A vehicle-based motor cooling intelligent control method and system

Abstract

The present invention discloses a vehicle-based intelligent motor cooling control method and system, which relates to the field of motor cooling. The method includes: extracting technical parameters based on the internal structure information of the vehicle motor, and using the technical parameters to generate a motor mathematical model to determine the heat generation rate of the motor when used in the vehicle; analyzing the upper temperature limit of the motor within the maximum allowable starting time of the vehicle according to the heat generation rate, and dividing the upper temperature limit based on the starting time to generate a temperature segmentation interval; generating a cooling combination based on the cooler structure, and using the cooling combination to simulate the optimal cooling power of the cooler under the temperature segmentation interval conditions; using the temperature segmentation interval and the corresponding optimal cooling power as the control basis, and adjusting the cooler based on the control basis to realize intelligent cooling control of the motor. The present invention realizes the automation of the cooling process, reduces the need for manual intervention, and improves the intelligence level of the cooler and the convenience of operation.

Description

Technical Field

[0001] The present invention relates to the field of motor cooling, and in particular to a vehicle-based motor cooling intelligent control method and system. Background Art

[0002] Vehicle motors refer to electric motors installed on various types of vehicles to provide power or perform specific functions. Vehicle motors are widely used in various types of vehicles such as automobiles, motorcycles, electric bicycles, electric tricycles, electric wheelchairs, electric forklifts, electric sightseeing vehicles, etc. The heat generated by vehicle motors mainly comes from their internal working principles and the energy conversion generated during operation.

[0003] When the motor is running, the current generates resistance loss through the winding, resulting in heat generation. This part of the loss is proportional to the square of the current, so high current will cause greater heat generation, and the heat generated by the motor must be dissipated through an effective cooler to keep the motor temperature within a safe operating range. If the motor is overheated, it may lead to performance degradation, shortened life, or even damage. Therefore, the motor cooler is essential to ensure the reliable operation of the motor.

[0004] However, the motor cooling control system in the prior art cannot set the cooling parameters of the cooler according to the motor temperature when used. As a result, under each temperature condition, the cooler cannot be in the most efficient working state, which not only reduces the cooling efficiency but also increases the energy consumption on a certain basis.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a vehicle-based motor cooling intelligent control method and system to achieve automation of the cooling process and reduce the need for manual intervention.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a vehicle-based motor cooling intelligent control method, the motor cooling intelligent control method comprising:

[0009] S1. Extracting technical parameters based on the internal structure information of the vehicle motor, and using the technical parameters to generate a motor mathematical model to determine the heat generation rate of the motor when used in the vehicle;

[0010] S2. Analyze the upper temperature limit of the motor within the maximum allowable starting time of the vehicle according to the heat generation rate, and divide the upper temperature limit into temperature segment intervals based on the starting time;

[0011] S3, generating a cooling combination based on the cooler structure, and using the cooling combination to simulate the optimal cooling power of the cooler under the temperature segmentation interval conditions;

[0012] S4. The temperature segment interval and the corresponding optimal cooling power are used as the control basis, and the cooler is adjusted based on the control basis to realize intelligent cooling control of the motor.

[0013] Preferably, extracting technical parameters based on the internal structure information of the vehicle motor, and using the technical parameters to generate a motor mathematical model to determine the heat generation rate of the motor when used in the vehicle includes:

[0014] S11, determining the specification parameters of the vehicle motor, and obtaining the rated power and rated speed of the motor from the specification parameters, and collecting the size information of the motor;

[0015] S12, establishing a proportional mathematical model of the motor based on the specification parameters and size information, and simulating the heat generation process of the vehicle when it is used by injecting alternating magnetic fields and current windings;

[0016] S13, setting different injection durations and injection efficiencies based on the heat generation process, and starting a motor mathematical model according to the injection duration and injection efficiency to obtain a temperature value of the motor;

[0017] S14. Analyze the heat generation rate of the motor when the vehicle is used according to the temperature value result and the injection time.

[0018] Preferably, generating a cooling combination based on the cooler structure, and using the cooling combination to simulate the optimal cooling power of the cooler under the temperature segmentation interval conditions includes:

[0019] S31, collecting structural information of internal components of the vehicle cooler, and obtaining parameter information of the air cooler, the oil cooler and the liquid cooler according to the structural information, and generating a corresponding three-dimensional model based on the parameter information;

[0020] S32, generating a cooling combination based on the corresponding working principles of the air cooler, the oil cooler and the liquid cooler, and using mathematical calculation software to randomly generate refrigeration parameters including air cooling pressure difference and wind flow, oil cooling viscosity and liquid cooling flow within the definition domain, and combining the generated parameters with the cooling combination to generate a random calculation condition;

[0021] S33, integrating the three-dimensional model and the calculation conditions into the motor mathematical model, calling the finite difference technology to start the numerical simulation, performing the motor cooling process and saving the simulation results into the database;

[0022] S34, analyzing the cooling efficiency of the motor and the power generated by the cooler when the cooling purpose is achieved under the temperature segmentation conditions based on the simulation results;

[0023] S35. Introduce natural language processing technology to analyze and calculate the nonlinear mapping relationship between working conditions and cooling efficiency and cooler power, and combine it with statistical experimental methods to perform optimal operation of motor cooling methods.

[0024] Preferably, the natural language processing technology is introduced to analyze and calculate the nonlinear mapping relationship between the working condition and the cooling efficiency and the cooler power, and the motor cooling method is optimized by combining the statistical experimental method. The operation includes:

[0025] S351. Perform dimensionality reduction processing on the calculation condition data using component analysis technology, and select several groups of relatively independent principal component variables based on the processing results;

[0026] S352. Introduce natural language processing technology to construct a nonlinear mapping relationship between the principal component variables and the cooling efficiency and cooler power, and predict the cooling time required under different motor temperature conditions;

[0027] S353. An evaluation system covering cooling efficiency and cooler power evaluation indicators is established based on cooling time and statistical test methods, and the evaluation system is used to select the optimal cooling method for each interval under the temperature segmentation interval conditions to obtain the optimal cooling power.

[0028] Preferably, the component analysis technology is used to perform dimension reduction processing on the calculation condition data, and several groups of relatively independent principal component variables are screened based on the processing results, including:

[0029] S3511, performing standardized dimension reduction processing on the calculation condition data, and setting the calculation condition data after dimension reduction as the input factor of the component analysis technology, and the cooling efficiency and cooling power as the output factors;

[0030] S3512, using component analysis technology to calculate the correlation coefficient matrix between the working condition and the cooling efficiency and cooling power, and analyzing and calculating the cumulative contribution rate of each refrigeration parameter in the working condition based on the correlation coefficient matrix;

[0031] S3513. Sort the cumulative contribution rates by order, and select the top three refrigeration parameters as the main component variables that affect the refrigeration efficiency.

[0032] Preferably, natural language processing technology is introduced to construct a nonlinear mapping relationship between the principal component variables and the cooling efficiency and the cooler power, and the cooling time required under different motor temperature conditions is predicted, including:

[0033] S3521. Use principal component variables, cooling efficiency, and cooler power to establish a dynamic model, and use the dynamic model as a state equation and efficiency equation for natural language processing technology;

[0034] S3522. Define the state value of the principal component variable and the control value of the cooler power based on the state equation and the efficiency equation;

[0035] S3523. Based on the state value and the control value, a cooling state equation is established as a prediction model of the prediction algorithm, and an optimization function and control constraints are defined to convert the cooling problem into an optimization problem;

[0036] S3524. Utilize the multiple shooting method to solve the optimization problem and optimize the prediction model, and use the prediction model to predict the cooling time required under different motor temperature conditions.

[0037] Preferably, based on the state value and the control value, a cooling state equation is established as a prediction model of the prediction algorithm, and an optimization function and control constraints are defined to convert the cooling problem into an optimization problem, including:

[0038] S35231. Define the state value as the key variable and the control value as the adjustment variable, and generate a cooling state equation to describe the influence relationship between the cooler and the motor temperature;

[0039] S35232. After discretizing the cooling state equation, establish a prediction model to predict the temperature change during the cooling process, and define an optimization function including the minimum cooling time and the minimum power consumption;

[0040] S35233. Set the state safety value and power increment limit of the cooler, and define the state safety value and power increment limit as control constraints, and form an optimization problem with the control constraints and the optimization function structure.

[0041] Preferably, the cooling state equation is expressed as:

[0042] ;

[0043] In the formula, Y Indicates the cooling status value, C m1 , C m2 , C m3 Respectively represent the power output of the air cooler, oil cooler and liquid cooler, S n1 and S n3 Respectively represent the physical upper and lower limits of the cooler power, v 1 means in T The air cooling pressure difference set by the time period cooler, v 2 means in T The wind flow rate set for the time period cooler, v 3 means in T The oil viscosity of the time period cooler setting, v 4 means in TThe liquid cooling flow rate set by the time period cooler, c T Indicated in T The temperature of the motor during the time period, t Indicates the upper temperature limit, g Indicates the motor cooling target temperature value.

[0044] Preferably, taking the temperature segment interval and the corresponding optimal cooling power as the control basis, and adjusting the cooler based on the control basis to realize the intelligent cooling control of the motor includes:

[0045] S41, constructing a control algorithm based on the temperature segmentation interval based on the vehicle controller, and adding the corresponding optimal cooling power to the control algorithm;

[0046] S42, when the vehicle is started, the real-time temperature of the motor feedback is monitored and matched with the temperature segment interval accordingly, and when the motor temperature rises to a certain value, the cooler enters the PID control mode;

[0047] S43, PID control drives the operating speed and operating state of the cooler according to the control algorithm, and turns off the cooler when the real-time temperature of the motor drops to a preset value.

[0048] In a second aspect, the present invention further provides a vehicle-based motor cooling intelligent control system, the motor cooling intelligent control system comprising:

[0049] A motor heat generation rate calculation module is used to extract technical parameters based on the internal structure information of the vehicle motor, and use the technical parameters to generate a motor mathematical model to determine the heat generation rate of the motor when it is used in the vehicle;

[0050] A temperature segmentation interval generation module is used to analyze the upper temperature limit of the motor within the maximum allowable starting time of the vehicle according to the heat generation rate, and divide the upper temperature limit into temperature segmentation intervals based on the starting time;

[0051] An optimal cooling power acquisition module is used to generate a cooling combination based on the cooler structure, and use the cooling combination to simulate the optimal cooling power of the cooler under the temperature segmentation interval conditions;

[0052] The motor intelligent cooling control module is used to take the temperature segmentation interval and the corresponding optimal cooling power as the control basis, and adjust the cooler based on the control basis to realize the intelligent cooling control of the motor.

[0053] The beneficial effects of the present invention are:

[0054] 1. The present invention generates an accurate motor mathematical model by extracting the technical parameters inside the motor, which is helpful for understanding the heating characteristics of the motor under different operating conditions. At the same time, the temperature upper limit of the motor within the maximum allowable starting time of the vehicle is analyzed according to the heat generation rate, and the temperature upper limit is divided into a temperature segment interval based on the starting time. The temperature upper limit is divided into multiple segment intervals, which is helpful for accurately controlling the cooling power output according to different temperature conditions, thereby avoiding excessive fluctuations in cooling power and improving cooling efficiency. A cooling combination is generated based on the cooler structure to simulate the optimal cooling power of the cooler under the temperature segment interval conditions. The optimal cooling power is simulated under different temperature segment conditions based on the cooling combination design, which can ensure that the cooler works in the most efficient state in each temperature interval, thereby reducing energy consumption, realizing the automation of the cooling process, reducing the need for manual intervention, and improving the intelligence level of the cooler and the convenience of operation.

[0055] 2. The present invention establishes a proportional mathematical model of the motor based on specification parameters and size information, and simulates the heat generation process of the vehicle when it is used by injecting alternating magnetic fields and current windings, especially the thermal effects caused by the alternating magnetic fields and current windings when the motor is working. This simulation can be close to the thermal behavior of the motor in actual operation, and different injection durations and injection efficiencies are set based on the heat generation process. The motor mathematical model is started according to the injection duration and injection efficiency to obtain the temperature value of the motor, and the heat generation rate of the motor when the vehicle is used is analyzed through the temperature value results and the injection time, which provides accurate input for the control and optimization of the cooler, so that the cooler can accurately respond to the heat generated by the motor.

[0056] 3. The present invention generates a cooling combination based on the corresponding working principles of air coolers, oil coolers and liquid coolers, and uses mathematical calculation software to randomly generate refrigeration parameters including air cooling pressure difference and wind flow, oil cooling viscosity and liquid cooling flow within the definition domain. The generated parameters are combined with the cooling combination to generate a random calculation condition, which can fully simulate the working conditions of the motor under different cooling conditions and provide more choices for cooling strategies. At the same time, the finite difference technology is used for numerical simulation, which can accurately simulate the temperature distribution and heat transfer in the motor cooling process, evaluate the cooling efficiency of different cooling combinations in different temperature segments, and provide a basis for selecting the optimal cooling strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0058] Figure 1 is a flow chart of a vehicle-based motor cooling intelligent control method according to an embodiment of the present invention;

[0059] Figure 2 The diagram is a principle block diagram of a vehicle-based motor cooling intelligent control system according to an embodiment of the present invention.

[0060] In the figure:

[0061] 1. Motor heat generation rate calculation module; 2. Temperature segmentation interval generation module; 3. Optimal cooling power acquisition module; 4. Motor intelligent cooling control module. DETAILED DESCRIPTION

[0062] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0063] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0064] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0065] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0066] See also Figure 1 The present invention provides a vehicle-based motor cooling intelligent control method, the motor cooling intelligent control method comprising:

[0067] S1. Extract technical parameters based on the internal structure information of the vehicle motor, and use the technical parameters to generate a motor mathematical model to determine the heat generation rate of the motor when used in the vehicle.

[0068] In this embodiment, the technical parameters are extracted based on the internal structure information of the vehicle motor, and the motor mathematical model is generated by using the technical parameters to determine the heat generation rate of the motor when it is used in the vehicle, including:

[0069] S11. Determine the specification parameters of the vehicle motor, obtain the rated power and rated speed of the motor from the specification parameters, and collect the size information of the motor.

[0070] In this embodiment, when determining the specification parameters of the vehicle motor and obtaining the rated power and rated speed of the motor from the specification parameters, and collecting the size information of the motor, it is necessary to clarify the model and purpose of the motor used, and obtain the basic specification data of the motor from the motor manufacturer's product manual or technical documents, including the rated power, rated speed, operating voltage, current and other basic parameters of the motor.

[0071] Rated power refers to the maximum power output of the motor when it runs stably for a long time at a specific voltage and speed; rated speed refers to the speed of the motor under rated load, usually measured in revolutions per minute (RPM).

[0072] Collect the physical dimension information of the motor, including the length, diameter, weight, mounting hole position, stator inner and outer diameters, rotor air hole diameter, stator slot width and slot type, core length and permanent magnet thickness, etc. Among them, the stator inner and outer diameters, rotor air hole diameter, stator slot width and slot type, core length and permanent magnet thickness are important components that affect motor cooling.

[0073] S12. A proportional mathematical model of the motor is established based on the specification parameters and size information, and the heat generation process of the vehicle during use is simulated by injecting alternating magnetic fields and current windings.

[0074] In this embodiment, a proportional mathematical model of the motor is established based on the specification parameters and size information, and the heat generation process of the vehicle during application is simulated by injecting alternating magnetic fields and current windings. Based on the collected size information, a geometric model of the motor can be established using computer-aided design tools. The geometric model needs to accurately reflect the internal structure of the motor, including the stator, rotor, windings, air gaps, etc. In order to facilitate calculation and simulation, the established geometric model should be scaled in proportion to the actual size to ensure that the model can effectively reflect the structural characteristics of the real motor and assign appropriate material properties to each component of the motor, such as the magnetic permeability of the core, the resistivity of the windings, and the thermal conductivity of the insulating material. Electromagnetic simulation software is used to inject an alternating magnetic field based on the operating frequency and design of the motor. Specifically, it can be completed by simulating the magnetic field generated by the stator windings of the motor, and the change of the magnetic field matches the actual operating conditions during operation. By injecting current into the windings in the model, the power-on state of the motor is simulated to reflect the actual working conditions of the motor under different loads. According to the operating state of the motor, different current and frequency conditions are set to reflect the actual working conditions of the motor under different loads and speeds, and different injection durations are set for the simulation to simulate the heat accumulation generated by the motor during various operating times. Different time parameters are used to evaluate the temperature rise of the motor during short and long operation periods; the heat generated inside the motor is calculated based on the injected current and the alternating magnetic field.

[0075] S13, setting different injection durations and injection efficiencies based on the heat generation process, and starting the motor mathematical model according to the injection duration and injection efficiency to obtain the temperature value of the motor.

[0076] In this embodiment, when different injection durations and injection efficiencies are set based on the heat generation process, and the mathematical model of the motor is started according to the injection duration and injection efficiency to obtain the temperature value of the motor, the load conditions during the operation of the motor can be determined, including the current magnitude, frequency, working time, etc., and the injection duration of the current and the alternating magnetic field is set according to different operating conditions to simulate the heat accumulation of the motor during different operating times. The injection duration can represent a short-term high-load condition or a long-term stable operating condition; the injection efficiency generally refers to the proportion of energy actually converted into heat during the operation of the motor. By adjusting the injection efficiency, the heat generation of the motor at different efficiencies is simulated; the location and type of the heat source are determined, including copper loss (heat loss caused by winding resistance), iron loss (hysteresis and eddy current loss), etc. Each heat source generates different heat distribution in different parts of the motor. According to the injected current and magnetic field, the heat intensity of each heat source is calculated to ensure that the heat source distribution during the simulation is consistent with the actual situation. According to different workloads or test requirements, different injection durations are defined, such as short-term startup loads (such as a few seconds of startup process) and long-term stable loads (such as long-term operation during vehicle cruising). Different efficiency parameters are adjusted to simulate the heating of the motor under different efficiencies. In the mathematical model of the motor, various input parameters are initialized, including injection current, magnetic field, duration and efficiency, and thermal simulation is prepared. The numerical calculation is started to simulate the working conditions of the motor under the specified injection duration and injection efficiency, and the temperature data at different time points are generated. The finite element analysis (FEA) tool can be used for thermal field calculation. In the thermal simulation, the temperature distribution of each component inside the motor is calculated, and how the heat is transferred from the heat source to the motor housing or other heat dissipation channels is analyzed. By simulating different injection durations, the temperature difference between short-term high-load operation and long-term stable operation is analyzed, and the influence of the heat accumulation effect on the motor temperature is evaluated. After the numerical calculation is completed, the temperature values ​​of each component of the motor are obtained, especially the key heating parts, such as the stator winding and the rotor core.

[0077] S14. Analyze the heat generation rate of the motor when the vehicle is used according to the temperature value result and the injection time.

[0078] In this embodiment, when analyzing the heat generation rate of the motor when the vehicle is used according to the temperature value results and the injection time, the temperature distribution data of each motor component (such as the stator, rotor, winding, etc.) at different time points can be obtained from the thermal simulation, and the temperature data can be smoothed as needed to eliminate the influence of short-term fluctuations or noise, so that the temperature change trend is more obvious; at each time point, the temperature change rate (Δ T / Δ t), that is, the rate of temperature change within a certain time interval. By analyzing the temperature change rate, the main heat source locations inside the motor (such as the winding area, core area, etc.) can be identified, and the heat accumulation and transfer process can be understood. The temperature change rates of different components can be compared, and the heat generation of each part under different working conditions can be analyzed. The heat generation rate of the motor is usually expressed as the heat generated per unit time. The heat generation rate can be calculated based on the temperature change: ;

[0079] In the formula, q ( t ) represents the heat generation rate, Q generated Indicates total calories, t Indicates time.

[0080] Therefore, a proportional mathematical model of the motor is established based on the specification parameters and size information, and the heat generation process of the vehicle when it is used is simulated by injecting alternating magnetic fields and current windings, especially the thermal effects caused by the alternating magnetic fields and current windings when the motor is working. This simulation can be close to the thermal behavior of the motor in actual operation. Different injection durations and injection efficiencies are set based on the heat generation process, and the motor mathematical model is started according to the injection duration and injection efficiency to obtain the temperature value of the motor. The heat generation rate of the motor when the vehicle is used is analyzed through the temperature value results and the injection time, which provides accurate input for the control and optimization of the cooler, so that the cooler can accurately respond to the heat generated by the motor.

[0081] S2. Analyze the upper temperature limit of the motor within the maximum allowable starting time of the vehicle according to the heat generation rate, and divide the upper temperature limit into temperature segment intervals based on the starting time.

[0082] In this embodiment, when analyzing the upper temperature limit of the motor within the maximum allowable start-up time of the vehicle based on the heat generation rate, and dividing the upper temperature limit value based on the start-up time to generate temperature segment intervals, it is possible to calculate whether the temperature of the motor reaches or approaches the upper temperature limit within the maximum allowable start-up time based on the integral of the heat generation rate and time. If the temperature exceeds the upper limit, additional cooling measures are required; based on the accumulated heat generation, a curve of the change of the motor temperature over time is generated; the maximum allowable start-up time and the corresponding temperature value are marked on the temperature curve as a basis for evaluating whether the motor reaches the upper temperature limit within the specified time; according to the temperature rise law of the motor and the response speed of the cooler, a reasonable temperature segmentation standard is set, and the start-up time can generally be divided in equal proportion, or divided into intervals according to the actual temperature change rate; according to the division standard, the temperature curve within the start-up time is divided into multiple intervals.

[0083] Usually the upper temperature limit (e.g. 130°C) can be used as the upper limit of the highest interval, and the intervals below this value are divided according to the temperature change rate, such as 0-30°C, 30-60°C, 60-90°C, 90-130°C.

[0084] Define the corresponding time period for each interval: For each temperature segment, determine the corresponding time period. For example, the first interval may be 0-10 seconds, the second interval may be 10-20 seconds, and then the maximum allowed startup time.

[0085] S3. Generate a cooling combination based on the cooler structure, and use the cooling combination to simulate the optimal cooling power of the cooler under the temperature segmentation range conditions.

[0086] In this embodiment, generating a cooling combination based on the cooler structure and using the cooling combination to simulate the optimal cooling power of the cooler under the temperature segmentation interval conditions includes:

[0087] S31. Collecting structural information of internal components of a vehicle cooler, and obtaining parameter information of an air cooler, an oil cooler, and a liquid cooler according to the structural information, and generating a corresponding three-dimensional model based on the parameter information.

[0088] In this embodiment, when collecting the structural information of the internal components of the vehicle cooler, and obtaining the parameter information of the air cooler, oil cooler and liquid cooler based on the structural information, and generating the corresponding three-dimensional model based on the parameter information, the internal structure of the cooler including the air cooler, oil cooler, liquid cooler and related components can be obtained, and the main parameters of the cooler are defined: the main parameters of the air cooler include the heat dissipation area, fin spacing, fan size and flow, air flow direction, wind resistance coefficient, etc.; the parameters of the oil cooler involve the heat exchange area, oil flow rate, oil viscosity, thermal conductivity, etc.; the parameters of the liquid cooler include liquid flow rate, heat exchange efficiency, liquid temperature change, pipeline size and layout, etc., involving factors such as fluid mechanics and heat conduction.

[0089] Determine the materials of each component of the cooler, obtain the material's thermal conductivity, specific heat capacity, density, temperature resistance and other parameters. These material properties are very important for subsequent thermal simulation and optimization. Collect parameters about the cooler's heat dissipation performance, such as the air flow efficiency of the air cooler, the effect of the oil flow viscosity of the oil cooler on the temperature, and the coolant thermal conductivity of the liquid cooler.

[0090] Use 3D modeling software (such as SolidWorks, AutoCAD, CATIA, etc.) to create a 3D model of the cooler. When selecting tools, you need to consider the software's support for complex structures and thermal analysis. Based on the collected air cooler parameters, create geometric models of heat sinks, fins, fans, and pipes. At the same time, based on the structural parameters of the oil cooler, create models of oil channels, heat exchanger surfaces, and fluid channels. At the same time, the modeling of the liquid cooler needs to accurately represent the pipeline layout, liquid flow path, and the geometric structure of the heat exchange surface. Use the parametric function of the modeling software to enable the model to automatically adjust according to different input parameters, and define parametric variables for each cooler's geometric features (such as fin thickness, pipe diameter, liquid flow path); based on parametric modeling, quickly adjust the design of air coolers, oil coolers, and liquid coolers, perform optimization simulations of different configurations, and assign material properties to each cooler component in the 3D model, including thermal conductivity, specific heat capacity, density, etc.

[0091] S32. Generate a cooling combination based on the corresponding working principles of the air cooler, oil cooler and liquid cooler, and use mathematical calculation software to randomly generate refrigeration parameters including air cooling pressure difference and wind flow, oil cooling viscosity and liquid cooling flow within the definition domain, and combine the generated parameters with the cooling combination to generate a random calculation condition.

[0092] In this embodiment, a cooling combination is generated based on the corresponding working principles of the air cooler, oil cooler and liquid cooler, and mathematical calculation software is used to randomly generate refrigeration parameters including air cooling pressure difference and wind flow, oil cooling viscosity and liquid cooling flow within the definition domain. When the generated parameters are combined with the cooling combination to generate a random calculation condition, the corresponding working principles of the air cooler, oil cooler and liquid cooler can be understood in advance, wherein the air cooler takes away heat through air flow, and key parameters include air velocity, flow, fan speed, heat exchange efficiency between air and fins, and pressure difference.

[0093] The oil cooler removes heat through the flow of oil. The parameters involved include oil flow rate, oil viscosity, oil thermal conductivity and inlet and outlet temperature difference.

[0094] The liquid cooler removes heat by circulating the coolant. The key parameters are liquid flow rate, flow rate, thermal conductivity of the coolant, heat capacity of the liquid and fluid pressure difference.

[0095] The cooling combinations may include six combinations: air cooler, oil cooler, liquid cooler, air cooler + oil cooler, oil cooler + liquid cooler, air cooler + liquid cooler and air cooler + oil cooler + liquid cooler.

[0096] Determine the domain of each parameter based on the operating range of the cooler. For each cooler, set a reasonable range of parameters, for example:

[0097] Air cooler: wind speed range (such as 0 to 10m / s), pressure difference range (such as 0 to 1000Pa).

[0098] Oil cooler: oil viscosity range (e.g. 0.01 to 1 Pa·s), oil flow rate range (e.g. 0 to 10 m / s).

[0099] Liquid cooler: liquid flow rate range (such as 0 to 5m / s), coolant flow range (such as 0 to 100L / min).

[0100] Parameter distribution type: determines the type of random distribution used to generate the parameters. Uniform distribution, normal distribution, or other suitable distribution can be used, depending on the physical scenario and the characteristics of the cooler.

[0101] Use mathematical computing software (such as MATLAB, Python, etc.) to write scripts, and use random number generation functions to generate multiple random combinations within the defined parameter range, including the pressure difference and flow of the air cooler, the viscosity and flow of the oil cooler, and the flow and inlet and outlet temperature difference of the liquid cooler.

[0102] Sample multiple different parameter combinations from the domain to ensure that different areas of the parameter space are covered. Specifically, you can use the rand or random function to generate random values.

[0103] The randomly generated cooling parameters are substituted into the mathematical model of the cooler to generate a complete calculation condition. Each cooling combination can calculate the cooling capacity, heat transfer efficiency and other performance indicators under specific working conditions based on the input air cooling, oil cooling and liquid cooling parameters. By randomly generating parameters multiple times, multiple different working conditions can be dynamically combined to evaluate the performance of the cooler under various possible conditions.

[0104] S33, integrating the three-dimensional model and the calculation conditions into the motor mathematical model, calling the finite difference technology to start the numerical simulation, executing the motor cooling process and saving the simulation results into the database.

[0105] In this embodiment, when the three-dimensional model and the calculation conditions are integrated into the mathematical model of the motor, the finite difference technology is called to start the numerical simulation, the motor cooling process is executed and the simulation results are saved in the database, the three-dimensional models of the air cooler, oil cooler, and liquid cooler can be imported into the mathematical model of the motor to ensure that the cooler geometry is accurately matched with the motor component geometry, and the heat transfer connection between the cooler and the motor components (such as the stator, rotor, winding) is defined in the motor model, the position of each cooler in the motor structure is determined, the physical processes of heat conduction, convection and radiation are simulated, and the generated random cooling conditions (including air cooling pressure difference, oil cooling viscosity, liquid cooling flow rate, etc.) are imported into the mathematical model of the motor as input conditions to drive the simulation of the cooling process, and thermodynamic equations are established in the motor model to describe the temperature distribution inside the motor and its change over time. The finite difference method (FDM) is used to discretize the continuous mathematical model of the motor, and the temperature field inside the motor and the heat conduction model of the cooler are discretized into grid nodes. The temperature change is calculated for each node, and the initial temperature distribution (such as ambient temperature) and boundary conditions (such as heat dissipation of the motor housing and temperature change of the heat exchange surface of the cooler) are set in the finite difference model. These conditions determine the temperature change process of the motor under different cooling conditions. At the same time, the time step is selected for time discretization to ensure that the change of the motor temperature over time can be accurately captured during the numerical simulation process; the numerical solution process of the finite difference technology is started, and the simulation software gradually calculates the temperature distribution of the motor at each time step, and simulates the influence of air cooling, oil cooling, and liquid cooling on the motor temperature. During the numerical simulation process, the temperature changes of key components inside the motor can be monitored, especially the temperature rise under high load and startup period. After the numerical simulation is completed, the temperature distribution data of each node inside the motor is extracted, the temperature rise of the motor under different cooling conditions is analyzed, and possible high temperature areas are identified. The thermal performance indicators of the motor are calculated based on the simulation results, and the output data of the numerical simulation is sorted out, including time series, temperature field data, heat flow distribution, etc., for subsequent analysis and storage.

[0106] S34. Based on the simulation results, the cooling efficiency of the motor and the power generated by the cooler when the cooling purpose is achieved under the temperature segmentation conditions are analyzed.

[0107] In this embodiment, when analyzing the cooling efficiency of the motor and the power generated by the cooler when the cooling purpose is achieved under the temperature segmentation conditions based on the simulation results, the temperature distribution data of the motor in different temperature segmentation intervals can be extracted from the numerical simulation results. For example, the simulated temperature data is classified according to the pre-divided intervals (such as 0-30°C, 30-60°C, 60-90°C, etc.), and the cooling efficiency is calculated;

[0108] Cooling efficiency can be defined as the ratio of the heat removed by the cooler to the total heat generated by the motor: ;

[0109] In the formula, η cool Indicates the heat ratio, Q removed is the heat taken away by the cooler, Q generated is the total heat generated when the motor is running.

[0110] The total heat generated by the motor in each temperature range is calculated by the temperature change rate and the specific heat capacity of the motor material: ;

[0111] In the formula, a Indicates the mass of the motor, b Indicates the specific heat capacity of the motor material, △ D Indicates temperature change.

[0112] According to the cooling conditions, the heat removed by the cooler is calculated using the performance parameters of the cooler (wind speed, oil flow rate, liquid flow rate, etc.), using the heat exchange formula: ;

[0113] In the formula, x represents the mass flow rate of the cooling medium, f fluid represents the specific heat capacity of the cooling medium, Δ D fluid Indicates the inlet and outlet temperature difference of the coolant.

[0114] Based on the extracted temperature data, the cooling efficiency is calculated according to different temperature ranges, and the cooling effect of the motor in different temperature ranges is analyzed. For example, the cooling efficiency of the motor in a lower temperature range (such as below 30°C) and a high temperature range (such as above 90°C) is compared, and a curve of the cooling efficiency changing with the temperature range is drawn. The working efficiency of the cooler in different ranges is analyzed. The cooling efficiency may be lower in a lower temperature range, while the cooling efficiency in a high temperature range is significantly improved.

[0115] The power consumption of the air cooler is usually related to the working power of the fan. The power of the air cooler can be calculated by the wind speed and fan efficiency: ;

[0116] In the formula, Δ P Indicates the pressure difference of the air cooler, Q air Indicates the air flow rate, η fan Indicates the efficiency of the fan.

[0117] The power consumption of the oil cooler is related to the power of the oil pump. The power of the oil pump is calculated based on the oil flow rate and viscosity: ;

[0118] In the formula, Δ P oil Indicates the pressure difference of the oil cooler, Q oil Indicates the oil flow rate, η pump Indicates the efficiency of the oil pump.

[0119] The power consumption of the liquid cooler mainly depends on the power of the coolant pump, which can be calculated by the liquid flow and pressure difference, specifically: ;

[0120] In the formula, Δ P liquid Indicates the pressure difference of the liquid cooler, Q liquid Indicates the liquid flow rate, η pump Indicates the efficiency of a liquid pump.

[0121] The total power consumption of the cooler is: ;

[0122] Compare the cooling efficiency and power consumption in different temperature ranges, and analyze the balance between cooling efficiency improvement and power consumption. Generally, the higher the cooling efficiency, the higher the power consumption of the cooler, so it is necessary to find the best balance point between cooling and energy consumption.

[0123] S35. Introduce natural language processing technology to analyze and calculate the nonlinear mapping relationship between working conditions and cooling efficiency and cooler power, and combine it with statistical experimental methods to perform optimal operation of motor cooling methods.

[0124] In this embodiment, natural language processing technology is introduced to analyze and calculate the nonlinear mapping relationship between the working condition and the cooling efficiency and the cooler power, and the motor cooling method is optimized by combining the statistical experimental method. The operation includes:

[0125] S351. Perform dimensionality reduction processing on the calculation condition data using component analysis technology, and select several groups of relatively independent principal component variables based on the processing results;

[0126] S352. Introduce natural language processing technology to construct a nonlinear mapping relationship between the principal component variables and the cooling efficiency and cooler power, and predict the cooling time required under different motor temperature conditions;

[0127] S353. An evaluation system covering cooling efficiency and cooler power evaluation indicators is established based on cooling time and statistical test methods, and the evaluation system is used to select the optimal cooling method for each interval under the temperature segmentation interval conditions to obtain the optimal cooling power.

[0128] In this embodiment, the component analysis technology is used to perform dimension reduction processing on the calculation condition data, and several groups of relatively independent principal component variables are screened based on the processing results, including:

[0129] S3511, performing standardized dimension reduction processing on the calculation condition data, and setting the calculation condition data after dimension reduction as the input factor of the component analysis technology, and the cooling efficiency and cooling power as the output factors;

[0130] S3512, using component analysis technology to calculate the correlation coefficient matrix between the working condition and the cooling efficiency and cooling power, and analyzing and calculating the cumulative contribution rate of each refrigeration parameter in the working condition based on the correlation coefficient matrix;

[0131] S3513. Sort the cumulative contribution rates by order, and select the top three refrigeration parameters as the main component variables that affect the refrigeration efficiency.

[0132] In this embodiment, natural language processing technology is introduced to construct a nonlinear mapping relationship between the principal component variables and the cooling efficiency and cooler power, and the cooling time required under different motor temperature conditions is predicted to include:

[0133] S3521. Use principal component variables, cooling efficiency, and cooler power to establish a dynamic model, and use the dynamic model as a state equation and efficiency equation for natural language processing technology;

[0134] S3522. Define the state value of the principal component variable and the control value of the cooler power based on the state equation and the efficiency equation;

[0135] S3523. Based on the state value and the control value, a cooling state equation is established as a prediction model of the prediction algorithm, and an optimization function and control constraints are defined to convert the cooling problem into an optimization problem;

[0136] S3524. Utilize the multiple shooting method to solve the optimization problem and optimize the prediction model, and use the prediction model to predict the cooling time required under different motor temperature conditions.

[0137] In this embodiment, based on the state value and the control value, a cooling state equation is established as a prediction model of the prediction algorithm, and an optimization function and control constraints are defined to convert the cooling problem into an optimization problem, including:

[0138] S35231. Define the state value as the key variable and the control value as the adjustment variable, and generate a cooling state equation to describe the influence relationship between the cooler and the motor temperature;

[0139] S35232. After discretizing the cooling state equation, establish a prediction model to predict the temperature change during the cooling process, and define an optimization function including the minimum cooling time and the minimum power consumption;

[0140] S35233. Set the state safety value and power increment limit of the cooler, and define the state safety value and power increment limit as control constraints, and form an optimization problem with the control constraints and the optimization function structure.

[0141] Specifically, the expression of the cooling state equation is: ;

[0142] In the formula, Y Indicates the cooling status value, C m1 , C m2 , C m3 Respectively represent the power output of the air cooler, oil cooler and liquid cooler, S n1 and S n3 Respectively represent the physical upper and lower limits of the cooler power, v 1 means in T The air cooling pressure difference set by the time period cooler, v 2 means in T The wind flow rate set for the time period cooler, v 3 means in T The oil viscosity of the time period cooler setting, v 4 means in T The liquid cooling flow rate set by the time period cooler, c T Indicated in T The temperature of the motor during the time period, t Indicates the upper temperature limit, g Indicates the motor cooling target temperature value.

[0143] Therefore, a cooling combination is generated based on the corresponding working principles of the air cooler, oil cooler and liquid cooler, and mathematical calculation software is used to randomly generate refrigeration parameters including air cooling pressure difference and wind flow, oil cooling viscosity and liquid cooling flow within the definition domain. The generated parameters are combined with the cooling combination to generate random calculation conditions, which can fully simulate the working conditions of the motor under different cooling conditions and provide more options for cooling strategies. At the same time, numerical simulation using finite difference technology can accurately simulate the temperature distribution and heat transfer in the motor cooling process, evaluate the cooling efficiency of different cooling combinations in different temperature segments, and provide a basis for selecting the optimal cooling strategy.

[0144] S4. The temperature segment interval and the corresponding optimal cooling power are used as the control basis, and the cooler is adjusted based on the control basis to realize intelligent cooling control of the motor.

[0145] In this embodiment, the temperature segment interval and the corresponding optimal cooling power are used as the control basis, and the cooler is adjusted based on the control basis to realize the intelligent cooling control of the motor, including:

[0146] S41. Construct a control algorithm based on the temperature segmentation interval based on the vehicle controller, and add the corresponding optimal cooling power into the control algorithm.

[0147] In this embodiment, when a control algorithm based on temperature segment intervals is constructed based on a vehicle controller and the corresponding optimal cooling power is added to the control algorithm, a classification of different temperature intervals can be established based on the previously analyzed temperature segment intervals (e.g., 0-30°C, 30-60°C, 60-90°C, 90-130°C), and the most suitable cooler power is determined in each temperature segment interval. A control algorithm is constructed in the vehicle controller to monitor the motor temperature in real time and adjust the cooler power according to temperature feedback. The algorithm can be based on PID control (proportional-integral-differential control) or other suitable control methods, as follows:

[0148] Input variable: Real-time motor temperature.

[0149] Output variables: cooler power, fan speed, liquid flow rate, etc.

[0150] Control logic: Set the target temperature (for example, below 90°C), output the corresponding cooling power according to the current temperature range, and use the PID control algorithm to adjust the cooling power to ensure smooth temperature changes and avoid overcooling or temperature fluctuations.

[0151] The optimal cooling power corresponding to each temperature segment interval is added to the control algorithm, so that the controller automatically adjusts the cooling power when the temperature changes: the target cooling power for each interval is set in the control algorithm, and when the temperature reaches a certain interval, the algorithm automatically calls the corresponding cooling power.

[0152] For example, the system can set the cooling power to 70% when the temperature is between 60°C and 90°C, and increase the cooling power to 100% when the temperature is above 90°C.

[0153] S42, when the vehicle is started, the real-time temperature of the motor feedback is monitored and matched with the temperature segment interval accordingly, and when the motor temperature rises to a certain value, the cooler enters the PID control mode;

[0154] S43, PID control drives the operating speed and operating state of the cooler according to the control algorithm, and turns off the cooler when the real-time temperature of the motor drops to a preset value.

[0155] In order to facilitate understanding of the above technical solution of the present invention, the motor intelligent cooling control method in the actual process of the present invention is described in detail below.

[0156] Step 1: Calculate heat generation rate;

[0157] Determine the specifications of the vehicle motor, and obtain the rated power and rated speed of the motor from the specifications. Collect the size information of the motor, including:

[0158] The rated power of the motor is 100kW; the rated speed is 3000RPM; the operating voltage is 400V; the operating current is 250A;

[0159] The physical dimensions of the motor are as follows: the motor length is 400mm; the motor diameter is 200mm; the stator inner diameter is 150mm; the stator outer diameter is 180mm; and the permanent magnet thickness is 10mm.

[0160] The mathematical model of the motor is established based on the specification parameters and size information, and is scaled to a 1:1 ratio. The current is injected with 250A, current frequency, 50Hz, and magnetic field strength of 1.2T by injecting alternating magnetic field and current windings;

[0161] Under alternating magnetic field and current winding, the heat generation process of the motor is as follows:

[0162] Copper loss: According to the winding resistance ( R winding =0.05Ω), calculate the copper loss power: P copper = I 2 × R winding =2502×0.05=3.125kW;

[0163] Iron loss: Assuming the hysteresis loss is 0.5kW and the eddy current loss is 0.2kW, the total iron loss is P iron =0.5+0.2=0.7kW;

[0164] The total heat generation is: Q generated = P copper + P iron =3.125+0.7=3.825kW.

[0165] Based on the heat generation process, different injection durations and injection efficiencies are set to simulate short-term high loads and long-term stable loads.

[0166] Injection duration: 10 seconds for short-term high load and 60 seconds for long-term stable load.

[0167] Injection efficiency: Assume 90% efficiency at short-term high load and 80% efficiency at long-term stable load.

[0168] Temperature simulation results: short-term high-load temperature rise is 30°C; long-term stable operation temperature rise is 60°C.

[0169] The short-term high load heat generation rate is: ;

[0170] The heat generation rate under long-term high load is: ;

[0171] That is, the short-term heat generation rate is 0.3825kW / s, and the long-term heat generation rate is 0.06375kW / s.

[0172] Step 2: Temperature range generation;

[0173] Set the maximum allowed vehicle startup time to 90 seconds and the upper temperature limit to 130°C. Calculate the upper temperature limit based on the heat generation rate and startup time: T max = T ambient + Heat rate ×Δ t ;

[0174] Assuming the ambient temperature is 25°C, the temperature rise in 90 seconds is calculated as:

[0175] T max =25+0.06375×90=80.7375°C.

[0176] Divide the temperature into intervals: 0-30°C, 30-60°C, 60-90°C, 90-130°C.

[0177] Step 3: Obtaining the optimal cooling power;

[0178] Assume that the internal structural parameters of the cooler are: the wind speed of the air cooler is 5m / s, the air cooling pressure difference is 300Pa, and the heat exchange area is 1m²; the oil flow rate of the oil cooler is 3m / s and the viscosity is 0.05Pa·s; the liquid flow rate of the liquid cooler is 2m / s and the heat exchange area is 0.5m 2 .

[0179] The cooling combinations include six combinations: air cooler, oil cooler, liquid cooler, air cooler + oil cooler, oil cooler + liquid cooler, air cooler + liquid cooler and air cooler + oil cooler + liquid cooler. Randomly generated air cooling, oil cooling and liquid cooling parameters are combined, and numerical simulation is performed using the finite difference method to obtain the temperature distribution during the motor cooling process and save it in the database.

[0180] Step 4: Intelligent cooling control;

[0181] Monitor the motor temperature in real time. When the temperature reaches 60°C, start the cooler and enter the PID control mode. The cooler speed is dynamically adjusted according to the control algorithm to ensure that the motor temperature is controlled between 30°C-90°C.

[0182] See also Figure 2 The present invention also provides a vehicle-based motor cooling intelligent control system, the motor cooling intelligent control system comprising:

[0183] The motor heat generation rate calculation module 1 is used to extract technical parameters based on the internal structure information of the vehicle motor, and use the technical parameters to generate a motor mathematical model to determine the heat generation rate of the motor when it is used in the vehicle;

[0184] The temperature segmentation interval generation module 2 is used to analyze the upper temperature limit of the motor within the maximum allowable starting time of the vehicle according to the heat generation rate, and divide the upper temperature limit into temperature segmentation intervals based on the starting time;

[0185] The optimal cooling power acquisition module 3 is used to generate a cooling combination based on the cooler structure, and use the cooling combination to simulate the optimal cooling power of the cooler under the temperature segmentation interval condition;

[0186] The motor intelligent cooling control module 4 is used to take the temperature segmentation interval and the corresponding optimal cooling power as the control basis, and adjust the cooler based on the control basis to realize the intelligent cooling control of the motor.

[0187] To sum up, with the help of the above-mentioned technical scheme of the present invention, the present invention generates an accurate motor mathematical model by extracting the technical parameters inside the motor, which is helpful to understand the heating characteristics of the motor under different operating conditions, and at the same time analyzes the temperature upper limit of the motor within the maximum allowable starting time of the vehicle according to the heat generation rate, and divides the temperature upper limit into a temperature segment interval based on the starting time, and divides the temperature upper limit into multiple segment intervals, which helps to accurately control the cooling power output according to different temperature conditions, thereby avoiding excessive fluctuations in cooling power and improving cooling efficiency. A cooling combination is generated based on the cooler structure to simulate the optimal cooling power of the cooler under the temperature segment interval conditions, and the optimal cooling power is simulated under different temperature segment conditions based on the cooling combination design, which can ensure that the cooler works in the most efficient state in each temperature interval, thereby reducing energy consumption, realizing the automation of the cooling process, reducing the need for manual intervention, and improving the intelligence level of the cooler and the convenience of operation.

[0188] The present invention establishes a proportional mathematical model of the motor based on specification parameters and size information, and simulates the heat generation process of the vehicle when it is used by injecting alternating magnetic fields and current windings, especially the thermal effects caused by the alternating magnetic fields and current windings when the motor is working. This simulation can be close to the thermal behavior of the motor in actual operation, and different injection durations and injection efficiencies are set based on the heat generation process. The motor mathematical model is started according to the injection duration and injection efficiency to obtain the temperature value of the motor, and the heat generation rate of the motor when the vehicle is used is analyzed through the temperature value results and the injection time, which provides accurate input for the control and optimization of the cooler, so that the cooler can accurately respond to the heat generated by the motor. The present invention generates a cooling combination based on the corresponding working principles of air coolers, oil coolers and liquid coolers, and uses mathematical calculation software to randomly generate refrigeration parameters including air cooling pressure difference and wind flow, oil cooling viscosity and liquid cooling flow within the definition domain. The generated parameters are combined with the cooling combination to generate a random calculation condition, which can comprehensively simulate the working conditions of the motor under different cooling conditions and provide more choices for cooling strategies. At the same time, the finite difference technology is used for numerical simulation, which can accurately simulate the temperature distribution and heat transfer in the motor cooling process, evaluate the cooling efficiency of different cooling combinations in different temperature segment intervals, and provide a basis for selecting the optimal cooling strategy.

[0189] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0190] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A vehicle-based motor cooling intelligent control method, characterized in that: The motor cooling intelligent control method comprises: S1. Extracting technical parameters based on the internal structure information of the vehicle motor, and using the technical parameters to generate a motor mathematical model to determine the heat generation rate of the motor when used in the vehicle; S2. Analyze the upper temperature limit of the motor within the maximum allowable starting time of the vehicle according to the heat generation rate, and divide the upper temperature limit into temperature segment intervals based on the starting time; S3, generating a cooling combination based on the cooler structure, and using the cooling combination to simulate the optimal cooling power of the cooler under the temperature segmentation interval conditions; S4, taking the temperature segment interval and the corresponding optimal cooling power as the control basis, and adjusting the cooler based on the control basis to realize intelligent cooling control of the motor; The generating of cooling combination based on cooler structure and simulating the optimal cooling power of the cooler under the temperature segmentation interval condition by using the cooling combination comprises: S31, collecting structural information of internal components of the vehicle cooler, and obtaining parameter information of the air cooler, the oil cooler and the liquid cooler according to the structural information, and generating a corresponding three-dimensional model based on the parameter information; S32, generating a cooling combination based on the corresponding working principles of the air cooler, the oil cooler and the liquid cooler, and using mathematical calculation software to randomly generate refrigeration parameters including air cooling pressure difference and wind flow, oil cooling viscosity and liquid cooling flow within the definition domain, and combining the generated parameters with the cooling combination to generate a random calculation condition; S33, integrating the three-dimensional model and the calculation conditions into the motor mathematical model, calling the finite difference technology to start the numerical simulation, performing the motor cooling process and saving the simulation results into the database; S34, analyzing the cooling efficiency of the motor and the power generated by the cooler when the cooling purpose is achieved under the temperature segmentation conditions based on the simulation results; S35, introducing natural language processing technology to analyze and calculate the nonlinear mapping relationship between working conditions and cooling efficiency and cooler power, and combining statistical experimental methods to perform motor cooling method optimization operations; The introduction of natural language processing technology to analyze and calculate the nonlinear mapping relationship between the working condition and the cooling efficiency and the cooler power, and combining the statistical experimental method to optimize the motor cooling method includes: S351. Perform dimensionality reduction processing on the calculation condition data using component analysis technology, and select several groups of relatively independent principal component variables based on the processing results; S352. Introduce natural language processing technology to construct a nonlinear mapping relationship between the principal component variables and the cooling efficiency and cooler power, and predict the cooling time required under different motor temperature conditions; S353. Establish an evaluation system covering cooling efficiency and cooler power evaluation indicators based on cooling time and statistical test method, and use the evaluation system to select the optimal cooling method for each interval under the temperature segmentation interval condition to obtain the optimal cooling power; The component analysis technology is used to reduce the dimension of the calculation condition data, and several groups of relatively independent principal component variables are selected based on the processing results, including: S3511, performing standardized dimension reduction processing on the calculation condition data, and setting the calculation condition data after dimension reduction as the input factor of the component analysis technology, and the cooling efficiency and cooling power as the output factors; S3512, using component analysis technology to calculate the correlation coefficient matrix between the working condition and the cooling efficiency and cooling power, and analyzing and calculating the cumulative contribution rate of each refrigeration parameter in the working condition based on the correlation coefficient matrix; S3513. Sort the cumulative contribution rates by order, and select the top three refrigeration parameters as the main component variables that affect the refrigeration efficiency.

2. The vehicle-based motor cooling intelligent control method according to claim 1, characterized in that: The extracting of technical parameters based on the internal structure information of the motor of the vehicle and using the technical parameters to generate a motor mathematical model to determine the heat generation rate of the motor when used in the vehicle includes: S11, determining the specification parameters of the vehicle motor, and obtaining the rated power and rated speed of the motor from the specification parameters, and collecting the size information of the motor; S12, establishing a proportional mathematical model of the motor based on the specification parameters and size information, and simulating the heat generation process of the vehicle when it is used by injecting alternating magnetic fields and current windings; S13, setting different injection durations and injection efficiencies based on the heat generation process, and starting a motor mathematical model according to the injection duration and injection efficiency to obtain a temperature value of the motor; S14. Analyze the heat generation rate of the motor when the vehicle is used according to the temperature value result and the injection time.

3. The vehicle-based motor cooling intelligent control method according to claim 1, characterized in that: The introduction of natural language processing technology to construct a nonlinear mapping relationship between the principal component variables and the cooling efficiency and cooler power, and predicting the cooling time required under different motor temperature conditions includes: S3521. Use principal component variables, cooling efficiency, and cooler power to establish a dynamic model, and use the dynamic model as a state equation and efficiency equation for natural language processing technology; S3522. Define the state value of the principal component variable and the control value of the cooler power based on the state equation and the efficiency equation; S3523. Based on the state value and the control value, a cooling state equation is established as a prediction model of the prediction algorithm, and an optimization function and control constraints are defined to convert the cooling problem into an optimization problem; S3524. Utilize the multiple shooting method to solve the optimization problem and optimize the prediction model, and use the prediction model to predict the cooling time required under different motor temperature conditions.

4. The vehicle-based motor cooling intelligent control method according to claim 3, characterized in that: The cooling state equation is established as a prediction model of the prediction algorithm based on the state value and the control value, and the optimization function and control constraints are defined to convert the cooling problem into an optimization problem, including: S35231. Define the state value as the key variable and the control value as the adjustment variable, and generate a cooling state equation to describe the influence relationship between the cooler and the motor temperature; S35232. After discretizing the cooling state equation, establish a prediction model to predict the temperature change during the cooling process, and define an optimization function including the minimum cooling time and the minimum power consumption; S35233. Set the state safety value and power increment limit of the cooler, and define the state safety value and power increment limit as control constraints, and form an optimization problem with the control constraints and the optimization function structure.

5. The vehicle-based motor cooling intelligent control method according to claim 4, characterized in that: The expression of the cooling state equation is: ; In the formula, Y Indicates the cooling status value, C m1 , C m2 , C m3 Respectively represent the power output of the air cooler, oil cooler and liquid cooler, S n1 and S n3 Respectively represent the physical upper and lower limits of the cooler power, v 1 means in T The air cooling pressure difference set by the time period cooler, v 2 means in T The wind flow rate set for the time period cooler, v 3 means in T The oil viscosity of the time period cooler setting, v 4 means in T The liquid cooling flow rate set by the time period cooler, c T Indicated in T The temperature of the motor during the time period, t Indicates the upper temperature limit, g Indicates the motor cooling target temperature value.

6. The vehicle-based motor cooling intelligent control method according to claim 1, characterized in that: The method of using the temperature segment interval and the corresponding optimal cooling power as a control basis and adjusting the cooler based on the control basis to realize intelligent cooling control of the motor includes: S41, constructing a control algorithm based on the temperature segmentation interval based on the vehicle controller, and adding the corresponding optimal cooling power to the control algorithm; S42, when the vehicle is started, the real-time temperature of the motor feedback is monitored and matched with the temperature segment interval accordingly, and when the motor temperature rises to a certain value, the cooler enters the PID control mode; S43, PID control drives the operating speed and operating state of the cooler according to the control algorithm, and turns off the cooler when the real-time temperature of the motor drops to a preset value.

7. A vehicle-based motor cooling intelligent control system, used to implement the vehicle-based motor cooling intelligent control method according to any one of claims 1 to 6, characterized in that: The motor cooling intelligent control system includes: A motor heat generation rate calculation module is used to extract technical parameters based on the internal structure information of the vehicle motor, and use the technical parameters to generate a motor mathematical model to determine the heat generation rate of the motor when it is used in the vehicle; A temperature segmentation interval generation module is used to analyze the upper temperature limit of the motor within the maximum allowable starting time of the vehicle according to the heat generation rate, and divide the upper temperature limit into temperature segmentation intervals based on the starting time; An optimal cooling power acquisition module is used to generate a cooling combination based on the cooler structure, and use the cooling combination to simulate the optimal cooling power of the cooler under the temperature segmentation interval conditions; The motor intelligent cooling control module is used to take the temperature segmentation interval and the corresponding optimal cooling power as the control basis, and adjust the cooler based on the control basis to realize the intelligent cooling control of the motor.

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