A fan heat dissipation control method, system and related equipment based on fuzzy control

Abstract

The present invention discloses a fan heat dissipation control method, system and related equipment based on fuzzy control, which belongs to the field of power electronics technology. The method includes: obtaining the real-time working data of the charging module and the preset maximum speed of the fan, calculating the module temperature and the vehicle charging current based on the working data as input variables; setting the fan speed adjustment rate based on the maximum speed as the output variable; selecting appropriate fuzzy variables to describe the module temperature, vehicle charging current and fan speed adjustment rate respectively; defining the membership of each fuzzy variable; and establishing fuzzy control rules based on the membership; performing fuzzy reasoning based on the fuzzy control rules to obtain the fuzzy output value of the fan speed adjustment rate; using the weighted average method to defuzzify the fuzzy output value of the fan speed adjustment rate to obtain the real value of the fan speed adjustment rate, and controlling the fan speed based on the real value.

Description

Technical Field

[0001] The invention belongs to the technical field of power electronics and relates to a fan heat dissipation control method and system based on fuzzy control and related equipment. Background Art

[0002] With the vigorous development of the new energy vehicle industry and the continuous improvement of the popularity of electric vehicles, the charging pile industry has ushered in unprecedented development opportunities and challenges. As the key infrastructure for electric vehicle energy replenishment, the performance, efficiency and intelligence level of charging piles are directly related to the user's charging experience and the operational efficiency of the energy replenishment network. In this context, intelligent transformation has become an irreversible trend in the charging pile industry. With its high efficiency, convenience and safety, smart charging piles are gradually becoming the mainstream products in the future charging pile market. In the technological innovation of charging piles, the optimal design of the heat dissipation system is one of the key factors to ensure the stable operation of the equipment and extend its service life. At present, the heat dissipation methods of charging piles are mainly divided into two categories: fan heat dissipation and liquid cooling. Fan heat dissipation has a dominant position in the market with its simple structure and low maintenance cost. However, this heat dissipation method also faces significant challenges, especially in noise control. When the fan runs at full speed, the noise generated is often large, which is easy to interfere with the living environment of surrounding residents and affect the normal rest and quality of life of residents. Although the fan noise can be effectively reduced when it is not running at full speed, how to achieve precise control of the fan speed while ensuring the heat dissipation effect to minimize the impact of noise has become an urgent problem to be solved.

[0003] Further observation of the working characteristics of the charging piles revealed that when the charging vehicle enters the late charging stage, as the battery power approaches saturation, the charging current will significantly decrease, and the corresponding temperature rise in the pile will be relatively limited. This means that at this stage, the demand for heat dissipation capacity of the charging pile is actually reduced, and the traditional constant high-speed fan operation strategy is too radical, which not only causes unnecessary energy consumption, but also may aggravate the noise problem. Summary of the invention

[0004] The purpose of the present invention is to solve the technical problem that the demand for heat dissipation capacity of charging piles in the later stage of charging in the prior art is actually reduced, and the traditional constant high-speed fan operation strategy is too radical, which not only causes unnecessary energy consumption but also may aggravate the noise problem. A fan heat dissipation control method, system and related equipment based on fuzzy control are provided.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention discloses a fan heat dissipation control method based on fuzzy control, comprising:

[0007] Acquire the real-time working data of the charging module and the preset maximum speed of the fan, calculate the module temperature and the vehicle charging current as input variables based on the working data; set the fan speed adjustment rate based on the maximum speed as the output variable;

[0008] Select appropriate fuzzy variables to describe the module temperature, vehicle charging current and fan speed adjustment rate respectively;

[0009] Defining the membership degree of each of the fuzzy variables; and establishing fuzzy control rules based on the membership degree;

[0010] Perform fuzzy reasoning based on the fuzzy control rule to obtain a fuzzy output value of the fan speed adjustment rate;

[0011] The fuzzy output value of the fan speed adjustment rate is defuzzified by using a weighted average method to obtain a real value of the fan speed adjustment rate, and the fan speed is controlled based on the real value.

[0012] Further improvements are:

[0013] The module temperature and vehicle charging current are calculated based on the working data as input variables. Specifically:

[0014]

[0015] Where FI represents the module temperature; SP represents the vehicle charging current; f(i) represents the module outlet temperature value collected by the sensor at the current i-th moment; f max Indicates the maximum temperature value that the current module outlet can accept; f min represents the lowest temperature value that the current module outlet can accept; S(i) represents the current vehicle charging current value at the i-th moment; S max Indicates the maximum current value of the current vehicle charging current; S min Indicates the minimum current value of the current vehicle charging current.

[0016] The module temperature is described by selecting fuzzy variables. Specifically:

[0017] The module temperature is described by fuzzy variables as {small, medium, large}, and is represented by characters as {IS, IM, IB}, with a value range of [0, 1]; the feedback value of the module temperature "small" is -20~15℃, and the character representation value range is [0, 0.3]. The value range increases by 3 / 350 for every 1℃ increase in the feedback value temperature; the feedback value of the module temperature "medium" is 16~35℃, and the character representation value range is [0.3, 0.7], and the value range increases by 1 / 5 for every 1℃ increase in the feedback value temperature; the feedback value of the module temperature "large" is 36~55℃, and the character representation value range is [0.7, 1.0], and the value range increases by 1 / 5 for every 1℃ increase in the feedback value temperature;

[0018] The fuzzy variables selected to describe the vehicle charging current are specifically:

[0019] The vehicle charging current is described by fuzzy variables as {small, medium, large}, represented by characters as {OS, OM, OB}, and the value range is [0, 1]; the feedback value of the charging current "small" is 15~60A, the character representation value range is [0, 0.3], and the feedback value charging current increases by 3 / 460 for every 1A increase; the feedback value of the charging current "medium" is 61~130℃, the character representation value range is [0.3, 0.7], and the charging current increases by 1A, and the value range increases by 1 / 175; the feedback value of the charging current "large" is 131~250A, the character representation value range is [0.7, 1.0], and the charging current increases by 1A, and the value range increases by 1 / 400;

[0020] The fuzzy variables selected for the fan speed adjustment rate are described as follows:

[0021] The fan speed adjustment rate is described by fuzzy variables as {small, smaller, medium, larger, large}, and represented by characters as {S, MS, M, ML, L}, with a value range of [0.05, 0.99]; the value range of the fan speed adjustment rate "small" is [0.05, 0.25]; the value range of the fan speed adjustment rate "small" is [0.41, 0.55]; the value range of the fan speed adjustment rate "medium" is [0.56, 0.70]; the value range of the fan speed adjustment rate "large" is [0.71, 0.85]; the value range of the fan speed adjustment rate "large" is [0.86, 1.00].

[0022] The membership degree of each fuzzy variable is specifically defined as:

[0023] The fuzzy membership functions of module temperature, vehicle charging current and fan speed adjustment rate are all symmetrical triangular membership functions, and the membership function formula is:

[0024]

[0025] Among them, a, b, and c are the three parameters of the triangular membership function respectively; a, b, and c are the three vertices of the defined triangle, b is the center point of the triangle, a is the left vertex of the triangle, c is the right vertex of the triangle, the distance between a and c determines the width of the triangle, and the position of b determines the height of the triangle; the area center function is selected by defuzzification.

[0026] Fuzzy control rules are established based on the membership degree. Specifically:

[0027] The fuzzy control rule of fan speed adjustment rate is:

[0028] if(FIis IS)and(SP is OS),then(PC is S);

[0029] if(FI is IM)and(SP is OS),then(PC is MS);

[0030] if(FI is IB)and(SP is OS),then(PC is ML);

[0031] if(FI is IS)and(SP is OM),then(PC is M);

[0032] if(FI is IM)and(SP is OM),then(PC is M);

[0033] if(FI is IB)and(SP is OM),then(PC is L);

[0034] if(FI is IS)and(SP is OB),then(PC is M);

[0035] if(FI is IM)and(SP is OB),then(PC is L);

[0036] if(FI is IB)and(SP is OB),then(PC is L).

[0037] Based on the fuzzy control rules, fuzzy reasoning is performed to obtain the fuzzy output value of the fan speed adjustment rate. Specifically:

[0038] Based on "IF FI is Aand SP is B, THEN Pc is C",

[0039] Where A = [μ A (S)μ A (M)μ A(B)],

[0040] B=[μ B (IS)μ B (MS)μ B (FS)],

[0041] C=[μ C (S)μ C (MS)μ C (M)μ C (ML)μ C (L)];

[0042] Use Mamdani operation to find the fuzzy implication relation RC:

[0043] R C =(AandB)→C (4)

[0044] The intersection operation is used to calculate A and B. The operation process is as follows:

[0045]

[0046] In the formula, is a composition operator, AT is the transposed matrix of matrix A, and the result of the intersection operation of A and B is recorded as:

[0047]

[0048] Write A and B in vector form:

[0049]

[0050] In the formula, R C is a matrix with 9 rows and 5 columns, for The transposed matrix of

[0051] Assuming FI is A′ and SP is B′, then according to fuzzy reasoning:

[0052]

[0053] The weighted average method is used to defuzzify the fuzzy output value of the fan speed adjustment rate. Specifically:

[0054] The weighted average method is used to defuzzify the output variables. The weighted average method converts the fuzzy output into a clear output by taking the weighted average of the fuzzy output and the corresponding weight. The calculation formula is:

[0055]

[0056] In the formula, P′C A real value representing the speed adjustment rate of the cooling fan; μ′ c (z i ) represents the cooling fan speed adjustment rate P C The fuzzy value of is also understood as the reflection value of the triangular fuzzy membership function; Zi represents the weight value. Different factors and rules are assigned different weights to oppose their relative importance, so as to better reflect the actual meaning and value of the fuzzy output.

[0057] In a second aspect, the present invention discloses a fan heat dissipation control system based on fuzzy control, comprising:

[0058] An input and output variable determination module is used to obtain the real-time working data of the charging module and the preset maximum speed of the fan, calculate the module temperature and the vehicle charging current as input variables based on the working data; and set the fan speed adjustment rate based on the maximum speed as the output variable;

[0059] A fuzzy variable description module, used to select appropriate fuzzy variables to describe the module temperature, vehicle charging current and fan speed adjustment rate respectively;

[0060] A fuzzy control rule establishment module, used for defining the membership degree of each of the fuzzy variables; and establishing fuzzy control rules based on the membership degree;

[0061] A fuzzy reasoning module, used for performing fuzzy reasoning based on the fuzzy control rule to obtain a fuzzy output value of the fan speed adjustment rate;

[0062] The defuzzification module is used to defuzzify the fuzzy output value of the fan speed adjustment rate by using a weighted average method to obtain a real value of the fan speed adjustment rate, and control the fan speed based on the real value.

[0063] In a third aspect, the present invention discloses 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 above-mentioned fuzzy control-based fan heat dissipation control method when executing the computer program.

[0064] In a fourth aspect, the present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned fan heat dissipation control method based on fuzzy control are implemented.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The invention discloses a fan heat dissipation control method based on fuzzy control. The method obtains the working data of the charging module and the preset maximum speed of the fan in real time, and calculates the module temperature and the vehicle charging current as input variables based on these data, thereby intelligently adjusting the fan speed. When the charging vehicle enters the late charging stage, when the charging current is significantly reduced and the temperature rise in the pile is limited, the method can automatically reduce the fan speed, avoiding unnecessary energy consumption under the traditional constant high speed operation strategy, thereby realizing the effective utilization of energy. By intelligently adjusting the fan speed, the method can significantly reduce the noise generated by the fan operation while ensuring the heat dissipation demand of the charging pile. Especially in the late charging stage, when the fan speed is reduced according to actual needs, the noise level will also decrease accordingly, providing a quieter living environment for surrounding residents and improving the charging experience of users. Fans that run at a constant high speed for a long time are prone to shortening their service life due to excessive wear. The method reduces excessive use of the fan by intelligently adjusting the fan speed, prolongs the service life of the equipment, and thus reduces maintenance costs. The method adopts a fuzzy control strategy, and can flexibly adjust the fan speed according to the actual working condition of the charging module and the preset maximum speed of the fan. This strategy is not only applicable to different types of charging piles and charging vehicles, but also can adapt to different environmental conditions, improving the flexibility and adaptability of the system. In summary, this method can maintain the stability of the internal temperature of the charging pile and minimize energy waste and noise pollution through precise fan speed management, thereby improving the user experience and promoting the sustainable development of the charging pile industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0068] Figure 1 It is a flow chart of a fan heat dissipation control method based on fuzzy control in the present invention;

[0069] Figure 2 A logic diagram of a fan heat dissipation control method based on fuzzy control in the present invention;

[0070] Figure 3 It is a module diagram of a fan heat dissipation control system based on fuzzy control in the present invention;

[0071] Figure 4 This is a module diagram of an electronic device in the present invention. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0073] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0075] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0076] See also Figure 1 The embodiment of the present invention discloses a fan heat dissipation control method based on fuzzy control, comprising:

[0077] S1, obtaining the real-time working data of the charging module and the preset maximum speed of the fan, calculating the module temperature and the vehicle charging current based on the working data as input variables; setting the fan speed adjustment rate based on the maximum speed as the output variable;

[0078] S2, selecting appropriate fuzzy variables to describe the module temperature, vehicle charging current and fan speed adjustment rate respectively;

[0079] S3, defining the membership degree of each of the fuzzy variables; and establishing a fuzzy control rule based on the membership degree;

[0080] S4, performing fuzzy reasoning based on the fuzzy control rule to obtain a fuzzy output value of the fan speed adjustment rate;

[0081] S5, using a weighted average method to defuzzify the fuzzy output value of the fan speed adjustment rate to obtain a real value of the fan speed adjustment rate, and controlling the fan speed based on the real value.

[0082] The invention discloses a fan heat dissipation control method based on fuzzy control. The method obtains the working data of the charging module and the preset maximum speed of the fan in real time, and calculates the module temperature and the vehicle charging current as input variables based on these data, thereby intelligently adjusting the fan speed. When the charging vehicle enters the late charging stage, when the charging current is significantly reduced and the temperature rise in the pile is limited, the method can automatically reduce the fan speed, avoiding unnecessary energy consumption under the traditional constant high speed operation strategy, thereby realizing the effective utilization of energy. By intelligently adjusting the fan speed, the method can significantly reduce the noise generated by the fan operation while ensuring the heat dissipation demand of the charging pile. Especially in the late charging stage, when the fan speed is reduced according to actual needs, the noise level will also decrease accordingly, providing a quieter living environment for surrounding residents and improving the charging experience of users. Fans that run at a constant high speed for a long time are prone to shortening their service life due to excessive wear. The method reduces excessive use of the fan by intelligently adjusting the fan speed, prolongs the service life of the equipment, and thus reduces maintenance costs. The method adopts a fuzzy control strategy, and can flexibly adjust the fan speed according to the actual working condition of the charging module and the preset maximum speed of the fan. This strategy is not only applicable to different types of charging piles and charging vehicles, but also can adapt to different environmental conditions, improving the flexibility and adaptability of the system. In summary, this method can maintain the stability of the internal temperature of the charging pile and minimize energy waste and noise pollution through precise fan speed management, thereby improving the user experience and promoting the sustainable development of the charging pile industry.

[0083] See also Figure 2 The following is a detailed description of the present invention in conjunction with specific embodiments:

[0084] This solution mainly uses a fuzzy control algorithm, combined with the real-time acquisition of module temperature by sensors, to adjust the system fan in real time, so that the entire pile is in a suitable working range, ensuring that the charging pile always operates efficiently and stably during the entire operation process. It includes the following steps.

[0085] Step 1: Determine input variables, output variables and controller structure

[0086] The fuzzy algorithm selects the module temperature FI and the vehicle charging current SP as input variables, and the cooling fan speed adjustment rate P c As the output variable (Note: the cooling fan speed adjustment rate is calculated based on the maximum speed value. For example, in this application, the maximum fan speed is set to 12,000 rpm. When the adjustment rate is 50%, the output fan speed is 6,000 rpm). The calculation formula for module temperature FI and charging current SP is:

[0087]

[0088] Where f(i) is the module outlet temperature value collected by the sensor at the current i-th moment, f max is the maximum temperature value that the current module outlet can accept (the maximum temperature value is set to 55°C in this application), f min is the lowest temperature value that the current module outlet can accept (the lowest temperature value is set to -20℃ in this program). S(i) is the current vehicle charging current value at the i-th moment, S max is the maximum current value of the current vehicle charging current (the maximum current value is set to 250A in this application), S min The minimum current value of the current vehicle charging current (the minimum current value is set to 15A in this application).

[0089] Step 2: Select language variables to describe input and output

[0090] Use appropriate fuzzy variables to describe the input and output variables of the fuzzy controller.

[0091] The module temperature FI can be described in language as {small, medium, large}, and represented by characters as {IS, IM, IB}, with a value range of [0, 1]. The feedback value of the module temperature "small" is -20-15℃, and the character represents the value range of [0, 0.3]. The value range increases by 3 / 350 for every 1℃ increase in the feedback value temperature; the feedback value of the module temperature "medium" is 16-35℃, and the character represents the value range of [0.3, 0.7[, and the value range increases by 1 / 5 for every 1℃ increase in the feedback value temperature; the feedback value of the module temperature "large" is 36-55℃, and the character represents the value range of [0.7, 1.0], and the value range increases by 1 / 5 for every 1℃ increase in the feedback value temperature.

[0092] The vehicle charging current SP can be described in words as {small, medium, large}, and represented by characters as {OS, OM, OB}, with a value range of [0, 1]. The feedback value of the charging current "small" is 15-60A, and the character representation value range is [0, 0.3]. The feedback value range increases by 3 / 460 for every 1A increase in the charging current; the feedback value of the charging current "medium" is 61-130℃, and the character representation value range is [0.3, 0.7]. The value range increases by 1 / 175 for every 1A increase in the charging current; the feedback value of the charging current "large" is 131-250A, and the character representation value range is [0.7, 1.0]. The value range increases by 1 / 400 for every 1A increase in the charging current.

[0093] Cooling fan speed adjustment rate P c It can be described in words as {small, smaller, medium, larger, large}, and expressed in characters as {S,MS,M,ML,L}, P cThe value range of the cooling fan speed adjustment rate is [0.05, 0.99]. The value range of the cooling fan speed adjustment rate "small" is [0.05, 0.25]. c The setting value of "small" is 0.17; the value range of the cooling fan speed adjustment rate "small" is [0.41,0.55]. c The setting value of "smaller" is 0.42; the value range of the cooling fan speed adjustment rate "medium" is [0.56, 0.70]. c The setting value of "medium" is 0.67; the value range of the cooling fan speed adjustment rate "large" is [0.71,0.85]. c The setting value of "large" is 0.84; the value range of the cooling fan speed adjustment rate "large" is [0.86,1.00]. c The setting value of "large" is 1.00. Cooling fan speed adjustment rate P C Described in character language is [0.17, 0.42, 0.67, 0.84, 1.00].

[0094] Step 3: Determine the membership degree of fuzzy variables

[0095] The fuzzy membership functions of all variables are symmetrical triangular membership functions, and the membership function formula is:

[0096]

[0097] Where: a, b, c are the three parameters of the triangular membership function. a, b, c are the three vertices of the defined triangle, b is the center point of the triangle, a is the left vertex of the triangle, c is the right vertex of the triangle, the distance between a and c determines the width of the triangle, and the position of b determines the height of the triangle. Defuzzification selects the area center function.

[0098] Step 4: Establish fuzzy control rules

[0099] Cooling fan speed adjustment rate P c The control rules are shown in Table 1.

[0100] Table 1P c and P m Control rules

[0101]

[0102] That is, if(FI is IS)and(SP is OS),then(PC is S);

[0103] if(FI is IM)and(SP is OS),then(PC is MS);

[0104] if(FI is IB)and(SP is OS),then(PC is ML);

[0105] if(FI is IS)and(SP is OM),then(PC is M);

[0106] if(FI is IM)and(SP is OM),then(PC is M);

[0107] if(FI is IB)and(SP is OM),then(PC is L);

[0108] if(FI is IS)and(SP is OB),then(PC is M);

[0109] if(FI is IM)and(SP is OB),then(PC is L);

[0110] if(FI is IB)and(SP is OB),then(PC is L);

[0111] Step 5: Fuzzy Reasoning

[0112] According to Table 1, "IF FI is A and SP is B, THEN Pc is C",

[0113] Where A = [μ A (S)μ A (M)μ A (B)],

[0114] B=[μ B (IS)μ B (MS)μ B (FS)],

[0115] C=[μ C (S)μ C (MS)μ C (M)μ C (ML)μ C (L)].

[0116] Use Mamdani operation to find the fuzzy implication relation RC:

[0117] R C =(AandB)→C (4)

[0118] The intersection operation is used to calculate A and B. The operation process is as follows:

[0119]

[0120] In the formula, is a composition operator, AT is the transposed matrix of matrix A, and the result of the intersection operation of A and B is recorded as:

[0121]

[0122] Write A and B in vector form

[0123]

[0124] In the formula, R C is a matrix with 9 rows and 5 columns, for The transposed matrix of .

[0125] Assuming FI is A′ and SP is B′, then according to fuzzy reasoning we get

[0126]

[0127] Step 6: Defuzzification of output

[0128] The weighted average method is used to defuzzify the output variables. The weighted average method converts the fuzzy output into a clear output by weighted averaging the fuzzy output and the corresponding weights (different factors and rules give different weights to oppose their relative importance, so as to better reflect the actual meaning and value of the fuzzy output). The relevant calculation formula is:

[0129]

[0130] In the formula, P′ C A real value representing the speed adjustment rate of the cooling fan, μ′ c (z i ) is the cooling fan speed adjustment rate P C The fuzzy value can also be understood as the triangle fuzzy membership function response value, Zi is the weight value, different factors and rules are assigned different weights to oppose their relative importance, so as to better reflect the actual meaning and value of the fuzzy output. Five weight values ​​are defined in this application, namely, charging power weight 1.1, charging time weight 0.7, ambient temperature weight 0.4, module temperature weight 1.3, and charging current weight 1.2; when μ′ c (z i ) is 0.67, substitute P' C The value obtained is 0.6298, that is, the real value of the cooling fan speed adjustment rate is 62.98%.

[0131] See also Figure 3 The embodiment of the present invention further discloses a fan heat dissipation control system based on fuzzy control, comprising:

[0132] An input and output variable determination module is used to obtain the real-time working data of the charging module and the preset maximum speed of the fan, calculate the module temperature and the vehicle charging current as input variables based on the working data; and set the fan speed adjustment rate based on the maximum speed as the output variable;

[0133] A fuzzy variable description module, used to select appropriate fuzzy variables to describe the module temperature, vehicle charging current and fan speed adjustment rate respectively;

[0134] A fuzzy control rule establishment module, used for defining the membership degree of each of the fuzzy variables; and establishing fuzzy control rules based on the membership degree;

[0135] A fuzzy reasoning module, used for performing fuzzy reasoning based on the fuzzy control rule to obtain a fuzzy output value of the fan speed adjustment rate;

[0136] The defuzzification module is used to defuzzify the fuzzy output value of the fan speed adjustment rate by using a weighted average method to obtain a real value of the fan speed adjustment rate, and control the fan speed based on the real value.

[0137] This system can accurately reflect the heat dissipation requirements by real-time monitoring of the working data of the charging module, including module temperature and vehicle charging current. Combined with the fuzzy control strategy, the dynamic adjustment of the fan speed is realized, which effectively improves the heat dissipation efficiency, ensures that the charging module operates within the optimal operating temperature range, and prolongs the service life of the equipment. Using fuzzy control technology, this system can handle complex heat dissipation control problems such as nonlinearity and time-varying without the need for precise mathematical models. By defining the membership of fuzzy variables and establishing fuzzy control rules, the system can adaptively adjust the fan speed to adapt to the heat dissipation requirements under different working conditions, improving the intelligence level and adaptability of the system. By accurately controlling the fan speed, this system avoids the energy waste caused by excessive fan operation in traditional heat dissipation methods. While ensuring the heat dissipation effect, it minimizes energy consumption, which is in line with the environmental protection concept of green energy saving. This system has a clear structure and reasonable module division, which is easy to implement and maintain. The modules communicate through standardized interfaces, which improves the scalability and maintainability of the system. At the same time, the adjustment and optimization of fuzzy control rules are relatively simple, which is convenient for customization and optimization according to actual application needs. By precisely controlling the heat dissipation process, the system can ensure the stable operation of the charging module, reduce problems such as charging interruption or device damage due to overheating, and improve user experience and satisfaction.

[0138] See also Figure 4The third object of the present invention is to provide 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 fan heat dissipation control method based on fuzzy control when executing the computer program.

[0139] The fan heat dissipation control method based on fuzzy control comprises the following steps:

[0140] Acquire the real-time working data of the charging module and the preset maximum speed of the fan, calculate the module temperature and the vehicle charging current as input variables based on the working data; set the fan speed adjustment rate based on the maximum speed as the output variable;

[0141] Select appropriate fuzzy variables to describe the module temperature, vehicle charging current and fan speed adjustment rate respectively;

[0142] Defining the membership degree of each of the fuzzy variables; and establishing fuzzy control rules based on the membership degree;

[0143] Perform fuzzy reasoning based on the fuzzy control rule to obtain a fuzzy output value of the fan speed adjustment rate;

[0144] The fuzzy output value of the fan speed adjustment rate is defuzzified by using a weighted average method to obtain a real value of the fan speed adjustment rate, and the fan speed is controlled based on the real value.

[0145] A fourth object of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the fan heat dissipation control method based on fuzzy control are implemented.

[0146] The fan heat dissipation control method based on fuzzy control comprises the following steps:

[0147] Acquire the real-time working data of the charging module and the preset maximum speed of the fan, calculate the module temperature and the vehicle charging current as input variables based on the working data; set the fan speed adjustment rate based on the maximum speed as the output variable;

[0148] Select appropriate fuzzy variables to describe the module temperature, vehicle charging current and fan speed adjustment rate respectively;

[0149] Defining the membership degree of each of the fuzzy variables; and establishing fuzzy control rules based on the membership degree;

[0150] Perform fuzzy reasoning based on the fuzzy control rule to obtain a fuzzy output value of the fan speed adjustment rate;

[0151] The fuzzy output value of the fan speed adjustment rate is defuzzified by using a weighted average method to obtain a real value of the fan speed adjustment rate, and the fan speed is controlled based on the real value.

[0152] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0154] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0156] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A fan heat dissipation control method based on fuzzy control, characterized in that: include: Obtain the real-time working data of the charging module and the preset maximum speed of the fan, and calculate the module temperature and vehicle charging current as input variables based on the working data; Setting a fan speed adjustment rate based on the maximum speed as an output variable; Select appropriate fuzzy variables to describe the module temperature, vehicle charging current and fan speed adjustment rate respectively; Defining the membership degree of each of the fuzzy variables; and establishing fuzzy control rules based on the membership degree; Perform fuzzy reasoning based on the fuzzy control rule to obtain a fuzzy output value of the fan speed adjustment rate; The fuzzy output value of the fan speed adjustment rate is defuzzified by using a weighted average method to obtain a real value of the fan speed adjustment rate, and the fan speed is controlled based on the real value.

2. The fan heat dissipation control method based on fuzzy control according to claim 1, characterized in that: The module temperature and vehicle charging current calculated based on the working data as input variables are specifically: Where FI represents the module temperature; SP represents the vehicle charging current; f(i) represents the module outlet temperature value collected by the sensor at the current i-th moment; f max Indicates the maximum temperature value that the current module outlet can accept; f min represents the lowest temperature value that the current module outlet can accept; S(i) represents the current vehicle charging current value at the i-th moment; S max Indicates the maximum current value of the current vehicle charging current; S min Indicates the minimum current value of the current vehicle charging current.

3. The fan heat dissipation control method based on fuzzy control according to claim 2, characterized in that: The fuzzy variables for selecting the module temperature are described as follows: The module temperature is described by fuzzy variables as {small, medium, large}, and is represented by characters as {IS, IM, IB}, with a value range of [0, 1]; the feedback value of the module temperature "small" is -20~15℃, and the character representation value range is [0, 0.3]. The feedback value range increases by 3 / 350 for every 1℃ increase in the feedback value temperature; the feedback value of the module temperature "medium" is 16~35℃, and the character representation value range is [0.3, 0.7], and the feedback value range increases by 1 / 5 for every 1℃ increase in the feedback value temperature; the feedback value of the module temperature "large" is 36~55℃, and the character representation value range is [0.7, 1.0], and the feedback value range increases by 1 / 5 for every 1℃ increase in the feedback value temperature; The fuzzy variables selected to describe the vehicle charging current are specifically: The vehicle charging current is described by fuzzy variables as {small, medium, large}, represented by characters as {OS, OM, OB}, and the value range is [0, 1]; the feedback value of the charging current "small" is 15~60A, the character representation value range is [0, 0.3], and the feedback value charging current increases by 3 / 460 for every 1A increase; the feedback value of the charging current "medium" is 61~130℃, the character representation value range is [0.3, 0.7], and the charging current increases by 1 / 175 for every 1A increase; the feedback value of the charging current "large" is 131~250A, the character representation value range is [0.7, 1.0], and the charging current increases by 1 / 400 for every 1A increase; The fuzzy variables selected for the fan speed adjustment rate are described as follows: The fan speed adjustment rate is described by fuzzy variables as {small, smaller, medium, larger, large}, and represented by characters as {S, MS, M, ML, L}, with a value range of [0.05, 0.99]; the value range of the fan speed adjustment rate "small" is [0.05, 0.25]; the value range of the fan speed adjustment rate "small" is [0.41, 0.55]; the value range of the fan speed adjustment rate "medium" is [0.56, 0.70]; the value range of the fan speed adjustment rate "large" is [0.71, 0.85]; the value range of the fan speed adjustment rate "large" is [0.86, 1.00].

4. The fan heat dissipation control method based on fuzzy control according to claim 3, characterized in that: The membership degree of each fuzzy variable is specifically defined as: The fuzzy membership functions of module temperature, vehicle charging current and fan speed adjustment rate are all symmetrical triangular membership functions, and the membership function formula is: Among them, a, b, and c are the three parameters of the triangular membership function respectively; a, b, and c are the three vertices of the defined triangle, b is the center point of the triangle, a is the left vertex of the triangle, c is the right vertex of the triangle, the distance between a and c determines the width of the triangle, and the position of b determines the height of the triangle; the area center function is selected by defuzzification.

5. The fan heat dissipation control method based on fuzzy control according to claim 4, characterized in that: The fuzzy control rules established based on the membership degree are as follows: The fuzzy control rule of fan speed adjustment rate is: if(FIis IS)and(SP is OS),then(PC is S); if(FI is IM)and(SP is OS),then(PC is MS); if(FI is IB)and(SP is OS),then(PC is ML); if(FI is IS)and(SP is OM),then(PC is M); if(FI is IM)and(SP is OM),then(PC is M); if(FI is IB)and(SP is OM),then(PC is L); if(FI is IS)and(SP is OB),then(PC is M); if(FI is IM)and(SP is OB),then(PC is L); if(FI is IB)and(SP is OB),then(PC is L).

6. The fan heat dissipation control method based on fuzzy control according to claim 5, characterized in that: Based on the fuzzy control rule, fuzzy reasoning is performed to obtain the fuzzy output value of the fan speed adjustment rate: Based on "IF FI is Aand SP is B, THEN Pc is C", where A = [μ A (S)μ A (M)μ A (B)], B=[μ B (IS)μ B (MS)μ B (FS)], C=[μ C (S)μ C (MS)μ C (M)μ C (ML)μ C (L)]; Use Mamdani operation to find the fuzzy implication relation RC: R C =(A and B)→C (4) The intersection operation is used to calculate A and B. The operation process is as follows: In the formula, is a composition operator, AT is the transposed matrix of matrix A, and the result of the intersection operation of A and B is recorded as: Write A and B in vector form: In the formula, R C is a matrix with 9 rows and 5 columns, for The transposed matrix of Assuming FI is A′ and SP is B′, then according to fuzzy reasoning:

7. The fan heat dissipation control method based on fuzzy control according to claim 6, characterized in that: The weighted average method is used to defuzzify the fuzzy output value of the fan speed adjustment rate as follows: The weighted average method is used to defuzzify the output variables. The weighted average method converts the fuzzy output into a clear output by taking the weighted average of the fuzzy output and the corresponding weight. The calculation formula is: In the formula, P′ C A real value representing the speed adjustment rate of the cooling fan; μ′ c (z i ) represents the cooling fan speed adjustment rate P C The fuzzy value of is also understood as the reflection value of the triangular fuzzy membership function; Zi represents the weight value. Different factors and rules are assigned different weights to oppose their relative importance, so as to better reflect the actual meaning and value of the fuzzy output.

8. A fan heat dissipation control system based on fuzzy control, characterized in that: include: An input and output variable determination module is used to obtain the real-time working data of the charging module and the preset maximum speed of the fan, calculate the module temperature and the vehicle charging current as input variables based on the working data; and set the fan speed adjustment rate based on the maximum speed as the output variable; A fuzzy variable description module, used to select appropriate fuzzy variables to describe the module temperature, vehicle charging current and fan speed adjustment rate respectively; A fuzzy control rule establishment module, used for defining the membership degree of each of the fuzzy variables; and establishing fuzzy control rules based on the membership degree; A fuzzy reasoning module, used for performing fuzzy reasoning based on the fuzzy control rule to obtain a fuzzy output value of the fan speed adjustment rate; The defuzzification module is used to defuzzify the fuzzy output value of the fan speed adjustment rate by using a weighted average method to obtain a real value of the fan speed adjustment rate, and control the fan speed based on the real value.

9. 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 fan heat dissipation control method based on fuzzy control as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the fan heat dissipation control method based on fuzzy control according to any one of claims 1 to 7.

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