Electric drive system thermal management method and device, vehicle and medium

Abstract

The invention provides an electric drive system heat management method and device, a vehicle and a medium. The method comprises the steps that a first set of fuzzy information is determined according to the heat transfer temperature difference, a second set of fuzzy information is determined according to the cooling potential temperature difference, and fan control parameters are determined according to mapping of the first set of fuzzy information and the second set of fuzzy information in a rule base; and controlling the working state of the fan according to the fan control parameters. In the embodiment of the invention, the heat transfer temperature difference and the cooling potential temperature difference are subjected to fuzzy processing to obtain the fuzzy information, and the fan control parameters matched with the fuzzy information are determined through mapping of the fuzzy information in the rule base, so that the working state of the fan is more matched with the temperature of the electric drive system, and heat dissipation response delay or fan energy consumption waste is reduced.

Description

Technical Field

[0001] This application relates to the field of thermal management technology for electric vehicles, and in particular to a thermal management method, device, vehicle, and medium for an electric drive system. Background Technology

[0002] As the core of electric vehicle power output, the operating temperature of the electric drive system directly affects the vehicle's power output, range, and lifespan. Therefore, thermal management of the electric drive system is a crucial aspect of electric vehicle thermal management. Coolant is typically used as the heat transfer medium, in conjunction with a fan for auxiliary cooling, to transfer heat from the electric drive system to the environment, thus achieving thermal management of the electric drive system.

[0003] In related technologies, multiple temperature thresholds are typically preset to control the operating state of the fan. When the temperature of the electric drive system reaches a certain preset temperature threshold, the fan is controlled to reach the corresponding operating state. For example, if the temperature of the electric drive system reaches the medium-high temperature threshold, the fan is controlled to run at medium-high intensity; if the temperature of the electric drive system reaches the high temperature threshold, the fan is controlled to run at high intensity.

[0004] However, once the electric drive system temperature reaches the threshold, the temperature may continue to change due to the temperature change trend of the electric drive system. This can cause a mismatch between the fan's operating state and the electric drive system temperature, resulting in delayed heat dissipation response or wasted fan energy.

[0005] It should be noted that the information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application, and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] This application provides a thermal management method, device, vehicle, and medium for an electric drive system, which helps to solve the problem of mismatch between the operating state of the fan and the temperature of the electric drive system, resulting in delayed heat dissipation response or wasted fan energy.

[0007] In a first aspect, embodiments of this application provide a thermal management method for an electric drive system, comprising: determining a first set of fuzzy information based on a heat transfer temperature difference, wherein the heat transfer temperature difference is the difference between the temperature of the electric drive system and the temperature of the coolant, and the first set of fuzzy information includes all the fuzzy sets corresponding to the heat transfer temperature difference and the membership degree of all the fuzzy sets. The second set of fuzzy information is determined based on the cooling potential temperature difference, which is the difference between the coolant temperature and the ambient temperature. The second set of fuzzy information includes all the fuzzy sets corresponding to the cooling potential temperature difference and the membership degree of all the fuzzy sets. Based on the mapping of the first set of fuzzy information and the second set of fuzzy information in the rule base, fan control parameters are determined; The fan's operating state is controlled according to the aforementioned fan control parameters.

[0008] In one possible implementation, determining the fan control parameters based on the mapping of the first set of fuzzy information and the second set of fuzzy information in the rule base includes: The third set of fuzzy information is determined based on the working state of the coolant pump. The third set of fuzzy information includes all the fuzzy sets and the membership degrees of all the fuzzy sets corresponding to the working state of the coolant pump. The fan control parameters are determined based on the mapping of the first set of fuzzy information, the second set of fuzzy information, and the third set of fuzzy information in the rule base.

[0009] In one possible implementation, determining the fan control parameters based on the mapping of the first set of fuzzy information, the second set of fuzzy information, and the third set of fuzzy information in the rule base includes: Based on the mapping of the first set of fuzzy information, the second set of fuzzy information, and the third set of fuzzy information in the rule base, all activation rules for the fan control parameters are determined. Based on all activation rules of the fan control parameters, the fuzzy information of the fan control parameters is determined. The fuzzy information of the fan control parameters includes the activation fuzzy set corresponding to all activation rules and the activation degree of all activation fuzzy sets. The fuzzy information of the fan control parameters is defuzzified to determine the fan control parameters.

[0010] In one possible implementation, the activation degree of the activated fuzzy set is the minimum value among all the membership degrees of the fuzzy sets corresponding to the activation rule.

[0011] In one possible implementation, the step of defuzzifying the fuzzy information of the fan control parameters to determine the fan control parameters includes: Through the formula: Determine the fan control parameters; Among them, D f Here are the fan control parameters, where n is the number of active rules and μ is the number of active rules. i x is the activation degree of the i-th activation rule. i It is the representative point corresponding to the i-th activation rule.

[0012] In one possible implementation, before controlling the operating state of the fan according to the fan control parameters, the method further includes: If the temperature of the electric drive system is greater than or equal to the high temperature threshold, then the fan control parameters are determined to be high temperature control parameters. And / or, If the temperature of the electric drive system is less than or equal to the energy-saving temperature threshold, then the fan control parameters are determined to be energy-saving control parameters. And / or, If the temperature of the electric drive system is invalid, then the fan control parameters are determined to be the default control parameters.

[0013] In one possible implementation, the membership fuzzy set corresponding to the heat transfer temperature difference includes low, medium, and high; The membership fuzzy set corresponding to the cooling potential temperature difference includes low, medium, and high; The fuzzy set corresponding to the operating state of the coolant pump includes low, medium, and high; The activation fuzzy set corresponding to the fan control parameters includes very low, low, medium, high, and very high.

[0014] Secondly, embodiments of this application provide a thermal management device for an electric drive system, comprising: The first information determination module is used to determine a first set of fuzzy information based on the heat transfer temperature difference, wherein the heat transfer temperature difference is the difference between the temperature of the electric drive system and the temperature of the coolant, and the first set of fuzzy information includes all the fuzzy sets corresponding to the heat transfer temperature difference and the membership degree of all the fuzzy sets. The second information determination module is used to determine a second set of fuzzy information based on the cooling potential temperature difference, wherein the cooling potential temperature difference is the difference between the coolant temperature and the ambient temperature, and the second set of fuzzy information includes all the fuzzy sets corresponding to the cooling potential temperature difference and the membership degree of all the fuzzy sets. The control parameter determination module is used to determine fan control parameters based on the mapping of the first set of fuzzy information and the second set of fuzzy information in the rule base; The operating status control module is used to control the operating status of the fan according to the fan control parameters.

[0015] Thirdly, embodiments of this application provide a vehicle, including: A controller, wherein the controller is configured to be used in any of the first aspects of the method.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a controller, implements the method described in any one of the first aspects.

[0017] In this embodiment, the heat transfer temperature difference and the cooling potential temperature difference are fuzzy processed to obtain fuzzy information. The fan control parameters that match the fuzzy information are determined by mapping the fuzzy information to the rule base, so that the fan operating state is more matched with the temperature of the electric drive system, reducing heat dissipation response delay or fan energy waste. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a structural diagram illustrating an application scenario provided in an embodiment of this application. Figure 2 A schematic flowchart illustrating a thermal management method for an electric drive system provided in an embodiment of this application; Figure 3 A flowchart illustrating another thermal management method for an electric drive system provided in an embodiment of this application; Figure 4 A flowchart illustrating another thermal management method for an electric drive system provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a thermal management device for an electric drive system provided in an embodiment of this application; Figure 6 This is a structural schematic diagram of a vehicle provided in an embodiment of this application. Detailed Implementation

[0020] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0021] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0022] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0024] To better understand the technical solution of this application, some concepts in the embodiments of this application will be described in detail below.

[0025] In practical applications, the set used to express fuzzy concepts can be called a fuzzy set. For example, the set expressing the fuzzy concept "high temperature value" can be called the fuzzy set "medium-high"; the set expressing the fuzzy concept "very high temperature value" can be called the fuzzy set "high".

[0026] It is understandable that a fuzzy set refers to the entirety of objects possessing the attributes described by a fuzzy concept. Since the concepts themselves are not clear-cut or well-defined, the membership of objects to a set is also not explicit or mutually exclusive. For example, the fuzzy concepts "higher temperature value" and "very high temperature value" are not clear-cut or well-defined; therefore, the membership of a certain temperature value to the fuzzy sets "medium-high" and "high" is also not explicit or mutually exclusive. In other words, a certain temperature value can belong to both the fuzzy set "medium-high" and the fuzzy set "high".

[0027] Furthermore, by setting preset conditions or thresholds, the boundaries of a fuzzy concept can be defined, making the fuzzy concept itself clear. Mapping relationships can also be set to describe the membership of objects to sets. In this way, fuzzy features in real-world scenarios can be transformed into quantifiable mathematical models through fuzzy sets and membership relationships. Meanwhile, those skilled in the art will understand that membership relationships can also be called membership degrees.

[0028] For example, the boundary of the fuzzy concept "high temperature value" is defined as 60℃-90℃, and the set of temperature values ​​in the range of 60℃-90℃ is called the fuzzy set "medium-high"; the boundary of the fuzzy concept "very high temperature value" is defined as 75℃-105℃, and the set of temperature values ​​in the range of 75℃-105℃ is called the fuzzy set "high".

[0029] The mapping relationship "When the temperature rises from 60℃ to 75℃, the membership degree of the fuzzy set 'Medium-High' linearly increases from 0% to 100%; when the temperature rises from 75℃ to 90℃, the membership degree of the fuzzy set 'Medium-High' linearly decreases from 100% to 0%" is used to describe the membership relationship of a certain temperature value to the fuzzy set "Medium-High". Similarly, the mapping relationship "When the temperature rises from 75℃ to 90℃, the membership degree of the fuzzy set 'High' linearly increases from 0% to 100%; when the temperature rises from 90℃ to 105℃, the membership degree linearly decreases from 100% to 0%" is used to describe the membership relationship of a certain temperature value to the fuzzy set "High".

[0030] As the core of electric vehicle power output, the operating temperature of the electric drive system directly affects the vehicle's power output, range, and lifespan. Therefore, thermal management of the electric drive system is a crucial aspect of electric vehicle thermal management. Coolant is typically used as the heat transfer medium, in conjunction with a fan for auxiliary cooling, to transfer heat from the electric drive system to the environment, thus achieving thermal management of the electric drive system.

[0031] See Figure 1 This is a structural diagram illustrating an application scenario provided in an embodiment of this application, such as... Figure 1 As shown, the vehicle 100 includes a controller 101, an electric drive system 102, a coolant circulation system 103, and a fan 104. It is understood that the electric drive system 102 typically includes components such as a motor and an inverter, and usually comes into contact with the coolant circulation system 103 for heat transfer. In the coolant circulation system 103, the coolant serves as the heat transfer medium, and with the assistance of the fan 104, the heat from the electric drive system is transferred to the environment. The controller 101 can acquire the operating temperature of the electric drive system 102, monitor the operating status of the coolant circulation system 103, and control the operating status of the fan 104 to assist in heat dissipation, thereby achieving thermal management of the electric drive system.

[0032] Understandable, such as Figure 1 In the application scenarios shown, vehicles include, but are not limited to, sedans, MPVs, and SUVs; electric drive systems include, but are not limited to, split electric drive systems, integrated electric drive systems, pure electric electric drive systems, and hybrid electric drive systems; controllers include, but are not limited to, microcontroller units (MCUs) and system-on-chips (SOCs).

[0033] In related technologies, multiple temperature thresholds are typically preset to control the operating state of the fan. When the temperature of the electric drive system 102 reaches a certain preset temperature threshold, the fan 104 is controlled to reach the corresponding operating state. For example, if the temperature of the electric drive system 102 reaches the medium-high temperature threshold, the fan 104 is controlled to operate at medium-high intensity; if the temperature of the electric drive system 102 reaches the high temperature threshold, the fan 104 is controlled to operate at high intensity.

[0034] However, once the electric drive system temperature reaches the threshold, the temperature may continue to change due to the temperature change trend of the electric drive system. This can cause a mismatch between the fan's operating state and the electric drive system temperature, resulting in delayed heat dissipation response or wasted fan energy.

[0035] To address the aforementioned issues, this application provides a thermal management method for an electric drive system, which helps to resolve the mismatch between the fan's operating state and the electric drive system's temperature, resulting in delayed heat dissipation response or wasted fan energy.

[0036] See Figure 2This is a flowchart illustrating a thermal management method for an electric drive system provided in an embodiment of this application. The method is applied to... Figure 1 The application scenarios shown are as follows: Figure 2 As shown, it mainly includes the following steps.

[0037] S201: Determine the first set of fuzzy information based on the temperature difference of heat transfer.

[0038] Among them, the heat transfer temperature difference is the difference between the temperature of the electric drive system and the temperature of the coolant. The first set of fuzzy information includes all the fuzzy sets corresponding to the heat transfer temperature difference and the membership degree of all the fuzzy sets.

[0039] In practical applications, the electric drive system temperature typically refers to the real-time temperature of the electric drive system during operation and is a crucial indicator for the thermal management of electric vehicles. The coolant temperature refers to the real-time temperature of the cooling medium used to transfer heat from the electric drive system, reflecting its current heat-carrying capacity and heat dissipation potential. As the difference between the electric drive system temperature and the coolant temperature, the heat transfer temperature difference reflects the thermal potential difference between the electric drive system and the coolant. A larger heat transfer temperature difference indicates a stronger driving force and faster heat transfer rate from the electric drive system to the coolant. Conversely, a smaller heat transfer temperature difference indicates a weaker driving force and slower heat transfer rate from the electric drive system to the coolant.

[0040] It is understandable that a fuzzy set of heat transfer temperature difference refers to a set used to express the fuzzy concept of "the magnitude of the heat transfer temperature difference". Based on the range of change of the heat transfer temperature difference, multiple fuzzy sets of heat transfer temperature differences can be determined, along with the boundaries, ranges, and mapping relationships describing the membership degrees of the corresponding fuzzy sets.

[0041] For example, the minimum heat transfer temperature difference is 0℃ and the maximum is 30℃. Based on the range of the heat transfer temperature difference, three fuzzy sets of heat transfer temperature differences—low, medium, and high—can be determined. The low fuzzy set ranges from 0℃ to 10℃; the medium fuzzy set ranges from 0℃ to 20℃; and the high fuzzy set ranges from 10℃ to 30℃. At 0℃, the membership degree of the lower fuzzy set is 100%, and at 10℃, the membership degree of the lower fuzzy set is 0%. During the process of heat transfer temperature difference from 0℃ to 10℃, the membership degree of the lower fuzzy set changes linearly. At 10℃, the membership degree of the fuzzy set is 100%, and at 0℃ or 20℃, the membership degree of the fuzzy set is 0%. During the process of heat transfer temperature difference from 10℃ to 0℃ or 20℃, the membership degree of the fuzzy set changes linearly. At 20℃, the membership degree of the higher fuzzy set is 100%, and at 10℃ or 30℃, the membership degree of the higher fuzzy set is 0%. During the process of heat transfer temperature difference from 20℃ to 10℃ or 30℃, the membership degree of the higher fuzzy set changes linearly.

[0042] Those skilled in the art will understand that, for the mapping relationship between the membership degrees of fuzzy sets described in the above exemplary statements, the membership functions of fuzzy sets can be used to describe the boundaries, ranges, and membership degrees of the fuzzy sets. For example, the membership function [0, 0, 10] of the lower fuzzy set can describe the range of the lower fuzzy set as 0℃-10℃. At 0℃, the membership degree of the lower fuzzy set is 100%, and at 10℃, the membership degree of the lower fuzzy set is 0%. During the process of heat transfer temperature difference changing from 0℃ to 10℃, the membership degree of the lower fuzzy set changes linearly. The membership function [0, 10, 20] of the fuzzy set can describe the fuzzy set's boundary, range, and membership degree. The fuzzy set has a range of 0℃-20℃. At 10℃, the membership degree of the fuzzy set is 100%. At 0℃ or 20℃, the membership degree of the fuzzy set is 0%. During the process of heat transfer temperature difference changing from 10℃ to 0℃ or 20℃, the membership degree of the fuzzy set changes linearly. The membership function [10, 20, 30] of the fuzzy set height can describe the range of the fuzzy set height of 10℃-30℃. At 20℃, the membership degree of the fuzzy set height is 100%. At 10℃ or 30℃, the membership degree of the fuzzy set height is 0%. During the process of heat transfer temperature difference changing from 20℃ to 10℃ or 30℃, the membership degree of the fuzzy set height changes linearly.

[0043] It should be noted that the fuzzy set setting of the heat transfer temperature difference described above is only an exemplary description. For example, other conditions can be selected to determine the fuzzy set of the heat transfer temperature difference according to the actual situation; the upper and lower limits of the range of change of the heat transfer temperature difference may be larger or smaller; the number of fuzzy sets of the heat transfer temperature difference may be two, four or more; the fuzzy set may also be set to include a set of a series of discrete temperature values; the boundary of each fuzzy set may also be set to other thresholds; other mapping relationships may also be set to determine the membership degree of the fuzzy set. This application does not impose specific limitations on these aspects.

[0044] Furthermore, the membership fuzzy set is the fuzzy set to which the heat transfer temperature difference belongs, and the heat transfer temperature difference can have multiple membership fuzzy sets. The total membership fuzzy sets of the heat transfer temperature difference and the membership degrees of all membership fuzzy sets constitute the first set of fuzzy information about the heat transfer temperature difference.

[0045] For example, when the heat transfer temperature difference is 5℃, it simultaneously belongs to both the low fuzzy set (0℃-10℃) and the fuzzy set (0℃-20℃). The low fuzzy set and the fuzzy set are the fuzzy sets to which the heat transfer temperature difference is 5℃. According to the mapping relationship, the membership degree of the low fuzzy set is determined to be 50%, and the membership degree of the fuzzy set is also determined to be 50%, together forming the first set of fuzzy information when the heat transfer temperature difference is 5℃. When the heat transfer temperature difference is 17.5℃, it simultaneously belongs to both the fuzzy set (0℃-20℃) and the high fuzzy set (10℃-30℃). The fuzzy set and the high fuzzy set are the fuzzy sets to which the heat transfer temperature difference is 17.5℃. According to the mapping relationship, the membership degree of the fuzzy set is determined to be 25%, and the membership degree of the high fuzzy set is determined to be 75%, together forming the first set of fuzzy information when the heat transfer temperature difference is 17.5℃.

[0046] S202: Determine the second set of fuzzy information based on the temperature difference of the cooling potential.

[0047] Among them, the cooling potential temperature difference is the difference between the coolant temperature and the ambient temperature, and the second set of fuzzy information includes all the fuzzy sets corresponding to the cooling potential temperature difference and the membership degree of all the fuzzy sets. In practical applications, coolant temperature refers to the real-time temperature of the cooling medium used to transfer heat from the electric drive system, reflecting the current heat load status of the cooling medium. Ambient temperature refers to the real-time temperature of the external environment, reflecting the external conditions under which the cooling medium dissipates heat to the environment. As the difference between coolant temperature and ambient temperature, the cooling potential temperature difference reflects the thermal potential difference between the coolant and the environment. The larger the cooling potential temperature difference, the stronger the driving force for the coolant to transfer heat to the environment and the greater its heat dissipation potential; the smaller the cooling potential temperature difference, the weaker the driving force for the coolant to transfer heat to the environment and the smaller its heat dissipation potential.

[0048] It is understandable that the fuzzy set of cooling potential temperature difference refers to the set used to express the fuzzy concept of "the magnitude of the cooling potential temperature difference". Based on the range of variation of the cooling potential temperature difference, multiple fuzzy sets of cooling potential temperature differences, the boundaries and ranges of the fuzzy sets, and the mapping relationship describing the membership degrees of the corresponding fuzzy sets can be determined.

[0049] For example, the minimum cooling potential temperature difference is 0℃ and the maximum is 25℃. Based on the range of variation of the cooling potential temperature difference, three fuzzy sets of cooling potential temperature differences—low, medium, and high—can be determined. The range of the low fuzzy set is 0℃-5℃; the range of the medium fuzzy set is 0℃-15℃; and the range of the high fuzzy set is 5℃-25℃. At 0℃, the membership degree of the fuzzy set is 100%. At 5℃, the membership degree of the fuzzy set is 0%. During the process of the cooling potential temperature difference changing from 0℃ to 5℃, the membership degree of the fuzzy set changes linearly, that is, the membership function of the fuzzy set is [0, 0, 5]. At 5℃, the membership degree of the fuzzy set is 100%. At 0℃ or 15℃, the membership degree of the fuzzy set is 0%. During the process of the cooling potential temperature difference changing from 5℃ to 0℃ or 15℃, the membership degree of the fuzzy set changes linearly, that is, the membership function of the fuzzy set is [0, 5, 15]. At 15℃, the membership degree of the fuzzy set is 100%. At 5℃ or 25℃, the membership degree of the fuzzy set is 0%. During the process of the cooling potential temperature difference changing from 15℃ to 5℃ or 25℃, the membership degree of the fuzzy set changes linearly, that is, the membership function of the fuzzy set is [5, 15, 25].

[0050] It should be noted that the fuzzy set setting of the cooling potential temperature difference described above is only an exemplary description. For example, other conditions can be selected to determine the fuzzy set of the cooling potential temperature difference according to the actual situation; the upper and lower limits of the variation range of the cooling potential temperature difference may be larger or smaller; the number of fuzzy sets of the cooling potential temperature difference may be two, four or more; the fuzzy set may also be set to include a set of a series of discrete temperature values; the boundary of each fuzzy set may also be set to other thresholds; other mapping relationships may also be set to determine the membership degree of the fuzzy set. This application does not impose specific limitations on these aspects.

[0051] Furthermore, the membership fuzzy set is the fuzzy set to which the cooling potential temperature difference belongs, and the cooling potential temperature difference can have multiple membership fuzzy sets. The total membership fuzzy sets of the cooling potential temperature difference and the membership degrees of all membership fuzzy sets constitute the second set of fuzzy information of the cooling potential temperature difference.

[0052] For example, when the cooling potential temperature difference is 5℃, it belongs only to the fuzzy set (0℃-15℃). The fuzzy set is the fuzzy set to which the cooling potential temperature difference is 5℃. According to the mapping relationship, the membership degree of the fuzzy set is determined to be 100%, forming the second set of fuzzy information when the cooling potential temperature difference is 5℃. When the cooling potential temperature difference is 10℃, it belongs to both the fuzzy set (0℃-15℃) and the fuzzy set high (5℃-25℃). The fuzzy set and the fuzzy set high are the fuzzy sets to which the cooling potential temperature difference is 10℃. According to the mapping relationship, the membership degree of the fuzzy set is determined to be 50%, and the membership degree of the fuzzy set high is 50%, together forming the second set of fuzzy information when the cooling potential temperature difference is 10℃.

[0053] S203: Determine the fan control parameters based on the mapping of the first set of fuzzy information and the second set of fuzzy information in the rule base.

[0054] In practical applications, control logic can be constructed based on the actual operating characteristics of the electric drive system's thermal management. The collection of all control logic constitutes the rule base. It can be understood that, in this embodiment, the rule base establishes the association between input and output information. Specifically, the rule base contains multiple sets of preset mapping rules, each consisting of input conditions and an output result. In one possible implementation, the input conditions are a combination of a first set of fuzzy information and a second set of fuzzy information, and the output result is fan control parameters adapted to the current situation.

[0055] Typically, the first and second sets of fuzzy information are input into a rule base. The rule base compares the combination of the first and second sets of fuzzy information with the input conditions in preset mapping rules, filters out the active mapping rules, and then uses the output of the active mapping rules as the final fan control parameters. The first set of fuzzy information reflects the dynamic characteristics of heat transfer between the electric drive system and the coolant, while the second set of fuzzy information reflects the heat dissipation potential characteristics between the coolant and the environment. The combination of the two can accurately reflect the actual situation of the current electric drive system, and the output is the fan control parameters adapted to the current situation. In this way, controlling the fan's operating state according to these fan control parameters is more in line with the temperature of the electric drive system, which can reduce heat dissipation response delay or fan energy waste.

[0056] S204: Control the working state of the fan according to the fan control parameters.

[0057] In practical applications, controlling the fan's operating state can be achieved through signal conversion and execution drive. Typically, a corresponding control signal (such as a PWM signal) is generated based on determined fan control parameters. This signal is then transmitted to the fan's operating state adjustment execution module. The execution module adjusts the fan speed or output power according to the signal parameters, ensuring that the fan's actual operating state matches the requirements of the fan control parameters. This makes the fan's operating state more closely match the temperature of the electric drive system, reducing heat dissipation response delays and fan energy waste.

[0058] Of course, those skilled in the art can also choose other methods to control the working state of the fan according to the fan control parameters according to actual needs. For example, the drive circuit inside the fan can be directly controlled by digital signals, and the output voltage or current can be adjusted according to the fan control parameter requirements to achieve control of the fan working state. This application does not impose specific limitations on this.

[0059] In practical applications, there may be issues such as insufficient fan control adaptability when the electric drive system is in extreme temperature scenarios such as high temperature or low energy-saving temperature, and inability to effectively control the electric drive system when the temperature is invalid.

[0060] To address the aforementioned technical problems, in one possible implementation, if the temperature of the electric drive system is greater than or equal to a high-temperature threshold, then the fan control parameters are determined to be high-temperature control parameters; and / or, If the temperature of the electric drive system is less than or equal to the energy-saving temperature threshold, then the fan control parameters are determined to be energy-saving control parameters; and / or, If the temperature of the electric drive system is invalid, then the fan control parameters are determined to be the default control parameters.

[0061] Understandably, a high-temperature threshold is typically set based on the upper limit of the safe operating temperature of the electric drive system. The fan's high-temperature operating state is then set based on whether the electric drive system temperature reaches or exceeds this threshold. If the electric drive system temperature is greater than or equal to the high-temperature threshold, the fan control parameters are determined to be the high-temperature control parameters corresponding to the high-temperature operating state. This is used for more efficient auxiliary heat dissipation, avoiding sustained temperature increases due to delayed heat dissipation, and ensuring the electric drive system operates at a reasonable temperature.

[0062] An energy-saving temperature threshold can be set based on a reasonable value for the safe operating temperature of the electric drive system. The fan's energy-saving operating state can then be set if the electric drive system temperature does not exceed this threshold. If the electric drive system temperature is less than or equal to the energy-saving temperature threshold, the fan control parameters are determined to be the energy-saving control parameters for the corresponding energy-saving operating state, thereby reducing the fan's energy consumption.

[0063] The fan can also be set to a default operating state to prevent ineffective control when the electric drive system temperature cannot be obtained. If the electric drive system temperature cannot be obtained normally or is significantly outside a reasonable range, the electric drive system temperature can be considered invalid, and the fan control parameters will be set to the default control parameters corresponding to the default operating state.

[0064] It should be noted that the high temperature threshold, energy-saving temperature threshold, high temperature control parameters, energy-saving control parameters, and default control parameters can all be set values ​​selected by those skilled in the art based on actual conditions. Of course, those skilled in the art can also choose other methods to set the above parameters based on actual conditions, such as determining dynamic parameters based on the analysis of the working status of past electric drive systems and fans. This application does not impose specific limitations on this.

[0065] See Figure 3 This is a flowchart illustrating another thermal management method for an electric drive system provided in an embodiment of this application. Figure 3 As shown, in Figure 2 Based on the method embodiment shown, step S203 specifically includes the following steps.

[0066] S301: Determine the third set of fuzzy information based on the operating status of the coolant pump.

[0067] The third set of fuzzy information includes all the fuzzy sets and the membership degrees of all the fuzzy sets corresponding to the working state of the coolant pump. In practical applications, the real-time operating parameters of the coolant pump, such as speed and power, can reflect the pump's working status and demonstrate the efficiency of the coolant in transferring heat within the circulation system. A stronger operating condition results in a faster coolant circulation speed and a greater ability to transfer heat from the electric drive system to the heat dissipation end; conversely, a weaker operating condition leads to a slower coolant circulation speed and relatively lower heat transfer efficiency.

[0068] It is understandable that the fuzzy set of coolant pump operating states refers to the set used to express the fuzzy concept of "strength or weakness of coolant pump operating state". Based on the range of changes in coolant pump operating states, the fuzzy sets of multiple coolant pump operating states, the boundaries and ranges of the fuzzy sets, and the mapping relationship describing the corresponding membership degrees of the fuzzy sets can be determined.

[0069] For example, the minimum value of the coolant pump's operating state is 0%, and the maximum value is 100%. Based on the range of variation of the coolant pump's operating state, fuzzy sets of three coolant pump operating states—low, medium, and high—can be determined. The low fuzzy set ranges from 0% to 40%; the medium fuzzy set ranges from 30% to 70%; and the high fuzzy set ranges from 60% to 100%. At 0%, the membership degree of the lower fuzzy set is 100%; at 40%, the membership degree of the lower fuzzy set is 0%. During the change of working state from 0% to 40%, the membership degree of the lower fuzzy set changes linearly, that is, the membership function of the lower fuzzy set is [0, 0, 40]. At 40%, the membership degree of the fuzzy set is 100%; at 30% or 70%, the membership degree of the fuzzy set is 0%. During the change of working state from 40% to 30% or 70%, the membership degree of the fuzzy set changes linearly, that is, the membership function of the fuzzy set is [30, 40, 70]. At 70%, the membership degree of the higher fuzzy set is 100%; at 60% or 100%, the membership degree of the higher fuzzy set is 0%. During the change of working state from 70% to 60% or 100%, the membership degree of the higher fuzzy set changes linearly, that is, the membership function of the higher fuzzy set is [60, 70, 100].

[0070] It should be noted that the fuzzy set setting of the coolant pump's operating state described above is merely an exemplary description. For example, other conditions can be selected to determine the fuzzy set according to actual conditions; the operating state of the coolant pump can also be reflected in other forms; the number of fuzzy sets can be two, four, or more; the fuzzy set can also be set to include a set of discrete operating condition values; the boundary of each fuzzy set can also be set to other thresholds; other mapping relationships can also be set to determine the membership degree of the fuzzy set. This application does not impose specific limitations on these aspects.

[0071] Furthermore, the membership fuzzy set is the fuzzy set to which the operating state of the coolant pump belongs, and the operating state of the coolant pump can have multiple membership fuzzy sets. The total membership fuzzy sets of the coolant pump's operating state and the membership degrees of all membership fuzzy sets constitute its third set of fuzzy information.

[0072] For example, when the coolant pump is operating at 40%, it belongs only to the fuzzy set (30%-70%). The fuzzy set is the fuzzy set belonging to this operating state. According to the mapping relationship, the membership degree of the fuzzy set is determined to be 100%, forming the third set of fuzzy information at this time. When the operating state is 65%, it belongs to both the fuzzy set (30%-70%) and the fuzzy set height (60%-100%). The fuzzy set and the fuzzy set height are the fuzzy sets belonging to this operating state. According to the mapping relationship, the membership degree of the fuzzy set is determined to be 17%, and the membership degree of the fuzzy set height is 50%, together forming the third set of fuzzy information at this time.

[0073] S302: Determine the fan control parameters based on the mapping of the first set of fuzzy information, the second set of fuzzy information, and the third set of fuzzy information in the rule base.

[0074] Similar to step S203, control logic can be constructed based on the actual operating characteristics of the electric drive system's thermal management. The set of all control logics constitutes the rule base. It can be understood that in this embodiment, the rule base establishes the association between input and output information. Specifically, the rule base contains multiple sets of preset mapping rules, each consisting of input conditions and an output result. In one possible implementation, the input conditions are a combination of a first set of fuzzy information, a second set of fuzzy information, and a third set of fuzzy information, and the output result is fan control parameters adapted to the current situation.

[0075] Typically, the first, second, and third sets of fuzzy information are input into the rule base. The rule base compares the combination of these three sets with the input conditions in the preset mapping rules, filters out the activated mapping rules, and then uses the output results corresponding to the activated mapping rules as the final fan control parameters. The first set of fuzzy information reflects the dynamic characteristics of heat transfer between the electric drive system and the coolant; the second set reflects the heat dissipation potential characteristics between the coolant and the environment; and the third set reflects the circulation capacity characteristics of the coolant pump. The combination of these three sets can more comprehensively and accurately reflect the actual situation of the current electric drive system, and the output results are fan control parameters adapted to the current situation.

[0076] See Figure 4 This is a flowchart illustrating another thermal management method for an electric drive system provided in an embodiment of this application. Figure 4 As shown, in Figure 3 Based on the method embodiment shown, step S302 specifically includes the following steps.

[0077] S401: Based on the mapping of the first set of fuzzy information, the second set of fuzzy information, and the third set of fuzzy information in the rule base, determine all activation rules for the fan control parameters.

[0078] In one possible implementation, the rule base takes as input conditions the membership fuzzy sets from the first, second, and third sets of fuzzy information, and outputs as the active fuzzy sets of fan control parameters that fit the current situation. It's understood that since each set of fuzzy information may include multiple membership fuzzy sets, typically all membership fuzzy sets from the first, second, and third sets are input into the rule base. The rule base then compares the input conditions with preset mapping rules, filters each active mapping rule, and thus determines all active rules for the fan control parameters.

[0079] S402: Determine the fuzzy information of the fan control parameters according to all activation rules of the fan control parameters.

[0080] The fuzzy information of the fan control parameters includes the activation fuzzy set corresponding to all activation rules and the activation degree of all activation fuzzy sets.

[0081] As mentioned above, the input conditions for the rule base are the membership fuzzy sets from the first, second, and third sets of fuzzy information, and the output is the activation fuzzy set of the fan control parameters that matches the current situation. Based on all the activation rules of the fan control parameters, the corresponding output activation fuzzy set is determined.

[0082] Similarly, the activation degree can be used to describe the activation degree of mapping rules or activation fuzzy sets. The activation degree of all activation rules can be preset to a fixed value. Of course, those skilled in the art can also choose other methods to determine the activation degree of activation fuzzy sets according to the actual situation, such as setting a weighting coefficient according to the number of activation rules to determine the activation degree of activation fuzzy sets, etc. The embodiments of this application do not impose specific limitations on this.

[0083] In one possible implementation, the activation degree of the activated fuzzy set is the minimum value among all the membership degrees of the fuzzy sets corresponding to the activation rule.

[0084] It is understandable that, since the input conditions of each activation rule are composed of the membership fuzzy sets corresponding to the first, second, and third groups of fuzzy information, and each membership fuzzy set has a corresponding membership degree, we can quantify the actual activation degree of the activation rule on the output result based on the property of "logical AND" in fuzzy logic, that is, the degree of validity of a rule is determined by the condition with the smallest membership degree. In other words, the activation degree is the minimum value among the membership degrees of all membership fuzzy sets corresponding to the activation rule.

[0085] In one possible implementation, the membership fuzzy set corresponding to the heat transfer temperature difference includes low, medium, and high; the membership fuzzy set corresponding to the cooling potential temperature difference includes low, medium, and high; the membership fuzzy set corresponding to the working state of the coolant pump includes low, medium, and high; and the activation fuzzy set corresponding to the fan control parameters includes very low, low, medium, high, and very high.

[0086] For example, one rule base in this implementation is shown in the table below: If the first set of fuzzy information only includes the membership fuzzy set with a membership degree of 100%; the second set of fuzzy information only includes the membership fuzzy set with a membership degree of 100%; and the third set of fuzzy information only includes the membership fuzzy set with a membership degree of 75%, then activation rule R13 is triggered. The fuzzy information of the fan control parameters includes the activation fuzzy set corresponding to activation rule R13, and the activation degree is the minimum value among the membership degrees of all membership fuzzy sets corresponding to the activation rule, i.e., 75%.

[0087] If the first set of fuzzy information includes a fuzzy membership set (100% membership); the second set includes a fuzzy membership set (50% membership) and a high-level fuzzy membership set (50% membership); and the third set includes a low-level fuzzy membership set (75% membership), then activation rules R13 and R16 are triggered. The fuzzy information for the fan control parameters includes the activation fuzzy set corresponding to activation rule R13 (50% activation) and the low-level activation fuzzy set corresponding to activation rule R16 (50% activation). If the first set of fuzzy information includes a fuzzy membership set with a membership degree of 100%; the second set of fuzzy information includes a fuzzy membership set (with a membership degree of 50%) and a high-level fuzzy membership set (with a membership degree of 50%); and the third set of fuzzy information includes a low-level fuzzy membership set (with a membership degree of 25%) and a fuzzy membership set (with a membership degree of 75%), then activation rules R13, R14, R16, and R17 are triggered. The fuzzy information of the fan control parameters includes the activation fuzzy set corresponding to activation rule R13 (with an activation degree of 25%), the activation fuzzy set corresponding to activation rule R14 (with an activation degree of 50%), the activation fuzzy set corresponding to activation rule R16 (with an activation degree of 25%), and the activation fuzzy set corresponding to activation rule R17 (with a very low activation degree of 50%).

[0088] It should be noted that the above is only an exemplary description. Those skilled in the art can also formulate other rule bases according to the actual situation, such as modifying the output activation fuzzy set average to be higher or lower, or partially adjusting the output results, etc. The embodiments of this application do not impose specific limitations on this.

[0089] S403: Defuzzify the fuzzy information of the fan control parameters to determine the fan control parameters.

[0090] Specifically, the fuzzy information of the fan control parameters includes the activation fuzzy set corresponding to all activation rules and the activation degree of all activation fuzzy sets. The activation fuzzy set corresponding to the fan control parameters and the activation degree of all activation fuzzy sets are defuzzified to determine the fan control parameters.

[0091] Of course, those skilled in the art can choose various algorithms to defuzzify the fuzzy information of the fan control parameters according to the actual situation. For example, they can identify the fuzzy set with the largest membership degree among all active fuzzy sets, use the feature parameter value corresponding to the fuzzy set as the defuzzification result of the fan control parameters, or determine a representative parameter value for each active fuzzy set, and then use the activation degree of each active fuzzy set as the weight to calculate a weighted average of all representative parameter values. The embodiments of this application do not impose specific limitations on this.

[0092] In one possible implementation, via the formula: Determine the fan control parameters.

[0093] Among them, D f Here are the fan control parameters, where n is the number of active rules and μ is the number of active rules. i x is the activation degree of the i-th activation rule. i It is the representative point corresponding to the i-th activation rule.

[0094] Specifically, the representative point represents the core feature parameter value corresponding to each activated fuzzy set, that is, simplifying the overall feature of the activated fuzzy set into a specific numerical value for defuzzification calculation. The activation degree μ of each activation rule is used as the reference. i As the weight, for the representative point x of each rule i After weighted summation, dividing by the sum of all activations yields the final fan control parameter D. f .

[0095] To better understand the technical solution of this application, the following describes the embodiments of this application in detail, taking a complete example of fan control parameter determination and fan working state control. For example, a rule base as shown in the table above is established, and three fuzzy sets, low, medium and high, are set to correspond to the heat transfer temperature difference. The membership function of the low fuzzy set is [0, 0, 10], the membership function of the medium fuzzy set is [0, 10, 20], and the membership function of the high fuzzy set is [10, 20, 30].

[0096] The cooling potential temperature difference corresponds to three fuzzy sets: low, medium, and high. The membership function of the low fuzzy set is [0, 0, 5], the membership function of the medium fuzzy set is [0, 5, 15], and the membership function of the high fuzzy set is [5, 15, 25].

[0097] The operating state of the coolant pump corresponds to three fuzzy sets: low, medium, and high. The membership function of the low fuzzy set is [0, 0, 40], the membership function of the medium fuzzy set is [30, 40, 70], and the membership function of the high fuzzy set is [60, 70, 100].

[0098] If the current heat transfer temperature difference is 10℃, the cooling potential temperature difference is 5℃, and the coolant pump's operating state is 10%, then the first set of fuzzy information only includes the fuzzy set of membership, with a membership degree of 100%; the second set of fuzzy information only includes the fuzzy set of membership, with a membership degree of 100%; and the third set of fuzzy information only includes the fuzzy set of membership, with a membership degree of 75%.

[0099] Based on the mapping of the first, second, and third sets of fuzzy information in the rule base shown in the table above, the trigger activation rule for the fan control parameters is determined to be R13. The fuzzy information of the fan control parameters includes the activation fuzzy set corresponding to activation rule R13, with an activation degree of 75% and a representative point of 40% in the activation fuzzy set.

[0100] Through the formula: Set the fan control parameters to 30%. Generate a PWM signal with a 30% duty cycle to control the fan's operating state.

[0101] Corresponding to the above embodiments, this application also provides a thermal management device for an electric drive system.

[0102] See Figure 5 This is a schematic diagram of the structure of a thermal management device for an electric drive system provided in an embodiment of this application. Figure 5 As shown, the electric drive system thermal management device 500 includes a first information determination module 501, a second information determination module 502, a control parameter determination module 503, and a working status control module 504.

[0103] The first information determination module 501 is used to determine a first set of fuzzy information based on the heat transfer temperature difference, wherein the heat transfer temperature difference is the difference between the temperature of the electric drive system and the temperature of the coolant, and the first set of fuzzy information includes all the fuzzy sets corresponding to the heat transfer temperature difference and the membership degree of all the fuzzy sets. The second information determination module 502 is used to determine a second set of fuzzy information based on the cooling potential temperature difference, wherein the cooling potential temperature difference is the difference between the coolant temperature and the ambient temperature, and the second set of fuzzy information includes all the fuzzy sets corresponding to the cooling potential temperature difference and the membership degree of all the fuzzy sets. The control parameter determination module 503 is used to determine fan control parameters based on the mapping of the first set of fuzzy information and the second set of fuzzy information in the rule base; The working status control module 504 is used to control the working status of the fan according to the fan control parameters.

[0104] In one possible implementation, the control parameter determination module 503 is further configured to determine a third set of fuzzy information based on the operating state of the coolant pump, the third set of fuzzy information including all membership fuzzy sets and membership degrees of all membership fuzzy sets corresponding to the operating state of the coolant pump; and determine fan control parameters based on the mapping of the first set of fuzzy information, the second set of fuzzy information and the third set of fuzzy information in the rule base.

[0105] In one possible implementation, the control parameter determination module 503 is further configured to: determine all activation rules for the fan control parameters based on the mapping of the first set of fuzzy information, the second set of fuzzy information, and the third set of fuzzy information in the rule base; determine fuzzy information for the fan control parameters based on the all activation rules, wherein the fuzzy information for the fan control parameters includes the activation fuzzy set corresponding to all activation rules and the activation degree of all activation fuzzy sets; and defuzzify the fuzzy information for the fan control parameters to determine the fan control parameters.

[0106] In one possible implementation, the control parameter determination module 503 is also used to determine the parameters using the formula: Determine the fan control parameters.

[0107] Among them, D f Here are the fan control parameters, where n is the number of active rules and μ is the number of active rules. i x is the activation degree of the i-th activation rule. i It is the representative point corresponding to the i-th activation rule.

[0108] For details regarding the embodiments of this application, please refer to the description of the above method embodiments. For the sake of brevity, these details will not be repeated here.

[0109] Corresponding to the above embodiments, this application also provides a vehicle.

[0110] See Figure 6 This is a structural schematic diagram of a vehicle provided in an embodiment of this application. Figure 6 As shown, vehicle 600 includes controller 601. Controller 601 is configured to perform some or all of the steps in the method embodiments.

[0111] For details regarding the embodiments of this application, please refer to the description of the above method embodiments. For the sake of brevity, these details will not be repeated here.

[0112] Corresponding to the above embodiments, this application also provides a computer-readable storage medium, wherein the computer-readable storage medium may store a program, and when the program runs, it can control the device where the computer-readable storage medium is located to execute some or all of the steps in the above method embodiments. In specific implementation, the computer-readable storage medium may be a magnetic disk, an optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0113] For details regarding the embodiments of this application, please refer to the description of the above method embodiments. For the sake of brevity, these details will not be repeated here.

[0114] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0115] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the above-described apparatus, controller, and computer storage medium can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0117] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, 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 part 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.

[0118] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A thermal management method for an electric drive system, characterized in that, include: The first set of fuzzy information is determined based on the heat transfer temperature difference, which is the difference between the temperature of the electric drive system and the temperature of the coolant. The first set of fuzzy information includes all the fuzzy sets corresponding to the heat transfer temperature difference and the membership degree of all the fuzzy sets. The second set of fuzzy information is determined based on the cooling potential temperature difference, which is the difference between the coolant temperature and the ambient temperature. The second set of fuzzy information includes all the fuzzy sets corresponding to the cooling potential temperature difference and the membership degree of all the fuzzy sets. Based on the mapping of the first set of fuzzy information and the second set of fuzzy information in the rule base, fan control parameters are determined; The fan's operating state is controlled according to the aforementioned fan control parameters.

2. The method according to claim 1, characterized in that, The step of determining fan control parameters based on the mapping of the first set of fuzzy information and the second set of fuzzy information in the rule base includes: The third set of fuzzy information is determined based on the working state of the coolant pump. The third set of fuzzy information includes all the fuzzy sets and the membership degrees of all the fuzzy sets corresponding to the working state of the coolant pump. The fan control parameters are determined based on the mapping of the first set of fuzzy information, the second set of fuzzy information, and the third set of fuzzy information in the rule base.

3. The method according to claim 2, characterized in that, The step of determining fan control parameters based on the mapping of the first set of fuzzy information, the second set of fuzzy information, and the third set of fuzzy information in the rule base includes: Based on the mapping of the first set of fuzzy information, the second set of fuzzy information, and the third set of fuzzy information in the rule base, all activation rules for the fan control parameters are determined. Based on all activation rules of the fan control parameters, the fuzzy information of the fan control parameters is determined. The fuzzy information of the fan control parameters includes the activation fuzzy set corresponding to all activation rules and the activation degree of all activation fuzzy sets. The fuzzy information of the fan control parameters is defuzzified to determine the fan control parameters.

4. The method according to claim 3, characterized in that, The activation degree of the activated fuzzy set is the minimum value among all the membership degrees of the fuzzy sets corresponding to the activation rule.

5. The method according to claim 4, characterized in that, The process of defuzzifying the fuzzy information of the fan control parameters to determine the fan control parameters includes: Through the formula: Determine the fan control parameters; Among them, D f Here are the fan control parameters, where n is the number of active rules and μ is the number of active rules. i x is the activation degree of the i-th activation rule. i It is the representative point corresponding to the i-th activation rule.

6. The method according to claim 1, characterized in that, Before controlling the operating state of the fan according to the fan control parameters, the method further includes: If the temperature of the electric drive system is greater than or equal to the high temperature threshold, then the fan control parameters are determined to be high temperature control parameters. And / or, If the temperature of the electric drive system is less than or equal to the energy-saving temperature threshold, then the fan control parameters are determined to be energy-saving control parameters. And / or, If the temperature of the electric drive system is invalid, then the fan control parameters are determined to be the default control parameters.

7. The method according to claim 3, characterized in that, The membership fuzzy set corresponding to the heat transfer temperature difference includes low, medium, and high; The membership fuzzy set corresponding to the cooling potential temperature difference includes low, medium, and high; The fuzzy set corresponding to the operating state of the coolant pump includes low, medium, and high; The activation fuzzy set corresponding to the fan control parameters includes very low, low, medium, high, and very high.

8. A thermal management device for an electric drive system, characterized in that, include: The first information determination module is used to determine a first set of fuzzy information based on the heat transfer temperature difference, wherein the heat transfer temperature difference is the difference between the temperature of the electric drive system and the temperature of the coolant, and the first set of fuzzy information includes all the fuzzy sets corresponding to the heat transfer temperature difference and the membership degree of all the fuzzy sets. The second information determination module is used to determine a second set of fuzzy information based on the cooling potential temperature difference, wherein the cooling potential temperature difference is the difference between the coolant temperature and the ambient temperature, and the second set of fuzzy information includes all the fuzzy sets corresponding to the cooling potential temperature difference and the membership degree of all the fuzzy sets. The control parameter determination module is used to determine fan control parameters based on the mapping of the first set of fuzzy information and the second set of fuzzy information in the rule base; The operating status control module is used to control the operating status of the fan according to the fan control parameters.

9. A vehicle, characterized in that, include: A controller configured to perform the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by the controller, implements the method described in any one of claims 1-7.

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