Power control method and device for wireless charging, equipment and storage medium

Abstract

The invention provides a wireless charging power control method, device and equipment and a storage medium, and the method comprises the steps: obtaining the value ranges of a temperature variable and a power variable of a wireless charger in a vehicle, and the temperature variable comprises an environment temperature and a charging temperature; interval division is carried out on the value ranges of the different variable types, and a plurality of environment temperature intervals, a plurality of charging temperature intervals and a plurality of power intervals are determined; performing fuzzification processing on each environment temperature interval, each charging temperature interval and each power interval to obtain a first fuzzy set, a second fuzzy set and a third fuzzy set; constructing a fuzzy relation matrix between the temperature variable and the power variable according to the first fuzzy set, the second fuzzy set and the third fuzzy set; and constructing a fuzzy reasoning function based on the fuzzy relation matrix so as to control a power variable of wireless charging according to the fuzzy reasoning function. In this way, the efficiency and stability of wireless charging in the vehicle are ensured.

Description

Technical Field

[0001] The present application relates to the field of wireless charging technology, and in particular to a power control method, device, equipment and storage medium for wireless charging. Background Art

[0002] As a convenient and efficient charging method, wireless charging technology has been widely used in the automotive field and has become an important trend to enhance user experience. In-vehicle wireless charging technology uses the principle of electromagnetic induction to perform contactless power transmission between the vehicle's built-in wireless charging device (such as a wireless charging pad) and the wireless charging receiving end of portable electronic devices such as smartphones. In related technologies, the vehicle's built-in wireless charger usually uses a fixed output power for charging. Users do not need to manually adjust the charger's settings. They only need to place the device on the charging pad to start charging. The operation is simple and quick.

[0003] However, for the wireless charging method with fixed output power, on the one hand, wireless charging cannot be intelligently adjusted according to the changes in the temperature environment inside the vehicle, which affects the efficiency and stability of charging and may make the charging efficiency not optimal. Especially in high temperature environments, the temperature of the wireless charging device and the charged device is prone to rise. When the temperature is too high, the performance of the electronic components in the wireless charging device will decline, thereby reducing the energy conversion efficiency and significantly affecting the charging efficiency. On the other hand, in a high temperature environment, the fixed output power may cause the charging device or the charged device to overheat, which in turn causes safety hazards and makes it impossible to ensure the safety of the charging process. Summary of the invention

[0004] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0005] In view of the above-mentioned shortcomings of the prior art, the present application discloses a power control method, device, equipment and storage medium for wireless charging to solve the above-mentioned technical problem of how to ensure the charging efficiency and safety of in-vehicle wireless charging.

[0006] In a first aspect, the present application provides a power control method for wireless charging, comprising: obtaining the respective value ranges of a temperature variable and a power variable of a wireless charger in a vehicle, the temperature variable comprising an ambient temperature and a charging temperature; dividing the value ranges of different variable types into intervals to determine a plurality of ambient temperature intervals, a plurality of charging temperature intervals and a plurality of power intervals; performing fuzzification processing on each of the ambient temperature intervals, each of the charging temperature intervals and each of the power intervals to obtain a first fuzzy set, a second fuzzy set and a third fuzzy set; constructing a fuzzy relationship matrix between the temperature variable and the power variable based on the first fuzzy set, the second fuzzy set and the third fuzzy set; constructing a fuzzy inference function based on the fuzzy relationship matrix to control the power variable of the wireless charger according to the fuzzy inference function, the fuzzy inference function being used to determine the mapping relationship between the temperature variable and the power variable.

[0007] In one embodiment of the present application, the construction of a fuzzy relationship matrix between the temperature variable and the power variable includes: determining a first membership distribution of the ambient temperature in the first fuzzy set, a second membership distribution of the charging temperature in the second fuzzy set, and a third membership distribution of the power variable in the third fuzzy set; performing fuzzy reasoning according to the first membership distribution, the second membership distribution and a preset fuzzy rule table to determine a mapping relationship between the first membership distribution, the second membership distribution and the third membership distribution, and use the mapping relationship as the fuzzy relationship matrix, wherein the preset fuzzy rule table includes a one-to-one correspondence between fuzzy subsets in the first fuzzy set, the second fuzzy set and the third fuzzy set.

[0008] In one embodiment of the present application, the constructing of the fuzzy relationship matrix between the temperature variable and the power variable further includes: determining a first membership of the ambient temperature in each first fuzzy subset, a second membership of the charging temperature in each second fuzzy subset, and a third membership of the power variable in each third fuzzy subset, wherein the first fuzzy subset belongs to the first fuzzy set and corresponds one-to-one to the ambient temperature range, the second fuzzy subset belongs to the second fuzzy set and corresponds one-to-one to the charging temperature range, and the third fuzzy subset belongs to the third fuzzy set and corresponds one-to-one to the power range; for each of the first The fuzzy subsets are combined with the second fuzzy subsets to obtain temperature fuzzy subsets of different combinations, each of which includes a first fuzzy subset and a second fuzzy subset; fuzzy reasoning is performed according to the first membership and the second membership corresponding to each group of the temperature fuzzy subsets, and the preset fuzzy rule table to determine the fuzzy relationship between the first membership, the second membership corresponding to each group of the temperature fuzzy subsets and the third membership corresponding to the target fuzzy subset, and use it as the fuzzy relationship matrix, the target fuzzy subset is the third fuzzy subset corresponding to the temperature fuzzy subsets one by one.

[0009] In one embodiment of the present application, the determining of the first membership of the ambient temperature in each first fuzzy subset, the second membership of the charging temperature in each second fuzzy subset, and the third membership of the power variable in each third fuzzy subset includes: defining the membership functions of each first fuzzy subset, each second fuzzy subset, and each third fuzzy subset respectively; mapping different values ​​of the ambient temperature to the first fuzzy subsets corresponding to the same ambient temperature interval based on the membership functions, determining the first membership of each value of the ambient temperature in the first fuzzy subset corresponding to the same ambient temperature interval, so as to obtain the first membership of the ambient temperature in each first fuzzy subset. The first membership; based on the membership function, mapping different values ​​of the charging temperature to the second fuzzy subsets corresponding to the same charging temperature range, determining the second membership of each value of the charging temperature in the second fuzzy subset corresponding to the same charging temperature range, so as to obtain the second membership of the charging temperature in each second fuzzy subset; based on the membership function, mapping different values ​​of the power variable to the third fuzzy subsets corresponding to the same power range, determining the third membership of each value of the power variable in the third fuzzy subset corresponding to the same power range, so as to obtain the third membership of the power variable in each third fuzzy subset.

[0010] In one embodiment of the present application, constructing a fuzzy inference function based on the fuzzy relationship matrix includes: calculating the first membership and the second membership corresponding to the temperature variables with different values ​​based on the fuzzy relationship matrix, and determining the third membership of the power variable corresponding to each value of the temperature variable; performing clarification processing according to each third membership to obtain the power variable values ​​corresponding to the temperature variables with different values; and constructing the fuzzy inference function according to the power variable values ​​corresponding to the temperature variables with different values.

[0011] In one embodiment of the present application, the clarification processing is performed according to the third membership to obtain the power variable values ​​corresponding to the temperature variable with different values, including: matching according to the temperature interval corresponding to any value of the temperature variable, and determining at least one temperature fuzzy subset as the target temperature fuzzy subset, the temperature interval including the ambient temperature interval and the charging temperature interval; calculating the first membership and the second membership corresponding to each of the target temperature fuzzy subsets based on the fuzzy relationship matrix to obtain the third membership of each target temperature fuzzy subset corresponding to the third fuzzy subset; performing clarification processing according to the maximum third membership to obtain the value of the power variable corresponding to the maximum third membership as the power variable value corresponding to any value of the temperature variable.

[0012] In one embodiment of the present application, controlling the power variable of the wireless charger according to the fuzzy inference function includes: collecting the current ambient temperature value and the current charging temperature value of the wireless charger; querying the fuzzy inference function according to the current ambient temperature value and the current charging temperature value to determine the target power value of the wireless charger.

[0013] In a second aspect, the present application provides a power control device for wireless charging, comprising: an acquisition module, used to obtain the respective value ranges of the temperature variable and the power variable of the wireless charger in the vehicle, the temperature variable including the ambient temperature and the charging temperature; a division module, used to divide the value ranges of different variable types into intervals, and determine multiple ambient temperature intervals, multiple charging temperature intervals and multiple power intervals; a fuzzification module, used to perform fuzzification processing on each of the ambient temperature intervals, each of the charging temperature intervals and each of the power intervals, respectively, to obtain a first fuzzy set, a second fuzzy set and a third fuzzy set; a construction module, used to construct a fuzzy relationship matrix between the temperature variable and the power variable according to the first fuzzy set, the second fuzzy set and the third fuzzy set; a control module, used to construct a fuzzy inference function based on the fuzzy relationship matrix to control the power variable of the wireless charger according to the fuzzy inference function, and the fuzzy inference function is used to determine the mapping relationship between the temperature variable and the power variable.

[0014] In a third aspect, the present application also provides an electronic device, comprising: a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement the power control method of wireless charging as described in the above embodiments.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor of a computer, the computer executes the power control method for wireless charging as described in the above embodiments.

[0016] Beneficial effects of the present application: The present application proposes a power control method, device, equipment and storage medium for wireless charging. By obtaining the respective value ranges of the temperature variable and the power variable of the wireless charger in the vehicle, the temperature variable includes the ambient temperature and the charging temperature; the value ranges of different variable types are divided into intervals to determine multiple ambient temperature intervals, multiple charging temperature intervals and multiple power intervals; each ambient temperature interval, each charging temperature interval and each power interval are fuzzified to obtain the first fuzzy set, the second fuzzy set and the third fuzzy set; according to the one-to-one correspondence between the fuzzy subsets in the first fuzzy set, the second fuzzy set and the third fuzzy set, a fuzzy relationship matrix between the temperature variable and the power variable is constructed; based on the fuzzy relationship matrix, a fuzzy inference function is constructed to control the power variable of the wireless charger according to the fuzzy inference function, and the fuzzy inference function characterizes the mapping relationship between the temperature variable and the power variable. In this way, the charging power can be dynamically adjusted in combination with the ambient temperature and the charging temperature of the wireless charger, so that the wireless charging can achieve the best charging efficiency under different temperature conditions, the efficiency and stability of the wireless charging are high, and the safety hazards caused by overheating or overcharging in a high temperature environment are avoided, thereby improving the safety of the wireless charging.

[0017] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0019] Figure 1 is a schematic diagram of an implementation environment of a wireless charging power control method shown in an exemplary embodiment of the present application;

[0020] Figure 2 is a flow chart of a wireless charging power control method shown in an exemplary embodiment of the present application;

[0021] Figure 3 is a schematic diagram of a first membership degree distribution shown in an exemplary embodiment of the present application;

[0022] Figure 4 is a schematic diagram of a second membership degree distribution shown in an exemplary embodiment of the present application;

[0023] Figure 5is a schematic diagram of a third membership degree distribution shown in an exemplary embodiment of the present application;

[0024] Figure 6 is a schematic diagram of a first degree of membership of an ambient temperature shown in an exemplary embodiment of the present application;

[0025] Figure 7 is a schematic diagram of a second degree of membership of a charging temperature shown in an exemplary embodiment of the present application;

[0026] Figure 8 is a schematic diagram of fuzzy rule matching shown in an exemplary embodiment of the present application;

[0027] Fig. 9 is a schematic diagram of a third degree of membership of a power variable shown in an exemplary embodiment of the present application;

[0028] Fig.10 is a schematic diagram of a third degree of membership of another power variable shown in an exemplary embodiment of the present application;

[0029] Fig.11 is a schematic diagram of calculating a power variable value shown in an exemplary embodiment of the present application;

[0030] Fig.12 is a schematic diagram of a fuzzy inference function shown in an exemplary embodiment of the present application;

[0031] Fig.13 is a schematic diagram of adjusting power based on fuzzy control according to an exemplary embodiment of the present application;

[0032] Fig.14 is a schematic diagram of a hardware structure of power control shown in an exemplary embodiment of the present application;

[0033] Fig.15 is a block diagram of a wireless charging power control device shown in an exemplary embodiment of the present application;

[0034] Fig.16 It is a structural diagram of a computer system suitable for implementing the electronic device of the present application, shown as an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0035] The following will describe the implementation methods of the present application with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, not for limiting the scope of protection of the present application.

[0036] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0037] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0038] See also Figure 1 , is a schematic diagram of an implementation environment of a rearview mirror display control method shown in an exemplary embodiment of the present application. Figure 1 As shown, the implementation environment includes a vehicle 110 and a controller 120, wherein the controller 120 is embedded in the vehicle 110, including but not limited to a vehicle system, a vehicle-mounted computer, etc., for implementing a power control method for wireless charging.

[0039] See also Figure 2 , is a flowchart of a wireless charging power control method shown in an exemplary embodiment of the present application. The method can be applied to Figure 1 The implementation environment shown in FIG. 1 is shown in FIG. 1 . It should be understood that the method may also be applicable to other exemplary implementation environments, and this embodiment does not limit the implementation environment to which the method is applicable. Figure 2 As shown, in an exemplary embodiment, the power control method of wireless charging includes at least steps S210 to S250, which are described in detail as follows:

[0040] Step S210, obtaining respective value ranges of a temperature variable and a power variable of the wireless charger in the vehicle, wherein the temperature variable includes an ambient temperature and a charging temperature.

[0041] In one embodiment of the present application, since the stability of the charging efficiency of the wireless charger is directly affected by the charging power (i.e., power variable), and in the process of wireless charging of the vehicle to the charged device (such as a mobile phone), the wireless charger will generate a certain amount of heat due to the energy loss in the power conversion process. If the charging temperature of the wireless charger is too high, the resistance of its internal electronic components may increase, resulting in a reduction in energy conversion efficiency, thereby affecting the power of wireless charging, and the ambient temperature is one of the important factors affecting the increase in charging temperature. When the ambient temperature and the heat related to the charging temperature act together, the wireless charging power will also be further reduced, resulting in a reduction in charging efficiency and charging speed. Therefore, it is necessary to adjust the power variable in real time according to the ambient temperature and charging temperature to maximize the charging efficiency and its stability while ensuring the charging speed.

[0042] In addition, during the wireless charging process, excessively high temperatures may also cause damage to the internal components of the charging board, reduce the life of the battery in the charged device, and even cause safety accidents such as fire. Therefore, the power needs to be adjusted within the safe charging temperature range and the ambient temperature range, and the adjustment range of the power variable cannot exceed the safe range. In other words, the value ranges of the temperature variable and the power variable are respectively within their corresponding preset safety ranges, and can be adjusted according to actual safety needs without any restrictions.

[0043] In one embodiment of the present application, the ambient temperature can be acquired by a temperature sensor installed inside the vehicle, and the location of the temperature sensor inside the vehicle can be set according to actual needs, for example, the temperature sensor can be placed under the dashboard of the vehicle or inside the passenger compartment, etc., without limitation. The charging temperature can be acquired by a temperature sensor built into a wireless charging module (Wireless Charging Module, WCM), which is a key component in a wireless charger to realize the wireless charging function.

[0044] Step S220 , dividing the value ranges of different variable types into intervals, and determining a plurality of ambient temperature intervals, a plurality of charging temperature intervals, and a plurality of power intervals.

[0045] In one embodiment of the present application, taking the value range of the variable charging temperature as [-40, 85], with the unit of Celsius as an example, the interval division of the charging temperature can be shown in Table 1:

[0046] Table 1: Charging temperature range

[0047] Charging temperature Fuzzy definition -40--20 Very low (EL) -20-0 Low (L) 0-50 High (H) 50-70 Higher(MH) 70-85 Very High (EH)

[0048] Among them, Table 1 divides the value range of the charging temperature into five charging temperature intervals of [-40, 20], [-20, 0], [0, 50], [50, 70] and [70, 85], and fuzzily defines the five charging temperature intervals as extremely low, low, high, relatively high and extremely high, respectively.

[0049] In one embodiment of the present application, taking the value range of the variable of ambient temperature as [-40, 45], with the unit of Celsius as an example, the interval division of ambient temperature can be shown in Table 2:

[0050] Table 2: Ambient temperature range

[0051] Ambient temperature Fuzzy definition -40--20 Very Low (ES) -20-0 Low(S) 0-35 High (B) 35-40 Higher(MB) 40-45 Very high (EB)

[0052] Among them, Table 2 divides the range of charging temperature into five ambient temperature intervals of [-40, 20], [-20, 0], [0, 35], [35, 40] and [40, 45]. The five charging temperature intervals are fuzzily defined as extremely low, low, high, relatively high and extremely high, respectively.

[0053] In one embodiment of the present application, taking the value range of the power variable as [5, 50], with the unit being watt as an example, the division of the power intervals may be as shown in Table 3:

[0054] Table 3: Power range

[0055] Wireless charging power Fuzzy definition 5-10 Very Low (PEL) 10-20 Low (PL) 20-30 High (PH) 30-45 Higher (PEH) 45-50 Very high (PMH)

[0056] In Table 3, the value range of the power variable is divided into five power intervals of [5, 10], [10, 20], [20, 30], [30, 45] and [45, 50]. The five power intervals are fuzzily defined as very low, low, high, relatively high and very high, respectively.

[0057] In one embodiment of the present application, the fuzzy definition is actually to define the specific value of the variable, that is, the clear value, as a fuzzy language, that is, to fuzzify the value of the variable and define it as a fuzzy subset. Each fuzzified subset can be regarded as a representation of a fuzzy language, which is more in line with the habits of natural language and is easy to understand and apply. In addition, when dividing the value range of each type of variable, it can be divided in an overlapping manner, and there can be overlap between different intervals of the variable obtained by the division.

[0058] In one embodiment of the present application, the value range of each type of variable is divided into intervals, and the number and size of the obtained intervals can be adjusted according to actual needs. Tables 1 to 3 are only examples and are not limited to this.

[0059] Step S230, fuzzification is performed on each ambient temperature interval, each charging temperature interval and each power interval to obtain a first fuzzy set, a second fuzzy set and a third fuzzy set. The first fuzzy set includes a plurality of first fuzzy subsets, and the first fuzzy subsets correspond to the ambient temperature intervals one-to-one; the second fuzzy set includes a plurality of second fuzzy subsets, and the second fuzzy subsets correspond to the charging temperature intervals one-to-one; the third fuzzy set includes a plurality of third fuzzy subsets, and the third fuzzy subsets correspond to the ambient temperature intervals one-to-one.

[0060] In one embodiment of the present application, to perform fuzzy processing, it is necessary to first determine the domain of each type of variable, that is, to take the value range of each type of variable as the domain of the type of variable, and then within the domain, convert the multiple intervals corresponding to each variable type into fuzzy subsets one by one according to the membership function to form an overall fuzzy set.

[0061] In one embodiment of the present application, the membership distribution refers to the set of the membership of all elements in a domain to a fuzzy subset, that is, the membership distribution describes the degree distribution of each element in the domain belonging to the fuzzy subset. This distribution is usually expressed in the form of a membership function, which can map each element in the domain to its corresponding membership. The membership is the basis of the membership distribution, which is used to describe the degree to which an element belongs to a fuzzy subset. The relationship between an element and a fuzzy subset is not absolute, but has a certain membership. The value range of the membership is between 0 and 1, where 0 means that the element does not belong to the fuzzy subset at all, 1 means that the element completely belongs to the fuzzy subset, and the value between 0 and 1 means that the element belongs to the fuzzy set to a certain extent or possibility. Among them, each element in the domain can be understood as a value of a variable. Therefore, the first membership distribution of the ambient temperature variable in the first fuzzy set is the set of the first memberships of the ambient temperature variable in each first fuzzy subset, that is, the set of the first memberships of each value in the corresponding value range of the environmental variable in each first fuzzy subset. The same is true for the second membership distribution corresponding to the charging temperature and the third membership distribution corresponding to the power variable.

[0062] See also Figures 3 to 5 , which are schematic diagrams of the first membership distribution, the second membership distribution and the third membership distribution, respectively.

[0063] like Figure 3 As shown, a triangular membership function is used to represent the first membership distribution corresponding to the ambient temperature. Figure 3The horizontal axis represents the value of the ambient temperature, and the vertical axis represents the first membership. The first fuzzy set includes five first fuzzy subsets: extremely low (ES), low (S), high (B), relatively high (MB) and extremely high (EB), and the corresponding ambient temperature intervals are [-40, -20], [-40, 0], [-20, 35], [0, 40] and [35, 50]. At this time, the value range of the ambient temperature is [-40, 50]. Taking the ambient temperature value of -30 as an example, the first membership of the five first fuzzy subsets arranged from low to high in fuzzy language are 0.5, 0.5, 0, 0 and 0 respectively.

[0064] like Figure 4 As shown, a triangular membership function is used to represent the second membership distribution corresponding to the charging temperature. Figure 4 The horizontal axis in represents the value of charging temperature, and the vertical axis represents the second membership. The second fuzzy set includes five second fuzzy subsets: extremely low (EL), low (L), high (H), relatively high (MH) and extremely high (EH), and the corresponding charging temperature intervals are [-40, -20], [-40, 0], [-20, 45], [0, 70] and [50, 85]. The value range of charging temperature is [-40, 85]. Taking the value of ambient temperature as 60 as an example, the second membership of the five second fuzzy subsets arranged from low to high in fuzzy language are 0, 0, 0, 0.5 and 0.5 respectively.

[0065] like Figure 5 As shown, the triangular membership function is used to represent the third membership distribution corresponding to the power variable. Figure 5 The horizontal axis represents the value of the power variable, and the vertical axis represents the third membership. The third fuzzy set includes five third fuzzy subsets: extremely low (PEL), low (PL), high (PH), relatively high (PEH) and extremely high (PMH), and the corresponding power intervals are [5, 10], [5, 20], [10, 30], [20, 45] and [30, 50], respectively. At this time, the value range of the power variable is [5, 50]. Taking the value of the power variable as 15 as an example, the third membership of the five third fuzzy subsets arranged from low to high in fuzzy language are 0, 0.5, 0.5, 0 and 0 respectively.

[0066] In one embodiment of the present application, there are Figures 3 to 5It can be seen that since the intervals corresponding to the same type of variables can overlap, when different intervals of the same type of variables overlap, the same value of the variable may have a certain degree of membership in multiple fuzzy subsets, but there is also a value whose membership in at least one fuzzy subset is 0, which makes the fuzzification processing speed slow and may not have practical significance in subsequent calculations. Instead, it increases the amount of data and computational complexity of subsequent calculations, affecting the computational efficiency. Based on this, the membership is determined in the following ways: the membership functions of the first fuzzy subsets, the second fuzzy subsets and the third fuzzy subsets are defined respectively; based on the membership function, different values ​​of the ambient temperature are mapped to the first fuzzy subsets corresponding to the same ambient temperature interval, and the first membership of each value of the ambient temperature in the same ambient temperature interval is determined; based on the membership function, different values ​​of the charging temperature are mapped to the second fuzzy subsets corresponding to the same charging temperature interval, and the second membership of each value of the charging temperature in the same charging temperature interval is determined; based on the membership function, different values ​​of the power variable are mapped to the third fuzzy subsets corresponding to the same power interval, and the third membership of each value of the power variable in the same power interval is determined. In this way, in the process of fuzzy processing, unnecessary membership calculations of the values ​​of the variables are avoided, and only the membership of the fuzzy subsets that have a certain correlation with the values ​​of the variables are calculated, which optimizes the fuzzification process and improves the processing efficiency.

[0067] In one embodiment of the present invention, the membership functions that can be used include, but are not limited to, a triangular membership function, a trapezoidal membership function, a bell-shaped membership function, and a Gaussian membership function, and the type of the membership function is not limited.

[0068] Step S240: construct a fuzzy relationship matrix between the temperature variable and the power variable according to the first fuzzy set, the second fuzzy set and the fuzzy subsets in the third fuzzy set.

[0069] In one embodiment of the present application, the one-to-one correspondence between the fuzzy subsets in the first fuzzy set, the second fuzzy set, and the third fuzzy set is characterized by a preset fuzzy rule table, the preset fuzzy rule table includes a plurality of fuzzy rules, and the construction of the preset fuzzy rule table can be shown in Table 4:

[0070] Table 4: Preset fuzzy rules table

[0071]

[0072] Among them, ES, S, B, MB and EB in Table 4 are five first fuzzy subsets corresponding to the variable of ambient temperature, EL, L, H, MH and EH are five second fuzzy subsets corresponding to the variable of charging temperature, and EEL, PL, PH, PMH and PEH are five third fuzzy subsets corresponding to the power variable. As can be seen from Table 4, the preset fuzzy rule table includes multiple fuzzy rules, each of which represents a first fuzzy subset and a third fuzzy subset corresponding to a second fuzzy subset. For example, if the value of the ambient temperature corresponds to the first fuzzy subset ES, and the value of the charging temperature corresponds to the second fuzzy subset L, then the third fuzzy subset corresponding to the value of the power variable is the third fuzzy subset PL. In other words, the fuzzy rule can be understood as when the ambient temperature is extremely low and the charging temperature is extremely low, the power variable is also extremely low. In addition, because the fuzzy subsets corresponding to the same type of parameter variables overlap, it is possible that multiple fuzzy rules are satisfied at the same time. For example, if the value of the ambient temperature corresponds to the first fuzzy subset ES, and the value of the charging temperature corresponds to the two second fuzzy subsets EL and L, then the third fuzzy subsets corresponding to the value of the power variable are the two third fuzzy subsets PL and PEL.

[0073] In one embodiment of the present application, fuzzy reasoning is performed based on the first membership distribution, the second membership distribution and the preset fuzzy rule table to determine the mapping relationship between the first membership distribution, the second membership distribution and the third membership distribution, and use it as a fuzzy relationship matrix. Its essence is to perform fuzzy reasoning based on the first membership corresponding to each of the first fuzzy subsets in the first membership distribution, the second membership corresponding to each of the second fuzzy subsets in the second membership distribution and the preset fuzzy rule table, determine multiple temperature fuzzy subsets formed by one-to-one corresponding combinations of different first fuzzy subsets and second fuzzy subsets, and calculate the third membership corresponding to the combination formed by any first membership and second membership in the third fuzzy subset corresponding to each temperature fuzzy subset.

[0074] In one embodiment of the present application, the fuzzy relationship matrix is ​​a ternary fuzzy relationship matrix, and the calculation expression thereof is as follows:

[0075] R ABCi =(A X (x)×B Y (y)) T1 ×C Z (z) Formula (1)

[0076] Among them, R in formula (1) ABCi A represents the fuzzy relationship corresponding to the i-th fuzzy rule in the preset fuzzy rule table, x represents the ambient temperature, y represents the charging temperature, z represents the power variable, and × is a small operation symbol; A X(x) represents the fuzzy vector of the first fuzzy subset X included in the i-th fuzzy rule, that is, the first membership degree of the variable of ambient temperature in the first fuzzy subset X (that is, the set of the first membership degrees of different values ​​of ambient temperature in the first fuzzy subset X), B Y (y) represents the fuzzy vector of the charging temperature in the second fuzzy subset Y included in the i-th fuzzy rule, that is, the second membership degree of the charging temperature variable in the second fuzzy subset Y (that is, the set of the second membership degrees of different values ​​of the charging temperature in the second fuzzy subset Y); B Y (y) represents the fuzzy vector of the power variable in the third fuzzy subset Z included in the i-th fuzzy rule, that is, the third membership degree of the power variable in the third fuzzy subset Z (that is, the set of the third membership degrees of different values ​​of the power variable in the third fuzzy subset Z); Yes X (x)×B Y (y)) m×n The m×n column vector is constructed, where n and m are the number of the first fuzzy subsets and the second fuzzy subsets respectively.

[0077] In one embodiment of the present application, the expression of the fuzzy relationship matrix corresponding to the preset fuzzy rule is:

[0078] R ABC =R ABC1 ∪R ABC2 ∪R ABC3 …∪R ABCn Formula (2)

[0079] Among them, R in formula (2) ABC Represents the fuzzy relationship matrix corresponding to the preset fuzzy rule table, R ABCn Indicates the fuzzy relationship corresponding to the Nth fuzzy rule in the preset fuzzy rule table,

[0080] In one embodiment of the present application, it can be seen from formula (1) and formula (2) that the combination of the first fuzzy subset and the second fuzzy subset is different, and the fuzzy rules involved are also different. Therefore, it is necessary to separately calculate the fuzzy relationship between each combination of the first fuzzy subset and the second fuzzy subset and the target fuzzy subset. Therefore, each first fuzzy subset and each second fuzzy subset are combined to obtain temperature fuzzy subsets of different combinations, and each temperature fuzzy subset includes a first fuzzy subset and a second fuzzy subset; fuzzy reasoning is performed based on the first membership and the second membership corresponding to each group of temperature fuzzy subsets, and the preset fuzzy rule table, and the fuzzy relationship between the first membership, the second membership corresponding to each group of temperature fuzzy subsets and the third membership corresponding to the target fuzzy subset is determined, and used as a fuzzy relationship matrix, and the target fuzzy subset is the third fuzzy subset corresponding to the temperature fuzzy subset.

[0081] Step S250, constructing a fuzzy inference function based on the fuzzy relationship matrix to control the power variable of the wireless charger according to the fuzzy inference function, wherein the fuzzy inference function is used to determine a mapping relationship between the temperature variable and the power variable.

[0082] Specifically, based on the fuzzy relationship matrix, the first membership and the second membership corresponding to the temperature variables with different values ​​are calculated to determine the third membership of the power variable corresponding to each temperature variable with each value; the power variable values ​​corresponding to the temperature variables with different values ​​are obtained by clarifying the third memberships; and the fuzzy inference function is constructed according to the power variable values ​​corresponding to the temperature variables with different values.

[0083] In one embodiment of the present application, in one embodiment of the present application, the clarification processing can adopt the Zadeh method (i.e., the fuzzy reasoning method based on the maximum and minimum rules or arithmetic rules of conditional propositions), the Mamdani reasoning method (i.e., using direct product to represent fuzzy relationships), the weighted average method, the maximum membership method and the centroid method, etc., without limitation.

[0084] Specifically, according to the temperature interval corresponding to any value of the temperature variable, matching is performed, and at least one temperature fuzzy subset is determined as the target temperature fuzzy subset, and the temperature interval includes the ambient temperature interval and the charging temperature interval; based on the fuzzy relationship matrix, the first membership and the second membership corresponding to each target temperature fuzzy subset are calculated to obtain the third membership of the third fuzzy subset corresponding to each target temperature fuzzy subset; according to the maximum third membership, clarification processing is performed to obtain the value of the power variable corresponding to the maximum third membership, as the power variable value corresponding to any value of the temperature variable.

[0085] In one embodiment of the present application, taking an example in which a value of the ambient temperature is simultaneously in two ambient temperature intervals and a value of the charging temperature is simultaneously in two charging temperature intervals, the two first fuzzy subsets corresponding to the two ambient temperature intervals are used as two target first fuzzy subsets, and the two second fuzzy subsets corresponding to the two charging temperature intervals are used as two target second fuzzy subsets; the two target first fuzzy subsets and the two target second fuzzy subsets can form four combinations, i.e., four target temperature fuzzy subsets, and based on the fuzzy relationship matrix, the third fuzzy subset corresponding to each target temperature fuzzy subset can be determined, so as to determine the third membership of the third fuzzy subset corresponding to each target temperature fuzzy subset according to the first membership and the second membership corresponding to the value of the ambient temperature and the value of the charging temperature in each target temperature fuzzy subset.

[0086] See also Figures 6 to 11 , Figure 6 The horizontal axis represents the value of the ambient temperature, and the vertical axis represents the first membership degree. Figure 7The horizontal axis in represents the value of charging temperature, and the vertical axis represents the second membership degree. Figures 9 to 11 The horizontal axis in represents the value of the power variable, and the vertical axis represents the third membership degree. Figures 6 to 8 As shown, taking the ambient temperature value of -5 degrees Celsius (℃) and the charging temperature value of 20 degrees Celsius as an example, when the ambient temperature is -5 degrees Celsius, its first membership in the first fuzzy subset B representing high ambient temperature is 0.7, and its first membership in the first fuzzy subset S representing low ambient temperature is 0.3; when the charging temperature is 20 degrees Celsius, its second membership in the second fuzzy subset H representing high charging temperature is 0.6, and its second membership in the second fuzzy subset L representing low charging temperature is 0.4; at this time, there is Figure 8 The four combinations of the first fuzzy subsets and the second fuzzy subsets are matched with a fuzzy rule according to the preset fuzzy rule table, and each combination corresponds to four third fuzzy subsets of PEH, PH, PMH and PL. Fig. 9 Shown is a combination of a first fuzzy subset B with a first membership of 0.7 and a second fuzzy subset H with a second membership of 0.6, and its corresponding third fuzzy subset PEH; Fig.10 The third membership degree corresponding to the three third fuzzy subsets of PEH, PH and PMH is shown in Fig.10 For example, the union result of the third membership degree corresponding to the three third fuzzy subsets is as follows Fig.11 As shown, the largest third membership degree 0.6 is selected and clarified to obtain two values ​​of the power variable corresponding to the third membership degree 0.6, 38 watts and 47 watts, and the average of the two values ​​is taken as the corresponding power variable value when the ambient temperature is -5 and the charging temperature is 20.

[0087] In one embodiment of the present application, the current ambient temperature value and the current charging temperature value of the wireless charger are collected; a fuzzy inference function is queried according to the current ambient temperature value and the current charging temperature value to determine the target power value of the wireless charger.

[0088] See also Fig.12 , is a schematic diagram of a fuzzy inference function shown in an exemplary embodiment of the present application. The fuzzy inference function can be expressed as follows Fig.12 The surface shown can also be expressed in other output forms, Fig.12 T_ambient in the equation represents the ambient temperature, T_charging_plate represents the charging temperature, and P_wireless represents the power variable. Fig.12As shown in the figure, the value of the power variable corresponding to any value of the ambient temperature and any value of the charging temperature can be intuitively understood based on the fuzzy surface. In this way, the charging power is dynamically adjusted according to the real-time changes of the ambient temperature and charging temperature of the wireless charger, so that the wireless charging can achieve the best charging efficiency under different temperature conditions, improve the efficiency and stability of wireless charging, avoid the safety hazards caused by overheating or overcharging in high temperature environments, and improve the safety of wireless charging.

[0089] See also Fig.13 , is a schematic diagram of adjusting power based on fuzzy control, shown as an exemplary embodiment of the present application. Fig.13 As shown, the power control method of wireless charging of the present application is implemented based on fuzzy control. Simply put, the precise values ​​of the ambient temperature (T_ambient) and the charging temperature (T_charging_plate) are used as the input of the fuzzy controller to fuzzify them, and the fuzzified ambient temperature (AC) and the charging temperature (TC) are obtained, that is, the first membership corresponding to the ambient temperature and the second membership corresponding to the charging temperature, and then combined with the fuzzy matching rules, that is, the preset fuzzy rules, the fuzzified power variable (PW) corresponding to the fuzzified ambient temperature and the charging temperature is obtained, that is, the third membership, and finally the fuzzified power variable is defuzzified to obtain the precise value of the power variable (P_wireless).

[0090] See also Fig.14 , is a schematic diagram of a hardware structure of power control shown in an exemplary embodiment of the present application. Fig.14 The hardware structure shown can be applied to the power control method of wireless charging. The temperature sensor used to collect the ambient temperature in the vehicle is connected to the AC controller (air conditioning and refrigeration controller) in the vehicle. The AC controller uploads the current ambient temperature to the BCM controller (body controller) in the vehicle. The WCM in the vehicle uploads the current charging temperature collected by its built-in temperature sensor to the BCM controller. The BCM controller adjusts the power variable of the wireless charger based on the received current ambient temperature and current charging temperature, and can send the power change to the vehicle's IVI (in-vehicle infotainment system). The in-vehicle infotainment system can intuitively display the power change in the form of icons. Among them, the temperature sensor and WC, AC controller and ambient temperature sensor in this hardware structure can be connected through the DI line (digital input line), and other hardware can be connected based on the CAN (controller area network) bus.

[0091] in addition, Fig.14 The hardware structure in the figure is only an example and can be adjusted according to the actual situation.

[0092] See also Fig.15 , is a block diagram of a wireless charging power control device shown in an exemplary embodiment of the present application. The device can be applied to Figure 1 It should be understood that the present application does not limit the implementation environment of the device. Fig.15 As shown, in an exemplary embodiment, the power control device for wireless charging includes at least an acquisition module 1510, a division module 1520, a fuzzification module 1530, a construction module 1540 and a control module 1560, which are described in detail as follows:

[0093] An acquisition module 1510 is used to acquire the value ranges of the temperature variable and the power variable of the wireless charger in the vehicle, wherein the temperature variable includes the ambient temperature and the charging temperature;

[0094] A division module 1520 is used to divide the value ranges of different variable types into intervals, and determine multiple ambient temperature intervals, multiple charging temperature intervals, and multiple power intervals;

[0095] A fuzzification module 1530 is used to perform fuzzification processing on each ambient temperature interval, each charging temperature interval and each power interval to obtain a first fuzzy set, a second fuzzy set and a third fuzzy set;

[0096] A construction module 1540 is used to construct a fuzzy relationship matrix between temperature variables and power variables according to the first fuzzy set, the second fuzzy set and the third fuzzy set;

[0097] The control module 1550 is used to construct a fuzzy inference function based on the fuzzy relationship matrix to control the power variable of the wireless charger according to the fuzzy inference function, and the fuzzy inference function is used to determine the mapping relationship between the temperature variable and the power variable.

[0098] It should be noted that the wireless charging power control device provided in the above embodiment and the wireless charging power control method provided in the above embodiment belong to the same concept, wherein the contents of the operations performed by each module have been described in detail in the method embodiment and will not be repeated here.

[0099] The present application also provides an electronic device, comprising: a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement a power control method for wireless charging as in the above-mentioned embodiment.

[0100] See also Fig.16 , shows a schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application. It should be noted that, Fig.16The computer system 1600 of the electronic device shown is only an example and should not bring any limitation to the functions and application scope of the embodiments of the present application.

[0101] like Fig.16 As shown, the computer system 1600 includes a central processing unit (CPU) 1601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1602 or the program loaded from the storage part 1607 to the random access memory (RAM) 1603, such as executing the method in the above embodiment. In RAM 1603, various programs and data required for system operation are also stored. CPU 1601, ROM 1602 and RAM 1603 are connected to each other through bus 1604. Input / output (I / O) interface 1605 is also connected to bus 1604.

[0102] The following components are connected to the I / O interface 1605: an input section 1606 including a keyboard, a mouse, etc.; an output section 1607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1608 including a hard disk, etc.; and a communication section 1604 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1609 performs communication processing via a network such as the Internet. A drive 1610 is also connected to the I / O interface 1609 as needed. A removable medium 1611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1610 as needed so that a computer program read therefrom is installed into the storage section 1608 as needed.

[0103] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1609, and / or installed from the removable medium 1611. When the computer program is executed by the central processing unit (CPU) 1601, various functions defined in the system of the present application are executed. It should be noted that the power control method of wireless charging and the power control device of wireless charging have been described in detail in the above-mentioned method embodiments, and will not be repeated here.

[0104] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor of a computer, the computer executes the wireless charging power control method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0105] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, system or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a 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, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, system or device. A computer program contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0106] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing 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 boxes represented in succession 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 or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0107] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0108] The above embodiments are merely illustrative of the principles and effects of the present application, and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A wireless charging power control method, characterized in that: include: Obtaining respective value ranges of a temperature variable and a power variable of a wireless charger in a vehicle, wherein the temperature variable includes an ambient temperature and a charging temperature; Dividing the value ranges of different variable types into intervals respectively to determine a plurality of ambient temperature intervals, a plurality of charging temperature intervals, and a plurality of power intervals; Performing fuzzy processing on each of the ambient temperature intervals, each of the charging temperature intervals, and each of the power intervals to obtain a first fuzzy set, a second fuzzy set, and a third fuzzy set; constructing a fuzzy relationship matrix between the temperature variable and the power variable according to the first fuzzy set, the second fuzzy set and the third fuzzy set; A fuzzy inference function is constructed based on the fuzzy relationship matrix to control the power variable of the wireless charger according to the fuzzy inference function, wherein the fuzzy inference function is used to determine a mapping relationship between the temperature variable and the power variable.

2. The wireless charging power control method according to claim 1, characterized in that: The constructing of a fuzzy relationship matrix between the temperature variable and the power variable includes: determining a first membership distribution of the ambient temperature in the first fuzzy set, a second membership distribution of the charging temperature in the second fuzzy set, and a third membership distribution of the power variable in the third fuzzy set; Fuzzy reasoning is performed according to the first membership distribution, the second membership distribution and a preset fuzzy rule table to determine a mapping relationship among the first membership distribution, the second membership distribution and the third membership distribution, and use the fuzzy relationship matrix as the fuzzy relationship matrix, wherein the preset fuzzy rule table includes a one-to-one correspondence between fuzzy subsets in the first fuzzy set, the second fuzzy set and the third fuzzy set.

3. The wireless charging power control method according to claim 2, characterized in that: The constructing of the fuzzy relationship matrix between the temperature variable and the power variable also includes: Determining a first membership degree of the ambient temperature in each first fuzzy subset, a second membership degree of the charging temperature in each second fuzzy subset, and a third membership degree of the power variable in each third fuzzy subset, wherein the first fuzzy subset belongs to the first fuzzy set and corresponds one-to-one to the ambient temperature interval, the second fuzzy subset belongs to the second fuzzy set and corresponds one-to-one to the charging temperature interval, and the third fuzzy subset belongs to the third fuzzy set and corresponds one-to-one to the power interval; Combining each of the first fuzzy subsets and each of the second fuzzy subsets to obtain temperature fuzzy subsets of different combinations, each of the temperature fuzzy subsets including one of the first fuzzy subsets and one of the second fuzzy subsets; Fuzzy reasoning is performed according to the first membership and the second membership corresponding to each group of the temperature fuzzy subsets and the preset fuzzy rule table to determine the fuzzy relationship between the first membership, the second membership corresponding to each group of the temperature fuzzy subsets and the third membership corresponding to the target fuzzy subset, and use it as the fuzzy relationship matrix, and the target fuzzy subset is the third fuzzy subset corresponding one-to-one to the temperature fuzzy subsets.

4. The wireless charging power control method according to claim 3, characterized in that: The determining of the first membership of the ambient temperature in each of the first fuzzy subsets, the second membership of the charging temperature in each of the second fuzzy subsets, and the third membership of the power variable in each of the third fuzzy subsets comprises: Respectively define the membership functions of each of the first fuzzy subsets, each of the second fuzzy subsets, and each of the third fuzzy subsets; Based on the membership function, different values ​​of the ambient temperature are respectively mapped to the first fuzzy subsets corresponding to the same ambient temperature interval, and the first membership degree of each value of the ambient temperature corresponding to the first fuzzy subset in the same ambient temperature interval is determined to obtain the first membership degree of the ambient temperature in each first fuzzy subset; Based on the membership function, different values ​​of the charging temperature are respectively mapped to the second fuzzy subsets corresponding to the same charging temperature interval, and the second membership degree of each value of the charging temperature corresponding to the second fuzzy subset in the same charging temperature interval is determined to obtain the second membership degree of the charging temperature in each second fuzzy subset; Based on the membership function, different values ​​of the power variable are respectively mapped to the third fuzzy subsets corresponding to the same power interval, and the third membership of each value of the power variable in the third fuzzy subset corresponding to the same power interval is determined to obtain the third membership of the power variable in each third fuzzy subset.

5. The wireless charging power control method according to claim 3, characterized in that: The step of constructing a fuzzy inference function based on the fuzzy relationship matrix comprises: Calculating the first membership degree and the second membership degree corresponding to the temperature variable with different values ​​based on the fuzzy relationship matrix, and determining the third membership degree of the power variable corresponding to each value of the temperature variable; Performing clarification processing according to each of the third membership degrees to obtain power variable values ​​corresponding to the temperature variables with different values; The fuzzy inference function is constructed according to the power variable values ​​corresponding to the temperature variables with different values.

6. The wireless charging power control method according to claim 5, characterized in that: The clarifying process according to the third membership degree to obtain power variable values ​​corresponding to the temperature variables with different values ​​includes: According to the temperature interval corresponding to any value of the temperature variable, at least one temperature fuzzy subset is matched to determine as the target temperature fuzzy subset, wherein the temperature interval includes the ambient temperature interval and the charging temperature interval; Calculating the first degree of membership and the second degree of membership corresponding to each of the target temperature fuzzy subsets based on the fuzzy relationship matrix to obtain the third degree of membership of each of the target temperature fuzzy subsets corresponding to the third fuzzy subset; A clarification process is performed according to the maximum third membership degree to obtain a value of the power variable corresponding to the maximum third membership degree, which is used as the power variable value corresponding to any value of the temperature variable.

7. The wireless charging power control method according to any one of claims 1 to 6, characterized in that: The controlling the power variable of the wireless charger according to the fuzzy inference function comprises: Collecting the current ambient temperature value and the current charging temperature value of the wireless charger; The fuzzy inference function is queried according to the current ambient temperature value and the current charging temperature value to determine the target power value of the wireless charger.

8. A power control device for wireless charging, characterized in that: include: An acquisition module, used to acquire respective value ranges of a temperature variable and a power variable of a wireless charger in a vehicle, wherein the temperature variable includes an ambient temperature and a charging temperature; A division module, used to divide the value ranges of different variable types into intervals, and determine multiple ambient temperature intervals, multiple charging temperature intervals, and multiple power intervals; a fuzzification module, configured to perform fuzzification processing on each of the ambient temperature intervals, each of the charging temperature intervals, and each of the power intervals, respectively, to obtain a first fuzzy set, a second fuzzy set, and a third fuzzy set; A construction module, configured to construct a fuzzy relationship matrix between the temperature variable and the power variable according to the first fuzzy set, the second fuzzy set and the third fuzzy set; A control module is used to construct a fuzzy inference function based on the fuzzy relationship matrix to control the power variable of the wireless charger according to the fuzzy inference function, wherein the fuzzy inference function is used to determine a mapping relationship between the temperature variable and the power variable.

9. An electronic device, characterized in that: include: processor, memory, and communications bus; The communication bus is used to connect the processor and the memory; The processor is configured to execute the computer program stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is used to make a computer execute the method according to any one of claims 1 to 7.

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