CN-122008960-A - Thermal management control method and vehicle

Abstract

The application provides a thermal management control method and a vehicle, and relates to the technical field of thermal management, wherein the method comprises the steps of determining the predicted temperature of each heat source based on measured temperature, temperature measurement noise and model estimated temperature, and determining the temperature weight coefficient corresponding to each heat source based on measured state parameters; and determining a global equivalent temperature based on the actually measured state parameters, the predicted temperature and the temperature weight coefficient of the heat source to perform thermal management control based on the global equivalent temperature, wherein the model estimated temperature is determined based on a pre-built multi-heat-source temperature coupling state model. According to the application, by constructing the multi-heat-source temperature coupling state model, multiple heat sources with independent temperature control originally are brought into a unified thermodynamic frame, a foundation is laid for realizing a global collaborative optimization thermal management strategy, and the generated global equivalent temperature is used for comprehensively planning the thermal management requirement of a multi-heat-source system by a single target, so that the heat redundancy exchange phenomenon caused by control strategy cracking in the traditional scheme is reduced.

Inventors

• FANG BO
• ZHANG YANYUN

Assignees

• 长城汽车股份有限公司

Dates

Publication Date

20260512

Application Date

20260228

Claims (10)

1. 1. A thermal management control method, comprising: Acquiring actual measurement state parameters of a plurality of heat sources of the vehicle, wherein the actual measurement state parameters comprise actual measurement temperatures and temperature measurement noise of the heat sources; determining the predicted temperature of each heat source based on the actually measured temperature, the temperature measurement noise and the model estimated temperature, and determining the temperature weight coefficient corresponding to each heat source based on the actually measured state parameter; Determining a global equivalent temperature based on the measured state parameter, the predicted temperature and the temperature weight coefficient of the heat source so as to perform thermal management control based on the global equivalent temperature; The model estimated temperature is determined based on a pre-built multi-heat-source temperature coupling state model, and the multi-heat-source temperature coupling state model is built based on the heat generation power of each heat source, the cooling power acting on each heat source, and the pre-stored heat capacity parameters and heat conductance parameters of each heat source.
2. 2. The method of claim 1, wherein determining a global equivalent temperature based on the measured state parameter, the predicted temperature, and the temperature weight coefficient of the heat source comprises: determining a first equivalent temperature of each heat source based on the temperature weight coefficient of each heat source and the predicted temperature of each heat source; Determining at least one target heat source in the heat sources, and determining a target weight coefficient corresponding to each target heat source based on the actually measured state parameter of each target heat source so as to determine a second equivalent temperature of each target heat source based on the actually measured temperature of each target heat source and the corresponding target weight coefficient; Determining a first confidence coefficient corresponding to the first equivalent temperature and a second confidence coefficient corresponding to the second equivalent temperature, and determining a process equivalent temperature based on the first equivalent temperature, the first confidence coefficient, the second equivalent temperature and the second confidence coefficient; and carrying out safety verification on the process equivalent temperature, and obtaining the global equivalent temperature.
3. 3. The method of claim 2, wherein the target heat source is a motor controller and a battery, and the measured state parameter corresponding to the motor controller further comprises a motor output torque; the determining the target weight coefficient corresponding to each target heat source based on the actually measured state parameter of each target heat source comprises the following steps: Determining a target heat source temperature difference based on the measured temperature of the motor controller and the measured temperature of the battery; invoking a pre-stored weight sensitivity coefficient corresponding to the target heat source temperature difference and a pre-stored load weight coefficient corresponding to the motor output torque; Determining target weight coefficients corresponding to the motor controller and the battery respectively based on the target heat source temperature difference, the weight sensitivity coefficient, the motor output torque and the load weight coefficient; And the target heat source temperature difference and the motor output torque are positively correlated with the target weight coefficient corresponding to the battery.
4. 4. A method according to claim 3, wherein said performing a security check on said process equivalent temperature and obtaining a global equivalent temperature comprises: Based on the measured temperature of the battery, the measured temperature of the motor controller and a preset safety margin, respectively determining the corrected measured temperature of the battery and the corrected measured temperature of the motor controller; Determining the maximum value among the process equivalent temperature, the corrected battery predicted temperature and the corrected motor controller measured temperature as the verification process equivalent temperature; Determining a future predicted temperature of the battery after a preset time period; Correcting the equivalent temperature of the verification process based on the future predicted temperature of the battery, and determining the corrected equivalent temperature of the verification process as the global equivalent temperature.
5. 5. The method of claim 4, wherein the modifying the process equivalent temperature based on the future predicted temperature of the battery and determining the modified process equivalent temperature as the global equivalent temperature comprises: in response to determining that the difference value between the future predicted temperature of the battery and the upper limit of the safe temperature of the battery is larger than a preset value, correcting the equivalent temperature of the verification process based on the difference value of the verification temperature, and determining the corrected equivalent temperature of the verification process as the global equivalent temperature; And in response to determining that the check temperature difference value is smaller than or equal to a preset value, correcting the equivalent temperature of the check process based on the preset value, and determining the corrected equivalent temperature of the check process as the global equivalent temperature.
6. 6. The method of claim 4, wherein determining the future predicted temperature of the battery after the predetermined period of time comprises: Determining an error covariance of the last moment based on the predicted temperature of the last moment of the battery and the actual temperature of the last moment of the battery; determining a gain coefficient at the current moment based on the error covariance at the previous moment and the temperature measurement noise of the battery; Determining the predicted temperature of the battery at the current moment based on the gain coefficient at the current moment, the measured temperature at the current moment of the battery and the predicted temperature at the last moment; And determining the future predicted temperature of the battery after the preset time based on the predicted temperature of the battery at the current moment and the multi-heat-source temperature coupling state model.
7. 7. The method of claim 1, wherein determining the predicted temperature for each heat source based on the measured temperature, temperature measurement noise, and a pre-constructed multi-heat source temperature coupling state model comprises: constructing an observation function for reflecting the relation between the measured temperature of each heat source and the estimated temperature of the model; Determining a measurement noise matrix based on temperature measurement noise of each heat source, and constructing a weighted residual cost function based on the observation function and the weight matrix by taking an inverse matrix of the measurement noise matrix as the weight matrix; and solving to obtain the predicted temperature of each heat source by minimizing the weighted residual cost function.
8. 8. The method of claim 2, wherein the determining a first confidence level corresponding to the first equivalent temperature comprises: determining a measurement noise matrix at the current moment based on temperature measurement noise of each heat source at the current moment, and determining a first error matrix by the measurement noise matrix at the current moment; determining an error covariance matrix at the previous moment based on the predicted temperature at the previous moment of each heat source and the actual temperature at the previous moment of each heat source, and determining a gain matrix at the current moment based on the error covariance matrix at the previous moment and a measurement noise matrix determined by the temperature measurement noise of each heat source Updating the error covariance matrix of the previous moment based on the gain matrix to obtain an error covariance matrix of the current moment so as to determine a second error matrix through the error covariance matrix of the current moment; Determining the first confidence level based on the first error matrix and the second error matrix; Wherein the first confidence level increases with an increase in the first error matrix and decreases with an increase in the second error matrix.
9. 9. The method of claim 1, wherein the measured state parameters further comprise a water pump speed, a pedal opening value, and a motor output torque, and wherein determining the temperature weight coefficient corresponding to each heat source based on the measured state parameters comprises: For each of the heat sources: determining a base weight based on the temperature measurement noise of the heat source; Determining a heat exchange dynamic factor based on the real-time thermal resistance from the heat source to the cooling liquid, the rotating speed of the water pump and the prestored cooling liquid flow influence coefficient; Determining a dynamic working condition factor based on the pedal opening value, the motor output torque, the prestored pedal dynamic influence coefficient and the prestored torque influence coefficient; Determining a safety boundary factor based on the measured temperature of the heat source, a prestored safety enhancement factor, a prestored upper safety temperature limit and a prestored safety margin; And determining a temperature weight coefficient corresponding to the heat source based on the basic weight, the heat exchange dynamic factor, the dynamic working condition factor and the safety boundary factor.
10. 10. A vehicle comprising an electronic device including a memory, a processor and a computer program stored on the memory and running on the processor, the processor implementing the method of any one of claims 1 to 9 when executing the program.

Description

Thermal management control method and vehicle Technical Field The present application relates to the field of thermal management technologies, and in particular, to a thermal management control method and a vehicle. Background With the rapid development of the global new energy automobile industry, the whole automobile thermal management system has become one of key technologies for influencing the endurance mileage, the safety performance and the driving experience of the electric automobile. Compared with the traditional fuel oil vehicle, the thermal management system of the new energy vehicle is more complex, and the temperature control requirements of multiple heat sources such as a power battery, a driving motor, a motor controller and the like are simultaneously considered. However, the existing thermal management strategies lack unified planning and collaborative scheduling of multiple heat source temperatures and need improvement. Disclosure of Invention In view of the above, the present application is directed to a thermal management control method and a vehicle, so as to solve the problem that the thermal management strategy in the related art lacks unified planning and collaborative scheduling for multiple heat source temperatures. Based on the above object, the present application provides a thermal management control method, the method comprising: Acquiring actual measurement state parameters of a plurality of heat sources of the vehicle, wherein the actual measurement state parameters comprise actual measurement temperatures and temperature measurement noise of the heat sources; determining the predicted temperature of each heat source based on the actually measured temperature, the temperature measurement noise and the model estimated temperature, and determining the temperature weight coefficient corresponding to each heat source based on the actually measured state parameter; Determining a global equivalent temperature based on the measured state parameter, the predicted temperature and the temperature weight coefficient of the heat source so as to perform thermal management control based on the global equivalent temperature; The model estimated temperature is determined based on a pre-built multi-heat-source temperature coupling state model, and the multi-heat-source temperature coupling state model is built based on the heat generation power of each heat source, the cooling power acting on each heat source, and the pre-stored heat capacity parameters and heat conductance parameters of each heat source. Further, the determining the global equivalent temperature based on the measured state parameter, the predicted temperature, and the temperature weight coefficient of the heat source includes: determining a first equivalent temperature of each heat source based on the temperature weight coefficient of each heat source and the predicted temperature of each heat source; Determining at least one target heat source in the heat sources, and determining a target weight coefficient corresponding to each target heat source based on the actually measured state parameter of each target heat source so as to determine a second equivalent temperature of each target heat source based on the actually measured temperature of each target heat source and the corresponding target weight coefficient; Determining a first confidence coefficient corresponding to the first equivalent temperature and a second confidence coefficient corresponding to the second equivalent temperature, and determining a process equivalent temperature based on the first equivalent temperature, the first confidence coefficient, the second equivalent temperature and the second confidence coefficient; and carrying out safety verification on the process equivalent temperature, and obtaining the global equivalent temperature. Further, the target heat source is a motor controller and a battery, and the actually measured state parameters corresponding to the motor controller further comprise motor output torque; the determining the target weight coefficient corresponding to each target heat source based on the actually measured state parameter of each target heat source comprises the following steps: Determining a target heat source temperature difference based on the measured temperature of the motor controller and the measured temperature of the battery; invoking a pre-stored weight sensitivity coefficient corresponding to the target heat source temperature difference and a pre-stored load weight coefficient corresponding to the motor output torque; Determining target weight coefficients corresponding to the motor controller and the battery respectively based on the target heat source temperature difference, the weight sensitivity coefficient, the motor output torque and the load weight coefficient; And the target heat source temperature difference and the motor output torque are positively correlated with the target weight coefficient corr