (T) to improve the accuracy of the model. The determination formula is as follows:

[0040] T(t) = a1sin(a2t + a3) + a4

[0041]

[0042] In the formula, a1, a2, a3, b1, b2, and b3 are all fitting coefficients, and b1, b2, and b3 are different in the heating and cooling states. EC4 is the power consumption per 100 kilometers considering air conditioner usage, with the unit of kWh. Among them, by substituting the temperature T(t) at different times of the day into f T (T), the probability p i of turning on the air conditioner at different times can be obtained, and then the probability of turning on the air conditioner throughout the day can be calculated. Then, the average power consumption per 100 kilometers considering air conditioner usage on that day is approximately the above formula (7).

[0043] So far, after evaluating the impact of the above factors on power consumption, the S3 step can be executed, that is, establishing a power consumption total model per 100 kilometers to obtain the expected value EC total of the power consumption per 100 kilometers of the electric vehicle considering all factors:

[0044] EC total = EC1 + EC2 + EC3 + EC4 (9).

[0045] It is understandable that establishing the total power consumption model per 100 kilometers takes into account the power consumption characteristics of electric vehicles of different brands. By statistically analyzing the proportion of electric vehicle brands in each interval and the average power consumption in each interval to calculate the expected value of the total power consumption, it is possible to more accurately predict the power consumption behavior of electric vehicles and provide more accurate data support for subsequent charging load prediction.

[0046] In an embodiment of the present invention, in step S4, the input parameters include: the number of active vehicles N for determining the total number of vehicles participating in charging within the target area active , the temperature change for considering the impact of environmental temperature on vehicle energy consumption and charging efficiency, represented by the temperature function T(t) of the current day, the battery capacity C for quantifying the battery capacity of each electric vehicle, which affects the charging demand bat , the charging power P bat and the initial state of charge SOC begin factor. Since the initial state of charge SOC begin is the state of charge at the start of a day's journey or at the end of charging, thus SOC begin = 1.

[0047] In step S4, based on the above technical concept, during the process of predicting and generating the load curve of the total charging demand of all electric vehicles changing with time, first, it is necessary to combine the travel statistical data of local electric vehicles and the temperature conditions of the current day to determine each key parameter affecting the power consumption, so as to obtain the accurate total power consumption per 100 kilometers EC of the electric vehicle during driving total . The purpose is to improve the prediction accuracy, that is, by accurately calculating the total power consumption per 100 kilometers, the accuracy of charging load prediction can be improved, thereby more effectively managing the grid load; optimize resource allocation, that is, accurate power consumption data helps grid operators better plan and allocate resources to meet the charging needs of electric vehicles while avoiding grid overload.

[0048] After obtaining the accurate total power consumption EC total , during the process of predicting and generating the load curve, continue to execute the following steps:

[0049] S4-1. Use the Monte Carlo method to simulate the probability distribution of the first charging time t d-s and the driving mileage D d , the purpose of which is to improve the accuracy of the model.

[0050] S4-2. Calculate the remaining state of charge SOC0 at the first charging time, and calculate the charging duration T of the electric vehicle according to the remaining power and the charging efficiency η (η is specified as 0.95) bat ,

[0051]

[0052] S4-3. Generate the load curve of the total charging demand of all electric vehicles in the target area changing with time by iteratively calculating and superimposing the charging loads of multiple electric vehicles. where N active is the number of electric vehicles, and this load curve will show the charging demand at different time points, providing data support for grid load management and the formulation of electric vehicle charging strategies.

[0053] Based on the above technical concept, it should be noted that when using the Monte Carlo method to simulate the probability distributions of the first charging time t d-s and the driving mileage D d , the first charging time t d-s and the driving mileage D d need to satisfy the following probability distribution models. This is because it can achieve: a. Authentic simulation, that is, by simulating the probability distribution consistent with actual driving behavior, it can more realistically reflect the charging behavior and driving habits of electric vehicle users; b. Ensure the activity and relevance of data, thereby improving the accuracy of the model; c. Randomness consideration, that is, the charging time and driving mileage of electric vehicles have randomness, and this randomness can be captured by satisfying the relationship in the probability distribution model, making the model closer to the actual situation.

[0054] The process is as follows:

[0055] For the first charging time t d-s , its probability distribution needs to satisfy the normal distribution model:

[0056]

[0057] where σ d-s and μ d-s are the variance and standard deviation of the starting charging time t d-s of the electric vehicle satisfying the normal distribution respectively;

[0058] For the driving mileage D d , its probability distribution needs to satisfy the lognormal distribution model:

[0059]

[0060] where σ d and μ d are the variance and standard deviation of the driving mileage D d of the electric vehicle satisfying the normal distribution respectively.

[0061] The technical scope of the present invention is not limited to the content described above. Those skilled in the art can make various deformations and modifications to the above embodiments without departing from the technical idea of the present invention, and these deformations and modifications shall fall within the protection scope of the present invention.

Claims

1. Method for constructing electric vehicle charging load prediction model considering multiple power consumption factors, characterized in that: Including the steps: By obtaining and cleaning the electric vehicle ownership and travel statistics data within the target area, excluding vehicles that have not been used for a long time, and distinguishing the travel patterns on holidays and non-holidays, the number of daily active electric vehicles is determined; Analyze multiple power consumption factors including the brand, driving road conditions, degree of aging, and in-vehicle equipment usage of the electric vehicles, evaluate the impact of these factors on power consumption, divide the power consumption per 100 kilometers of electric vehicles of different brands into multiple intervals, calculate the proportion of brands in each interval, and then establish a total power consumption model per 100 kilometers to obtain the expected value of the power consumption per 100 kilometers of electric vehicles considering all factors; Based on the total power consumption model, using the Monte Carlo simulation method, set the input parameters affecting the charging load prediction, simulate the probability distributions of the first charging time and driving mileage through the probability distribution model, and finally predict and generate a load curve representing the total charging demand of all electric vehicles in the target area over time.

2. The method for constructing an electric vehicle charging load prediction model considering multiple power consumption factors according to claim 1, characterized in that: When excluding vehicles that have not been used for a long time, define vehicles used within two weeks as active electric vehicles, and calculate the total number of active electric vehicles based on the following formula through data obtained from surveys or relevant institutions: N active = N total -N nonuse = α a N total Where, N active is the total number of active electric vehicles; N nonuse is the total number of electric vehicles not used for a long time; N total is the electric vehicle ownership in the target area; α a is the electric vehicle activity factor; meanwhile, Taking 14 days as a cycle, by calculating the daily expected number of vehicles traveling on holidays and non-holidays N Dunr-exp , distinguish the travel patterns on holidays and non-holidays: where D r is the number of holiday days, and 14 - D r is the number of non - holiday days.

3. The method for constructing an electric vehicle charging load prediction model considering multiple power consumption factors according to claim 1, characterized in that: When evaluating the impact of multiple power consumption factors on power consumption, the operations are as follows: By statistically calculating the proportions β1, β2, β3, and β4 of electric vehicles of different brands in each power consumption interval, calculate the power consumption per 100 kilometers EC1 to evaluate the impact of vehicle type on power consumption, EC1 = 10.5β1 + 13.5β2 + 16.5β3 + 19.5β4; By introducing the average congestion factor γ, the power consumption is adjusted based on the traffic conditions to evaluate the impact of the traffic conditions on the power consumption EC2, EC2 = γEC1, where is the average congestion distance per 100 kilometers of the electric vehicle, is the average power increase rate during congestion; By considering the aging coefficients η1 and η2 of the battery and motor of the electric vehicle to quantify the impact of the degree of aging on power consumption EC3, EC3 = (η1 + η2)EC1; By analyzing the air conditioner usage probability and its power consumption ratio in the on and off states to evaluate the impact of air conditioner usage on power consumption EC4, where K is the ratio of power consumption when the air conditioner is on to that when it is off, and p i is the probability of turning on the air conditioner at different times, and is the probability of turning on the air conditioner throughout the day.

4. The method for constructing an electric vehicle charging load prediction model considering multiple power consumption factors according to claim 1 or 3, characterized in that: The total power consumption model is obtained by dividing the power consumption into four intervals and calculating the power consumption in each interval. Among them, the power consumption intervals for different brands of electric vehicles are [9 kWh, 12 kWh], (12 kWh, 15 kWh], (15 kWh, 18 kWh], (18 kWh, 21 kWh]; The established total power consumption model is: EC total = EC1 + EC2 + EC3 + EC4, where EC total is the expected power consumption per 100 kilometers during the driving of the electric vehicle.

5. The method for constructing an electric vehicle charging load prediction model considering multiple power consumption factors according to claim 1, characterized in that: The input parameters include: the number of active vehicles N used to determine the total number of vehicles participating in charging within the target area active , the temperature change used to consider the influence of ambient temperature on vehicle energy consumption and charging efficiency, represented by the daily temperature function T(t), the battery capacity C used to quantify the battery capacity of each electric vehicle and affect the charging demand bat , the charging power P bat and the initial state of charge SOC begin Factor.

6. The method for constructing an electric vehicle charging load prediction model considering multiple power consumption factors according to claim 4, characterized in that: In the process of predicting the load curve of the total charging demand of all electric vehicles changing over time, it is first necessary to combine the travel statistics of local electric vehicles and the temperature conditions of the day to determine each key parameter affecting the power consumption, so as to obtain the accurate total power consumption EC per 100 kilometers during the driving of electric vehicles total .

7. The method for constructing an electric vehicle charging load prediction model considering multiple power consumption factors according to claim 1 or 6, characterized in that: After obtaining the exact total power consumption EC total During the process of predicting and generating the load curve, continue to execute the following steps: Simulate the probability distribution of the first charging time t d-s and the driving range D d using the Monte Carlo method; Calculate the remaining state of charge SOC0 at the first charging moment of the day, and calculate the charging duration T of the electric vehicle according to the remaining power and charging efficiency η bat , Finally, by iteratively calculating and superimposing the charging loads of multiple electric vehicles, generate the load curve of the total charging demand of all electric vehicles in the target area changing with time, where N active is the number of electric vehicles.

8. The method for constructing an electric vehicle charging load prediction model considering multiple power consumption factors according to claim 7, characterized in that: Simulate the first charging time t using the Monte Carlo method d-s and the driving range D d When the probability distributions of d-s the first charging time t and the driving range D d are considered, the first charging time t and the driving range D need to satisfy the following probability distribution model to improve the accuracy of the model. Among them, For the first charging time t d-s , its probability distribution needs to satisfy the normal distribution model: where σ d-s and μ d-s are the variance and standard deviation that satisfy the normal distribution respectively at the starting charging time t d-s of the electric vehicle; For the driving mileage D d , its probability distribution needs to satisfy the lognormal distribution model: where σ d and μ d are the variance and standard deviation of the driving range D of the electric vehicle d that satisfy the normal distribution, respectively.

9. The method for constructing an electric vehicle charging load prediction model considering multiple power consumption factors according to claim 3, characterized in that: When calculating the ratio K of the power consumption when the air conditioner is on to the power consumption when it is off, according to statistical laws, it is necessary to determine based on the relationship between the ambient temperature T(t) at different times of the day and the air conditioner turning-on probability f T (T) to improve the accuracy of the model. The determination formula is as follows: T(t) = a1sin(a2t + a3) + a4 Wherein, a1, a2, a3, b1, b2, and b3 are all fitting coefficients, and b1, b2, and b3 are different in heating and cooling states. EC4 is the power consumption per 100 kilometers considering the use of the air conditioner, with the unit of kWh. Among them, the temperature T(t) at different times of a day is substituted into f T (T), and the probability p of turning on the air conditioner at different times can be obtained i , and then the probability of turning on the air conditioner throughout the day is calculated 10. The method for constructing an electric vehicle charging load prediction model considering multiple power consumption factors according to claim 9, wherein: The ambient temperature T(t) is obtained from the meteorological temperature data of the day.

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