Wireless charging method and device for electric vehicle, electric vehicle and electronic equipment

Abstract

The present application provides a wireless charging method, device, electric vehicle, electronic device and storage medium for electric vehicles, wherein the method includes: constructing a wireless charging model; controlling the wireless charging model to operate in a constant current state and a constant voltage state respectively to obtain current data and voltage data; performing equivalent circuit analysis on the current data and the voltage data to obtain input variables; inputting the input variables into a pre-constructed neural network model to predict the state of charge and obtain the state of charge information; and wirelessly charging the electric vehicle according to the state of charge information. The implementation of the present application can reduce the voltage and current fluctuations during the charging process when the charging state is switched, simplify the complexity of the design parameters, improve the charging control efficiency and the automation of the charging switching, and reduce the device stress during charging.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a wireless charging method, device, electronic device and storage medium for an electric vehicle. Background Art

[0002] There are many wireless charging methods for electric vehicles, but they all have objective disadvantages. For example, 1. The parameters of the constant current / constant voltage switching process change greatly: the method of switching the primary and secondary resonant networks at the same time is adopted. At the moment of switching, it will have a serious impact on the working conditions of the electric vehicle wireless charging system, such as overvoltage, overcurrent, excessive device stress, etc., which increases the voltage and current fluctuations in the constant current / constant voltage switching process of electric vehicle wireless charging; 2. The constant current / constant voltage switching conditions are unclear: the conditions for switching the switch are not explained, that is, the charge state information of the electric vehicle battery load is not analyzed and explained, and the self-switching function of the switching switch cannot be realized; 3. The degree of automation of the constant current / constant voltage switching process is not high: the constant current / constant voltage charging mode switching is realized by artificial switching topology, which reduces the degree of automation of the electric vehicle system operation; 4. The circulation effect is large: the method of connecting multiple single-tube inverters in parallel increases the circulation effect of the system, the stress of the device, etc., and the current sharing function of the electric vehicle wireless charging system needs to be designed to suppress the circulation effect between the electric vehicle wireless charging systems. Summary of the invention

[0003] The purpose of the present application is to provide a wireless charging method, device, electric vehicle, electronic device and storage medium for an electric vehicle, which can reduce the voltage and current fluctuations when switching the charging state during the charging process, simplify the complexity of the design parameters, improve the charging control efficiency and the automation of charging switching, and reduce the device stress during charging.

[0004] In a first aspect, an embodiment of the present application provides a wireless charging method for an electric vehicle, the method comprising:

[0005] Build a wireless charging model;

[0006] Controlling the wireless charging model to operate in a constant current state and a constant voltage state respectively to obtain current data and voltage data;

[0007] Performing equivalent circuit analysis on the current data and the voltage data to obtain input variables;

[0008] Inputting the input variables into a pre-built neural network model to perform state of charge prediction to obtain state of charge information;

[0009] Wireless charging control is performed on the electric vehicle according to the charge state information.

[0010] In the above implementation process, an equivalent circuit analysis is performed based on the data under the constant current and constant voltage state, and the obtained input variables are input into the neural network model for charge state prediction, and then charging control is performed. This can slow down the voltage and current fluctuations when the charging state is switched during the charging process, simplify the complexity of the design parameters, improve the charging control efficiency and the degree of automation of charging switching, and reduce device stress during charging.

[0011] Furthermore, the step of constructing the wireless charging model includes:

[0012] constructing a hybrid topology model according to the first topology model and the second topology model;

[0013] Determining the hybrid topology model as a wireless charging basic model;

[0014] The charging status of the wireless charging basic model is analyzed to obtain the wireless charging model.

[0015] In the above implementation process, the charging status analysis is performed based on the hybrid topology model constructed by the first topology model and the second topology model, so as to improve the consistency between the infinite charging model and the actual charging status of the electric vehicle battery and improve the control of charging control.

[0016] Furthermore, the step of performing equivalent circuit analysis on the current data and the voltage data to obtain input variables includes:

[0017] Inputting the current data and the voltage data into a pre-built basic equivalent circuit model to obtain an equivalent circuit model including state of charge parameters;

[0018] An equivalent circuit analysis is performed on the equivalent circuit model including the state of charge parameters to obtain the input variables.

[0019] In the above implementation process, inputting data into the basic equivalent circuit model for equivalent circuit analysis can improve the accuracy of input parameters, improve the effectiveness of input parameters, and reduce errors in the equivalent circuit analysis process.

[0020] Furthermore, the step of inputting the input variables into a pre-built neural network model to predict the state of charge and obtain the state of charge information includes:

[0021] Obtaining weight data corresponding to the input variable in the neural network model;

[0022] The state of charge is predicted for the input variables of the neural network model according to the weight data to obtain the state of charge information.

[0023] In the above implementation process, the charge state of the input variables of the neural network model is predicted according to the weight data, which can reduce the calculation error of the input variables in the neural network model and improve the accuracy.

[0024] Furthermore, the state of charge information is obtained by predicting the state of charge of the input variables input into the neural network model according to the weight data using the following formula:

[0025] ;

[0026] in, , , , , , , , is the input variable, SOC is the charge state information, , , , , , , is the weight data.

[0027] Furthermore, the step of controlling the wireless charging of the electric vehicle according to the charge state information includes:

[0028] If the state of charge information is greater than a first threshold value and less than a second threshold value, controlling the battery of the electric vehicle to enter a constant current state for charging;

[0029] If the state of charge information is greater than the second threshold and less than the third threshold, controlling the battery of the electric vehicle to enter a constant voltage state for charging;

[0030] If the state of charge information is equal to the third threshold, the battery of the electric vehicle is controlled to enter a standby state.

[0031] In the above implementation process, the battery is controlled to enter different states for charging according to the charge state information, which can improve the stability and safety of the battery during charging and reduce the device stress during charging.

[0032] In a second aspect, an embodiment of the present application further provides a wireless charging device for an electric vehicle, the device comprising:

[0033] Building blocks for building wireless charging models;

[0034] A data acquisition module, used to control the wireless charging model to operate in a constant current state and a constant voltage state respectively, to obtain current data and voltage data;

[0035] An equivalent circuit analysis module, used for performing equivalent circuit analysis on the current data and the voltage data to obtain input variables;

[0036] A prediction module, used for inputting the input variables into a pre-built neural network model to perform state of charge prediction and obtain state of charge information;

[0037] A charging control module is used to control the wireless charging of the electric vehicle according to the charge state information.

[0038] In the above implementation process, an equivalent circuit analysis is performed based on the data under the constant current and constant voltage state, and the obtained input variables are input into the neural network model for charge state prediction, and then charging control is performed. This can slow down the voltage and current fluctuations when the charging state is switched during the charging process, simplify the complexity of the design parameters, improve the charging control efficiency and the degree of automation of charging switching, and reduce device stress during charging.

[0039] In a third aspect, an embodiment of the present application provides an electric vehicle, comprising the wireless charging device of the electric vehicle of the second aspect.

[0040] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in any one of the first aspects when executing the computer program.

[0041] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed on a computer, the computer executes the method described in any one of the first aspects.

[0042] In a sixth aspect, an embodiment of the present application provides a computer program product, which, when running on a computer, enables the computer to execute the method as described in any one of the first aspects.

[0043] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by implementing the above-mentioned technology of the present disclosure.

[0044] And it can be implemented according to the contents of the specification. The following is a detailed description of the preferred embodiments of the present application with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the range values. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 A schematic diagram of a flow chart of a wireless charging method for an electric vehicle provided in an embodiment of the present application;

[0047] Figure 2 A schematic diagram of the structure of a wireless charging model for an electric vehicle provided in an embodiment of the present application;

[0048] Figure 3 A schematic diagram of the constant current and constant voltage operation of an electric vehicle provided in an embodiment of the present application;

[0049] Figure 4 A schematic diagram of the structural composition of an equivalent circuit model of an electric vehicle provided in an embodiment of the present application;

[0050] Figure 5 A schematic diagram of the structure of a wireless charging device for an electric vehicle provided in an embodiment of the present application;

[0051] Figure 6 A schematic diagram of the structural composition of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0053] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0054] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application, but are not used to limit the scope of the present application.

[0055] Embodiment 1

[0056] Figure 1 is a flow chart of a wireless charging method for an electric vehicle provided in an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0057] S10, building a wireless charging model;

[0058] S20, controlling the wireless charging model to operate in a constant current state and a constant voltage state respectively, to obtain current data and voltage data;

[0059] S30, performing equivalent circuit analysis on the current data and the voltage data to obtain input variables;

[0060] S40, inputting the input variables into a pre-built neural network model to perform state of charge prediction and obtain state of charge information;

[0061] S50, wireless charging control of the electric vehicle is performed according to the charge state information.

[0062] In the above implementation process, an equivalent circuit analysis is performed based on the data under the constant current and constant voltage state, and the obtained input variables are input into the neural network model for charge state prediction, and then charging control is performed. This can slow down the voltage and current fluctuations when the charging state is switched during the charging process, simplify the complexity of the design parameters, improve the charging control efficiency and the degree of automation of charging switching, and reduce device stress during charging.

[0063] Furthermore, S10 includes:

[0064] constructing a hybrid topology model according to the first topology model and the second topology model;

[0065] Determine the hybrid topology model as the basic model for wireless charging;

[0066] The charging status of the wireless charging basic model is analyzed to obtain the wireless charging model.

[0067] In the above implementation process, the charging status analysis is performed based on the hybrid topology model constructed by the first topology model and the second topology model, so as to improve the consistency between the infinite charging model and the actual charging status of the electric vehicle battery and improve the control of charging control.

[0068] The embodiment of the present application adopts a hybrid circuit model based on the primary side LCC topology, the secondary side LCC topology and the S topology to construct a wireless charging model. Figure 2 shown.

[0069] in, is a DC voltage source, S1, S2, S3, and S4 are four MOSFET power switch tubes that form a single-phase full-bridge inverter. To compensate the inductance of the primary circuit, is the primary circuit compensation capacitor, For the primary side LCC compensation network, is the self-inductance of the primary circuit coil, , is the self-inductance of the secondary circuit coil, is the mutual inductance of the coil, , and is the internal resistance, D1, D2, D3, and D4 form the secondary side rectifier circuit. For voltage stabilizing filter capacitor, For electric vehicle battery load.

[0070] K1 and K2 are topology switching switches. For the secondary-side double-switching hybrid topology, when K1 is turned off and K2 is turned off, the secondary-side circuit coil self-inductance , and , Form a secondary side S-type compensation network; when K1 is turned on and K2 is turned on, the secondary side circuit coil self-inductance and , The secondary side LCC compensation network is formed, where: is the series resonant capacitor 1 of the secondary circuit, is the parallel resonant capacitor of the secondary circuit, is the series resonant capacitor 2 of the secondary circuit.

[0071] In S20, Figure 3 As shown in the figure, it is a schematic diagram of battery load operation, where: and They are the switching time between constant current and constant voltage charging states and the time required to fully charge the battery. and They are the battery capacity when switching between constant current and constant voltage charging states and the battery full charge capacity.

[0072] The output current at the load end has nothing to do with the battery load, and the wireless charging model presents a constant current characteristic. The output voltage at the load end has nothing to do with the battery load, and the wireless charging model presents a constant voltage characteristic.

[0073] Furthermore, S30 includes:

[0074] Inputting current data and voltage data into a pre-built basic equivalent circuit model to obtain an equivalent circuit model including state of charge parameters;

[0075] An equivalent circuit analysis is performed on an equivalent circuit model including state of charge parameters to obtain input variables.

[0076] In the above implementation process, inputting data into the basic equivalent circuit model for equivalent circuit analysis can improve the accuracy of input parameters, improve the effectiveness of input parameters, and reduce errors in the equivalent circuit analysis process.

[0077] like Figure 4For an equivalent circuit model, Kirchhoff's law is used in the embodiment of the present application to analyze the equivalent circuit to obtain input variables, where: For electric vehicle battery load, is the battery equivalent model circuit capacitance, is the voltage across the battery load (voltage data), is the charging current (current data), and some input variables are , , , , , for time The derivative is:

[0078] ;

[0079] Get input variables , , , , , , , .in, is the battery charge voltage, SOC is the battery charge state information, is the battery equivalent model line impedance The voltage on , , They are , , SOC vs. time t The derivative of .

[0080] Furthermore, S40 includes:

[0081] Obtain the weight data corresponding to the input variables in the neural network model;

[0082] The state of charge is predicted for the input variables of the neural network model according to the weight data to obtain the state of charge information.

[0083] In the above implementation process, the charge state of the input variables of the neural network model is predicted according to the weight data, which can reduce the calculation error of the input variables in the neural network model and improve the accuracy.

[0084] Furthermore, the state of charge is predicted for the input variables of the neural network model according to the weight data by the following formula to obtain the state of charge information:

[0085] ;

[0086] in, , , , , , , , is the input variable, SOC is the charge status information, , , , , , , is the weight data.

[0087] Furthermore, S50 includes:

[0088] If the state of charge information is greater than the first threshold and less than the second threshold, the battery of the electric vehicle is controlled to enter a constant current state for charging;

[0089] If the state of charge information is greater than the second threshold value and less than the third threshold value, the battery of the electric vehicle is controlled to enter a constant voltage state for charging;

[0090] If the state of charge information is equal to the third threshold, the battery of the electric vehicle is controlled to enter a standby state.

[0091] In the above implementation process, the battery is controlled to enter different states for charging according to the charge state information, which can improve the stability and safety of the battery during charging and reduce the device stress during charging.

[0092] During the charging process, the state of charge information (SOC) is divided into three categories, and the primary side inverter MOS tube and the dual hybrid topology switch are controlled according to the corresponding state of charge information (SOC) to achieve wireless charging control:

[0093] when , DSP controls the primary inverter MOS tube to work normally, K1 is turned on, K2 is turned on, and enters the constant current state for charging.

[0094] when , DSP controls the primary inverter MOS tube to work normally, K1 is turned off, K2 is turned off, and it enters the constant voltage state for charging.

[0095] when , DSP controls the primary inverter MOS tube to turn off and enter the standby mode, where the first threshold is 0, the third threshold is 1, and the second threshold is , It is the battery capacity ratio when switching between constant voltage and constant current charging states.

[0096] Since there are constant current and constant voltage processes in the charging process of electric vehicles, the self-switching of the secondary circuit resonant network is realized by using the topology switch switching method, and the secondary circuit is designed by combining the S-type topology and LCC-type topology dual hybrid design;

[0097] The embodiment of the present application realizes constant current output and constant voltage output under the LCC-LCC electric vehicle wireless charging model by controlling the on and off of the topology switching switch, and predicts the charge state information of the battery load of the electric vehicle wireless charging model based on the long short-term memory recurrent neural network method.

[0098] The wireless charging model is simplified and analyzed by controlling the on-off of the topology switching switch. The design and analysis difficulty of the constant current / constant voltage mode of the electric vehicle wireless charging model is reduced, the switching speed of the electric vehicle constant current and constant voltage wireless charging process is improved, and the current and voltage fluctuations of the electric vehicle wireless charging constant current / constant voltage switching process are ensured to be small.

[0099] Based on the long short-term memory recurrent neural network, the selected input parameters are analyzed and the charge state information of the electric vehicle wireless charging model is predicted. It is suitable for the analysis of the nonlinear change process of the electric vehicle battery load and can be used to monitor the health status of the electric vehicle battery load, thereby improving the accuracy of the prediction of the charge state information of the electric vehicle.

[0100] The present application can effectively improve the comprehensive performance of the self-switching of the constant current / constant voltage charging process of wireless charging of electric vehicles, and provide an effective solution for realizing flexible switching of the wireless charging process, accurate prediction of battery load information, and self-switching of the charging process.

[0101] Embodiment 2

[0102] In order to execute the method corresponding to the above embodiment 1 to achieve the corresponding functions and technical effects, a wireless charging device for an electric vehicle is provided below, such as Figure 5 As shown, the device comprises:

[0103] Building module 1, used to build a wireless charging model;

[0104] A data acquisition module 2 is used to control the wireless charging model to operate in a constant current state and a constant voltage state respectively to obtain current data and voltage data;

[0105] An equivalent circuit analysis module 3 is used to perform equivalent circuit analysis on the current data and the voltage data to obtain input variables;

[0106] Prediction module 4, used for inputting the input variables into a pre-built neural network model to perform state of charge prediction and obtain state of charge information;

[0107] The charging control module 5 is used to control the wireless charging of the electric vehicle according to the charge state information.

[0108] In the above implementation process, an equivalent circuit analysis is performed based on the data under the constant current and constant voltage state, and the obtained input variables are input into the neural network model for charge state prediction, and then charging control is performed. This can slow down the voltage and current fluctuations when the charging state is switched during the charging process, simplify the complexity of the design parameters, improve the charging control efficiency and the degree of automation of charging switching, and reduce device stress during charging.

[0109] Furthermore, the building block 1 is also used for:

[0110] constructing a hybrid topology model according to the first topology model and the second topology model;

[0111] Determine the hybrid topology model as the basic model for wireless charging;

[0112] The charging status of the wireless charging basic model is analyzed to obtain the wireless charging model.

[0113] In the above implementation process, the charging status analysis is performed based on the hybrid topology model constructed by the first topology model and the second topology model, so as to improve the consistency between the infinite charging model and the actual charging status of the electric vehicle battery and improve the control of charging control.

[0114] Furthermore, the equivalent circuit analysis module 3 is also used for:

[0115] Inputting current data and voltage data into a pre-built basic equivalent circuit model to obtain an equivalent circuit model including state of charge parameters;

[0116] An equivalent circuit analysis is performed on an equivalent circuit model including state of charge parameters to obtain input variables.

[0117] In the above implementation process, inputting data into the basic equivalent circuit model for equivalent circuit analysis can improve the accuracy of input parameters, improve the effectiveness of input parameters, and reduce errors in the equivalent circuit analysis process.

[0118] Furthermore, the prediction module 4 is also used for:

[0119] Obtain the weight data corresponding to the input variables in the neural network model;

[0120] The state of charge is predicted for the input variables of the neural network model according to the weight data to obtain the state of charge information.

[0121] In the above implementation process, the charge state of the input variables of the neural network model is predicted according to the weight data, which can reduce the calculation error of the input variables in the neural network model and improve the accuracy.

[0122] Furthermore, the prediction module 4 is also used to predict the state of charge of the input variables of the input neural network model according to the weight data through the following formula to obtain the state of charge information:

[0123] ;

[0124] in, , , , , , , , is the input variable, SOC is the charge status information, , , , , , , is the weight data.

[0125] Furthermore, the charging control module 5 is also used for:

[0126] If the state of charge information is greater than the first threshold and less than the second threshold, the battery of the electric vehicle is controlled to enter a constant current state for charging;

[0127] If the state of charge information is greater than the second threshold value and less than the third threshold value, the battery of the electric vehicle is controlled to enter a constant voltage state for charging;

[0128] If the state of charge information is equal to the third threshold, the battery of the electric vehicle is controlled to enter a standby state.

[0129] In the above implementation process, the battery is controlled to enter different states for charging according to the charge state information, which can improve the stability and safety of the battery during charging and reduce the device stress during charging.

[0130] The wireless charging device for the electric vehicle can implement the method of the first embodiment. The options in the first embodiment are also applicable to this embodiment and will not be described in detail here.

[0131] The rest of the contents of the embodiments of the present application can refer to the contents of the above-mentioned embodiment 1, and will not be repeated in this embodiment.

[0132] Embodiment 3

[0133] An embodiment of the present application provides an electric vehicle, including the wireless charging device for the electric vehicle of the second embodiment.

[0134] Embodiment 4

[0135] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the wireless charging method for an electric vehicle of embodiment 1.

[0136] Optionally, the above-mentioned electronic device may be a server.

[0137] See also Figure 6 , Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include a processor 61, a communication interface 62, a memory 63 and at least one communication bus 64. The communication bus 64 is used to realize direct connection and communication between these components. The communication interface 62 of the device in the embodiment of the present application is used to communicate signaling or data with other node devices. The processor 61 may be an integrated circuit chip with signal processing capabilities.

[0138] The processor 61 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor 61 can also be any conventional processor, etc.

[0139] The memory 63 may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc. The memory 63 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 61, the device may execute the above-mentioned Figure 1 The method embodiment involves various steps.

[0140] Optionally, the electronic device may further include a storage controller and an input / output unit. The memory 63, the storage controller, the processor 61, the peripheral interface, and the input / output unit are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components may be electrically connected to each other via a communication bus 64. The processor 61 is used to execute executable modules stored in the memory 63, such as software function modules or computer programs included in the device.

[0141] The input and output unit is used to provide users with the task creation and to create an optional time period or preset execution time for the task to enable interaction between the user and the server. The input and output unit can be, but is not limited to, a mouse and a keyboard.

[0142] Understandably, Figure 6 The structure shown is for illustration only. The electronic device may also include Figure 6 More or fewer components as shown, or with Figure 6 Different configurations are shown. Figure 6 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0143] In addition, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the wireless charging method for an electric vehicle of the first embodiment.

[0144] The embodiment of the present application also provides a computer program product, which, when running on a computer, enables the computer to execute the method described in the method embodiment.

[0145] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of boxes in the block diagram and / or the flowchart, can be implemented with a dedicated hardware-based device that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0146] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0147] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0148] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0149] The above is only a specific implementation of the present application, but the protection range of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical range disclosed in the present application, which should be included in the protection range of the present application. Therefore, the protection range of the present application should be based on the protection range of the claims.

[0150] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A wireless charging method for an electric vehicle, characterized in that: The method comprises: Build a wireless charging model; Controlling the wireless charging model to operate in a constant current state and a constant voltage state respectively to obtain current data and voltage data; Performing equivalent circuit analysis on the current data and the voltage data to obtain input variables; Inputting the input variables into a pre-built neural network model to perform state of charge prediction to obtain state of charge information; Performing wireless charging control on the electric vehicle according to the state of charge information; The step of inputting the input variables into a pre-built neural network model to predict the state of charge and obtain the state of charge information includes: Obtaining weight data corresponding to the input variable in the neural network model; Performing state of charge prediction on input variables input into the neural network model according to the weight data to obtain the state of charge information; The state of charge information is obtained by predicting the state of charge of the input variables of the neural network model according to the weight data using the following formula: ； in, , , , , , , , is the input variable, SOC is the charge state information, , , , , , , is the weight data, is the battery charge voltage, is the battery equivalent model line impedance The voltage on is the voltage data, is the current data, t For time, , , They are , , SOC vs. time t The derivative of for The corresponding weight data, for The corresponding weight data, for The corresponding weight data, for The corresponding weight data, for The corresponding weight data, for The corresponding weight data, for The corresponding weight data.

2. The wireless charging method for an electric vehicle according to claim 1, characterized in that: The step of constructing the wireless charging model includes: constructing a hybrid topology model according to the first topology model and the second topology model; Determining the hybrid topology model as a wireless charging basic model; The charging status of the wireless charging basic model is analyzed to obtain the wireless charging model.

3. The wireless charging method for an electric vehicle according to claim 1, characterized in that: The step of performing equivalent circuit analysis on the current data and the voltage data to obtain input variables comprises: Inputting the current data and the voltage data into a pre-built basic equivalent circuit model to obtain an equivalent circuit model including state of charge parameters; An equivalent circuit analysis is performed on the equivalent circuit model including the state of charge parameters to obtain the input variables.

4. The wireless charging method for an electric vehicle according to claim 1, characterized in that: The step of controlling the wireless charging of the electric vehicle according to the charge state information comprises: If the state of charge information is greater than a first threshold value and less than a second threshold value, controlling the battery of the electric vehicle to enter a constant current state for charging; If the state of charge information is greater than the second threshold and less than the third threshold, controlling the battery of the electric vehicle to enter a constant voltage state for charging; If the state of charge information is equal to the third threshold, the battery of the electric vehicle is controlled to enter a standby state.

5. A wireless charging device for an electric vehicle, characterized in that: The device comprises: Building blocks for building wireless charging models; A data acquisition module, used to control the wireless charging model to operate in a constant current state and a constant voltage state respectively, to obtain current data and voltage data; An equivalent circuit analysis module, used for performing equivalent circuit analysis on the current data and the voltage data to obtain input variables; A prediction module, used for inputting the input variables into a pre-built neural network model to perform state of charge prediction and obtain state of charge information; A charging control module, used for controlling wireless charging of the electric vehicle according to the charge state information; The data acquisition module is also used for: Obtaining weight data corresponding to the input variable in the neural network model; Performing state of charge prediction on input variables input into the neural network model according to the weight data to obtain the state of charge information; The state of charge information is obtained by predicting the state of charge of the input variables of the neural network model according to the weight data using the following formula: ； in, , , , , , , , is the input variable, SOC is the charge state information, , , , , , , is the weight data, is the battery charge voltage, is the battery equivalent model line impedance The voltage on is the voltage data, is the current data, t For time, , , They are , , SOC vs. time t The derivative of for The corresponding weight data, for The corresponding weight data, for The corresponding weight data, for The corresponding weight data, for The corresponding weight data, for The corresponding weight data, for The corresponding weight data.

6. An electric vehicle, characterized in that: The electric vehicle comprises the wireless charging device for the electric vehicle according to claim 5.

7. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the wireless charging method for an electric vehicle according to any one of claims 1 to 4.

8. A storage medium, characterized in that: A computer program is stored therein, and when the computer program is executed by a processor, the wireless charging method for an electric vehicle as claimed in any one of claims 1 to 4 is implemented.

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