Intelligent temperature control cooling system for new energy automobile driving motor

Abstract

The invention provides an intelligent temperature control cooling system for a new energy automobile driving motor. The intelligent temperature control cooling system comprises an electronic water pump, a temperature sensor array and a control unit. Wherein the temperature sensor array is used for collecting temperature data of each part of the motor in real time and sending the temperature data to the control unit; the control unit is used for dynamically adjusting the data acquisition frequency of each temperature sensor in the temperature sensor array according to the temperature data of each part of the motor; meanwhile, a rotating speed dynamic compensation coefficient and a flow dynamic compensation coefficient are obtained according to the received temperature data, and the rotating speed and the cooling liquid flow of the electronic water pump are adjusted in a self-adaptive mode through the rotating speed dynamic compensation coefficient and the flow dynamic compensation coefficient; the electronic water pump is used for adjusting the rotating speed and the cooling liquid flow according to instructions corresponding to the rotating speed and the cooling liquid flow given by the control unit, and precise adjustment of the motor temperature is achieved.

Description

Technical Field

[0001] The invention proposes an intelligent temperature control cooling system for a new energy vehicle drive motor, belonging to the technical field of intelligent temperature control cooling control. Background Art

[0002] As new energy vehicles are an important trend in future travel, the working principles of the core components of the drive motor and cooling system are particularly important. As the power source of new energy vehicles, the performance of the drive motor system directly affects the vehicle's driving efficiency, mileage and safety. The cooling system is the key to dissipating heat from the drive motor and control system to prevent overheating damage and ensure the stable operation of the motor.

[0003] In the prior art, the cooling system of new energy vehicles usually adopts the traditional mechanical water pump and fixed temperature sensor configuration. This configuration has the following shortcomings: Inflexible water pump control: The speed and coolant flow of a mechanical water pump are usually driven by the engine and cannot be dynamically adjusted according to the real-time temperature of the motor, resulting in low cooling efficiency and high energy consumption.

[0004] Limited accuracy of temperature sensors: A fixed temperature sensor acquisition frequency may not accurately reflect the temperature changes in various parts of the motor, especially when the motor is running at high speed or the load changes. The temperature distribution may be more complex, requiring higher-precision temperature monitoring.

[0005] Lack of intelligent adjustment mechanism: The existing system lacks an intelligent adjustment mechanism to dynamically adjust the sensor data acquisition frequency, water pump speed, and coolant flow rate according to temperature data, resulting in less than ideal cooling effect and may affect the service life and performance of the motor. Summary of the invention

[0006] The present invention provides an intelligent temperature control cooling system for a new energy vehicle drive motor to solve the technical problems in the above-mentioned prior art. The technical solutions adopted are as follows: An intelligent temperature control cooling system for a new energy vehicle drive motor, the intelligent temperature control cooling system comprising an electronic water pump, a temperature sensor array and a control unit; wherein: The temperature sensor array is used to collect temperature data of various parts of the motor in real time and send the temperature data to the control unit; The control unit is used to dynamically adjust the data acquisition frequency of each temperature sensor in the temperature sensor array according to the temperature data of each part of the motor; at the same time, obtain the speed dynamic compensation coefficient and the flow dynamic compensation coefficient according to the received temperature data, and adaptively adjust the speed and coolant flow of the electronic water pump through the speed dynamic compensation coefficient and the flow dynamic compensation coefficient; The electronic water pump is used to adjust the rotation speed and coolant flow according to the instructions corresponding to the rotation speed and coolant flow given by the control unit, so as to achieve accurate regulation of the motor temperature.

[0007] Further, the temperature sensor array includes an ambient temperature sensor, a stator temperature sensor, a rotor surface temperature sensor, and a motor housing temperature sensor; The temperature correlation coefficients between the target objects corresponding to the temperature sensors included in the temperature sensor array are as follows: The temperature correlation coefficients between the ambient temperature sensor and the stator temperature sensor, the rotor surface temperature sensor, and the motor housing temperature sensor are respectively l 01 =0.2, l 02 = 0.1 and l 03 =0.4; The temperature correlation coefficients between the stator temperature sensor, the rotor surface temperature sensor, and the motor housing temperature sensor are respectively l 04 =0.6 and l 05 =0.5; The temperature correlation coefficient between the rotor surface temperature sensor and the motor housing temperature sensor k 01 l 04 + k 02 l 05 ,in, k 01 and k 02 They respectively represent the temperature influence coefficient between the stator and the rotor and the influence coefficient of the motor housing temperature on the stator temperature, and the specific values ​​of the influence coefficients are obtained by means of experimental simulation.

[0008] Furthermore, dynamically adjusting the data acquisition frequency of each temperature sensor in the temperature sensor array according to the temperature data of each part of the motor includes: Real-time monitoring of the temperature values ​​obtained by each temperature sensor included in the temperature sensor array; Obtaining a temperature variation coefficient of a target object monitored by each temperature sensor according to the temperature data collected by each temperature sensor; comparing the temperature variation coefficient with a preset temperature variation coefficient threshold; When any one of the temperature variation coefficients exceeds a preset temperature variation coefficient threshold, the temperature sensor whose temperature variation coefficient exceeds the preset temperature variation coefficient threshold is used as a target sensor, and the temperature sensor whose temperature variation coefficient does not exceed the preset temperature variation coefficient threshold is used as an associated sensor; The temperature acquisition frequency of the target sensor is adjusted using the temperature variation coefficient of the target sensor; The temperature acquisition frequency of the associated sensor is adjusted by using the temperature variation coefficient of the target sensor in combination with the temperature variation coefficient of the associated sensor.

[0009] Furthermore, the temperature variation coefficient of the target object monitored by each temperature sensor is obtained according to the temperature data collected by each temperature sensor, including: Extract each temperature sensor n The temperature data collected is used to n The temperature data collected this time forms a temperature vector corresponding to each temperature sensor; wherein the structure of the temperature vector is as follows:

[0010] in, T represents the temperature vector; T 1. T 2.…… T n Respectively n The temperature data collected; The temperature vector is standardized to obtain a temperature standard vector after the standardization process, wherein the structure of the temperature standard vector is as follows:

[0011] in, S represents the temperature standard vector, and S=[1+exp(-T)] -1 ; T represents the temperature vector; Using the temperature standard vector corresponding to each temperature sensor, obtain the temperature variation coefficient of the target object monitored by each temperature sensor; The temperature variation coefficient of the target object monitored by each temperature sensor is obtained by the following formula:

[0012] in, G Indicates the temperature variation coefficient of the target object monitored by each temperature sensor; D r Represents the first-order difference vector of the temperature standard vector, which is used to capture the temperature change trend; ⊙ represents the dot multiplication operation of the corresponding elements of the vector;S represents the temperature standard vector; S J Represents the mean vector of the temperature standard vector; Δ S represents the second-order difference vector of the temperature standard vector; s (Δ S ) is the second-order difference of the temperature standard vector Δ S The corresponding standard deviation function; I represents the identity matrix; S smooth represents the smoothed vector after smoothing the temperature standard vector; d ( S ) represents the variance corresponding to the temperature standard vector.

[0013] Furthermore, the temperature acquisition frequency of the target sensor is adjusted using the temperature variation coefficient of the target sensor, including: Extract the temperature variation coefficient corresponding to the target sensor and the temperature acquisition frequency of the target sensor; Obtaining an adjusted temperature acquisition frequency of the target sensor using a temperature variation coefficient corresponding to the target sensor; The adjusted temperature acquisition frequency of the target sensor is obtained by the following formula:

[0014] in, f a Indicates the adjusted temperature acquisition frequency of the target sensor; f a0 Indicates the temperature acquisition frequency of the target sensor before adjustment; G m Indicates the temperature variation coefficient of the target object monitored by the target sensor; α Indicates the influence factor of the preset temperature variation coefficient on the frequency adjustment, with a value range of 0.26-0.51; β Indicates the influence factor of the preset temperature change on the adjustment range, with a value range of 0.14-0.33; c Represents a preset nonlinear factor, which is used to further control the smoothness and sensitivity of the adjustment, and its value range is 0.58-1.73.

[0015] Furthermore, the temperature acquisition frequency of the associated sensor is adjusted by using the temperature variation coefficient of the target sensor in combination with the temperature variation coefficient of the associated sensor, including: Extracting a temperature correlation coefficient between the temperature of the target object corresponding to each preset associated sensor and the temperature of the target object corresponding to each target sensor; Obtaining the adjusted temperature acquisition frequency corresponding to each associated sensor by using the temperature correlation coefficient between the temperature of the target object corresponding to each associated sensor and the temperature of the target object corresponding to each target sensor in combination with the temperature variation coefficient of the associated sensor and the temperature variation coefficient of the target sensor; The adjusted temperature acquisition frequency corresponding to each associated sensor is obtained by the following formula:

[0016] in, f b Indicates the adjusted temperature acquisition frequency of the associated sensor; f b0 Indicates the temperature acquisition frequency of the associated sensor before adjustment; G mi Indicates the first i The temperature variation coefficient of the target object monitored by each target sensor; G g Indicates the temperature variation coefficient of the target object monitored by the associated sensor; w i Indicates the association of the sensor with the i The temperature correlation coefficient between the target objects monitored by the target sensors; α Indicates the influence factor of the preset temperature variation coefficient on the frequency adjustment, with a value range of 0.26-0.51; β Indicates the influence factor of the preset temperature change on the adjustment range, with a value range of 0.14-0.33; c Represents a preset nonlinear factor, which is used to further control the smoothness and sensitivity of the adjustment, and its value range is 0.58-1.73.

[0017] Further, a speed dynamic compensation coefficient and a flow rate dynamic compensation coefficient are obtained according to the received temperature data, and the speed and coolant flow rate of the electronic water pump are adaptively adjusted by the speed dynamic compensation coefficient and the flow rate dynamic compensation coefficient, including: The control unit receives temperature data sent by the temperature sensor array in real time; Using the temperature data sent by the temperature sensor array, a rotation speed dynamic compensation coefficient and a flow rate dynamic compensation coefficient are obtained; The rotation speed dynamic compensation coefficient is used to adaptively adjust the rotation speed of the electronic water pump, and at the same time, the flow rate dynamic compensation coefficient is used to adaptively adjust the coolant flow rate of the electronic water pump.

[0018] Furthermore, the temperature data sent by the temperature sensor array is used to obtain a rotation speed dynamic compensation coefficient, including: Extract temperature data sent by the temperature sensor array; Comparing the temperature data collected by each temperature sensor included in the temperature sensor array with its corresponding temperature threshold; When the temperature data collected by any temperature sensor reaches or exceeds its corresponding temperature threshold, the rotation speed dynamic compensation coefficient is obtained by using the temperature data collected by each temperature sensor included in the temperature sensor array; The speed dynamic compensation coefficient is obtained by the following formula:

[0019] in, B r Indicates the speed dynamic compensation coefficient; x Indicates the number of target objects whose temperature data reaches or exceeds the corresponding temperature threshold; y Indicates the number of target objects whose temperature data is lower than the corresponding temperature threshold; T xi Indicates i The temperature data of the target object corresponding to the temperature data reaching or exceeding the corresponding temperature threshold; T zxi Indicates i The temperature threshold value corresponding to the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T yi Indicates i The temperature data of the target object corresponding to the temperature threshold value corresponding to the temperature data is lower than the temperature data of the target object corresponding to the temperature data; T zyi Indicates i The temperature threshold value corresponding to the target object whose temperature data is lower than the corresponding temperature threshold; J 01 and J 02 represents the first adjustment coefficient and the second adjustment coefficient; The first adjustment coefficient is obtained by the following formula:

[0020] in, J 01 represents the first adjustment coefficient; T xmaxi and T xmini Indicates i The maximum and minimum temperature values ​​of the target objects whose temperature data reaches or exceeds the corresponding temperature threshold value; f xbi Indicatesi The standard deviation of the temperature acquisition frequency of the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T xbi Indicates i The standard deviation of the temperature data of the target object whose temperature data reaches or exceeds its corresponding temperature threshold; At the same time, the first adjustment coefficient is obtained by the following formula:

[0021] in, J 02 represents the second adjustment coefficient; T ymaxi and T ymini Indicates i The maximum and minimum temperature values ​​that appear for the target objects whose temperature data is lower than the corresponding temperature threshold value; f ybi Indicates i The standard deviation of the temperature acquisition frequency of the target object whose temperature data is lower than its corresponding temperature threshold; T ybi Indicates i The standard deviation of the temperature data of the target object whose temperature data is lower than its corresponding temperature threshold.

[0022] Furthermore, the flow dynamic compensation coefficient is obtained by using the temperature data sent by the temperature sensor array, including: Extract temperature data sent by the temperature sensor array; Comparing the temperature data collected by each temperature sensor included in the temperature sensor array with its corresponding temperature threshold; When the temperature data collected by any temperature sensor reaches or exceeds its corresponding temperature threshold, the flow dynamic compensation coefficient is obtained using the temperature data collected by each temperature sensor included in the temperature sensor array; The flow dynamic compensation coefficient is obtained by the following formula:

[0023] in, B v Indicates the flow dynamic compensation coefficient; x Indicates the number of target objects whose temperature data reaches or exceeds the corresponding temperature threshold; y Indicates the number of target objects whose temperature data is lower than the corresponding temperature threshold; T xi Indicates iThe temperature data of the target object corresponding to the temperature data reaching or exceeding the corresponding temperature threshold; T zxi Indicates i The temperature threshold value corresponding to the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T yi Indicates i The temperature data of the target object corresponding to the temperature threshold value corresponding to the temperature data is lower than the temperature data of the target object corresponding to the temperature data; T zyi Indicates i The temperature threshold value corresponding to the target object whose temperature data is lower than the corresponding temperature threshold; f xbp express x The average value of the standard deviation of the temperature acquisition frequency of the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T xbp express x The average value of the standard deviation of the temperature data of the target object whose temperature data reaches or exceeds its corresponding temperature threshold; f ybp express y The average value of the standard deviation of the temperature acquisition frequency of the target object whose temperature data is lower than the corresponding temperature threshold; T ybp express y The average value of the standard deviation of the temperature data of the target objects whose temperature data is lower than the corresponding temperature threshold.

[0024] Furthermore, the speed of the electronic water pump is adaptively adjusted by using the speed dynamic compensation coefficient, and at the same time, the coolant flow of the electronic water pump is adaptively adjusted by using the flow dynamic compensation coefficient, including: The speed of the electronic water pump is compensated and adjusted using the speed dynamic compensation coefficient to obtain the speed after compensation adjustment; The speed after compensation adjustment is obtained by the following formula:

[0025] in, R Indicates the speed after compensation adjustment; R 0 means the speed before compensation adjustment; B r Indicates the speed dynamic compensation coefficient; x Indicates the number of target objects whose temperature data reaches or exceeds the corresponding temperature threshold; T xi Indicates i The temperature data of the target object corresponding to the temperature data reaching or exceeding the corresponding temperature threshold;T zxi Indicates i The temperature threshold value corresponding to the target object whose temperature data reaches or exceeds its corresponding temperature threshold; or represents a control correlation coefficient of preset temperature and speed control; and the control correlation coefficient of temperature and speed control is obtained through experiments or simulations; Controlling the motor of the electronic water pump to run at a speed adjusted by compensation; Using the dynamic flow compensation coefficient to compensate and adjust the coolant flow of the electronic water pump, and obtain the compensated and adjusted coolant flow; The coolant flow rate after compensation adjustment is obtained by the following formula:

[0026] in, V Indicates the coolant flow after compensation adjustment; V 0 means the coolant flow before compensation adjustment; B v Indicates the flow dynamic compensation coefficient; x represents a control correlation coefficient between a preset temperature and a coolant flow rate control; and the control correlation coefficient between the temperature and the coolant flow rate control is obtained through experiments or simulations; The electronic water pump is controlled to operate according to the coolant flow rate after compensation adjustment.

[0027] Beneficial effects of the present invention: The intelligent temperature control cooling system for driving motors of new energy vehicles proposed by the present invention can more accurately monitor and adjust the temperature of the motor by dynamically adjusting the data acquisition frequency of the temperature sensor, the speed of the electronic water pump, and the coolant flow rate. This not only improves the cooling efficiency, but also reduces unnecessary energy consumption. Accurate cooling control helps to keep the motor running within the optimal operating temperature range and reduces the risk of damage caused by overheating. This helps to extend the service life of the motor and reduce maintenance costs. The intelligent temperature control cooling system can adaptively adjust the cooling parameters to cope with different working conditions and external environmental changes. This improves the stability of the entire new energy vehicle drive system and ensures the driving safety and reliability of the vehicle. By optimizing the operating efficiency of the cooling system, the intelligent temperature control cooling system helps to reduce the energy consumption and emissions of new energy vehicles. This meets the current global requirements for energy conservation, emission reduction and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The figure is a system principle diagram of the system described in the present invention. DETAILED DESCRIPTION

[0029] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0030] An embodiment of the present invention provides an intelligent temperature control cooling system for a new energy vehicle drive motor, such as Figure 1 As shown, the intelligent temperature control cooling system includes an electronic water pump, a temperature sensor array and a control unit; wherein, The temperature sensor array is used to collect temperature data of various parts of the motor in real time and send the temperature data to the control unit; The control unit is used to dynamically adjust the data acquisition frequency of each temperature sensor in the temperature sensor array according to the temperature data of each part of the motor; at the same time, obtain the speed dynamic compensation coefficient and the flow dynamic compensation coefficient according to the received temperature data, and adaptively adjust the speed and coolant flow of the electronic water pump through the speed dynamic compensation coefficient and the flow dynamic compensation coefficient; The electronic water pump is used to adjust the rotation speed and coolant flow according to the instructions corresponding to the rotation speed and coolant flow given by the control unit, so as to achieve accurate regulation of the motor temperature.

[0031] The working principle of the above technical solution is as follows: the temperature sensor array is arranged at each key part of the new energy vehicle drive motor to collect the temperature data of these parts in real time. These temperature data are converted into electrical signals by the circuit inside the sensor and sent to the control unit. The control unit receives the temperature data from the temperature sensor array and dynamically adjusts the data acquisition frequency of each temperature sensor according to these data. If the temperature of a certain part changes rapidly or the temperature is high, the control unit may increase the data acquisition frequency of the temperature sensor of the part to monitor the temperature change more accurately. The control unit calculates the dynamic compensation coefficient of the speed and the dynamic compensation coefficient of the flow rate through the built-in algorithm based on the received temperature data. The control unit sends the calculated speed and coolant flow instructions to the electronic water pump. The electronic water pump adjusts its speed and coolant flow according to these instructions to achieve accurate regulation of the motor temperature. During the whole process, the control unit continuously receives temperature data from the temperature sensor array and continuously adjusts the data acquisition frequency, compensation coefficient and adjustment instructions of the electronic water pump according to these data. This feedback mechanism ensures that the cooling system can respond to the temperature changes of the motor in real time and always maintain the best cooling effect.

[0032] The effect of the above technical solution is: by dynamically adjusting the data acquisition frequency of the temperature sensor and the speed and coolant flow of the electronic water pump, the intelligent temperature control cooling system can more accurately monitor and adjust the temperature of the motor. This not only improves the cooling efficiency, but also reduces unnecessary energy consumption. Accurate cooling control helps keep the motor running within the optimal operating temperature range and reduces the risk of damage due to overheating. This helps to extend the service life of the motor and reduce maintenance costs. The intelligent temperature control cooling system can adaptively adjust the cooling parameters to cope with different working conditions and external environmental changes. This improves the stability of the entire new energy vehicle drive system and ensures the driving safety and reliability of the vehicle. By optimizing the operating efficiency of the cooling system, the intelligent temperature control cooling system helps to reduce the energy consumption and emissions of new energy vehicles. This is in line with the current global requirements for energy conservation, emission reduction and sustainable development.

[0033] In summary, this intelligent temperature control cooling system provides a more efficient, reliable and environmentally friendly cooling solution for new energy vehicle drive motors through its unique working principle and advanced technical effects.

[0034] In one embodiment of the present invention, the temperature sensor array includes an ambient temperature sensor, a stator temperature sensor, a rotor surface temperature sensor, and a motor housing temperature sensor; The temperature correlation coefficients between the target objects corresponding to the temperature sensors included in the temperature sensor array are as follows: The temperature correlation coefficients between the ambient temperature sensor and the stator temperature sensor, the rotor surface temperature sensor, and the motor housing temperature sensor are respectively l 01 =0.2, l 02 = 0.1 and l 03 =0.4; The temperature correlation coefficients between the stator temperature sensor, the rotor surface temperature sensor, and the motor housing temperature sensor are respectively l 04 =0.6 and l 05 =0.5; The temperature correlation coefficient between the rotor surface temperature sensor and the motor housing temperature sensor k 01 l 04 + k 02 l 05 ,in, k 01 and k 02They respectively represent the temperature influence coefficient between the stator and the rotor and the influence coefficient of the motor housing temperature on the stator temperature, and the specific values ​​of the influence coefficients are obtained by means of experimental simulation.

[0035] The working principle of the above technical solution is: Ambient temperature sensor: used to monitor the temperature of the environment in which the new energy vehicle drive motor is located.

[0036] Stator temperature sensor: placed inside the motor stator to monitor the temperature of the stator winding.

[0037] Rotor surface temperature sensor: used to directly measure the temperature of the motor rotor surface, which is the key to evaluating the rotor heat dissipation effect and thermal status.

[0038] Motor casing temperature sensor: monitors the temperature of the motor casing and reflects the overall heat dissipation of the motor.

[0039] Temperature data collection and transmission: Each temperature sensor collects the temperature data of the corresponding target object in real time. These data are transmitted to the control unit as the basis for subsequent temperature correlation analysis and cooling system adjustment.

[0040] Application of temperature correlation coefficient: The temperature correlation coefficient (such as λ01, λ02, λ03, λ04, λ05) describes the degree of correlation between temperature changes between different temperature sensors. The temperature correlation coefficient between the rotor surface temperature sensor and the motor housing temperature sensor is determined by the temperature influence coefficient k01 between the stator and the rotor and the influence coefficient k02 of the motor housing temperature on the stator temperature, which reflects the complexity and interrelationship of temperature changes. The control unit performs intelligent analysis based on the received temperature data and temperature correlation coefficient. Based on the analysis results, the control unit dynamically adjusts the parameters of the cooling system (such as the speed of the electronic water pump and the coolant flow rate) to achieve precise control of the motor temperature. At the same time, the system continuously monitors temperature changes and adjusts the data acquisition frequency of the temperature sensor and the adjustment strategy of the cooling system as needed.

[0041] The effect of the above technical solution is that by arranging multiple temperature sensors and considering the temperature correlation coefficient between them, the system can monitor the temperature state of the motor more comprehensively. This helps to accurately identify temperature anomalies and improve the accuracy and reliability of temperature monitoring. Using the temperature correlation coefficient for intelligent analysis, the control unit can adjust the parameters of the cooling system more accurately. This helps to maximize cooling efficiency while reducing unnecessary energy consumption. Accurate temperature monitoring and cooling system adjustment help keep the motor running within the optimal operating temperature range. This reduces the risk of damage caused by overheating and improves the stability and safety of the entire new energy vehicle drive system. The specific values ​​of the temperature correlation coefficient and the influence coefficient are obtained by means of experimental simulation, which helps to verify and optimize the performance of the system. Based on the experimental data, the system can be further optimized and improved to improve its adaptability and reliability.

[0042] In summary, this technical solution achieves accurate monitoring and intelligent regulation of the temperature of the new energy vehicle drive motor through the application of temperature sensor array and temperature correlation coefficient. This helps to improve the stability and safety of the system, while optimizing the performance of the cooling system and reducing energy consumption.

[0043] In one embodiment of the present invention, the data acquisition frequency of each temperature sensor in the temperature sensor array is dynamically adjusted according to the temperature data of each part of the motor, including: Real-time monitoring of the temperature values ​​obtained by each temperature sensor included in the temperature sensor array; Obtaining a temperature variation coefficient of a target object monitored by each temperature sensor according to the temperature data collected by each temperature sensor; comparing the temperature variation coefficient with a preset temperature variation coefficient threshold; When any one of the temperature variation coefficients exceeds a preset temperature variation coefficient threshold, the temperature sensor whose temperature variation coefficient exceeds the preset temperature variation coefficient threshold is used as a target sensor, and the temperature sensor whose temperature variation coefficient does not exceed the preset temperature variation coefficient threshold is used as an associated sensor; The temperature acquisition frequency of the target sensor is adjusted using the temperature variation coefficient of the target sensor; The temperature acquisition frequency of the associated sensor is adjusted by using the temperature variation coefficient of the target sensor in combination with the temperature variation coefficient of the associated sensor.

[0044] The working principle of the above technical solution is as follows: the system first monitors the temperature values ​​obtained by each temperature sensor included in the temperature sensor array in real time. These temperature values ​​reflect the current temperature status of each part of the motor. Then, the system calculates the temperature variation coefficient of the target object monitored by each temperature sensor according to the temperature data collected by each temperature sensor. This coefficient reflects the speed or intensity of the temperature change of the target object. The system compares the calculated temperature variation coefficient with the preset temperature variation coefficient threshold. This threshold is set according to the normal working conditions and temperature monitoring requirements of the motor. When the temperature variation coefficient of any temperature sensor exceeds the preset threshold, the sensor is identified as the target sensor. At the same time, other sensors whose temperature variation coefficients do not exceed the threshold are used as associated sensors. For the target sensor, the system directly adjusts its temperature acquisition frequency using its temperature variation coefficient. If the temperature variation coefficient is large, it means that the temperature of the target object changes rapidly, and temperature data needs to be collected more frequently for real-time monitoring. For the associated sensor, the system not only considers its own temperature variation coefficient, but also adjusts its temperature acquisition frequency in combination with the temperature variation coefficient of the target sensor. This is because the associated sensor may be affected by the temperature change of the object monitored by the target sensor, or there is a certain temperature correlation between them.

[0045] The effect of the above technical solution is: by dynamically adjusting the data acquisition frequency of the temperature sensor, the system can more efficiently monitor the temperature changes of various parts of the motor. When the temperature changes rapidly, increasing the data acquisition frequency can ensure that the system can capture the temperature anomaly in time and take corresponding adjustment measures. Dynamic adjustment of the data acquisition frequency can also optimize the allocation of system resources. For parts with slower temperature changes, reducing the data acquisition frequency can reduce the energy consumption and data processing burden of the system. When a temperature anomaly occurs in a certain part of the motor, the system can quickly identify and adjust the data acquisition frequency of the relevant temperature sensor, so as to respond to temperature changes more quickly and ensure the safe operation of the motor. By adjusting the data acquisition frequency in combination with the temperature change coefficients of the target sensor and the associated sensor, the system can more accurately reflect the temperature correlation and changes between the various parts of the motor, thereby improving the accuracy of temperature monitoring.

[0046] In summary, this technical solution achieves accurate monitoring and efficient response to temperature changes in various parts of the motor by dynamically adjusting the data acquisition frequency of each temperature sensor in the temperature sensor array. This helps to improve the stability and safety of the system, while optimizing resource allocation and reducing energy consumption.

[0047] In one embodiment of the present invention, obtaining the temperature variation coefficient of the target object monitored by each temperature sensor according to the temperature data collected by each temperature sensor includes: Extract each temperature sensor nThe temperature data collected is used to n The temperature data collected this time forms a temperature vector corresponding to each temperature sensor; wherein the structure of the temperature vector is as follows:

[0048] in, T represents the temperature vector; T 1. T 2.…… T n Respectively n The temperature data collected; The temperature vector is standardized to obtain a temperature standard vector after the standardization process, wherein the structure of the temperature standard vector is as follows:

[0049] in, S represents the temperature standard vector, and S=[1+exp(-T)] -1 ; T represents the temperature vector; Using the temperature standard vector corresponding to each temperature sensor, obtain the temperature variation coefficient of the target object monitored by each temperature sensor; The temperature variation coefficient of the target object monitored by each temperature sensor is obtained by the following formula:

[0050] in, G Indicates the temperature variation coefficient of the target object monitored by each temperature sensor; D r Represents the first-order difference vector of the temperature standard vector, which is used to capture the temperature change trend; ⊙ represents the dot multiplication operation of the corresponding elements of the vector; S represents the temperature standard vector; S J Represents the mean vector of the temperature standard vector; Δ S represents the second-order difference vector of the temperature standard vector; s (Δ S ) is the second-order difference of the temperature standard vector Δ S The corresponding standard deviation function; I represents the identity matrix; S smooth represents the smoothed vector after smoothing the temperature standard vector; d ( S ) represents the variance corresponding to the temperature standard vector.

[0051] The working principle of the above technical solution is: the system first extracts the temperature data collected by each temperature sensor n times, and these data constitute the temperature vector T corresponding to each temperature sensor. The temperature vector T is an array containing n elements, where T1, T2, ..., Tn represent the temperature data collected n times respectively.

[0052] Next, the system normalizes the temperature vector T to obtain the standardized temperature standard vector S. The purpose of standardization is to eliminate the differences between different temperature sensors due to factors such as range and accuracy, so that the temperature data are comparable. The formula for standardization is S=[1+exp(-T)]-1, where S represents the temperature standard vector and T represents the original temperature vector. After obtaining the temperature standard vector S, the system uses the vector to calculate the temperature change coefficient G of the target object monitored by each temperature sensor. This coefficient reflects the speed or severity of the temperature change of the target object.

[0053] The calculation formula of the temperature variation coefficient G involves multiple parameters and operation steps. First, the first-order difference vector Dr of the temperature standard vector S is calculated to capture the temperature variation trend. Then, the temperature variation coefficient G is finally obtained by using the first-order difference vector Dr, the temperature standard vector S, the mean vector SJ of the temperature standard vector, the second-order difference vector ΔS of the temperature standard vector and its standard deviation σ(ΔS), the unit matrix I, and the smoothed vector Ssmooth after smoothing the temperature standard vector.

[0054] The effect of the above technical solution is: by standardizing the temperature vector, the differences between different temperature sensors are eliminated, making the temperature data comparable. This helps to improve the accuracy of temperature monitoring. Using the first-order difference vector Dr to capture the temperature change trend can more sensitively reflect the temperature change. This is of great significance for timely detection of temperature anomalies and prevention of equipment failures. By calculating the temperature variation coefficient G, the speed or intensity of the temperature change of the target object is quantified. This provides an important basis for evaluating the thermal state of the equipment and optimizing the cooling system. The technical solution takes into account a variety of factors (such as first-order difference, second-order difference, smoothing, etc.), so that the system can analyze the temperature data more comprehensively and enhance the adaptability to different working conditions and environments. At the same time, the temperature variation coefficient is obtained through the above mathematical operation, which also improves the robustness of the system.

[0055] On the other hand, by standardizing the temperature vector, the differences between different temperature sensors due to factors such as range and accuracy are eliminated, making the temperature data comparable. This helps to improve the accuracy of temperature data acquisition and provide a reliable basis for the subsequent temperature variation coefficient calculation and acquisition frequency adjustment. The temperature variation coefficient is calculated using the above mathematical operations and multiple parameters (such as first-order difference, second-order difference, smoothing, etc.), which can more comprehensively reflect the temperature variation characteristics of the target object. This refined calculation method improves the accuracy of the temperature variation coefficient, thereby ensuring the accuracy of the dynamic adjustment of the temperature acquisition frequency. By calculating the first-order difference vector of the temperature standard vector, the temperature change trend can be sensitively captured. When the temperature changes slightly, the first-order difference vector will respond quickly and provide a timely signal for the dynamic adjustment of the temperature acquisition frequency. When the temperature variation coefficient exceeds the preset threshold, the system can quickly identify and adjust the data acquisition frequency of the relevant temperature sensor. This fast response mechanism ensures that the system can capture temperature anomalies in a timely manner and take effective adjustment measures. When adjusting the data acquisition frequency of the temperature sensor, not only the temperature variation coefficient of the target sensor is considered, but also the temperature variation coefficient of the associated sensor is comprehensively considered. This global adjustment strategy ensures that the data collection frequencies of each sensor can match each other, avoiding redundancy and loss of data collection. By dynamically adjusting the data collection frequency, the system can reasonably allocate resources according to actual needs. For parts with faster temperature changes, the data collection frequency is increased to ensure the timeliness of monitoring; for parts with slower temperature changes, the data collection frequency is reduced to reduce energy consumption and data processing burden. This optimization strategy improves the overall efficiency and performance of the system.

[0056] At the same time, the data acquisition frequency of the temperature sensor is dynamically adjusted, so that the system can more efficiently monitor the temperature changes of various parts of the motor. This helps to improve the monitoring efficiency of the system and reduce the monitoring cost. Through precise temperature monitoring and timely adjustment of the acquisition frequency, the system can more accurately reflect the temperature correlation and changes between various parts of the motor. This helps to enhance the stability of the system and improve the reliability and service life of the equipment. This technical solution provides strong support for the intelligent decision-making of the temperature monitoring system. By real-time analysis of the temperature change coefficient and dynamic adjustment of the data acquisition frequency, the system can automatically optimize the monitoring strategy and improve the accuracy and efficiency of monitoring.

[0057] In summary, the above technical solution has shown significant technical effects in terms of accuracy and sensitivity of dynamic adjustment of temperature acquisition frequency, and matching of data acquisition frequencies between various sensors. These effects jointly improve the overall performance and reliability of the temperature monitoring system, and provide a strong guarantee for the safe operation and intelligent management of the equipment. At the same time, the technical solution realizes the precise analysis of temperature data and accurate calculation of temperature variation coefficient through a series of mathematical operations and parameter settings mentioned above. This helps to improve the accuracy of temperature monitoring, capture temperature change trends, quantify the degree of temperature change, and enhance the adaptability and robustness of the system.

[0058] In one embodiment of the present invention, the temperature acquisition frequency of the target sensor is adjusted by using the temperature variation coefficient of the target sensor, including: Extract the temperature variation coefficient corresponding to the target sensor and the temperature acquisition frequency of the target sensor; Obtaining an adjusted temperature acquisition frequency of the target sensor using a temperature variation coefficient corresponding to the target sensor; The adjusted temperature acquisition frequency of the target sensor is obtained by the following formula:

[0059] in, f a Indicates the adjusted temperature acquisition frequency of the target sensor; f a0 Indicates the temperature acquisition frequency of the target sensor before adjustment; G m Indicates the temperature variation coefficient of the target object monitored by the target sensor; α Indicates the influence factor of the preset temperature variation coefficient on the frequency adjustment, with a value range of 0.26-0.51; β Indicates the influence factor of the preset temperature change on the adjustment range, with a value range of 0.14-0.33; c Represents a preset nonlinear factor, which is used to further control the smoothness and sensitivity of the adjustment, and its value range is 0.58-1.73.

[0060] The working principle of the above technical solution is as follows: the system first extracts the temperature variation coefficient Gm corresponding to the target sensor, which reflects the speed or intensity of the temperature change of the target object. At the same time, the system also extracts the current temperature acquisition frequency of the target sensor, that is, the acquisition frequency before adjustment. Using the extracted temperature variation coefficient and the preset influencing factors (α, β, γ), the adjusted temperature acquisition frequency of the target sensor is calculated through a specific formula. α in the formula represents the influencing factor of the temperature variation coefficient on the frequency adjustment, which determines the degree of influence of the temperature variation coefficient on the adjustment amplitude of the acquisition frequency. β represents the influencing factor of the temperature change amount on the adjustment amplitude, which reflects the direct contribution of the temperature change amount to the adjustment amplitude of the acquisition frequency. γ represents a nonlinear factor, which is used to further control the smoothness and sensitivity of the adjustment to ensure that the adjustment of the acquisition frequency is neither too drastic nor too slow. The system applies the calculated adjusted temperature acquisition frequency to the target sensor, thereby realizing dynamic adjustment of the temperature acquisition frequency.

[0061] The effect of the above technical solution is that by dynamically adjusting the acquisition frequency of the temperature sensor, the system can monitor the temperature change of the target object more efficiently. When the temperature changes rapidly, the acquisition frequency increases to ensure that the temperature anomaly is captured in time; when the temperature changes slowly, the acquisition frequency decreases to reduce unnecessary energy consumption and data processing burden. The technical solution can reasonably allocate resources according to actual needs. For parts with drastic temperature changes, the acquisition frequency is increased to ensure the timeliness of monitoring; for parts with gentle temperature changes, the acquisition frequency is reduced to reduce costs. This optimization strategy improves the overall efficiency and performance of the system. Through precise temperature monitoring and timely adjustment of the acquisition frequency, the system can more accurately reflect the temperature change characteristics of the target object. This helps to enhance the stability of the system and improve the reliability and service life of the equipment. The technical solution provides strong support for the intelligent decision-making of the temperature monitoring system. By real-time analysis of the temperature change coefficient and dynamic adjustment of the acquisition frequency, the system can automatically optimize the monitoring strategy and improve the accuracy and efficiency of monitoring. Due to the introduction of the nonlinear factor γ, the technical solution can more flexibly adapt to the temperature change characteristics under different working conditions and environments. Whether it is a fast change or a slow change, the system can ensure the accuracy and timeliness of monitoring by adjusting the acquisition frequency.

[0062] In summary, the above technical solution optimizes and improves the temperature monitoring system by using the temperature variation coefficient of the target sensor to dynamically adjust its temperature acquisition frequency. This technical solution not only improves monitoring efficiency, optimizes resource allocation, and enhances system stability, but also supports intelligent decision-making and has strong adaptability.

[0063] In one embodiment of the present invention, the temperature acquisition frequency of the associated sensor is adjusted by using the temperature variation coefficient of the target sensor in combination with the temperature variation coefficient of the associated sensor, including: Extracting a temperature correlation coefficient between the temperature of the target object corresponding to each preset associated sensor and the temperature of the target object corresponding to each target sensor; Obtaining the adjusted temperature acquisition frequency corresponding to each associated sensor by using the temperature correlation coefficient between the temperature of the target object corresponding to each associated sensor and the temperature of the target object corresponding to each target sensor in combination with the temperature variation coefficient of the associated sensor and the temperature variation coefficient of the target sensor; The adjusted temperature acquisition frequency corresponding to each associated sensor is obtained by the following formula:

[0064] in, f b Indicates the adjusted temperature acquisition frequency of the associated sensor; f b0 Indicates the temperature acquisition frequency of the associated sensor before adjustment; G mi Indicates the first i The temperature variation coefficient of the target object monitored by each target sensor; G g Indicates the temperature variation coefficient of the target object monitored by the associated sensor; w i Indicates the association of the sensor with the i The temperature correlation coefficient between the target objects monitored by the target sensors; α Indicates the influence factor of the preset temperature variation coefficient on the frequency adjustment, with a value range of 0.26-0.51; β Indicates the influence factor of the preset temperature change on the adjustment range, with a value range of 0.14-0.33; c Represents a preset nonlinear factor, which is used to further control the smoothness and sensitivity of the adjustment, and its value range is 0.58-1.73.

[0065] The working principle of the above technical solution is as follows: the system first extracts the temperature correlation coefficient wi between the temperature of the target object corresponding to each preset associated sensor and the temperature of the target object corresponding to each target sensor. These coefficients reflect the correlation between the temperature change between the associated sensor and the target sensor. The system then obtains the temperature change coefficient of the target object corresponding to the associated sensor, and the temperature change coefficient of each target sensor that has an associated relationship with the target object corresponding to the associated sensor. Using the extracted temperature correlation coefficient, the temperature change coefficient of the associated sensor, the temperature change coefficient of the target sensor, and the preset influencing factors (α, β, γ), the adjusted temperature acquisition frequency corresponding to the associated sensor is calculated by a specific formula. α in the formula represents the influencing factor of the temperature change coefficient on the frequency adjustment, which determines the degree of influence of the temperature change coefficient on the adjustment amplitude of the acquisition frequency. β represents the influencing factor of the temperature change amount on the adjustment amplitude, which reflects the direct contribution of the temperature change amount to the adjustment amplitude of the acquisition frequency. γ represents a nonlinear factor, which is used to further control the smoothness and sensitivity of the adjustment to ensure that the adjustment of the acquisition frequency is neither too drastic nor too slow. w i As the temperature correlation coefficient, it reflects the correlation degree of the temperature change between the associated sensor and the target sensor, thus affecting the adjustment of the associated sensor acquisition frequency. The system applies the calculated adjusted temperature acquisition frequency fb to the associated sensor, thereby realizing the dynamic adjustment of the temperature acquisition frequency of the associated sensor.

[0066] The effect of the above technical solution is that by comprehensively considering the temperature correlation coefficient between the associated sensor and the target sensor and their respective temperature change coefficients, the system can more accurately reflect the temperature correlation and change between the target objects. This helps to improve the accuracy of temperature monitoring. The technical solution can reasonably allocate resources according to actual needs. For parts with drastic temperature changes and high correlation, the acquisition frequency is increased to ensure the timeliness of monitoring; for parts with gentle temperature changes or low correlation, the acquisition frequency is reduced to reduce costs. This optimization strategy improves the overall efficiency and performance of the system. Through precise temperature monitoring and timely adjustment of the acquisition frequency, the system can more accurately reflect the temperature change characteristics of the target object, thereby enhancing the stability of the system. This helps to improve the reliability and service life of the equipment. The technical solution provides strong support for the intelligent decision-making of the temperature monitoring system. By real-time analysis of the temperature change coefficient, the temperature correlation coefficient and dynamic adjustment of the acquisition frequency, the system can automatically optimize the monitoring strategy and improve the accuracy and efficiency of monitoring. Due to the introduction of the nonlinear factor γ and the temperature correlation coefficient, the technical solution can more flexibly adapt to the temperature change characteristics and the correlation between sensors under different working conditions and environments. Whether it is a fast change or a slow change, the system can ensure the accuracy and timeliness of monitoring by adjusting the acquisition frequency.

[0067] In summary, the above technical solution optimizes and improves the temperature monitoring system by dynamically adjusting the temperature acquisition frequency of the associated sensor by comprehensively considering the temperature variation coefficient of the target sensor and the associated sensor and the temperature correlation coefficient between them. This technical solution not only improves monitoring accuracy, optimizes resource allocation, and enhances system stability, but also supports intelligent decision-making and has strong adaptability.

[0068] In one embodiment of the present invention, a speed dynamic compensation coefficient and a flow rate dynamic compensation coefficient are obtained according to received temperature data, and the speed and coolant flow rate of the electronic water pump are adaptively adjusted by the speed dynamic compensation coefficient and the flow rate dynamic compensation coefficient, including: The control unit receives temperature data sent by the temperature sensor array in real time; Using the temperature data sent by the temperature sensor array, a rotation speed dynamic compensation coefficient and a flow rate dynamic compensation coefficient are obtained; The rotation speed dynamic compensation coefficient is used to adaptively adjust the rotation speed of the electronic water pump, and at the same time, the flow rate dynamic compensation coefficient is used to adaptively adjust the coolant flow rate of the electronic water pump.

[0069] The working principle of the above technical solution is as follows: the control unit receives the temperature data sent by the temperature sensor array in real time. These temperature data reflect the temperature conditions at different locations in the cooling system. Using the received temperature data, the system calculates the speed dynamic compensation coefficient and the flow dynamic compensation coefficient through a specific algorithm or model. These coefficients reflect the changes in demand for the electronic water pump speed and coolant flow under the current temperature conditions. According to the calculated speed dynamic compensation coefficient, the system adaptively adjusts the speed of the electronic water pump. When the temperature data indicates that the cooling effect needs to be increased, the speed dynamic compensation coefficient will increase accordingly, thereby increasing the speed of the electronic water pump and increasing the coolant circulation speed. At the same time, according to the flow dynamic compensation coefficient, the system adaptively adjusts the coolant flow of the electronic water pump. When the temperature data indicates that the coolant flow needs to be increased to improve the heat dissipation efficiency, the flow dynamic compensation coefficient will increase accordingly, thereby increasing the coolant flow.

[0070] The effect of the above technical solution is that by receiving temperature data in real time and dynamically adjusting the speed and coolant flow of the electronic water pump accordingly, the system can more accurately meet the needs of the cooling system. This helps to improve cooling efficiency and ensure that key components such as the engine are always kept within the optimal operating temperature range. The dynamic compensation coefficient adjustment mechanism enables the system to better cope with external interference such as temperature fluctuations and load changes. This helps to enhance the stability of the system and reduce the risk of failure due to abnormal temperature. By adaptively adjusting the speed and coolant flow of the electronic water pump, the system can avoid unnecessary energy consumption. When the temperature is low or the load is light, the system can reduce the speed and coolant flow of the electronic water pump to reduce energy consumption. A stable cooling system can ensure that the vehicle always maintains a good operating state and reduce the failure rate and maintenance cost caused by temperature problems. This helps to improve the user's driving experience and satisfaction. This technical solution embodies the concept of intelligent management. Through real-time monitoring and dynamic adjustment, the system can automatically optimize the performance of the cooling system and reduce the need for manual intervention.

[0071] In summary, the above technical solution realizes intelligent management of the cooling system by receiving temperature data in real time and dynamically adjusting the speed of the electronic water pump and the coolant flow rate accordingly. This technical solution not only improves cooling efficiency, enhances system stability, and reduces energy consumption, but also improves user experience and intelligent management level.

[0072] In one embodiment of the present invention, the temperature data sent by the temperature sensor array is used to obtain the rotation speed dynamic compensation coefficient, including: Extract temperature data sent by the temperature sensor array; Comparing the temperature data collected by each temperature sensor included in the temperature sensor array with its corresponding temperature threshold; When the temperature data collected by any temperature sensor reaches or exceeds its corresponding temperature threshold, the rotation speed dynamic compensation coefficient is obtained by using the temperature data collected by each temperature sensor included in the temperature sensor array; The speed dynamic compensation coefficient is obtained by the following formula:

[0073] in, B r Indicates the speed dynamic compensation coefficient; x Indicates the number of target objects whose temperature data reaches or exceeds the corresponding temperature threshold; y Indicates the number of target objects whose temperature data is lower than the corresponding temperature threshold; T xi Indicates iThe temperature data of the target object corresponding to the temperature data reaching or exceeding the corresponding temperature threshold; T zxi Indicates i The temperature threshold value corresponding to the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T yi Indicates i The temperature data of the target object corresponding to the temperature threshold value corresponding to the temperature data is lower than the temperature data of the target object corresponding to the temperature data; T zyi Indicates i The temperature threshold value corresponding to the target object whose temperature data is lower than the corresponding temperature threshold; J 01 and J 02 represents the first adjustment coefficient and the second adjustment coefficient; The first adjustment coefficient is obtained by the following formula:

[0074] in, J 01 represents the first adjustment coefficient; T xmaxi and T xmini Indicates i The maximum and minimum temperature values ​​of the target objects whose temperature data reaches or exceeds the corresponding temperature threshold value; f xbi Indicates i The standard deviation of the temperature acquisition frequency of the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T xbi Indicates i The standard deviation of the temperature data of the target object whose temperature data reaches or exceeds its corresponding temperature threshold; At the same time, the first adjustment coefficient is obtained by the following formula:

[0075] in, J 02 represents the second adjustment coefficient; T ymaxi and T ymini Indicates i The maximum and minimum temperature values ​​that appear for the target objects whose temperature data is lower than the corresponding temperature threshold value; f ybi Indicates iThe standard deviation of the temperature acquisition frequency of the target object whose temperature data is lower than its corresponding temperature threshold; T ybi Indicates i The standard deviation of the temperature data of the target object whose temperature data is lower than its corresponding temperature threshold.

[0076] The working principle of the above technical solution is as follows: the system first extracts the temperature data sent by the temperature sensor array. Then, the temperature data collected by each temperature sensor is compared with its corresponding temperature threshold. These temperature thresholds are preset according to system requirements and are used to determine whether the temperature is within the normal range. When the temperature data collected by any temperature sensor reaches or exceeds its corresponding temperature threshold, the calculation process of the speed dynamic compensation coefficient is triggered. This means that the system detects that there is a temperature abnormality or high temperature, and the speed of the electronic water pump needs to be adjusted to enhance the cooling effect. The calculation of the first adjustment coefficient takes into account the maximum temperature, minimum temperature, standard deviation of temperature acquisition frequency and standard deviation of temperature data of the target object whose temperature data reaches or exceeds the threshold. The calculation of the second adjustment coefficient takes into account the corresponding parameters of the target object whose temperature data is lower than the threshold. The calculation of these two adjustment coefficients helps to more accurately reflect the characteristics of the temperature data, thereby more accurately calculating the speed dynamic compensation coefficient. Finally, according to the calculated speed dynamic compensation coefficient, the system adaptively adjusts the speed of the electronic water pump. When the temperature data indicates that the cooling effect needs to be increased, the speed dynamic compensation coefficient will increase accordingly, thereby increasing the speed of the electronic water pump.

[0077] The effect of the above technical solution is that by real-time monitoring of temperature data and dynamically adjusting the speed of the electronic water pump according to temperature anomalies, the system can respond to cooling needs more accurately. This helps to improve cooling efficiency and ensure that the system is always in the best working state. The dynamic speed adjustment mechanism enables the system to better cope with external interference such as temperature fluctuations and load changes. This helps to enhance the stability of the system and reduce the risk of failure caused by temperature anomalies. Through precise temperature monitoring and speed adjustment, the system can avoid unnecessary energy consumption. When the temperature is low or the load is light, the system can reduce the speed of the electronic water pump to save energy. This technical solution embodies the concept of intelligent management. Through real-time monitoring and dynamic adjustment, the system can automatically optimize the performance of the cooling system and reduce the need for manual intervention. This technical solution takes into account a variety of temperature data characteristics (such as maximum value, minimum value, standard deviation, etc.), so that the system can more flexibly adapt to temperature changes under different working conditions and environments.

[0078] In summary, the above technical solution realizes intelligent management of the cooling system by real-time monitoring of temperature data and dynamically adjusting the speed of the electronic water pump according to abnormal temperature conditions. This technical solution not only improves cooling efficiency, enhances system stability, optimizes resource allocation, but also improves the level of intelligence and adaptability.

[0079] In one embodiment of the present invention, the flow dynamic compensation coefficient is obtained by using the temperature data sent by the temperature sensor array, including: Extract temperature data sent by the temperature sensor array; Comparing the temperature data collected by each temperature sensor included in the temperature sensor array with its corresponding temperature threshold; When the temperature data collected by any temperature sensor reaches or exceeds its corresponding temperature threshold, the flow dynamic compensation coefficient is obtained using the temperature data collected by each temperature sensor included in the temperature sensor array; The flow dynamic compensation coefficient is obtained by the following formula:

[0080] in, B v Indicates the flow dynamic compensation coefficient; x Indicates the number of target objects whose temperature data reaches or exceeds the corresponding temperature threshold; y Indicates the number of target objects whose temperature data is lower than the corresponding temperature threshold; T xi Indicates i The temperature data of the target object corresponding to the temperature data reaching or exceeding the corresponding temperature threshold; T zxi Indicates i The temperature threshold value corresponding to the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T yi Indicates i The temperature data of the target object corresponding to the temperature threshold value corresponding to the temperature data is lower than the temperature data of the target object corresponding to the temperature data; T zyi Indicates i The temperature threshold value corresponding to the target object whose temperature data is lower than the corresponding temperature threshold; f xbp express x The average value of the standard deviation of the temperature acquisition frequency of the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T xbp express x The average value of the standard deviation of the temperature data of the target object whose temperature data reaches or exceeds its corresponding temperature threshold;f ybp express y The average value of the standard deviation of the temperature acquisition frequency of the target object whose temperature data is lower than the corresponding temperature threshold; T ybp express y The average value of the standard deviation of the temperature data of the target objects whose temperature data is lower than the corresponding temperature threshold.

[0081] The working principle of the above technical solution is as follows: the system first extracts the temperature data sent by the temperature sensor array. Then, compare these temperature data with the preset temperature threshold. The temperature threshold is set according to the normal operating temperature range of the system or equipment, and is used to determine whether the current temperature deviates from the normal range. When the temperature data collected by any temperature sensor reaches or exceeds its corresponding temperature threshold, the calculation of the flow dynamic compensation coefficient is triggered. This means that the system detects a temperature abnormality or high temperature, and it may be necessary to increase the coolant flow to enhance the cooling effect. According to the calculated flow dynamic compensation coefficient Bv, the system adaptively adjusts the flow of the coolant. When the temperature data indicates that the cooling effect needs to be increased, the flow dynamic compensation coefficient will increase accordingly, thereby increasing the flow of the coolant.

[0082] The effect of the above technical solution is that by real-time monitoring of temperature data and dynamically adjusting the flow of coolant according to temperature anomalies, the system can respond to cooling needs more accurately. This helps to improve cooling efficiency and ensure that the system or equipment is always in the best working condition. The dynamic flow adjustment mechanism enables the system to better cope with external interference such as temperature fluctuations and load changes. This helps to enhance the stability of the system and reduce the risk of failure due to temperature anomalies. Through precise temperature monitoring and flow adjustment, the system can avoid unnecessary energy consumption. When the temperature is low or the load is light, the system can reduce the flow of coolant to save energy. This technical solution embodies the concept of intelligent management. Through real-time monitoring and dynamic adjustment, the system can automatically optimize the performance of the cooling system and reduce the need for manual intervention. This technical solution takes into account a variety of temperature data characteristics (such as the average value of the standard deviation of the temperature acquisition frequency, the average value of the standard deviation of the temperature data, etc.), so that the system can more flexibly adapt to temperature changes under different working conditions and environments.

[0083] In summary, the above technical solution realizes intelligent management of the cooling system by real-time monitoring of temperature data and dynamically adjusting the flow of coolant according to abnormal temperature conditions. This technical solution not only improves cooling efficiency, enhances system stability, optimizes resource allocation, but also improves the level of intelligence and adaptability.

[0084] In one embodiment of the present invention, the speed of the electronic water pump is adaptively adjusted using the speed dynamic compensation coefficient, and at the same time, the coolant flow of the electronic water pump is adaptively adjusted using the flow dynamic compensation coefficient, including: The speed of the electronic water pump is compensated and adjusted using the speed dynamic compensation coefficient to obtain the speed after compensation adjustment; The speed after compensation adjustment is obtained by the following formula:

[0085] in, R Indicates the speed after compensation adjustment; R 0 means the speed before compensation adjustment; B r Indicates the speed dynamic compensation coefficient; x Indicates the number of target objects whose temperature data reaches or exceeds the corresponding temperature threshold; T xi Indicates i The temperature data of the target object corresponding to the temperature data reaching or exceeding the corresponding temperature threshold; T zxi Indicates i The temperature threshold value corresponding to the target object whose temperature data reaches or exceeds its corresponding temperature threshold; or represents a control correlation coefficient of preset temperature and speed control; and the control correlation coefficient of temperature and speed control is obtained through experiments or simulations; Controlling the motor of the electronic water pump to run at a speed adjusted by compensation; Using the dynamic flow compensation coefficient to compensate and adjust the coolant flow of the electronic water pump, and obtain the compensated and adjusted coolant flow; The coolant flow rate after compensation adjustment is obtained by the following formula:

[0086] in, V Indicates the coolant flow after compensation adjustment; V 0 means the coolant flow before compensation adjustment; B v Indicates the flow dynamic compensation coefficient; x represents a control correlation coefficient between a preset temperature and a coolant flow rate control; and the control correlation coefficient between the temperature and the coolant flow rate control is obtained through experiments or simulations; The electronic water pump is controlled to operate according to the coolant flow rate after compensation adjustment.

[0087] The working principle of the above technical solution is as follows: the system first calculates the dynamic compensation coefficient of the speed based on the temperature data sent by the temperature sensor array. Then, the dynamic compensation coefficient of the speed is used to compensate and adjust the original speed of the electronic water pump to obtain the speed after compensation adjustment. The compensation adjustment process takes into account the number of target objects whose temperature data reaches or exceeds the temperature threshold, the number of target objects whose temperature data is lower than the temperature threshold, the relevant temperature data, and the preset temperature and speed control correlation coefficient. The control correlation coefficient is obtained through experiments or simulations, etc., and is used to reflect the control relationship between temperature and speed. Finally, the system controls the motor of the electronic water pump to run at the speed after compensation adjustment.

[0088] Similar to the dynamic compensation adjustment of the speed, the system also calculates the dynamic compensation coefficient of the flow rate based on the temperature data sent by the temperature sensor array. Then, the dynamic compensation coefficient of the flow rate is used to compensate and adjust the original coolant flow rate of the electronic water pump to obtain the compensated coolant flow rate. The compensation adjustment process also takes into account the number of target objects whose temperature data reaches or exceeds the temperature threshold, the number of target objects whose temperature data is lower than the temperature threshold, the relevant temperature data, and the preset temperature and coolant flow control correlation coefficient. The control correlation coefficient is also obtained through experiments or simulations to reflect the control relationship between temperature and coolant flow. Finally, the system controls the electronic water pump to operate according to the compensated coolant flow rate.

[0089] The effect of the above technical solution is that by real-time monitoring of temperature data and dynamically adjusting the speed and coolant flow of the electronic water pump according to temperature anomalies, the system can respond to cooling needs more accurately. This helps to improve cooling efficiency and ensure that the system or equipment is always in the best working state. The dynamic speed and flow adjustment mechanism enables the system to better cope with external interference such as temperature fluctuations and load changes. This helps to enhance the stability of the system and reduce the risk of failure caused by temperature anomalies. Through precise temperature monitoring and speed and flow adjustment, the system can avoid unnecessary energy consumption. When the temperature is low or the load is light, the system can reduce the speed and coolant flow of the electronic water pump to save energy. This technical solution embodies the concept of intelligent management. Through real-time monitoring and dynamic adjustment, the system can automatically optimize the performance of the cooling system and reduce the need for manual intervention. The technical solution takes into account a variety of temperature data characteristics (such as the number of target objects whose temperature data reaches or exceeds the temperature threshold, the number of target objects whose temperature data is lower than the temperature threshold, etc.), as well as the preset control correlation coefficient, so that the system can more flexibly adapt to temperature changes under different working conditions and environments. Through precise speed and flow regulation, the system can ensure that the cooling system always operates in the best condition, thereby extending the service life of the system or equipment and improving the reliability of the system.

[0090] In summary, the above technical solution realizes intelligent management of the cooling system by real-time monitoring of temperature data and dynamically adjusting the speed and coolant flow of the electronic water pump according to abnormal temperature conditions. This technical solution not only improves cooling efficiency, enhances system stability, optimizes resource allocation, but also improves the level of intelligence and adaptability, while enhancing system reliability.

[0091] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent temperature control cooling system for a new energy vehicle drive motor, characterized in that: The intelligent temperature control cooling system includes an electronic water pump, a temperature sensor array and a control unit; wherein, The temperature sensor array is used to collect temperature data of various parts of the motor in real time and send the temperature data to the control unit; The control unit is used to dynamically adjust the data acquisition frequency of each temperature sensor in the temperature sensor array according to the temperature data of each part of the motor; at the same time, obtain the speed dynamic compensation coefficient and the flow dynamic compensation coefficient according to the received temperature data, and adaptively adjust the speed and coolant flow of the electronic water pump through the speed dynamic compensation coefficient and the flow dynamic compensation coefficient; The electronic water pump is used to adjust the rotation speed and coolant flow according to the instructions corresponding to the rotation speed and coolant flow given by the control unit, so as to achieve accurate regulation of the motor temperature.

2. The intelligent temperature control cooling system for a new energy vehicle drive motor according to claim 1 is characterized in that: The temperature sensor array includes an ambient temperature sensor, a stator temperature sensor, a rotor surface temperature sensor, and a motor housing temperature sensor; The temperature correlation coefficients between the target objects corresponding to the temperature sensors included in the temperature sensor array are as follows: The temperature correlation coefficients between the ambient temperature sensor and the stator temperature sensor, the rotor surface temperature sensor, and the motor housing temperature sensor are respectively λ 01 =0.2, λ 02 = 0.1 and λ 03 =0.4; The temperature correlation coefficients between the stator temperature sensor, the rotor surface temperature sensor, and the motor housing temperature sensor are respectively λ 04 =0.6 and λ 05 =0.5; The temperature correlation coefficient between the rotor surface temperature sensor and the motor housing temperature sensor k 01 λ 04 + k 02 λ 05 ,in, k 01 and k 02 They respectively represent the temperature influence coefficient between the stator and the rotor and the influence coefficient of the motor casing temperature on the stator temperature.

3. The intelligent temperature control cooling system for a new energy vehicle drive motor according to claim 1 is characterized in that: The data acquisition frequency of each temperature sensor in the temperature sensor array is dynamically adjusted according to the temperature data of each part of the motor, including: Real-time monitoring of the temperature values ​​obtained by each temperature sensor included in the temperature sensor array; Obtaining a temperature variation coefficient of a target object monitored by each temperature sensor according to the temperature data collected by each temperature sensor; comparing the temperature variation coefficient with a preset temperature variation coefficient threshold; When any one of the temperature variation coefficients exceeds a preset temperature variation coefficient threshold, the temperature sensor whose temperature variation coefficient exceeds the preset temperature variation coefficient threshold is used as a target sensor, and the temperature sensor whose temperature variation coefficient does not exceed the preset temperature variation coefficient threshold is used as an associated sensor; The temperature acquisition frequency of the target sensor is adjusted using the temperature variation coefficient of the target sensor; The temperature acquisition frequency of the associated sensor is adjusted by using the temperature variation coefficient of the target sensor in combination with the temperature variation coefficient of the associated sensor.

4. The intelligent temperature control cooling system for a new energy vehicle drive motor according to claim 3 is characterized in that: The temperature variation coefficient of the target object monitored by each temperature sensor is obtained according to the temperature data collected by each temperature sensor, including: Extract each temperature sensor n The temperature data collected is used to n The temperature data collected this time forms a temperature vector corresponding to each temperature sensor; wherein the structure of the temperature vector is as follows: in, T represents the temperature vector; T 1. T 2.…… T n Respectively n The temperature data collected; The temperature vector is standardized to obtain a temperature standard vector after the standardization process, wherein the structure of the temperature standard vector is as follows: in, S represents the temperature standard vector, and S=[1+exp(-T)] -1 ; T represents the temperature vector; Using the temperature standard vector corresponding to each temperature sensor, obtain the temperature variation coefficient of the target object monitored by each temperature sensor; The temperature variation coefficient of the target object monitored by each temperature sensor is obtained by the following formula: in, G Indicates the temperature variation coefficient of the target object monitored by each temperature sensor; D r Represents the first-order difference vector of the temperature standard vector, which is used to capture the temperature change trend; ⊙ represents the dot multiplication operation of the corresponding elements of the vector; S represents the temperature standard vector; S J Represents the mean vector of the temperature standard vector; Δ S represents the second-order difference vector of the temperature standard vector; σ (Δ S ) is the second-order difference of the temperature standard vector Δ S The corresponding standard deviation function; I represents the identity matrix; S smooth represents the smoothed vector after smoothing the temperature standard vector; δ ( S ) represents the variance corresponding to the temperature standard vector.

5. The intelligent temperature control cooling system for a new energy vehicle drive motor according to claim 3 is characterized in that: The temperature acquisition frequency of the target sensor is adjusted using the temperature variation coefficient of the target sensor, including: Extract the temperature variation coefficient corresponding to the target sensor and the temperature acquisition frequency of the target sensor; Obtaining an adjusted temperature acquisition frequency of the target sensor using a temperature variation coefficient corresponding to the target sensor; The adjusted temperature acquisition frequency of the target sensor is obtained by the following formula: in, f a Indicates the adjusted temperature acquisition frequency of the target sensor; f a0 Indicates the temperature acquisition frequency of the target sensor before adjustment; G m Indicates the temperature variation coefficient of the target object monitored by the target sensor; α Indicates the influence factor of the preset temperature variation coefficient on the frequency adjustment, with a value range of 0.26-0.51; β Indicates the influence factor of the preset temperature change on the adjustment range, with a value range of 0.14-0.33; γ Represents a preset nonlinear factor, which is used to further control the smoothness and sensitivity of the adjustment, and its value range is 0.58-1.

73.

6. The intelligent temperature control cooling system for a new energy vehicle drive motor according to claim 3 is characterized in that: The temperature acquisition frequency of the associated sensor is adjusted by using the temperature variation coefficient of the target sensor in combination with the temperature variation coefficient of the associated sensor, including: Extracting a temperature correlation coefficient between the temperature of the target object corresponding to each preset associated sensor and the temperature of the target object corresponding to each target sensor; Obtaining the adjusted temperature acquisition frequency corresponding to each associated sensor by using the temperature correlation coefficient between the temperature of the target object corresponding to each associated sensor and the temperature of the target object corresponding to each target sensor in combination with the temperature variation coefficient of the associated sensor and the temperature variation coefficient of the target sensor; The adjusted temperature acquisition frequency corresponding to each associated sensor is obtained by the following formula: in, f b Indicates the adjusted temperature acquisition frequency of the associated sensor; f b0 Indicates the temperature acquisition frequency of the associated sensor before adjustment; G mi Indicates the first i The temperature variation coefficient of the target object monitored by each target sensor; G g Indicates the temperature variation coefficient of the target object monitored by the associated sensor; w i Indicates the association of the sensor with the i The temperature correlation coefficient between the target objects monitored by the target sensors; α Indicates the influence factor of the preset temperature variation coefficient on the frequency adjustment, with a value range of 0.26-0.51; β Indicates the influence factor of the preset temperature change on the adjustment range, with a value range of 0.14-0.33; γ Represents a preset nonlinear factor, which is used to further control the smoothness and sensitivity of the adjustment, and its value range is 0.58-1.

73.

7. The intelligent temperature control cooling system for a new energy vehicle drive motor according to claim 1, characterized in that: The method comprises: obtaining a speed dynamic compensation coefficient and a flow rate dynamic compensation coefficient according to the received temperature data, and adaptively adjusting the speed and coolant flow rate of the electronic water pump by using the speed dynamic compensation coefficient and the flow rate dynamic compensation coefficient. The control unit receives temperature data sent by the temperature sensor array in real time; Using the temperature data sent by the temperature sensor array, a rotation speed dynamic compensation coefficient and a flow rate dynamic compensation coefficient are obtained; The rotation speed dynamic compensation coefficient is used to adaptively adjust the rotation speed of the electronic water pump, and at the same time, the flow rate dynamic compensation coefficient is used to adaptively adjust the coolant flow rate of the electronic water pump.

8. The intelligent temperature control cooling system for a new energy vehicle drive motor according to claim 7, characterized in that: The temperature data sent by the temperature sensor array is used to obtain a rotation speed dynamic compensation coefficient, including: Extract temperature data sent by the temperature sensor array; Comparing the temperature data collected by each temperature sensor included in the temperature sensor array with its corresponding temperature threshold; When the temperature data collected by any temperature sensor reaches or exceeds its corresponding temperature threshold, the rotation speed dynamic compensation coefficient is obtained by using the temperature data collected by each temperature sensor included in the temperature sensor array; The speed dynamic compensation coefficient is obtained by the following formula: in, B r Indicates the speed dynamic compensation coefficient; x Indicates the number of target objects whose temperature data reaches or exceeds the corresponding temperature threshold; y Indicates the number of target objects whose temperature data is lower than the corresponding temperature threshold; T xi Indicates i The temperature data of the target object corresponding to the temperature data reaching or exceeding the corresponding temperature threshold; T zxi Indicates i The temperature threshold value corresponding to the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T yi Indicates i The temperature data of the target object corresponding to the temperature threshold value corresponding to the temperature data is lower than the temperature data of the target object corresponding to the temperature data; T zyi Indicates i The temperature threshold value corresponding to the target object whose temperature data is lower than the corresponding temperature threshold; J 01 and J 02 represents the first adjustment coefficient and the second adjustment coefficient; The first adjustment coefficient is obtained by the following formula: in, J 01 represents the first adjustment coefficient; T xmaxi and T xmini Indicates i The maximum and minimum temperature values ​​of the target objects whose temperature data reaches or exceeds the corresponding temperature threshold value; f xbi Indicates i The standard deviation of the temperature acquisition frequency of the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T xbi Indicates i The standard deviation of the temperature data of the target object whose temperature data reaches or exceeds its corresponding temperature threshold; At the same time, the first adjustment coefficient is obtained by the following formula: in, J 02 represents the second adjustment coefficient; T ymaxi and T ymini Indicates i The maximum and minimum temperature values ​​that appear for the target objects whose temperature data is lower than the corresponding temperature threshold value; f ybi Indicates i The standard deviation of the temperature acquisition frequency of the target object whose temperature data is lower than its corresponding temperature threshold; T ybi Indicates i The standard deviation of the temperature data of the target object whose temperature data is lower than its corresponding temperature threshold.

9. The intelligent temperature control cooling system for a new energy vehicle drive motor according to claim 7, characterized in that: The flow dynamic compensation coefficient is obtained by using the temperature data sent by the temperature sensor array, including: Extract temperature data sent by the temperature sensor array; Comparing the temperature data collected by each temperature sensor included in the temperature sensor array with its corresponding temperature threshold; When the temperature data collected by any temperature sensor reaches or exceeds its corresponding temperature threshold, the flow dynamic compensation coefficient is obtained using the temperature data collected by each temperature sensor included in the temperature sensor array; The flow dynamic compensation coefficient is obtained by the following formula: in, B v Indicates the flow dynamic compensation coefficient; x Indicates the number of target objects whose temperature data reaches or exceeds the corresponding temperature threshold; y Indicates the number of target objects whose temperature data is lower than the corresponding temperature threshold; T xi Indicates i The temperature data of the target object corresponding to the temperature data reaching or exceeding the corresponding temperature threshold; T zxi Indicates i The temperature threshold value corresponding to the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T yi Indicates i The temperature data of the target object corresponding to the temperature threshold value corresponding to the temperature data is lower than the temperature data of the target object corresponding to the temperature data; T zyi Indicates i The temperature threshold value corresponding to the target object whose temperature data is lower than the corresponding temperature threshold; f xbp express x The average value of the standard deviation of the temperature acquisition frequency of the target object whose temperature data reaches or exceeds its corresponding temperature threshold; T xbp express x The average value of the standard deviation of the temperature data of the target object whose temperature data reaches or exceeds its corresponding temperature threshold; f ybp express y The average value of the standard deviation of the temperature acquisition frequency of the target object whose temperature data is lower than the corresponding temperature threshold; T ybp express y The average value of the standard deviation of the temperature data of the target objects whose temperature data is lower than the corresponding temperature threshold.

10. The intelligent temperature control cooling system for a new energy vehicle drive motor according to claim 7, characterized in that: The speed of the electronic water pump is adaptively adjusted by using the speed dynamic compensation coefficient, and the coolant flow of the electronic water pump is adaptively adjusted by using the flow dynamic compensation coefficient, including: The speed of the electronic water pump is compensated and adjusted using the speed dynamic compensation coefficient to obtain the speed after compensation adjustment; The speed after compensation adjustment is obtained by the following formula: in, R Indicates the speed after compensation adjustment; R 0 means the speed before compensation adjustment; B r Indicates the speed dynamic compensation coefficient; x Indicates the number of target objects whose temperature data reaches or exceeds the corresponding temperature threshold; T xi Indicates i The temperature data of the target object corresponding to the temperature data reaching or exceeding the corresponding temperature threshold; T zxi Indicates i The temperature threshold value corresponding to the target object whose temperature data reaches or exceeds its corresponding temperature threshold; η Indicates the control correlation coefficient of the preset temperature and speed control; Controlling the motor of the electronic water pump to run at a speed adjusted by compensation; Using the dynamic flow compensation coefficient to compensate and adjust the coolant flow of the electronic water pump, and obtain the compensated and adjusted coolant flow; The coolant flow rate after compensation adjustment is obtained by the following formula: in, V Indicates the coolant flow after compensation adjustment; V 0 means the coolant flow before compensation adjustment; B v Indicates the flow dynamic compensation coefficient; ξ Indicates the control correlation coefficient between the preset temperature and the coolant flow control; The electronic water pump is controlled to operate according to the coolant flow rate after compensation adjustment.

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