High-temperature adaptive heat dissipation regulation method for ac-dc equipment based on multi-physical field coupling

Abstract

The application discloses a high-temperature self-adaptive heat dissipation regulation and control method for AC / DC equipment based on multi-physical field coupling, relates to the technical field of equipment high-temperature self-adaptive heat dissipation regulation and control, and comprises the following steps: testing the relationship between operating parameters and the temperature of different equipment components in AC / DC equipment, which is named as a temperature rise relationship; analyzing the relationship between the heat dissipation power and the heat dissipation efficiency of a heat dissipation device, which is named as a heat dissipation relationship; predicting the temperature of the equipment components based on the temperature rise relationship to obtain the predicted temperature of the equipment components; and adaptively regulating the heat dissipation power of different heat dissipation devices based on the predicted temperature of the equipment components and the heat dissipation relationship. The application is used to solve the problems that the existing equipment high-temperature self-adaptive heat dissipation regulation and control technology is too traditional in the regulation and control method of the heat dissipation power and is not accurate enough in the regulation and control of the heat dissipation power required by different components, so that the equipment components cannot obtain the best heat dissipation effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device high-temperature adaptive heat dissipation regulation, in particular to an AC / DC device high-temperature adaptive heat dissipation regulation method based on multi-physical field coupling. BACKGROUND

[0002] Device high-temperature adaptive heat dissipation regulation technology refers to a series of technical systems for intelligently regulating the heat dissipation system inside the device under high-temperature conditions, so that the heat dissipation effect is optimal, and the device is effectively cooled. It is very suitable for high-power charging piles, data center servers, and high-density power electronic converters, etc. which have very high requirements for thermal management.

[0003] The existing device high-temperature adaptive heat dissipation regulation technology usually uses threshold method to regulate the heat dissipation power, divides the heat dissipation power into different levels, and enables the corresponding heat dissipation level when the device reaches a certain temperature threshold. At the same time, the existing device high-temperature adaptive heat dissipation regulation technology only enables the corresponding heat dissipation level when the device temperature rises to a certain degree, but when the device temperature is too high, using stronger heat dissipation will cause the device to be damaged due to high temperature. In addition, the temperatures of the various components inside the device are not the same, and the existing device high-temperature adaptive heat dissipation regulation technology usually uses a unified heat dissipation level to cool all components inside the device, but some components do not need such high cooling capacity, which will cause waste of energy. For example, in the patent application with publication number CN114967787A, a "power supply regulation cabinet heat dissipation regulation system and method" is disclosed, which regulates the cooling capacity through a first temperature threshold and a second temperature threshold. The first temperature threshold and the second temperature threshold are not given, and are assumed to be set by humans, which is subjective. The existing device high-temperature adaptive heat dissipation regulation technology also has the problems of too traditional heat dissipation power regulation method and not accurate enough regulation of the heat dissipation power required by different components, resulting in that the components in the device cannot obtain the best heat dissipation effect. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the prior art by integrating operating parameters that affect the temperature of device components, obtaining different sets of temperature-influencing parameters, then constructing a temperature-influencing analysis system according to the sets of temperature-influencing parameters, analyzing the temperature-influencing analysis system to obtain an influence array, analyzing the influence array, converting the influence array into temperature characteristic points, composing a temperature relationship from the temperature characteristic points, connecting an independently controlled heat dissipation device to each device component in AC / DC equipment, analyzing the relationship between the heat dissipation power and the heat dissipation efficiency of the heat dissipation device, naming it as a heat dissipation relationship, combining real-time operating parameters of device components with the temperature-influencing analysis system to analyze real-time characteristic points of the device components, then predicting the temperature of the device components based on the temperature relationship and the real-time characteristic points to obtain a predicted temperature, and finally adaptively regulating the heat dissipation power of different heat dissipation devices based on the predicted temperature of the device components and the heat dissipation relationship, to solve the problem that the existing high-temperature adaptive heat dissipation regulation technology for equipment is too traditional in the regulation method of heat dissipation power and not accurate enough in the regulation of heat dissipation power required by different components, resulting in that each component in the equipment cannot obtain the best heat dissipation effect.

[0005] To achieve the above-mentioned purpose, the present application provides a high-temperature adaptive heat dissipation regulation method for AC / DC equipment based on multi-physical field coupling, which comprises the following steps:

[0006] Monitoring each operating parameter in the AC / DC equipment, testing the relationship between the operating parameters and the temperature of different device components in the AC / DC equipment, naming it as a temperature-influencing relationship;

[0007] Connecting an independently controlled heat dissipation device to each device component in the AC / DC equipment, analyzing the relationship between the heat dissipation power and the heat dissipation efficiency of the heat dissipation device, naming it as a heat dissipation relationship;

[0008] Predicting the temperature of the device components based on the temperature-influencing relationship to obtain a predicted temperature of the device components;

[0009] Adaptively regulating the heat dissipation power of different heat dissipation devices based on the predicted temperature of the device components and the heat dissipation relationship.

[0010] Further, monitoring each operating parameter in the AC / DC equipment, testing the relationship between the operating parameters and the temperature of different device components in the AC / DC equipment, naming it as a temperature-influencing relationship, which comprises the following sub-steps:

[0011] Integrating operating parameters that affect the temperature of device components to obtain different sets of temperature-influencing parameters;

[0012] Constructing a temperature-influencing analysis system according to the sets of temperature-influencing parameters, and analyzing the temperature-influencing analysis system to obtain an influence array;

[0013] Analyze the influence array, convert the influence array into a temperature rise feature point, and form a temperature rise relationship from the temperature rise feature point.

[0014] Further, integrate the operating parameters that affect the temperature of the device component to obtain different temperature rise influence parameter sets, including the following sub-steps:

[0015] Embed a temperature sensor in the device component, and name the monitored temperature as the component temperature;

[0016] Number the device component, and represent it by a symbol S n , where n is a positive integer and n is the serial number of S n , and the corresponding component temperature is marked as T n .

[0017] Different operating parameters are executed by different device components, and the operating parameters executed in S n are integrated into a temperature rise influence parameter set, marked as PU n .

[0018] Further, construct a temperature rise influence analysis system according to the temperature rise influence parameter set, and analyze the influence array from the temperature rise influence analysis system, including the following sub-steps:

[0019] Independently analyze different PU n , and number the operating parameters in PU n , represented by a symbol H(n,m), where m is a positive integer and (n,m) is the serial number of H;

[0020] Obtain the historical data of the operating parameters and the device temperature, and name them as historical parameters and historical temperature respectively, mark the minimum and maximum values of the operating parameters H(n,m) in the historical parameters as Min(n,m) and Max(n,m) respectively, construct a one-dimensional coordinate system for each operating parameter, and name it as a parameter independent coordinate system, the lengths of all parameter independent coordinate systems are the same and are in the form of a line segment, mark the parameter independent coordinate system corresponding to H(n,m) as CS(n,m), and the left end point on the CS(n,m) is Min(n,m) and the right end point is Max(n,m);

[0021] If the maximum m is 1, establish a two-dimensional coordinate system with H(n,1) as the X axis and the device temperature as the Y axis, name it as a temperature rise analysis graph, input the historical temperature into the temperature rise analysis graph according to the historical parameters, and then perform discrete regression analysis on the temperature rise analysis graph, and the obtained discrete function is the temperature rise relationship;

[0022] If the maximum value of m is greater than 1, the right end point of CS(n, m) is overlapped with the left end point of CS(n, m+1), and there is a certain angle between CS(n, m) and CS(n, m+1), so that all CS(n, m) are overlapped to form a regular polygon, and a temperature rise influence analysis system is obtained;

[0023] The historical parameters are substituted into the respective CS(n, m) of the temperature rise influence analysis system, the corresponding points of the historical parameters on the CS(n, m) are marked as R(n, m), and a perpendicular line of the CS(n, m) is drawn through R(n, m). Different intersection points will be formed between the perpendicular lines of each CS(n, m) and are named as influence points. An influence array is formed by all the influence points. Each historical parameter recorded at the same time in the temperature rise influence parameter set forms a historical information, and one historical information corresponds to one influence array.

[0024] Further, the influence array is analyzed and converted into a temperature rise feature point, and a temperature rise relationship is formed by the temperature rise feature point, including the following sub-steps:

[0025] The leftmost influence point in the influence array is taken as a target analysis point, the two influence points closest to the target analysis point are found and are named as target adjacent points, a triangle is formed by the target analysis point and the target adjacent points, the incenter of the triangle is obtained and is named as a secondary analysis point, and the target analysis point and the target adjacent points are excluded. The target analysis point is repeatedly searched and the secondary analysis point is analyzed until all the influence points are excluded;

[0026] The secondary analysis point is taken as a new influence point, and the above steps are repeated until only one secondary analysis point is left. The last remaining secondary analysis point is named as a temperature rise feature point. The temperature rise feature points of each historical information are analyzed, and a temperature rise relationship is formed by the temperature rise feature points. Each device component has an independent temperature rise relationship.

[0027] Further, a separately controlled heat dissipation device is connected to each device component in the AC / DC equipment, and a relationship between the heat dissipation power and the heat dissipation efficiency of the heat dissipation device is analyzed, which is named as a heat dissipation relationship, including the following sub-steps:

[0028] Obtain heat dissipation data, including heat dissipation power, initial temperature and heat dissipation temperature. The initial temperature is marked as AT, and the heat dissipation temperature is marked as FT;

[0029] Calculate (AT-FT) / AT, and the calculation result is named as heat dissipation efficiency;

[0030] A two-dimensional coordinate system is established with the heat dissipation power as the X axis and the heat dissipation efficiency as the Y axis, which is named as a heat dissipation analysis diagram. The heat dissipation efficiency is recorded in the heat dissipation analysis diagram according to the heat dissipation power;

[0031] Discrete regression analysis was performed on the heat dissipation analysis diagram, and the discrete function was named the heat dissipation relationship.

[0032] Furthermore, predicting the temperature of the equipment components based on the heating relationship, and obtaining the predicted temperature of the equipment components includes the following sub-steps:

[0033] The real-time characteristic points of the equipment components are analyzed by combining the real-time operating parameters of the equipment components with the temperature rise effect analysis system.

[0034] The predicted temperature is obtained by predicting the temperature of the equipment components based on the heating relationship and real-time feature points.

[0035] Furthermore, analyzing the real-time characteristic points of the equipment components by combining the real-time operating parameters of the equipment components with the temperature rise effect analysis system includes the following sub-steps:

[0036] Name the real-time operating parameters of the device components as real-time parameters;

[0037] Substitute the real-time parameters into the temperature rise impact analysis system corresponding to the equipment components and analyze the temperature rise characteristic points, which are named real-time characteristic points.

[0038] S n The real-time feature points are labeled as E n At the same time, S n The temperature rise relationship is denoted as W. n .

[0039] Furthermore, based on the heating relationship and real-time feature points, the temperature of the device components is predicted to obtain the predicted temperature, which includes the following sub-steps:

[0040] In W n Searching in E n The three closest heating characteristic points are labeled G1, G2 and G3 in order of distance from nearest to farthest. Connecting G2 and G3 yields the first deviation analysis auxiliary line.

[0041] Connect G1 and E n Connect and extend the line so that it intersects the first deviation analysis auxiliary line. Name the line obtained by connecting and extending the line the second deviation analysis auxiliary line. Name the intersection of the first deviation analysis auxiliary line and the second deviation analysis auxiliary line the deviation analysis auxiliary point.

[0042] E n The distances to G1 and the deviation analysis auxiliary point are marked as L1 and L2, respectively, and the distances between the deviation analysis auxiliary point and G2 and G3 are marked as L3 and L4, respectively.

[0043] Obtain the historical temperatures corresponding to G1, G2, and G3, and label them TH1, TH2, and TH3 respectively. Assume E nThe equipment temperature is assumed to be K, and the equipment temperature at the auxiliary point of the deviation analysis is assumed to be V.

[0044] Through formula Solve for V, then use the formula Solve for K; the obtained K is the predicted temperature of the equipment component. Calculate the predicted temperature of all equipment components.

[0045] Furthermore, the adaptive adjustment of the heat dissipation power of different heat dissipation devices based on the predicted temperature of the equipment components and the heat dissipation relationship includes the following sub-steps:

[0046] To obtain the optimal operating temperature for each device component, S n The optimal operating temperature is marked as CA n At the same time, S n The predicted temperature is labeled CB n ;

[0047] Calculation (CB) n -CA n ) / CB n The calculation result is named heat dissipation demand efficiency. Substituting the heat dissipation demand efficiency into the heat dissipation relationship, S is obtained. n The power required for heat dissipation by the heat dissipation device is called the heat dissipation demand power, based on different S n The power required for heat dissipation is adaptively adjusted to the heat dissipation power of different heat dissipation devices.

[0048] The beneficial effects of this invention are as follows: This invention integrates the operating parameters that affect the temperature of equipment components to obtain different sets of temperature rise influencing parameters. Then, it constructs a temperature rise influencing analysis system based on the temperature rise influencing parameter sets. The system analyzes the influencing array to obtain an influencing array, and then analyzes the influencing array to convert it into temperature rise feature points. These feature points form temperature rise relationships. The advantage is that the temperature rise feature points represent the temperature rise relationships corresponding to different equipment components. Different equipment components have different operating parameters, meaning that different operating parameters will affect the temperature of different equipment components. Moreover, a single equipment component can be affected by multiple operating parameters. By analyzing the temperature rise feature points, the temperature changes of the equipment components under the influence of multiple operating parameters can be obtained. This allows for the prediction of the equipment component temperature and the adaptive adjustment of heat dissipation power in advance. This ensures that the temperature of the equipment components will not rise and then fall, improving the accuracy and effectiveness of high-temperature adaptive heat dissipation control of the equipment.

[0049] This invention connects each component in an AC / DC equipment to an independently controlled heat dissipation device, analyzes the relationship between the heat dissipation power and efficiency of the heat dissipation device, and names it the heat dissipation relationship. By combining the real-time operating parameters of the equipment components with a temperature rise impact analysis system, the real-time characteristic points of the equipment components are analyzed. Then, based on the temperature rise relationship and the real-time characteristic points, the temperature of the equipment components is predicted to obtain the predicted temperature. Finally, based on the predicted temperature of the equipment components and the heat dissipation relationship, the heat dissipation power of different heat dissipation devices is adaptively adjusted. The advantages are that the heat dissipation is independently controlled for each equipment component, and the temperature change of the equipment can be predicted through the changing relationship, and the heat dissipation power can be adjusted in advance. Moreover, each equipment component can automatically adjust to the optimal heat dissipation efficiency according to its predicted temperature, which improves the effectiveness of high-temperature adaptive heat dissipation control of the equipment and saves heat dissipation resources. Attached Figure Description

[0050] Figure 1 This is a flowchart of the steps of the method of the present invention;

[0051] Figure 2 This is a schematic diagram of the parameter-independent coordinate system corresponding to the ambient temperature of the present invention;

[0052] Figure 3 This is a schematic diagram of the temperature rise effect analysis system of the present invention;

[0053] Figure 4 This is a schematic diagram of the points of influence in this invention;

[0054] Figure 5 This is a schematic diagram illustrating the heating relationship of the present invention;

[0055] Figure 6 This is a schematic diagram of the heat dissipation analysis of the present invention;

[0056] Figure 7 This is a schematic diagram of the real-time feature points of the present invention;

[0057] Figure 8 This is a schematic diagram of G1, G2 and G3 of the present invention;

[0058] Figure 9 This is a schematic diagram of the first deviation analysis auxiliary line and the second deviation analysis auxiliary line of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1, please refer to Figure 1 As shown, this application provides a high-temperature adaptive heat dissipation control method for AC / DC equipment based on multi-physics coupling, including the following steps:

[0061] Step S1 involves monitoring various operating parameters in the AC / DC equipment and testing the relationship between these parameters and the temperatures of different components within the AC / DC equipment, termed the "temperature rise relationship." Step S1 includes the following sub-steps:

[0062] Step S101: Integrate the operating parameters that affect the temperature of the equipment components to obtain different sets of temperature rise influencing parameters;

[0063] Step S101 includes the following sub-steps:

[0064] Step S1011: Embed a temperature sensor in the device component and name the monitored temperature as the component temperature.

[0065] Step S1012: Number the device components using the symbol S. n Let S be a sequence of numbers, where n is a positive integer and n is the index of S. n The corresponding component temperature is marked as T. n ;

[0066] Step S1013: Different operating parameters are executed by different device components, and S... n The operating parameters executed in the process are integrated into a set of temperature-influence parameters, labeled as PU. n ;

[0067] In specific implementation, the energy storage converter is an AC / DC device that can convert the AC power from the grid into DC power to charge the battery, and at the same time, it can convert the DC power from the battery into AC power to send back to the grid. In this embodiment, the energy storage converter is used as an example. The energy storage converter contains a variety of equipment components such as power conversion unit, DC support capacitor, filter device, transformer, main control board and relay. In this embodiment, only the above 6 types are used as examples, numbered as S1 to S6. The monitored component temperatures are T1 to T6. For example, the operating parameters of the DC support capacitor include output power and ripple current. In addition to the operating parameters of the equipment components themselves, the ambient temperature will also affect the temperature of the equipment components. Therefore, each temperature rise influencing parameter set includes the ambient temperature. That is, the temperature rise influencing parameter set corresponding to the DC support capacitor includes output power, ripple current and ambient temperature. The DC support capacitor is S2, that is, PU2 includes output power, ripple current and ambient temperature.

[0068] Step S102: Construct a temperature rise influence analysis system based on the temperature rise influence parameter set, and obtain the influence array from the temperature rise influence analysis system;

[0069] Step S102 includes the following sub-steps:

[0070] Step S1021, for different PUs n Independent analysis of PU n The running parameters are numbered and represented by the symbol H(n,m), where m is a positive integer and (n,m) is the sequence number of H;

[0071] Please see Figure 2 As shown, in step S1022, historical data of operating parameters and equipment temperature are obtained and named historical parameters and historical temperature, respectively. The minimum and maximum values ​​of operating parameter H(n,m) in the historical parameters are marked as Min(n,m) and Max(n,m), respectively. A one-dimensional coordinate system is constructed for each operating parameter and named parameter-independent coordinate system. All parameter-independent coordinate systems have the same length and are in the form of line segments. The parameter-independent coordinate system corresponding to H(n,m) is marked as CS(n,m). The left endpoint of CS(n,m) is Min(n,m) and the right endpoint is Max(n,m).

[0072] In specific implementation, different PUs n This embodiment analyzes the data independently, taking PU2 as an example. The operating parameters of PU2 are numbered to obtain H(2,1), H(2,2), and H(2,3), corresponding to output power, ripple current, and ambient temperature, respectively. Historical parameters for output power, ripple current, and ambient temperature are obtained, along with historical temperatures. For example, in a historical record, the output power, ripple current, and ambient temperature are 6750W, 15A, and 30℃, respectively, while the component temperature of the DC support capacitor is 85℃. Therefore, 6750W, 15A, and 30℃ represent the historical output power, ripple current, and ambient temperature. The parameters, 85℃, represent the historical temperature, and these belong to the same historical information. The definition of historical information is explained in step S1025. It's important to note that the acquired historical parameters and historical temperatures are provided by the manufacturer's testing, and no cooling device was connected during the testing process. Furthermore, the historical temperature is the device temperature when it stabilizes after the historical parameters have been raised, not the device temperature during the historical temperature rise process. Specific data is obtained and provided by the manufacturer's testing. Taking ambient temperature as an example, the obtained Min(2,3) and Max(2,3) of the ambient temperature are 5℃ and 40℃, respectively. Based on this, an independent coordinate system for the parameters corresponding to the ambient temperature is constructed as follows: Figure 2 As shown, CS(2,3) represents the positive direction of the X-axis, which is to the right.

[0073] Step S1023: If m is at most 1, then establish a two-dimensional coordinate system with H(n,1) as the X-axis and the equipment temperature as the Y-axis, and name it the temperature rise analysis chart. Enter the historical temperature into the temperature rise analysis chart according to the historical parameters, and then perform discrete regression analysis on the temperature rise analysis chart. The resulting discrete function is the temperature rise relationship.

[0074] Please see Figure 3 As shown, in step S1024, if the maximum value of m is greater than 1, the right endpoint of CS(n,m) is overlapped with the left endpoint of CS(n,m+1), and there is a certain angle between CS(n,m) and CS(n,m+1), so that all CS(n,m) overlap to form a regular polygon, and the temperature rise effect analysis system is obtained.

[0075] Please see Figure 4 As shown, in step S1025, the historical parameters are substituted into their respective CS(n,m) in the temperature rise effect analysis system. The points corresponding to the historical parameters on CS(n,m) are marked as R(n,m). A perpendicular line is drawn from R(n,m) to CS(n,m). Different intersection points will be formed between the perpendicular lines of each CS(n,m), which are named influence points. All influence points together form an influence array. Each historical parameter recorded at the same time in the temperature rise effect parameter set forms a historical information, and one historical information corresponds to one influence array.

[0076] In practice, since only one factor needs to be considered when m is at most 1, the relationship between equipment temperature and operating parameters can be directly analyzed through a regression model. The method and process are relatively simple, and will not be described in detail in this embodiment. In the example listed in this embodiment, the maximum value of m is 3. Therefore, the right endpoint of CS(2,m) is overlapped with the left endpoint of CS(2,m+1), and there is a certain angle between CS(2,m) and CS(2,m+1), so that all the overlapping CS(2,m) form an equilateral triangle, resulting in the temperature rise effect analysis system as follows. Figure 3 As shown, the angle between CS(2,m) and CS(2,m+1) varies depending on the maximum value of m, typically [(M-2)×180°] / M, where M represents the maximum value of m. Taking the previously listed output power, ripple current, ambient temperature, and component temperature of the DC support capacitor as examples of 6750W, 15A, 30℃, and 85℃ respectively, substituting 6750W, 15A, and 30℃ into CS(2,1), CS(2,2), and CS(2,3) respectively, we obtain R(2,1), R(2,2), and R(2,3). Then, we draw vertical lines and obtain the influence points as shown. Figure 4 As shown, Figure 4 In the diagram, the black coordinates represent R(n,m), and the white coordinates represent the influencing points, which are then removed. Figure 4By removing R(n,m) and the vertical line, and keeping only the white coordinate points, the influence array can be obtained.

[0077] Step S103: Analyze the influence array and convert it into heating feature points, which form a heating relationship.

[0078] Step S103 includes the following sub-steps:

[0079] Step S1031: Starting with the leftmost influence point in the influence array, name it the target analysis point. Find the two influence points closest to the target analysis point and name them the target neighbor points. The target analysis point and the target neighbor points together form a triangle. Obtain the incenter of the triangle and name it the secondary analysis point. At the same time, remove the target analysis point and the target neighbor points. Repeat the process of finding the target analysis point and analyzing the secondary analysis points until all influence points are removed.

[0080] Please see Figure 5 As shown, in step S1032, the secondary analysis point is used as a new influence point and the process is repeated until only one secondary analysis point remains. The remaining secondary analysis point is named the heating feature point. The heating feature point of each historical information is analyzed, and the heating relationship is formed by the heating feature points. Each device component has an independent heating relationship.

[0081] In practice, since the temperature rise influence parameters consist of only three operating parameters, the temperature rise influence analysis system presents a triangular shape, meaning there are exactly three influence points during the analysis. Figure 4 The incenter of the small triangle within the system is the secondary analysis point. If there are 4 influence points, after analyzing one secondary analysis point, only one influence point remains; this point is directly marked as the secondary analysis point. If there are 5 influence points, after analyzing one secondary analysis point according to step S1031, two influence points remain; the point between these two influence points is taken as the secondary analysis point. This process is repeated until only one secondary analysis point remains; this point is the temperature rise characteristic point. The temperature rise characteristic point reflects the temperature distribution of the equipment under the combined influence of multiple factors, i.e., under the coupling effect of multiple physical fields, using coordinate points. Analyzing the temperature rise characteristic points of each historical information yields the final temperature rise relationship as shown below. Figure 5 As shown, Figure 5 The gray coordinate points in the diagram are the characteristic points of the temperature rise.

[0082] Step S2 involves connecting each component in the AC / DC equipment to an independently controlled heat dissipation device, analyzing the relationship between the heat dissipation power and heat dissipation efficiency of the heat dissipation device, and naming this relationship as the heat dissipation relationship. Step S2 includes the following sub-steps:

[0083] Step S201: Obtain heat dissipation data, which includes heat dissipation power, initial temperature, and heat dissipation temperature. Mark the initial temperature as AT and the heat dissipation temperature as FT.

[0084] Step S202: Calculate (AT-FT) / AT, and name the calculation result as heat dissipation efficiency;

[0085] Please see Figure 6 As shown, in step S203, a two-dimensional coordinate system is established with heat dissipation power as the X-axis and heat dissipation efficiency as the Y-axis, named heat dissipation analysis diagram, and the heat dissipation efficiency is entered into the heat dissipation analysis diagram according to the heat dissipation power.

[0086] Step S204: Perform discrete regression analysis on the heat dissipation analysis diagram and name the discrete function as heat dissipation relationship;

[0087] In practice, the initial temperature is the component temperature of the device when the cooling system is not activated. Since the heat dissipation test is an experimental test, the initial temperature is defined as the component temperature after the cooling system has stabilized. The heat dissipation temperature is the component temperature after the cooling system is activated at the initial temperature, and the component temperature decreases and stabilizes. For example, in a certain test, the heat dissipation power is 2000W, the initial temperature AT is 75℃, and the heat dissipation temperature FT is 50℃. The calculated heat dissipation efficiency is 1 / 3, and the resulting heat dissipation analysis diagram is as follows. Figure 6 As shown, by performing a regression analysis on the heat dissipation diagram using the discrete regression method in existing discrete mathematics, the heat dissipation relationship is obtained as Y = -0.00000004 × X 2 +0.0003×X-0.07, where Y is the heat dissipation efficiency and X is the heat dissipation power. The heat dissipation relationship is universal in different heat dissipation components.

[0088] Step S3: Based on the heating relationship, predict the temperature of the equipment components to obtain the predicted temperature of the equipment components; Step S3 includes the following sub-steps:

[0089] Step S301: Analyze the real-time characteristic points of the equipment components by combining the real-time operating parameters of the equipment components with the temperature rise influence analysis system;

[0090] Step S301 includes the following sub-steps:

[0091] Step S3011: Name the real-time operating parameters of the device components as real-time parameters;

[0092] Please see Figure 7 As shown, in step S3012, the real-time parameters are substituted into the temperature rise influence analysis system corresponding to the equipment component and the temperature rise characteristic points are analyzed and named as real-time characteristic points.

[0093] Step S3013, S nThe real-time feature points are labeled as E n At the same time, S n The temperature rise relationship is denoted as W. n ;

[0094] In practical implementation, for example, during a certain use of the energy storage converter, the real-time parameters of the DC support capacitor's output power, ripple current, and ambient temperature are 5400W, 12A, and 30℃, respectively. Substituting these parameters, the real-time characteristic points are obtained as follows: Figure 7 As shown, Figure 7 The black coordinates in the image represent the real-time feature point, E2. Figure 7 The gray coordinates in the diagram represent the heating relationship, i.e., W2.

[0095] Step S302: Based on the heating relationship and real-time feature points, predict the temperature of the equipment components to obtain the predicted temperature;

[0096] Step S302 includes the following sub-steps:

[0097] Please see Figure 8 As shown, in step S3021, in W n Searching in E n The three closest heating characteristic points are labeled G1, G2 and G3 in order of distance from nearest to farthest. Connecting G2 and G3 yields the first deviation analysis auxiliary line.

[0098] In specific implementation, G1, G2, and G3 are obtained as follows: Figure 8 As shown, in the actual extraction of G1, G2, and G3, it is necessary to ensure that the real-time feature points are within the triangle formed by G1, G2, and G3. This can be achieved by constructing a rectangular coordinate system with the real-time feature points as the origin and the Y-axis as the direction from the real-time feature points to G1. Then, by searching for G2 and G3 in the third and fourth quadrants of the rectangular coordinate system, it can be ensured that the real-time feature points are within the triangle formed by G1, G2, and G3. This is because it is difficult for real-time feature points to completely overlap with any temperature rise feature points, but there will be a certain deviation. As long as there is a deviation, it means that the equipment temperature will also have a deviation, but the magnitude and direction of the deviation are unknown. By analyzing G1, G2, and G3, the actual deviation of the equipment temperature of the real-time feature points can be obtained by using the deviation of the equipment temperature from G1 to G2 and the deviation of the equipment temperature from G1 to G3 as references.

[0099] Please see Figure 9 As shown, in step S3022, G1 and E n Connect and extend the line so that it intersects the first deviation analysis auxiliary line. Name the line obtained by connecting and extending the line the second deviation analysis auxiliary line. Name the intersection of the first deviation analysis auxiliary line and the second deviation analysis auxiliary line the deviation analysis auxiliary point.

[0100] Step S3023, E n The distances to G1 and the deviation analysis auxiliary point are marked as L1 and L2, respectively, and the distances between the deviation analysis auxiliary point and G2 and G3 are marked as L3 and L4, respectively.

[0101] Step S3024: Obtain the historical temperatures corresponding to the historical information of G1, G2, and G3, and label them TH1, TH2, and TH3 respectively. Assume E n The equipment temperature is assumed to be K, and the equipment temperature at the auxiliary point of the deviation analysis is assumed to be V.

[0102] Step S3025, using the formula Solve for V, then use the formula Solve for K; the obtained K is the predicted temperature of the equipment component. Calculate the predicted temperature of all equipment components.

[0103] In practice, the first deviation analysis auxiliary line and the second deviation analysis auxiliary line are drawn as follows: Figure 9 As shown, the dashed line is the second deviation analysis auxiliary line, and the solid line is the first deviation analysis auxiliary line. The intersection point between them is the deviation analysis auxiliary point. In the actual analysis process, L1, L2, L3, and L4 are only used to calculate the deviation ratio between the deviation analysis auxiliary point and the equipment temperature of G2, and the deviation ratio between the real-time feature point and the equipment temperature of G1, namely L3 / (L3+L4) and L1 / (L1+L2). Therefore, the specific values ​​of L1, L2, L3, and L4 are not important. L3 / (L3+L4) and L1 / (L1+L2) can be directly obtained in the coordinate system. The obtained L3 / (L3+L4) and L1 / (L1+L2) are 0.377 and 0.578, respectively. The equipment temperature at the deviation analysis auxiliary point deviates from the equipment temperatures of G2 and G3, therefore, we obtain... For example, if TH1, TH2, and TH3 are obtained as 70℃, 75℃, and 78℃ respectively, the solution yields V as 76.131℃. The principle and Similarly, further calculation yields K as 73.5℃. The calculation result is rounded to one decimal place, meaning the predicted temperature is 73.5℃. This represents the instant when the output power, ripple current, and ambient temperature of the DC support capacitor are adjusted to 5400W, 12A, and 30℃, respectively. In this embodiment, it is predicted that the DC support capacitor will heat up to 73.5℃ after a period of time. This is because when the energy storage converter is activated, the equipment temperature needs a certain amount of time to rise. Existing heat dissipation control only dissipates heat after the temperature rises, while this embodiment can dissipate heat before the equipment components heat up, preventing large temperature differences in the equipment components within a short period of time.

[0104] Step S4 involves adaptively adjusting the heat dissipation power of different heat dissipation devices based on the predicted temperature of the device components and their heat dissipation relationship. Step S4 includes the following sub-steps:

[0105] Step S401: Obtain the optimal operating temperature for each device component, and set S... n The optimal operating temperature is marked as CA n At the same time, S n The predicted temperature is labeled CB n ;

[0106] Step S402, calculate (CB) n -CA n ) / CB n The calculation result is named heat dissipation demand efficiency. Substituting the heat dissipation demand efficiency into the heat dissipation relationship, S is obtained. n The power required for heat dissipation by the heat dissipation device is called the heat dissipation demand power, based on different S n The heat dissipation power of different heat dissipation devices is adaptively adjusted according to the heat dissipation power demand.

[0107] In practical implementation, taking the DC supporting capacitor as an example, the optimal operating temperature CA2 of the DC supporting capacitor is found to be 50℃, and the predicted temperature CB2 is 73.5℃. The calculated heat dissipation efficiency is 0.31. The calculation result is rounded to two decimal places. Substituting Y=0.31 into Y=-0.00000004×X 2 +0.0003×X-0.07, solving for X yields approximately 1614W or 5886W. The minimum non-negative value is selected as the required heat dissipation power, i.e., the required heat dissipation power is 1614W. The heat dissipation power of the DC support capacitor is adjusted to 1614W. The heat dissipation power is only adjusted when the operating parameters change, based on the predicted temperature. Similarly, the predicted temperature of other equipment components is analyzed and the required heat dissipation power is calculated, and then the heat dissipation power of their heat dissipation devices is adjusted.

[0108] Example 2: This application provides an electronic device, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call these instructions. When the processor executes a computer-readable instruction, it performs steps such as those in the "High-Temperature Adaptive Heat Dissipation Control Method for AC / DC Equipment Based on Multi-Physical Field Coupling" to achieve the following functions: testing the relationship between operating parameters and the temperatures of different equipment components in the AC / DC equipment, termed the temperature rise relationship; analyzing the relationship between the heat dissipation power and heat dissipation efficiency of the heat dissipation device, termed the heat dissipation relationship; predicting the temperature of the equipment components based on the temperature rise relationship to obtain the predicted temperature of the equipment components; and adaptively controlling the heat dissipation power of different heat dissipation devices based on the predicted temperature of the equipment components and the heat dissipation relationship.

[0109] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0110] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the high-temperature adaptive heat dissipation control method for AC / DC equipment based on multi-physics coupling provided by the above methods. The method includes: testing the relationship between operating parameters and the temperature of different equipment components in the AC / DC equipment, named the temperature rise relationship; analyzing the relationship between the heat dissipation power and heat dissipation efficiency of the heat dissipation device, named the heat dissipation relationship; predicting the temperature of the equipment components based on the temperature rise relationship to obtain the predicted temperature of the equipment components; and adaptively controlling the heat dissipation power of different heat dissipation devices based on the predicted temperature of the equipment components and the heat dissipation relationship.

[0111] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-mentioned AC / DC equipment high-temperature adaptive heat dissipation control method based on multi-physics coupling to achieve the following functions: testing the relationship between operating parameters and the temperatures of different equipment components in the AC / DC equipment, named the temperature rise relationship; analyzing the relationship between the heat dissipation power and heat dissipation efficiency of the heat dissipation device, named the heat dissipation relationship; predicting the temperature of the equipment components based on the temperature rise relationship to obtain the predicted temperature of the equipment components; and adaptively controlling the heat dissipation power of different heat dissipation devices based on the predicted temperature of the equipment components and the heat dissipation relationship.

[0112] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0113] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A high-temperature adaptive heat dissipation control method for AC / DC equipment based on multi-physics coupling, characterized in that, Includes the following steps: The various operating parameters in AC / DC equipment are monitored, and the relationship between the operating parameters and the temperature of different equipment components in the AC / DC equipment is tested and named the temperature rise relationship. Each component in the AC / DC equipment is connected to an independently controlled heat dissipation device. The relationship between the heat dissipation power and heat dissipation efficiency of the heat dissipation device is analyzed and named the heat dissipation relationship. The predicted temperature of the equipment components is obtained by predicting the temperature rise relationship. The heat dissipation power of different heat dissipation devices is adaptively adjusted based on the predicted temperature of the device components and the heat dissipation relationship. Monitoring various operating parameters in AC / DC equipment and testing the relationship between these operating parameters and the temperatures of different components within the AC / DC equipment, termed the "temperature rise relationship," includes the following sub-steps: By integrating the operating parameters that affect the temperature of equipment components, different sets of temperature rise influence parameters are obtained; A temperature rise influence analysis system is constructed based on the temperature rise influence parameter set, and the influence array is obtained by analyzing the temperature rise influence analysis system. The influence array is analyzed and converted into heating feature points, which form the heating relationship. Integrating the operating parameters affecting the temperature of equipment components to obtain different sets of temperature rise impact parameters includes the following sub-steps: Temperature sensors are embedded within the device components, and the monitored temperatures are named component temperatures. The equipment components are numbered using the symbol S. n Let S be a sequence of numbers, where n is a positive integer and n is the index of S. n The corresponding component temperature is marked as T. n ; Different operating parameters are executed by different device components, which will S n The operating parameters executed in the process are integrated into a set of temperature-influence parameters, labeled as PU. n ; Constructing a temperature rise influence analysis system based on the temperature rise influence parameter set, and obtaining the influence array from the temperature rise influence analysis system includes the following sub-steps: For different PUs n Independent analysis of PU n The running parameters are numbered and represented by the symbol H(n,m), where m is a positive integer and (n,m) is the sequence number of H; Historical data on operating parameters and equipment temperature are obtained and named historical parameters and historical temperature, respectively. The minimum and maximum values ​​of the operating parameter H(n,m) in the historical parameters are marked as Min(n,m) and Max(n,m), respectively. A one-dimensional coordinate system is constructed for each operating parameter and named the parameter-independent coordinate system. All parameter-independent coordinate systems have the same length and are in the form of line segments. The parameter-independent coordinate system corresponding to H(n,m) is marked as CS(n,m). The left endpoint of CS(n,m) is Min(n,m) and the right endpoint is Max(n,m). If m is at most 1, then a two-dimensional coordinate system is established with H(n,1) as the X-axis and the equipment temperature as the Y-axis, named the temperature rise analysis chart. The historical temperatures are entered into the temperature rise analysis chart according to the historical parameters. Then, discrete regression analysis is performed on the temperature rise analysis chart, and the resulting discrete function is the temperature rise relationship. If the maximum value of m is greater than 1, then the right endpoint of CS(n,m) is overlapped with the left endpoint of CS(n,m+1), and there is a certain angle between CS(n,m) and CS(n,m+1), so that all CS(n,m) overlap to form a regular polygon, and the temperature rise effect analysis system is obtained. Substitute the historical parameters into their respective CS(n,m) in the temperature rise effect analysis system, mark the points corresponding to the historical parameters on CS(n,m) as R(n,m), draw a perpendicular line from R(n,m) to CS(n,m), and each perpendicular line to CS(n,m) will form a different intersection point, which is named the influence point. All the influence points together form an influence array. Each historical parameter recorded at the same time in the temperature rise effect parameter set forms a historical information, and one historical information corresponds to one influence array. Analyzing the influence array and converting it into heating feature points, the heating relationship formed by these feature points includes the following sub-steps: Starting with the leftmost influence point in the influence array, name it the target analysis point. Find the two influence points closest to the target analysis point and name them the target neighbor points. The target analysis point and the target neighbor points together form a triangle. Obtain the incenter of the triangle and name it the secondary analysis point. At the same time, remove the target analysis point and the target neighbor points. Repeat the process of finding the target analysis point and analyzing the secondary analysis points until all influence points have been removed. The secondary analysis point is used as a new influence point, and the process is repeated until only one secondary analysis point remains. The remaining secondary analysis point is named the temperature rise feature point. The temperature rise feature point of each historical information is analyzed, and the temperature rise relationship is formed by the temperature rise feature points. Each device component has an independent temperature rise relationship.

2. The high-temperature adaptive heat dissipation control method for AC / DC equipment based on multi-physics coupling according to claim 1, characterized in that, Each component in the AC / DC equipment is connected to an independently controlled heat dissipation device. The relationship between the heat dissipation power and heat dissipation efficiency of the heat dissipation device is analyzed and named the heat dissipation relationship. The process includes the following sub-steps: Acquire heat dissipation data, which includes heat dissipation power, initial temperature, and heat dissipation temperature. The initial temperature is labeled as AT, and the heat dissipation temperature is labeled as FT. Calculate (AT-FT) / AT, and name the result heat dissipation efficiency; Establish a two-dimensional coordinate system with heat dissipation power as the X-axis and heat dissipation efficiency as the Y-axis, and name it heat dissipation analysis chart. Enter the heat dissipation efficiency into the heat dissipation analysis chart according to the heat dissipation power. Discrete regression analysis was performed on the heat dissipation analysis diagram, and the discrete function was named the heat dissipation relationship.

3. The high-temperature adaptive heat dissipation control method for AC / DC equipment based on multi-physics coupling according to claim 2, characterized in that, Predicting the temperature of equipment components based on the heating relationship involves the following sub-steps: The real-time characteristic points of the equipment components are analyzed by combining the real-time operating parameters of the equipment components with the temperature rise effect analysis system. The predicted temperature is obtained by predicting the temperature of the equipment components based on the heating relationship and real-time feature points.

4. The high-temperature adaptive heat dissipation control method for AC / DC equipment based on multi-physics coupling according to claim 3, characterized in that, Analyzing the real-time characteristic points of equipment components by combining real-time operating parameters with a temperature rise impact analysis system includes the following sub-steps: Name the real-time operating parameters of the device components as real-time parameters; Substitute the real-time parameters into the temperature rise impact analysis system corresponding to the equipment components and analyze the temperature rise characteristic points, which are named real-time characteristic points. S n The real-time feature points are labeled as E n At the same time, S n The temperature rise relationship is denoted as W. n .

5. The high-temperature adaptive heat dissipation control method for AC / DC equipment based on multi-physics coupling according to claim 4, characterized in that, The prediction of device component temperature based on heating relationship and real-time feature points includes the following sub-steps: In W n Searching in E n The three closest heating characteristic points are labeled G1, G2 and G3 in order of distance from nearest to farthest. Connecting G2 and G3 yields the first deviation analysis auxiliary line. Connect G1 and E n Connect and extend the line so that it intersects the first deviation analysis auxiliary line. Name the line obtained by connecting and extending the line the second deviation analysis auxiliary line. Name the intersection of the first deviation analysis auxiliary line and the second deviation analysis auxiliary line the deviation analysis auxiliary point. E n The distances to G1 and the deviation analysis auxiliary point are marked as L1 and L2, respectively, and the distances between the deviation analysis auxiliary point and G2 and G3 are marked as L3 and L4, respectively. Obtain the historical temperatures corresponding to G1, G2, and G3, and label them TH1, TH2, and TH3 respectively. Assume E n The equipment temperature is assumed to be K, and the equipment temperature at the auxiliary point of the deviation analysis is assumed to be V. Through formula Solve for V, then use the formula Solve for K; the obtained K is the predicted temperature of the equipment component. Calculate the predicted temperature of all equipment components.

6. The high-temperature adaptive heat dissipation control method for AC / DC equipment based on multi-physics coupling according to claim 5, characterized in that, Adaptive adjustment of the heat dissipation power of different heat dissipation devices based on the predicted temperature of the equipment components and the heat dissipation relationship includes the following sub-steps: To obtain the optimal operating temperature for each device component, S n The optimal operating temperature is marked as CA n At the same time, S n The predicted temperature is labeled CB n ; Calculation (CB) n -CA n ) / CB n The calculation result is named heat dissipation demand efficiency. Substituting the heat dissipation demand efficiency into the heat dissipation relationship, S is obtained. n The power required for heat dissipation by the heat dissipation device is called the heat dissipation demand power, based on different S n The power required for heat dissipation is adaptively adjusted to the heat dissipation power of different heat dissipation devices.

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