A wireless charging electric vehicle load prediction method, system and device

Abstract

The application discloses a wireless charging type electric vehicle load prediction method, system and device, relates to the electric vehicle load prediction technical field, receives electric vehicle related parameters, and inputs the electric vehicle related parameters into a pre-established wireless charging type electric vehicle charging load model to generate the daily driving mileage of a single electric vehicle, the starting charging time, the charging power selected according to the charging time and the charging duration; the generated daily driving mileage of the single electric vehicle, the starting charging time, the charging power selected according to the charging time and the charging duration are used to calculate the charging load of the single electric vehicle; the calculated charging load of the single electric vehicle is extracted m times by using a Monte Carlo mathematical sampling method, the charging loads of m electric vehicles are obtained, and the daily charging load curves of the m electric vehicles are obtained by superimposing the charging loads of the m electric vehicles.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle load prediction, in particular to a wireless charging type electric vehicle load prediction method, system and device. BACKGROUND

[0002] When a large number of electric vehicles access the power grid, on the one hand, if the charging infrastructure in the region is not reasonably planned, it will affect the charging experience of electric vehicle owners; on the other hand, the charging load of electric vehicles may pose a certain threat to the stable operation of the power grid. As a basic problem of studying the development of electric vehicles, the prediction of the charging load of electric vehicles has become a research hotspot for scholars around the world.

[0003] With the development of wireless charging technology in recent years, its application in electric vehicles has attracted much attention. At present, the mainstream charging method for electric vehicles is conductive charging, which has certain drawbacks. Overall, it can be divided into problems such as long charging time, charging limitations, danger, multiple interfaces, and diverse protocols. In addition, conductive charging requires a wire as a medium and relies on the physical connection between the power supply object and the powered object to transmit power, which greatly reduces the flexibility of charging. On the other hand, due to the physical contact of traditional conductive charging, sparks may occur in an instant, and the wire is exposed, which all increase the danger of charging. And the current new energy vehicles are of various types, and different vehicles have different charging interfaces and protocols, and there are also certain differences in the use of charging piles, which may result in different electric vehicles not being able to match the corresponding charging interface. Therefore, the wireless power transmission technology that has emerged in recent years is extremely necessary, as it can solve the problems that cannot be avoided by traditional conductive charging and has advantages that cannot be compared with traditional power transmission methods.

[0004] After investigation, scholars at home and abroad have conducted a lot of research on electric vehicle charging load prediction methods. The current research is all around the load prediction of traditional conductive charging electric vehicles and does not take into account the diversity of electric vehicle charging methods. For example, wireless charging type electric vehicles, due to the convenience of charging method, will have a major impact on the power system in future large-scale applications, so it is necessary to conduct load prediction research on wireless charging type electric vehicles. SUMMARY

[0005] To solve the problems mentioned in the background art, the purpose of the present application is to provide a wireless charging type electric vehicle load prediction method, system and device.

[0006] The purpose of the present application can be achieved by the following technical solution: a wireless charging type electric vehicle load prediction method, the method comprising the following steps:

[0007] Receiving electric vehicle related parameters and inputting the electric vehicle related parameters into a pre-established wireless charging electric vehicle charging load model to generate a single electric vehicle daily driving range, a starting charging time, a charging power selected according to a charging time and a charging duration;

[0008] The electric vehicle related parameters include a daily driving range of the electric vehicle, a starting charging time, a battery capacity, a maximum driving range, a charging power, a charging efficiency, a charging duration and a charging selection.

[0009] The generated single electric vehicle daily driving range, starting charging time, charging power selected according to charging time and charging duration are used to calculate a single electric vehicle charging load.

[0010] The calculated single electric vehicle charging load is extracted m times by using a Monte Carlo mathematical sampling method to obtain m electric vehicle charging loads, and the m electric vehicle charging loads are superimposed to obtain a daily charging load curve of m electric vehicles.

[0011] Preferably, the size of the battery capacity affects the charging duration of the electric vehicle, the maximum driving range determines the upper limit of the daily driving range of the electric vehicle, and the battery capacity and the maximum driving range of different types of electric vehicles are different.

[0012] Preferably, the charging power is divided into fast charging and slow charging, and vehicles with starting charging time in the a-b interval use fast charging, and vehicles not in the a-b interval use slow charging.

[0013] Preferably, the charging efficiency is not constant, when the frequency and load are constant, the transmission efficiency between coils is determined by the mutual inductance between coils, and the system transmission efficiency fluctuates in the range of , and the charging efficiency satisfies the normal distribution rule as follows:

[0014] .

[0015] wherein is the system transmission efficiency, is the mean of the system transmission efficiency, is the variance of the system transmission efficiency

[0016] Preferably, the charging duration is related to the state of charge, the charging power and the battery capacity, wherein the lower the state of charge, the larger the battery capacity and the smaller the charging power, the longer the charging duration, the smoother the charging load curve, and the charging duration is calculated based on the state of charge:

[0017]

[0018] Wherein, Tc is the charging time, the unit is h, a is the desired state of charge, set to 1; SOC is the initial state of charge; E is the capacity of the battery, the unit is kw / h, P is the charging power, and η is the charging efficiency.

[0019] Preferably, the charging selection of the wireless charging electric vehicle user is different from the conduction charging characteristics, and the initial state of charge has little effect on the charging probability of the wireless charging user placing the vehicle for a period of time. For the wireless charging electric vehicle, when the electric vehicle owner ends a trip and stays for a period of time, the private car state of charge SOC value is not 1, that is, the charging starts.

[0020] Preferably, the extraction process includes: holiday / weekday extraction, extraction day driving mileage, extraction initial charging time, extraction battery capacity, extraction transmission efficiency for a single electric vehicle, calculation of charging time, obtaining of single electric vehicle charging load, m times of extraction, and superposition of charging load to obtain m electric vehicle daily charging load curve.

[0021] Preferably, the wireless charging electric vehicle charging load model is simulated and established according to the travel habits of the electric vehicle owner and the vehicle and wireless charging device parameters.

[0022] A wireless charging electric vehicle load prediction system, comprising:

[0023] The model input module is used for receiving electric vehicle related parameters and inputting the electric vehicle related parameters into the pre-established wireless charging electric vehicle charging load model to generate single electric vehicle daily driving mileage, initial charging time, charging power selection according to charging time, and charging time calculation;

[0024] The load calculation module is used for calculating the single electric vehicle charging load obtained by the generated single electric vehicle daily driving mileage, initial charging time, charging power selection according to charging time, and charging time calculation;

[0025] The load extraction module uses the Monte Carlo mathematical sampling method to extract m times of the calculated single electric vehicle charging load to obtain m electric vehicle charging loads, and superimposes the m electric vehicle charging loads to obtain m electric vehicle daily charging load curve.

[0026] An apparatus comprising:

[0027] One or more processors;

[0028] Memory for storing one or more programs;

[0029] When one or more of the programs are executed by one or more of the processors, the one or more processors implement a wireless charging electric vehicle load prediction method as described above.

[0030] Advantages of the present application:

[0031] The present application takes into account the diversity of electric vehicle charging methods. Wireless charging electric vehicles will have a significant impact on the power system due to the convenience of charging methods in future large-scale applications. Load prediction research for wireless charging electric vehicles is crucial for subsequent research. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings;

[0033] Figure 1 is a flowchart of the method of the present application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0035] As shown in Figure 1 A wireless charging electric vehicle load prediction method includes the following steps:

[0036] Receiving electric vehicle related parameters and inputting the electric vehicle related parameters into a pre-established wireless charging electric vehicle charging load model to generate a single electric vehicle daily driving distance, a starting charging time, a charging power selected according to a charging time and a charging duration.

[0037] The electric vehicle related parameters include: daily driving distance of electric vehicle, starting charging time, battery capacity, maximum driving distance, charging power, charging efficiency, charging duration and charging selection.

[0038] The generated single electric vehicle daily driving distance, starting charging time, charging power selected according to charging time and charging duration are used to calculate the charging load of a single electric vehicle.

[0039] The calculated single electric vehicle charging load is extracted m times by using the Monte Carlo mathematical sampling method to obtain m electric vehicle charging loads, and the m electric vehicle charging loads are superimposed to obtain a daily charging load curve of the m electric vehicles.

[0040] It needs to be further explained that the probability density distribution function of the daily driving mileage meets the characteristics of the logarithmic normal distribution:

[0041]

[0042] Further, the starting charging time generally has a rule for private car owners, and private car owners generally choose one or two time periods for charging on weekdays, i.e. driving to the work area in the morning and driving home in the evening. It is assumed that the state of charge SOC of the electric private car is 1 before leaving home in the morning, i.e. the amount of electricity consumed for the journey between the residence and the workplace. The starting charging time of the electric vehicle on weekdays meets the normal distribution rule:

[0043]

[0044]

[0045] It needs to be further explained that the starting charging time on weekdays, i.e. represents the probability distribution of the starting charging time in the morning on weekdays, represents the probability distribution of the starting charging time in the evening on weekdays.

[0046] It needs to be further explained that the starting charging time on holidays, i.e. the private car owners generally choose to drive out for a trip or the like, and for them, the starting charging time is generally the first charging of the day after returning home from a day's trip. The starting charging time of the electric vehicle on holidays meets the normal distribution rule:

[0047]

[0048] It needs to be further explained that the starting charging time on holidays, i.e. represents the probability distribution of the starting charging time on weekends on holidays.

[0049] It needs to be further explained that the battery capacity affects the charging time of the electric vehicle, and the maximum driving range determines the upper limit of the extraction of the daily driving range of the electric vehicle. The battery capacity and maximum driving range of the best-selling electric vehicle brand are investigated, and the battery capacity and maximum driving range of each pure electric vehicle series of the electric vehicle brand are weighted and averaged.

[0050] Further, the wireless charging power is divided into 3.7kw and 7.7kw. The vehicle with the starting charging time in the interval of 7:00-18:00 uses 7.7kw charging power for charging, and otherwise uses 3.3kw for charging.

[0051] It needs to be further explained that the charging efficiency is not constant. When the frequency and load are constant, the transmission efficiency between the coils is mainly determined by the mutual inductance between the coils, and the charging efficiency satisfies the normal distribution rule:

[0052]

[0053] wherein is the system transmission efficiency, is the mean of the system transmission efficiency, is the variance of the system transmission efficiency.

[0054] Further, the charging duration is related to the state of charge, charging power, battery capacity and other factors. The lower the state of charge, the larger the battery capacity, the smaller the charging power, the longer the charging time, and the more smooth the charging load curve. The charging duration is calculated based on the state of charge:

[0055]

[0056] Further, in the charging duration calculation process, Tc is the charging duration, unit h, α is the expected completed state of charge (full is 1, assuming all are 1); SOC is the starting state of charge; E is the capacity of the battery, unit kw / h, P is the charging power, and η is the charging efficiency.

[0057] Further, the charging selection of the wireless charging electric vehicle is different from the conduction charging characteristics. The starting state of charge has little effect on the charging probability of the wireless charging user when the vehicle is placed for a period of time. For the wireless charging electric vehicle, when the electric vehicle owner ends a journey and stays for a period of time, the state of charge SOC value of the private car is not 1, i.e. the charging starts.

[0058] Preferably, the extraction process includes: extracting for holidays / working days, daily driving mileage, starting charging time, battery capacity, transmission efficiency for a single electric vehicle, calculating the charging duration, obtaining the charging load of a single electric vehicle, performing m times of extraction, and superimposing the charging load to obtain the daily charging load curve of m electric vehicles.

[0059] Preferably, the charging load model of the wireless charging electric vehicle is simulated and established according to the travel rules of the electric vehicle owner and the vehicle and wireless charging device parameters.

[0060] A wireless charging electric vehicle load prediction system comprises:

[0061] A model input module is configured to receive electric vehicle related parameters and input the electric vehicle related parameters into a pre-established wireless charging electric vehicle charging load model to generate daily driving mileage, starting charging time, charging power selected according to charging time and charging duration of a single electric vehicle.

[0062] A load calculation module is configured to calculate the charging load of a single electric vehicle based on the generated daily driving mileage, starting charging time, charging power selected according to charging time and charging duration of the single electric vehicle.

[0063] A load extraction module is configured to extract the charging load of the single electric vehicle m times by using a Monte Carlo mathematical sampling method to obtain the charging load of m electric vehicles, and to superimpose the charging load of the m electric vehicles to obtain a daily charging load curve of the m electric vehicles.

[0064] Based on the same inventive concept, the application further provides a computer device, which comprises one or more processors and a memory for storing one or more computer programs; the program comprises program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is used to implement one or more instructions, specifically to load and execute one or more instructions in the computer storage medium to realize the above-mentioned method.

[0065] It should be further noted that based on the same inventive concept, the present application further provides a computer storage medium, which stores a computer program, and the computer program is run by a processor to execute the above method. The storage medium can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0066] The following Table 1 is the battery capacity and maximum driving range of different brands of electric vehicles

[0067] Table 1

[0068]

[0069] In the description of the present specification, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0070] The basic principles, main features and advantages of the present disclosure are shown and described above. It should be understood by those skilled in the art that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, various changes and improvements can be made to the present disclosure, and these changes and improvements all fall within the scope of the claimed present disclosure.

Claims

1. A wireless charging electric vehicle load forecasting method, characterized by, The method comprises the following steps: Receiving electric vehicle related parameters and inputting the electric vehicle related parameters into a pre-established wireless charging electric vehicle charging load model to generate a single electric vehicle daily driving range, a starting charging time, a charging power selected according to a charging time, and a calculated charging duration; The electric vehicle related parameters include a daily driving range of the electric vehicle, a starting charging time, a battery capacity, a maximum driving range, a charging power, a charging efficiency, a charging duration, and a charging selection; The charging efficiency is not constant. When the frequency and load are constant, the transmission efficiency between coils is determined by mutual inductance between coils, and the system transmission efficiency fluctuates in the range of 0.5-0.7, and the charging efficiency satisfies the normal distribution rule as follows: wherein is the system transmission efficiency, is the mean of the system transmission efficiency, is the variance of the system transmission efficiency; The generated single electric vehicle daily driving range, starting charging time, charging power selected according to charging time, and calculated charging duration are used to calculate the charging load of a single electric vehicle; The calculated charging load of a single electric vehicle is extracted m times using the Monte Carlo mathematical sampling method to obtain the charging load of m electric vehicles, and the charging load of m electric vehicles is superimposed to obtain a daily charging load curve of m electric vehicles; The extraction process includes extracting the daily driving range, starting charging time, battery capacity, and transmission efficiency of a single electric vehicle, calculating the charging duration, obtaining the charging load of a single electric vehicle, extracting m times, and superimposing the charging load to obtain a daily charging load curve of m electric vehicles.

2. The wireless charging electric vehicle load forecasting method of claim 1, wherein, The size of the battery capacity affects the charging duration of the electric vehicle, and the maximum driving range determines the upper limit of the daily driving range extraction. The battery capacity and maximum driving range parameters of different types of electric vehicles are different.

3. The wireless charging electric vehicle load forecasting method of claim 1, wherein, The charging power is divided into fast charging and slow charging. Vehicles with a starting charging time within the a-b interval use fast charging, and vehicles not within the a-b interval use slow charging.

4. The wireless charging electric vehicle load forecasting method of claim 1, wherein, The charging duration is related to the state of charge, charging power, and battery capacity. The lower the state of charge, the larger the battery capacity, and the smaller the charging power, the longer the charging duration, and the smoother the charging load curve. The state of charge is calculated based on the state of charge: Where Tc is the charging duration, unit h, α is the expected completed state of charge, set to 1; SOC is the starting state of charge; E is the capacity of the battery, unit kw / h, P is the charging power, and η is the charging efficiency.

5. The wireless charging electric vehicle load forecasting method of claim 1, wherein, For the charging selection of wireless charging electric vehicle users: for wireless charging electric vehicles, when the electric vehicle owner ends a journey and stays for a period of time, the state of charge SOC value of the private car is not 1, i.e. charging starts.

6. The wireless charging electric vehicle load forecasting method of claim 1, wherein, The wireless charging electric vehicle charging load model is simulated and established according to the travel habits of electric vehicle owners and vehicle and wireless charging device parameters.

7. A wireless charging electric vehicle load forecasting system, which implements a wireless charging electric vehicle load forecasting method as claimed in claim 1, characterized in that, It includes: The model input module is used to receive electric vehicle related parameters and input the electric vehicle related parameters into a pre-established wireless charging electric vehicle charging load model to generate a single electric vehicle daily driving range, a starting charging time, a charging power selected according to a charging time, and a calculated charging duration; The load calculation module is used to calculate the charging load of a single electric vehicle from the generated single electric vehicle daily driving range, starting charging time, charging power selected according to charging time, and calculated charging duration; The load extraction module: the calculated single electric vehicle charging load is extracted m times by using the Monte Carlo mathematical sampling method to obtain m electric vehicle charging loads, and the m electric vehicle charging loads are superimposed to obtain the daily charging load curve of m electric vehicles.

8. A wireless charging electric vehicle load prediction device, characterized by, Comprise: One or more processors; Memory for storing one or more programs; When one or more of the programs are executed by one or more of the processors, one or more of the processors implement a wireless charging electric vehicle load prediction method as claimed in any one of claims 1-6.

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