Method and device for optimizing charging efficiency of electric vehicle and electric vehicle

Abstract

The present application provides a method, device, electric vehicle, electronic device and storage medium for optimizing the charging efficiency of an electric vehicle, wherein the method comprises: constructing a basic charging model of the electric vehicle; adjusting the voltage of the basic charging model under a constant voltage state to obtain the charging efficiency; obtaining variables that affect the charging efficiency; dynamically controlling the variables according to a pre-constructed fuzzy control model to obtain the error amount of the variables; optimizing the charging efficiency according to the error amount. The implementation of the present application can improve the charging efficiency and the stability during the charging process, and the optimization method is simpler and easier to use, with low complexity, suitable for a variety of scenarios, and convenient for adjusting the voltage during the charging process, and can achieve power regulation in a wide range.

Description

Technical Field

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

[0002] The efficiency improvement technology of the electric vehicle wireless charging system involves optimizing the efficiency of the electric vehicle wireless charging process by estimating the mutual inductance of the primary and secondary coils and the output voltage of the secondary rectifier side. Although the existing technology has made progress in improving the energy transmission efficiency of the electric vehicle wireless charging system, there are still many problems, such as real-time performance: the existing technology requires some circuit parameters of the primary and secondary circuits, such as voltage, current, phase and other information, to communicate in real time, which increases the crosstalk between circuit elements and increases the difficulty and cost of circuit system structure design; stability: constant voltage / constant current output is not considered in the design of the electric vehicle wireless charging system, and its resonant network design has defects, which affects the output power stability of the electric vehicle wireless charging system; complexity: in order to improve the efficiency of the receiving end of the electric vehicle wireless charging system, the anti-offset characteristics of the system are improved by increasing the number of transmitting coils, which increases the difficulty of analysis, reduces flexibility, increases crosstalk between coils, and is not suitable for practical applications, and has higher complexity; singleness: only the output voltage of the secondary rectifier circuit can be reduced or increased, and the output power and system efficiency adjustment in a wide range cannot be achieved. Summary of the invention

[0003] The purpose of this application is to provide a method, device, electric vehicle, electronic device and storage medium for optimizing the charging efficiency of an electric vehicle, which can improve the charging efficiency and stability during the charging process. The optimization method is simpler and easier to use, has low complexity, is applicable to a variety of scenarios, and is convenient for adjusting the voltage during the charging process, and can achieve power regulation within a wide range.

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

[0005] Constructing a basic charging model for the electric vehicle;

[0006] Under a constant voltage state, adjusting the voltage of the basic charging model to obtain a charging efficiency;

[0007] Obtaining variables that affect the charging efficiency;

[0008] Dynamically controlling the variable according to a pre-built fuzzy control model to obtain an error amount of the variable;

[0009] The charging efficiency is optimized according to the error amount.

[0010] In the above implementation process, the voltage of the basic charging model is adjusted to obtain the variables that affect the charging efficiency, and then the error of the variable is obtained according to the fuzzy control model. The charging efficiency is optimized according to the error, which can improve the charging efficiency and the stability during the charging process. The optimization method is simpler and easier to use, has low complexity, is suitable for a variety of scenarios, and is convenient for adjusting the voltage during the charging process, and can achieve power regulation within a wide range.

[0011] Furthermore, the step of adjusting the voltage of the basic charging model under the constant voltage state to obtain the charging efficiency includes:

[0012] Construct voltage regulation model;

[0013] Obtaining a conduction duty cycle range of the switch tube in the voltage regulation model;

[0014] According to the on-duty cycle range, the basic charging model is sequentially adjusted to reduce the voltage and to increase the voltage to obtain a charging power range;

[0015] Obtaining a battery load within the charging power range;

[0016] The charging efficiency is obtained according to the battery load.

[0017] In the above implementation process, the voltage of the basic charging model is adjusted in turn according to the conduction duty cycle range to obtain the charging power range and then obtain the charging efficiency, which can improve the calculation accuracy, reduce the error and make the obtained charging efficiency more accurate.

[0018] Furthermore, the step of obtaining the variable affecting the charging efficiency comprises:

[0019] After voltage adjustment is performed on the basic charging model, the basic charging model after voltage adjustment is controlled to be in a fully resonant state, thereby obtaining a primary coil current, a first output voltage, and a second output voltage;

[0020] Obtaining a mutual inductance parameter according to the primary coil current, the first output voltage, and the second output voltage;

[0021] The mutual inductance parameter is determined as the variable.

[0022] In the above implementation process, data is obtained in a completely resonant state, which can improve the anti-offset characteristics of the system, reduce the difficulty of analysis, increase flexibility, and improve the ability of the obtained variables to adjust the reaction charge.

[0023] Furthermore, the step of obtaining the variable affecting the charging efficiency further includes:

[0024] After the voltage of the basic charging model is adjusted, the basic charging model after the voltage adjustment is controlled to be in a fully resonant state, and the on-duty ratio is determined as the variable according to the basic parameters of the voltage adjustment model and the charging efficiency.

[0025] In the above implementation process, data is obtained in a fully resonant state, and the on-duty ratio is determined as a variable, which can improve the flexibility of variable application and reduce the error of the on-duty ratio on the charging efficiency.

[0026] Furthermore, the step of dynamically controlling the variable according to the pre-constructed fuzzy control model to obtain the error amount of the variable includes:

[0027] Get optimization variables;

[0028] Obtaining error data between the variable and the optimized variable;

[0029] The error data is determined to be an error amount of the variable.

[0030] In the above implementation process, the error amount between the variable and the optimized variable is obtained, which can increase the weight of the variable and improve the influence of the variable on the stability of the charging efficiency.

[0031] Furthermore, the step of obtaining the optimization variables includes:

[0032] Obtaining a first proportional coefficient, a first integral coefficient and a first differential coefficient according to a first fuzzy control model in the fuzzy control model;

[0033] Obtaining a second proportional coefficient, a second integral coefficient and a second differential coefficient according to a second fuzzy control model in the fuzzy control model;

[0034] The variables are dynamically controlled according to the first proportional coefficient, the first integral coefficient, the first differential coefficient, the second proportional coefficient, the second integral coefficient and the second differential coefficient to obtain the optimized variables.

[0035] In the above implementation process, the variables are dynamically controlled according to the first proportional coefficient, the first integral coefficient, the first differential coefficient, the second proportional coefficient, the second integral coefficient and the second differential coefficient, which can improve the accuracy of the optimized variables and reduce the errors in the calculation process.

[0036] In a second aspect, an embodiment of the present application further provides a device for optimizing charging efficiency of an electric vehicle, the device comprising:

[0037] A building module, used to build a basic charging model for the electric vehicle;

[0038] A voltage adjustment module, used to adjust the voltage of the basic charging model under a constant voltage state to obtain a charging efficiency;

[0039] A data acquisition module, used to obtain variables affecting the charging efficiency;

[0040] A dynamic control module, used to dynamically control the variable according to a pre-built fuzzy control model to obtain an error amount of the variable;

[0041] An optimization module is used to optimize the charging efficiency according to the error amount.

[0042] In the above implementation process, the voltage of the basic charging model is adjusted to obtain the variables that affect the charging efficiency, and then the error of the variable is obtained according to the fuzzy control model. The charging efficiency is optimized according to the error, which can improve the charging efficiency and the stability during the charging process. The optimization method is simpler and easier to use, has low complexity, is suitable for a variety of scenarios, and is convenient for adjusting the voltage during the charging process, and can achieve power regulation within a wide range.

[0043] In a third aspect, an embodiment of the present application provides an electric vehicle, comprising the charging efficiency optimization device of the electric vehicle according to the second aspect.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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

[0049] 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.

[0050] Figure 1 A schematic diagram of a flow chart of a method for optimizing charging efficiency of an electric vehicle provided in an embodiment of the present application;

[0051] Figure 2 A schematic diagram of the basic charging model structure of an electric vehicle provided in an embodiment of the present application;

[0052] Figure 3 A schematic diagram of the voltage regulation model structure of an electric vehicle provided in an embodiment of the present application;

[0053] Figure 4 A schematic diagram of the fuzzy control structure of an electric vehicle provided in an embodiment of the present application;

[0054] Figure 5 A schematic diagram of the structural composition of a charging efficiency optimization device for an electric vehicle provided in an embodiment of the present application;

[0055] Figure 6 A schematic diagram of the structural composition of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] 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.

[0057] 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.

[0058] 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.

[0059] Embodiment 1

[0060] Figure 1 is a flow chart of a method for optimizing the charging efficiency of an electric vehicle provided in an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0061] S10, building a basic charging model for electric vehicles;

[0062] S20, under a constant voltage state, adjusting the voltage of the basic charging model to obtain the charging efficiency;

[0063] S30, obtaining variables affecting charging efficiency;

[0064] S40, dynamically controlling the variable according to the pre-built fuzzy control model to obtain the error amount of the variable;

[0065] S50, optimizing the charging efficiency according to the error amount.

[0066] In the above implementation process, the voltage of the basic charging model is adjusted to obtain the variables that affect the charging efficiency, and then the error of the variable is obtained according to the fuzzy control model. The charging efficiency is optimized according to the error, which can improve the charging efficiency and the stability during the charging process. The optimization method is simpler and easier to use, has low complexity, is suitable for a variety of scenarios, and is convenient for adjusting the voltage during the charging process, and can achieve power regulation within a wide range.

[0067] In S10, the embodiment of the present application constructs a basic charging model based on the LCC-S type topology, such as Figure 2 As shown. Among them, 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, The primary side LCC compensation network is formed, and the secondary side circuit compensation inductor Compensation capacitor for the secondary circuit To form an S-type compensation network, is the self-inductance of the primary circuit coil, is the mutual inductance of the coil, , and All of them are internal resistances. D1, D2, D3, and D4 constitute the secondary side rectification circuit. For voltage stabilizing filter capacitor, The battery load of the electric vehicle.

[0068] Furthermore, S20 includes:

[0069] Construct voltage regulation model;

[0070] Obtain the on-duty cycle range of the switch tube in the voltage regulation model;

[0071] The basic charging model is adjusted in turn for buck and boost according to the on-duty cycle range to obtain a charging power range;

[0072] Get the battery load within the charging power range;

[0073] Get the charging efficiency based on the battery load.

[0074] In the above implementation process, the voltage of the basic charging model is adjusted in turn according to the conduction duty cycle range to obtain the charging power range and then obtain the charging efficiency, which can improve the calculation accuracy, reduce the error and make the obtained charging efficiency more accurate.

[0075] Mutual inductance in coils , inverter output voltage, primary circuit compensation inductance Under the condition that the voltage remains unchanged, the basic charging model constructed based on the LCC-S topology adopted in the embodiment of the present application can achieve constant voltage output and improve the stability of system operation.

[0076] like Figure 3 The voltage regulation model is shown in Figure 1, where is a DC voltage source, For the switch tube, is the inductor, For diodes, is the capacitance, is the resistance, according to the on-duty ratio , you can get, For resistance Upper voltage:

[0077] ;

[0078] in, For switch tube The on-duty ratio is When , the secondary circuit voltage is stepped down; when , the secondary circuit voltage is boosted.

[0079] After voltage adjustment, the electric vehicle battery load is:

[0080]

[0081] Since the system is in a completely resonant state, the charging efficiency can be obtained according to the above power calculation formula:

[0082] ;

[0083] in, is the actual charging power, For electric vehicle battery load, is the coefficient, is the on-duty ratio, is the load voltage, is the internal resistance of the primary coil, is the system angular frequency, To compensate the inductance of the primary circuit, is the internal resistance of the secondary circuit coil, is the load internal resistance.

[0084] According to the above formula, by adjusting the on-duty cycle of the switch tube, the system efficiency can be adjusted during the wireless charging of electric vehicles, and the charging power range can also be adjusted over a wide range.

[0085] Furthermore, S30 includes:

[0086] After the voltage of the basic charging model is adjusted, the basic charging model after the voltage adjustment is controlled to be in a fully resonant state, and the primary coil current, the first output voltage and the second output voltage are obtained;

[0087] Obtaining mutual inductance parameters according to the primary coil current, the first output voltage and the second output voltage;

[0088] The mutual inductance parameter is determined as a variable.

[0089] In the above implementation process, data is obtained in a completely resonant state, which can improve the anti-offset characteristics of the system, reduce the difficulty of analysis, increase flexibility, and improve the ability of the obtained variables to adjust the reaction charge.

[0090] In the fully resonant state, the primary coil current can be approximated as:

[0091] ;

[0092] in, To compensate the inductance of the primary circuit, is the inverter output voltage, is the system angular frequency.

[0093] Then the output voltage of the secondary rectifier end (first output voltage) is:

[0094] ;

[0095] in, is the mutual inductance of the coil, To compensate the inductance of the primary circuit, For electric vehicle battery load, is the internal resistance.

[0096] The output voltage (second output voltage) of the secondary voltage regulation model is:

[0097] ;

[0098] Combining the above formulas, we can get the mutual inductance parameter:

[0099] ;

[0100] in, is the on-duty ratio, To compensate the inductance of the primary circuit, is the internal resistance of the primary coil, is the system angular frequency, To compensate the inductance of the primary circuit, is the internal resistance of the secondary circuit coil.

[0101] It can be seen that when other parameters remain unchanged, during the wireless charging process of electric vehicles, as the battery load changes, parameter estimation can be performed based on the mutual inductance parameter. Therefore, the mutual inductance parameter is determined as a variable.

[0102] Furthermore, the step of obtaining the variables affecting the charging efficiency further includes:

[0103] After the voltage of the basic charging model is adjusted, the basic charging model after the voltage adjustment is controlled to be in a fully resonant state, and the conduction duty ratio is determined as a variable according to the basic parameters of the voltage adjustment model and the charging efficiency.

[0104] In the above implementation process, data is obtained in a fully resonant state, and the on-duty ratio is determined as a variable, which can improve the flexibility of variable application and reduce the error of the on-duty ratio on the charging efficiency.

[0105] By adjusting the on-duty cycle of the switch tube, the system efficiency can be adjusted during the wireless charging of the electric vehicle, and the charging power range can also be adjusted over a wide range. Therefore, the on-duty cycle is determined as a variable.

[0106] Furthermore, S40 includes:

[0107] Get optimization variables;

[0108] Obtain error data between variables and optimized variables;

[0109] Determine the error data as the error amount for the variable.

[0110] In the above implementation process, the error amount between the variable and the optimized variable is obtained, which can increase the weight of the variable and improve the influence of the variable on the stability of the charging efficiency.

[0111] Furthermore, the step of obtaining the optimization variables includes:

[0112] Obtaining a first proportional coefficient, a first integral coefficient, and a first differential coefficient according to a first fuzzy control model in the fuzzy control model;

[0113] Obtaining a second proportional coefficient, a second integral coefficient, and a second differential coefficient according to a second fuzzy control model in the fuzzy control model;

[0114] The variables are dynamically controlled according to the first proportional coefficient, the first integral coefficient, the first differential coefficient, the second proportional coefficient, the second integral coefficient and the second differential coefficient to obtain optimized variables.

[0115] In the above implementation process, the variables are dynamically controlled according to the first proportional coefficient, the first integral coefficient, the first differential coefficient, the second proportional coefficient, the second integral coefficient and the second differential coefficient, which can improve the accuracy of the optimized variables and reduce the errors in the calculation process.

[0116] The embodiment of the present application adopts a fuzzy control method to dynamically control the two variables and improve the system efficiency. The fuzzy control model uses the error between the variable and the optimization variable as the error amount, and calculates the proportional coefficient through the first fuzzy controller and the second fuzzy controller. , the second proportionality coefficient , the first integral coefficient , the second integral coefficient , the first differential coefficient , the second differential coefficient , and for variables , Control and get optimized , .

[0117] The embodiment of the present application realizes constant voltage output at the load end of the secondary circuit through a charging model based on the LCC-S topology, thereby improving the output voltage stability of the system.

[0118] By adjusting the on-duty cycle of the switch tube during the buck-boost process, the output power in a wide range of wireless charging for electric vehicles is achieved, the system transmission efficiency is optimized, and the efficiency of wireless charging for electric vehicles is improved.

[0119] The fuzzy control method is used to dynamically adjust the parameters, which improves the robustness of wireless charging of electric vehicles. The efficiency, economy and stability of wireless charging of electric vehicles are fully considered, and it has good applicability.

[0120] The present application effectively improves the comprehensive performance of wireless charging for electric vehicles, and provides an effective solution for achieving efficient, adjustable, stable wireless charging efficiency for electric vehicles with a wide charging power range.

[0121] Embodiment 2

[0122] In order to execute the method corresponding to the above embodiment 1 to achieve the corresponding functions and technical effects, a charging efficiency optimization device for an electric vehicle is provided below, such as Figure 5 As shown, the device comprises:

[0123] Building module 1, used to build a basic charging model for electric vehicles;

[0124] The voltage adjustment module 2 is used to adjust the voltage of the basic charging model under the constant voltage state to obtain the charging efficiency;

[0125] A data acquisition module 3 is used to obtain variables that affect charging efficiency;

[0126] The dynamic control module 4 is used to dynamically control the variables according to the pre-built fuzzy control model to obtain the error amount of the variables;

[0127] The optimization module 5 is used to optimize the charging efficiency according to the error amount.

[0128] In the above implementation process, the voltage of the basic charging model is adjusted to obtain the variables that affect the charging efficiency, and then the error of the variable is obtained according to the fuzzy control model. The charging efficiency is optimized according to the error, which can improve the charging efficiency and the stability during the charging process. The optimization method is simpler and easier to use, has low complexity, is suitable for a variety of scenarios, and is convenient for adjusting the voltage during the charging process, and can achieve power regulation within a wide range.

[0129] Furthermore, the voltage adjustment module 2 is also used for:

[0130] Construct voltage regulation model;

[0131] Obtain the on-duty cycle range of the switch tube in the voltage regulation model;

[0132] The basic charging model is adjusted in turn for buck and boost according to the on-duty cycle range to obtain a charging power range;

[0133] Get the battery load within the charging power range;

[0134] Get the charging efficiency based on the battery load.

[0135] In the above implementation process, the voltage of the basic charging model is adjusted in turn according to the conduction duty cycle range to obtain the charging power range and then obtain the charging efficiency, which can improve the calculation accuracy, reduce the error and make the obtained charging efficiency more accurate.

[0136] Furthermore, the data acquisition module 3 is also used for:

[0137] After the voltage of the basic charging model is adjusted, the basic charging model after the voltage adjustment is controlled to be in a fully resonant state, and the primary coil current, the first output voltage and the second output voltage are obtained;

[0138] Obtaining mutual inductance parameters according to the primary coil current, the first output voltage and the second output voltage;

[0139] The mutual inductance parameter is determined as a variable.

[0140] In the above implementation process, data is obtained in a completely resonant state, which can improve the anti-offset characteristics of the system, reduce the difficulty of analysis, increase flexibility, and improve the ability of the obtained variables to adjust the reaction charge.

[0141] Furthermore, the data acquisition module 3 is also used for:

[0142] After the voltage of the basic charging model is adjusted, the basic charging model after the voltage adjustment is controlled to be in a fully resonant state, and the conduction duty ratio is determined as a variable according to the basic parameters of the voltage adjustment model and the charging efficiency.

[0143] In the above implementation process, data is obtained in a fully resonant state, and the on-duty ratio is determined as a variable, which can improve the flexibility of variable application and reduce the error of the on-duty ratio on the charging efficiency.

[0144] Furthermore, the dynamic control module 4 is also used for:

[0145] Get optimization variables;

[0146] Obtain error data between variables and optimized variables;

[0147] Determine the error data as the error amount for the variable.

[0148] In the above implementation process, the error amount between the variable and the optimized variable is obtained, which can increase the weight of the variable and improve the influence of the variable on the stability of the charging efficiency.

[0149] Furthermore, the dynamic control module 4 is also used for:

[0150] Obtaining a first proportional coefficient, a first integral coefficient, and a first differential coefficient according to a first fuzzy control model in the fuzzy control model;

[0151] Obtaining a second proportional coefficient, a second integral coefficient, and a second differential coefficient according to a second fuzzy control model in the fuzzy control model;

[0152] The variables are dynamically controlled according to the first proportional coefficient, the first integral coefficient, the first differential coefficient, the second proportional coefficient, the second integral coefficient and the second differential coefficient to obtain optimized variables.

[0153] In the above implementation process, the variables are dynamically controlled according to the first proportional coefficient, the first integral coefficient, the first differential coefficient, the second proportional coefficient, the second integral coefficient and the second differential coefficient, which can improve the accuracy of the optimized variables and reduce the errors in the calculation process.

[0154] The above-mentioned device for optimizing the charging efficiency of an electric vehicle can implement the method of the above-mentioned embodiment 1. The options in the above-mentioned embodiment 1 are also applicable to this embodiment and will not be described in detail here.

[0155] 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.

[0156] Embodiment 3

[0157] An embodiment of the present application provides an electric vehicle, including the charging efficiency optimization device of the electric vehicle of embodiment 2.

[0158] Embodiment 4

[0159] 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 method for optimizing the charging efficiency of an electric vehicle of embodiment 1.

[0160] Optionally, the above-mentioned electronic device may be a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The input and output unit is used to provide users with the task creation and to create a start optional time period or preset execution time for the task to realize the interaction between the user and the server. The input and output unit can be, but is not limited to, a mouse and a keyboard.

[0166] 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 shown. Figure 6 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0167] 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 method for optimizing the charging efficiency of an electric vehicle of embodiment 1.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 method for optimizing charging efficiency of an electric vehicle, characterized in that: The method comprises: Constructing a basic charging model for the electric vehicle; Under a constant voltage state, adjusting the voltage of the basic charging model to obtain a charging efficiency; Obtaining variables that affect the charging efficiency; Dynamically controlling the variable according to a pre-built fuzzy control model to obtain an error amount of the variable; Optimizing the charging efficiency according to the error amount; The step of adjusting the voltage of the basic charging model under the constant voltage state to obtain the charging efficiency includes: Construct voltage regulation model; Obtaining a conduction duty cycle range of the switch tube in the voltage regulation model; According to the on-duty cycle range, the basic charging model is sequentially adjusted to reduce the voltage and to increase the voltage to obtain a charging power range; Obtaining a battery load within the charging power range; Obtaining the charging efficiency according to the battery load; The step of dynamically controlling the variable according to the pre-built fuzzy control model to obtain the error amount of the variable includes: Get optimization variables; Obtaining error data between the variable and the optimized variable; Determine the error data as the error amount of the variable; The step of obtaining the optimization variables comprises: Obtaining a first proportional coefficient, a first integral coefficient and a first differential coefficient according to a first fuzzy control model in the fuzzy control model; Obtaining a second proportional coefficient, a second integral coefficient and a second differential coefficient according to a second fuzzy control model in the fuzzy control model; The variables are dynamically controlled according to the first proportional coefficient, the first integral coefficient, the first differential coefficient, the second proportional coefficient, the second integral coefficient and the second differential coefficient to obtain the optimized variables.

2. The method for optimizing the charging efficiency of an electric vehicle according to claim 1, characterized in that: The step of obtaining the variable affecting the charging efficiency comprises: After voltage adjustment is performed on the basic charging model, the basic charging model after voltage adjustment is controlled to be in a fully resonant state, thereby obtaining a primary coil current, a first output voltage, and a second output voltage; Obtaining a mutual inductance parameter according to the primary coil current, the first output voltage, and the second output voltage; The mutual inductance parameter is determined as the variable.

3. The method for optimizing the charging efficiency of an electric vehicle according to claim 2, characterized in that: The step of obtaining the variable affecting the charging efficiency further includes: After the voltage of the basic charging model is adjusted, the basic charging model after the voltage adjustment is controlled to be in a fully resonant state, and the on-duty ratio is determined as the variable according to the basic parameters of the voltage adjustment model and the charging efficiency.

4. A device for optimizing charging efficiency of an electric vehicle, characterized in that: The device comprises: A building module, used to build a basic charging model for the electric vehicle; A voltage adjustment module, used to adjust the voltage of the basic charging model under a constant voltage state to obtain a charging efficiency; A data acquisition module, used to obtain variables affecting the charging efficiency; A dynamic control module, used to dynamically control the variable according to a pre-built fuzzy control model to obtain an error amount of the variable; An optimization module, used for optimizing the charging efficiency according to the error amount; The voltage adjustment module is also used for: Construct voltage regulation model; Obtaining a conduction duty cycle range of the switch tube in the voltage regulation model; According to the on-duty cycle range, the basic charging model is sequentially adjusted to reduce the voltage and to increase the voltage to obtain a charging power range; Obtaining a battery load within the charging power range; Obtaining the charging efficiency according to the battery load; The dynamic control module is also used for: Get optimization variables; Obtaining error data between the variable and the optimized variable; Determine the error data as the error amount of the variable; Obtaining a first proportional coefficient, a first integral coefficient and a first differential coefficient according to a first fuzzy control model in the fuzzy control model; Obtaining a second proportional coefficient, a second integral coefficient and a second differential coefficient according to a second fuzzy control model in the fuzzy control model; The variables are dynamically controlled according to the first proportional coefficient, the first integral coefficient, the first differential coefficient, the second proportional coefficient, the second integral coefficient and the second differential coefficient to obtain the optimized variables.

5. An electric vehicle, characterized in that: The electric vehicle comprises the charging efficiency optimization device for the electric vehicle as claimed in claim 4.

6. 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 method for optimizing the charging efficiency of an electric vehicle according to any one of claims 1 to 3.

7. A storage medium, characterized in that: A computer program is stored therein, and when the computer program is executed by a processor, the method for optimizing the charging efficiency of an electric vehicle as claimed in any one of claims 1 to 3 is implemented.

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