Control method of thermal management system, vehicle, storage medium, and program product

Abstract

The application discloses a control method of a thermal management system, a vehicle, a storage medium and a program product. The method comprises the following steps: monitoring the working mode of a power device on the vehicle; acquiring the thermal management demand of the vehicle when the working mode is a preset mode; constructing a target control parameter of the thermal management system on the vehicle based on the thermal management demand; and controlling the thermal management system to operate based on the target control parameter. The application solves the technical problem of high energy consumption when the thermal management system operates in the related art.

Description

Technical Field

[0001] This invention relates to the field of vehicle engineering, and more specifically, to a control method for a thermal management system, a vehicle, a storage medium, and a program product. Background Technology

[0002] In the field of new energy vehicles, thermal management technology is one of the key factors in ensuring the efficient and safe operation of vehicles. Because power equipment such as engines and motors generate a large amount of heat during operation, if not effectively managed, this heat can lead to overheating, thereby affecting the operating performance and lifespan of the equipment. However, current thermal management strategies result in high energy consumption.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a control method, vehicle, storage medium, and program product for a thermal management system, to at least solve the technical problem of high energy consumption during the operation of thermal management systems in related technologies.

[0005] According to one aspect of the present invention, a control method for a thermal management system is provided, comprising: monitoring the operating mode of a power device on a vehicle, wherein the power device is used to provide power to the vehicle; when the operating mode is a preset mode, acquiring the thermal management requirements of the vehicle, wherein the power device can meet the target power requirements of the vehicle when operating in the preset mode, and the thermal management requirements are used to characterize the heat requirements of different devices on the vehicle; constructing target control parameters for the thermal management system on the vehicle based on the thermal management requirements; and controlling the operation of the thermal management system based on the target control parameters.

[0006] Furthermore, the thermal management system includes: multiple thermal management devices, including power equipment; target control parameters include: equipment control parameters corresponding to any one of the thermal management devices; based on thermal management requirements, the target control parameters of the vehicle's thermal management system are constructed, including: obtaining temperature influence parameters of the current environment in which the vehicle is located, wherein the temperature influence parameters are used to characterize the parameters in the environment that are correlated with the temperature of different devices on the vehicle; based on thermal management requirements, determining the target temperature range of any one of the thermal management devices; inputting the target temperature range and temperature influence parameters into a parameter construction model, and using the parameter construction model to construct the equipment control parameters corresponding to the thermal management devices.

[0007] Furthermore, the target temperature range and temperature influence parameters are input into the parameter construction model, and the parameter construction model is used to construct the equipment control parameters corresponding to the thermal management equipment. This includes: obtaining the correlation degree between any temperature influence parameter and the thermal management equipment; determining the target influence parameter from the temperature influence parameters based on the correlation degree, wherein the correlation degree corresponding to the target influence parameter is greater than the correlation degree corresponding to other parameters; and inputting the target influence parameter and the target temperature range into the parameter construction model, and using the parameter construction model to construct the equipment control parameters.

[0008] Furthermore, the method also includes: acquiring the vehicle's driving status and the driver's driving intention; determining the target power demand based on the driving status and driving intention; inputting the target power demand into a pattern prediction model, and using the pattern prediction model to predict a preset pattern.

[0009] Furthermore, based on the driving status and driving intention, the target power demand is determined, including: inputting the driving status and driving intention into multiple demand prediction models, using multiple demand prediction models to predict the vehicle's power demand to obtain multiple initial power demands, wherein different demand prediction models use different computational logic; and weighting the multiple initial power demands to obtain the target power demand.

[0010] Furthermore, the power equipment includes an engine and a generator. Each demand prediction model includes an engine demand prediction module and a generator demand prediction module. Driving status and driving intention are input into multiple demand prediction models, and these models are used to predict the vehicle's power demand, resulting in multiple initial power demands. Specifically, during the prediction of the vehicle's power demand using any one demand prediction model, the engine demand prediction module processes the driving status and driving intention to obtain a first predicted demand for the engine; the generator demand prediction module processes the driving status and driving intention to obtain a second predicted demand for the generator; and the first and second predicted demands are fused to obtain the initial power demand corresponding to the demand prediction model.

[0011] Furthermore, the preset modes include: a first preset mode corresponding to the engine and a second preset mode corresponding to the generator. The mode prediction model includes: a feature extraction module, a first prediction module, and a second prediction module. Inputting the target power demand into the mode prediction model and using the mode prediction model to predict the preset modes includes: using the feature extraction module to extract features from the target power demand to obtain demand features; inputting the demand features into the first prediction module and using the first prediction module to predict the first preset mode; and inputting the demand features into the second prediction module and using the second prediction module to predict the second preset mode.

[0012] According to another aspect of the present invention, a control device for a thermal management system is also provided, comprising: a mode monitoring module for monitoring the operating mode of a power device on a vehicle, wherein the power device is used to provide power to the vehicle; a demand acquisition module for acquiring the thermal management demand of the vehicle when the operating mode is a preset mode, wherein the power device can meet the target power demand of the vehicle when operating in the preset mode, and the thermal management demand is used to characterize the heat demand of different devices on the vehicle; a parameter construction module for constructing target control parameters for the thermal management system on the vehicle based on the thermal management demand; and an operation control module for controlling the operation of the thermal management system based on the target control parameters.

[0013] According to another aspect of the present invention, a vehicle is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0015] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0016] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0017] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0018] In this embodiment of the invention, the operating mode of the vehicle's power equipment is monitored; when the operating mode is a preset mode, the vehicle's thermal management requirements are obtained; based on the thermal management requirements, target control parameters for the vehicle's thermal management system are constructed; and the operation of the thermal management system is controlled based on the target control parameters. By monitoring the operating mode of the power equipment to determine whether the operating mode is in the preset mode, when the operating mode is the preset mode, the vehicle's thermal management requirements are obtained, and the aforementioned target control parameters are further constructed based on the thermal management requirements. This allows for coordinated control of the thermal management system and the power equipment based on the target control parameters, thereby reducing the energy consumption of the thermal management system even when the power equipment itself generates less heat. This achieves the goal of improving the operating efficiency of the thermal management system, thus realizing the technical effect of reducing the energy consumption generated during the operation of the thermal management system, and solving the technical problem of high energy consumption generated during the operation of the thermal management system in related technologies. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0020] Figure 1 This is a flowchart of a control method for a thermal management system according to an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of a control device for a thermal management system according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] According to an embodiment of the present invention, an embodiment of a control method for a thermal management system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] Figure 1 This is a flowchart of a control method for a thermal management system according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0026] Step S102: Monitor the operating mode of the power equipment on the vehicle, wherein the power equipment is used to provide power to the vehicle.

[0027] The aforementioned power equipment can be equipment that provides driving force to the aforementioned vehicle, and may include, but is not limited to: engine, generator, clutch, battery system, etc.

[0028] The aforementioned operating modes refer to the operating methods of the aforementioned power equipment under different conditions, which can be switched according to the actual driving conditions of the vehicle, the driver's operating commands, and the vehicle's energy requirements. Taking an engine as an example, the aforementioned operating modes may include, but are not limited to: idling, acceleration, deceleration, high load, low load, etc. Taking a generator as an example, the aforementioned operating modes may include, but are not limited to: charging, auxiliary drive, stop, etc.

[0029] In one optional embodiment, considering the different heat generation of the power equipment under different operating modes, by monitoring the operating status of the power equipment, the operating status of the vehicle's thermal management system (such as cooling fan, water pump speed, coolant flow rate, etc.) can be dynamically adjusted to ensure that the operating temperature of the power equipment is within an optimal range, thereby improving the reliability of the power equipment and extending the service life of various components in the power equipment. Specifically, multiple sensors can be pre-installed on the vehicle, including but not limited to temperature sensors, pressure sensors, flow sensors, speed sensors, torque sensors, etc., for real-time monitoring of the operating status of the power equipment. For example, temperature sensors can monitor the temperature of the battery, motor, and engine, while speed and torque sensors can monitor the workload of the motor and engine. The data collected by each sensor can be transmitted to the control system of the thermal management system (hereinafter referred to as the control system) through an in-vehicle network such as CAN or LIN. Subsequently, the control system can use its own data processing and analysis functions to analyze the received sensor data in real time to determine the operating mode of the power equipment.

[0030] In another alternative embodiment, to reduce the computational burden on the control system, the operating data of the power equipment can be uploaded to a cloud server via an onboard communication system. The cloud server can then utilize big data analytics and machine learning models to perform in-depth analysis of the received data to identify the operating modes of the power equipment. Subsequently, the cloud server can feed the analysis results back to the vehicle, enabling the control system to monitor the operating modes of the power equipment and thus achieve more intelligent thermal management control decisions.

[0031] Step S104: When the working mode is the preset mode, obtain the vehicle's thermal management requirements. The power equipment can meet the vehicle's target power requirements when running in the preset mode. The thermal management requirements are used to characterize the heat requirements of different equipment on the vehicle.

[0032] The aforementioned thermal management requirements can refer to the temperature requirements needed to maintain the optimal operating condition of different equipment on the vehicle and extend the service life of the equipment.

[0033] In an optional embodiment, considering that when the above-mentioned operating mode is the preset mode, the power output of the above-mentioned power equipment can meet the target power demand of the vehicle, but at this time the power equipment may also generate a lot of heat during operation, in order to prevent the power equipment from overheating, thereby reducing the risk of heat-related failures and enhancing the safety of the vehicle, the control system can use sensors pre-deployed in the power equipment to collect the temperature data of the power equipment when the above-mentioned operating mode is the preset mode. Subsequently, the control system can analyze the heat demand of different power equipment on the vehicle, that is, the above-mentioned thermal management demand, based on the temperature data, so as to control the operation of the thermal management system and achieve efficient heat dissipation of the power equipment.

[0034] In another optional embodiment, in order to improve the accuracy of obtaining thermal management requirements, the control system can collect vehicle operation data in real time, including but not limited to vehicle speed, acceleration, battery temperature, motor temperature, and external ambient temperature, and combine the output characteristics of the power equipment in the preset mode with a pre-developed thermal management prediction model to calculate the heat generated during the operation of the power equipment. Based on this heat, the system can further predict the heat requirements of thermal management systems such as air conditioning systems and battery temperature control systems.

[0035] In another alternative embodiment, in order to improve the efficiency of obtaining thermal management requirements, the control system can use machine learning algorithms such as support vector machines, decision trees, and neural networks to establish a mapping relationship between thermal management requirements and the above-mentioned operating modes. When the vehicle's operating mode is in the above-mentioned preset mode, the control system can directly determine the above-mentioned thermal management requirements based on real-time vehicle operation data and using this mapping relationship.

[0036] Step S106: Based on thermal management requirements, construct the target control parameters for the vehicle's thermal management system.

[0037] The aforementioned target control parameters can be parameters used to control the operation of the aforementioned thermal management system, and may include, but are not limited to: temperature control parameters, energy consumption control parameters, heat dissipation efficiency parameters, and coordinated control parameters of the engine and generator.

[0038] In an optional embodiment, considering that the above-mentioned thermal management requirements reflect the heat dissipation requirements of the power equipment in the preset mode, in order to ensure that the power equipment meets the target power requirements without causing excessive wear of components due to excessively high operating temperature, the control system can first determine the operating temperature range of the power equipment based on the above-mentioned thermal management requirements. Within this operating temperature range, the power equipment can maintain a better power output to meet the target power requirements. Subsequently, the control system can generate corresponding target control parameters based on the operating temperature range to accurately control the operation of the thermal management system, achieve efficient heat dissipation of the power equipment, and keep the operating temperature of the power equipment within the operating temperature range.

[0039] Step S108: Control the operation of the thermal management system based on the target control parameters.

[0040] In an optional embodiment, considering that the target control parameters are set according to the thermal management requirements of the power system, controlling the operation of the thermal management system according to the target control parameters can enable the thermal management system to dissipate heat from the power equipment, so that the power system operates within a more suitable temperature range. This can meet the target power requirements of the vehicle while avoiding damage to the power equipment due to excessively high operating temperatures, thereby protecting the power equipment.

[0041] In this embodiment of the invention, the operating mode of the vehicle's power equipment is monitored; when the operating mode is a preset mode, the vehicle's thermal management requirements are obtained; based on the thermal management requirements, target control parameters for the vehicle's thermal management system are constructed; and the operation of the thermal management system is controlled based on the target control parameters. By monitoring the operating mode of the power equipment to determine whether the operating mode is in the preset mode, when the operating mode is the preset mode, the vehicle's thermal management requirements are obtained, and the aforementioned target control parameters are further constructed based on the thermal management requirements. This allows for coordinated control of the thermal management system and the power equipment based on the target control parameters, thereby reducing the energy consumption of the thermal management system even when the power equipment itself generates less heat. This achieves the goal of improving the operating efficiency of the thermal management system, thus realizing the technical effect of reducing the energy consumption generated during the operation of the thermal management system, and solving the technical problem of high energy consumption generated during the operation of the thermal management system in related technologies.

[0042] Furthermore, the thermal management system includes: multiple thermal management devices, including power equipment; target control parameters include: equipment control parameters corresponding to any one of the thermal management devices; based on thermal management requirements, the target control parameters of the vehicle's thermal management system are constructed, including: obtaining temperature influence parameters of the current environment in which the vehicle is located, wherein the temperature influence parameters are used to characterize the parameters in the environment that are correlated with the temperature of different devices on the vehicle; based on thermal management requirements, determining the target temperature range of any one of the thermal management devices; inputting the target temperature range and temperature influence parameters into a parameter construction model, and using the parameter construction model to construct the equipment control parameters corresponding to the thermal management devices.

[0043] The aforementioned thermal management equipment can refer to devices used to control and regulate the temperature of different equipment on a vehicle, including but not limited to air conditioning compressors, cooling pumps, radiators, heaters, heat exchangers, cooling fans, and temperature control valves.

[0044] The aforementioned equipment control parameters refer to parameters used to control the operating status of the aforementioned thermal management equipment. Taking an air conditioning compressor as an example, the equipment control parameters corresponding to the compressor may include, but are not limited to, compressor speed, operating time, or start / stop status. Taking a cooling pump as an example, the equipment control parameters corresponding to the cooling pump may include, but are not limited to, pump speed and flow rate. By adjusting the aforementioned equipment control parameters, the operating status of the thermal management equipment can be adjusted to adapt to different thermal management needs and ensure efficient vehicle operation.

[0045] The aforementioned temperature-affected parameters can refer to external parameters that directly affect the performance of the thermal management system in the vehicle's operating environment, including but not limited to: ambient temperature, humidity, vehicle speed, solar radiation intensity, etc.

[0046] The aforementioned target temperature range can be the temperature range that allows different devices on the vehicle to operate normally and helps extend the service life of these devices. When the temperature of different devices on the vehicle exceeds the aforementioned target temperature range, the thermal management system can adjust the operating status of the thermal management equipment through the corresponding equipment control parameters to achieve rapid temperature regulation and ensure that the temperature of different devices returns to a safe and efficient operating range.

[0047] The aforementioned parameter construction model can be a mathematical model or algorithm used to calculate and predict the equipment control parameters of thermal management equipment based on thermal management requirements, current environmental temperature influence parameters, and target temperature ranges of key components. To improve the prediction accuracy of the aforementioned parameter construction model, the model can be constructed based on vehicle operating data, thermodynamic principles, and control theory. This allows the model to comprehensively consider various factors such as component heat capacity, thermal conductivity, ambient temperature changes, and vehicle operating conditions, thereby outputting more accurate equipment control parameters to achieve precise control of the thermal management equipment.

[0048] In one optional embodiment, considering that the temperature of the thermal management device comes not only from the heat generated by the device itself during operation, but also from factors such as altitude, temperature, wind speed, and humidity of the vehicle's environment, the control system needs to consider the influence of these factors when constructing the target control parameters to ensure that the constructed target control parameters accurately meet the aforementioned thermal management requirements. Therefore, the control system can obtain temperature influence parameters of the vehicle's current environment, which reflect the factors in the environment related to the temperature of different devices on the vehicle. Then, to further clarify the effect of the aforementioned target control parameters and improve the accuracy of their construction, the control system can determine the target temperature range for any thermal management device based on the aforementioned thermal management requirements, thereby ensuring that the thermal management device can operate efficiently and maintain optimal working conditions. After determining the target temperature range and temperature influence parameters, the control system can input these parameters into the parameter building model. This model is responsible for calculating and outputting the equipment control parameters corresponding to the thermal management equipment, such as water pump speed and valve opening. This process accurately generates the aforementioned equipment control parameters to precisely control the thermal management equipment included in the thermal management system. This ensures that the thermal management equipment and the entire vehicle can operate with optimal power and low energy consumption under different operating conditions, while maintaining key components such as batteries and motors within a suitable temperature range. This effectively extends the service life of these components and reduces maintenance requirements.

[0049] Furthermore, the target temperature range and temperature influence parameters are input into the parameter construction model, and the parameter construction model is used to construct the equipment control parameters corresponding to the thermal management equipment. This includes: obtaining the correlation degree between any temperature influence parameter and the thermal management equipment; determining the target influence parameter from the temperature influence parameters based on the correlation degree, wherein the correlation degree corresponding to the target influence parameter is greater than the correlation degree corresponding to other parameters; and inputting the target influence parameter and the target temperature range into the parameter construction model, and using the parameter construction model to construct the equipment control parameters.

[0050] The aforementioned correlation can be used as an indicator to reflect the severity of the impact of the temperature-related parameters on the aforementioned thermal management equipment. For example, a high correlation between coolant temperature and water pump speed means that changes in coolant temperature will significantly affect the adjustment of water pump speed.

[0051] The aforementioned target influencing parameter can be a temperature-related parameter that has a significant impact on the aforementioned thermal management equipment. Specifically, the impact of the aforementioned target influencing parameter on the thermal management equipment is higher than the impact of other temperature-related parameters on the thermal management equipment.

[0052] In one alternative embodiment, considering that there may be various parameters in the vehicle's environment that affect the temperature of the thermal management equipment, and that the correlation between the temperature of different thermal management equipment and different temperature-affecting parameters may also be different, if all different temperature-affecting parameters are taken into account during the construction of the equipment control parameters, it will lead to high computational overhead. Furthermore, due to the lack of targeted adjustments for temperature-affecting parameters with a high degree of influence, the final generated equipment control parameters may be difficult to accurately meet the thermal management requirements of the thermal management equipment. Therefore, in order to improve the targeting and effectiveness of temperature control for thermal management equipment and reduce energy consumption in the temperature control process, the control system can analyze the correlation between any temperature-affecting parameter and the thermal management equipment, and identify the target influencing parameter by comparison, that is, the parameter that has a greater impact on the operating performance of the thermal management equipment than other temperature-affecting parameters. Next, the control system can take the aforementioned target influencing parameters and the preset target temperature range as inputs. The parameter building model will generate corresponding equipment control parameters based on this information, so that the thermal management equipment can operate in a more suitable temperature under the control of the aforementioned equipment control parameters. This process allows the thermal management system to self-adjust according to real-time temperature requirements and influencing factors, ensuring that key components such as batteries and motors work within a suitable temperature range, thereby reducing energy consumption, extending component life, and improving the overall driving performance of the vehicle.

[0053] For example, during vehicle operation, after determining the vehicle's required power based on current operating conditions and driving intentions, the control system can analyze multiple temperature-influencing parameters, such as ambient temperature, vehicle load, and driving speed, as well as the relationships between these parameters and thermal management equipment such as the engine, generator, and cooling system. During the analysis, the control system found that the correlation between ambient temperature and the cooling system is significantly higher than the correlation between vehicle load and driving speed. Therefore, the control system can use the aforementioned target temperature range and ambient temperature as inputs to build a parameter model and calculate the optimal equipment control parameters for the cooling system, including but not limited to the optimal speed of the cooling water pump and the optimal start-up time of the cooling fan. This ensures that while meeting temperature control requirements, the thermal management system operates efficiently and energy is used rationally.

[0054] Furthermore, the method also includes: acquiring the vehicle's driving status and the driver's driving intention; determining the target power demand based on the driving status and driving intention; inputting the target power demand into a pattern prediction model, and using the pattern prediction model to predict a preset pattern.

[0055] The aforementioned driving status can refer to the state of the vehicle during driving, and may include, but is not limited to: vehicle speed, acceleration, direction, battery charge, engine and motor temperature, etc.

[0056] The aforementioned driving intention can refer to the driver's expectations or needs regarding vehicle performance. It can be inferred from the driver's operations, such as the depth of the accelerator pedal, the force of the brake, and the angle of the steering wheel. It can also be inferred from instructions such as the set cruise speed or whether a rapid acceleration is needed to overtake, conveyed by advanced driver assistance systems or human-machine interfaces.

[0057] The aforementioned target power demand can be calculated based on the aforementioned driving conditions and driving intentions, representing the power output that the power equipment should provide.

[0058] The aforementioned pattern prediction model can be a mathematical model or algorithm that can predict a suitable operating mode of the power equipment based on the vehicle's driving status, the driver's driving intention, and the target power demand. To ensure the accuracy of the aforementioned pattern prediction model, it can be constructed based on machine learning, deep learning, or other advanced data processing technologies.

[0059] In one alternative embodiment, considering that the vehicle's driving status helps the control system understand the current environment and conditions, and thus determine how much power is needed to meet driving requirements (e.g., driving on a highway requires more power than driving on low-speed city roads), and that the driver's driving intentions are crucial for the control system to understand how the driver wants the vehicle to respond (e.g., pressing the accelerator pedal deeply may mean the driver wants to accelerate quickly), the control system can first collect the vehicle's driving status and the driver's driving intentions, including but not limited to parameters such as vehicle speed, acceleration, and driving mode selection. Based on this information, the control system can accurately determine the aforementioned target power demand, which reflects the vehicle's energy requirements under the current operating conditions. Subsequently, the control system can input the aforementioned target power demand into a mode prediction model. This model, through analysis and calculation, can predict the optimal operating mode of the power equipment under the current operating conditions, i.e., the aforementioned preset mode, thereby guiding the control system to adjust the operating status of the vehicle's power equipment, including but not limited to adjusting the operating status of the engine, generator, and thermal management system. The above steps enable intelligent adjustment of vehicle power and heat management, ensuring that while meeting driving requirements, key components such as engines and generators are kept within ideal temperature ranges, reducing overall vehicle energy consumption and improving the service life of various components in the power system and the vehicle's operating efficiency.

[0060] For example, when a vehicle is cruising at high speed, the control system can determine that the vehicle's current driving state is stable and the driver's intention is to maintain a constant speed. Based on this driving state and intention, the control system can further determine the target power demand as a low to moderate level of power output and input this target power demand into a pre-trained mode prediction model. This model predicts that the preset mode to be used at this time is for the engine and generator to work together under low load conditions to reduce energy consumption and improve the operating efficiency of the thermal management system. It should be noted that in the selection of the preset mode, the mode prediction model can comprehensively consider the operating efficiency of the engine and generator as well as the needs of the thermal management system, avoiding the unreasonable situation of pursuing the optimal efficiency of a single component while ignoring the overall thermal balance of the vehicle. This achieves coordinated adjustment of the power equipment and the thermal management system, improving the overall performance of the vehicle and the driving experience.

[0061] Furthermore, based on the driving status and driving intention, the target power demand is determined, including: inputting the driving status and driving intention into multiple demand prediction models, using multiple demand prediction models to predict the vehicle's power demand to obtain multiple initial power demands, wherein different demand prediction models use different computational logic; and weighting the multiple initial power demands to obtain the target power demand.

[0062] The aforementioned demand prediction model can be a mathematical model or algorithm that predicts the power output required by the vehicle during subsequent driving based on its driving status and driving intentions. To improve the accuracy of the demand prediction model in predicting target power demand, machine learning and dynamic principles can be used to construct the model. Furthermore, to comprehensively consider the various factors that affect the vehicle's power output during the prediction process, multiple demand prediction models can be pre-constructed. These different models can be based on different theories and datasets, predicting target power demand according to driving status and driving intentions.

[0063] The aforementioned initial power demand can be the vehicle power demand prediction value calculated by each individual demand forecasting model according to the inherent operational logic of the model. Since different demand forecasting models may focus on different forecasting factors or use different forecasting methods, the initial power demand output by different demand forecasting models may be different.

[0064] In one optional embodiment, multiple different demand forecasting models are used for power demand prediction. Each model is trained based on different datasets and algorithmic logic. This avoids the limitations of a single model and improves the stability and reliability of the prediction results. For example, when one model malfunctions or makes an inaccurate prediction, the outputs of other models can be used as a reference or substitute, ensuring the continuity and safety of the control system's decision-making. Furthermore, different models may focus on different aspects; for example, some models may consider driving intentions more, while others may focus more on driving conditions or environmental factors. By combining the outputs of different models, a more comprehensive and accurate power demand prediction can be obtained. Therefore, the control system can predict power demand based on vehicle driving conditions and driving intentions using multiple demand forecasting models to obtain a series of initial power demands. Different models employ different computational logics to comprehensively consider various factors. Subsequently, in order to integrate the prediction results of multiple demand forecasting models, the control system can weight these initial power demands, combine the actual operating conditions of the vehicle and the driver's operating intentions, and finally determine the target power demand. This process ensures the accuracy of power distribution, enabling the engine and generator to operate in coordination within an optimal operating range. At the same time, it takes into account the efficiency of the thermal management system, reduces unnecessary energy consumption and excessive heat dissipation, and achieves efficient coordination between the power system and the thermal management system.

[0065] For example, the control system can use the current driving state (such as vehicle speed and road conditions) and driving intention (such as acceleration, deceleration, or maintaining a constant speed) as inputs to first predict the vehicle's required power through multiple demand prediction models. Suppose that at a certain moment, model A predicts the power output required by the power unit is 'a', model B predicts it is 'b', and model C predicts it is 'c'. Then, the control system can weight these initial power demands, for example, assigning weights p, q, and w to the prediction results of models A, B, and C respectively, to calculate the target power demand 'a'. p+b q+c w, this target power requirement can be used to subsequently adjust the operating modes of the engine, generator, and thermal management system.

[0066] Furthermore, the power equipment includes an engine and a generator. Each demand prediction model includes an engine demand prediction module and a generator demand prediction module. Driving status and driving intention are input into multiple demand prediction models, and these models are used to predict the vehicle's power demand, resulting in multiple initial power demands. Specifically, during the prediction of the vehicle's power demand using any one demand prediction model, the engine demand prediction module processes the driving status and driving intention to obtain a first predicted demand for the engine; the generator demand prediction module processes the driving status and driving intention to obtain a second predicted demand for the generator; and the first and second predicted demands are fused to obtain the initial power demand corresponding to the demand prediction model.

[0067] The engine demand forecasting module mentioned above can be the module in the demand forecasting model used to predict the required output power of the engine.

[0068] The generator demand forecasting module mentioned above can be the module in the demand forecasting model used to forecast the load demand of generators.

[0069] The aforementioned first predicted demand may refer to the power level that the engine is expected to achieve in the future, as obtained after processing by the engine demand prediction module.

[0070] The aforementioned second predicted demand can refer to the level of electrical energy that the generators are expected to provide in the future, as obtained through processing by the generator demand prediction module.

[0071] In one optional embodiment, considering that both the engine and generator have their own optimal power ranges, in order to ensure that their optimal power ranges overlap, thereby maximizing the operation of both the engine and generator within their optimal power ranges and thus improving vehicle performance while reducing energy consumption, the control system needs to determine the actual operating power of the engine or generator by separately predicting the first predicted demand of the engine and the second predicted demand of the generator. Based on this, the control system can design multiple demand prediction models, including an engine demand prediction module and a generator demand prediction module, tailored to the dynamic characteristics of the vehicle's driving state and driving intentions. Subsequently, the control system can input the aforementioned driving state and driving intentions into the multiple demand prediction models, and obtain the first predicted demand of the engine and the second predicted demand of the generator by independently predicting the engine and generator. The control system can then fuse these two predicted demands to determine the initial power demand corresponding to each demand prediction model. The core of this strategy lies in achieving coordinated control of the engine and generator through accurate prediction of their operating demands, ensuring that while meeting the vehicle's power requirements, the workload of the thermal management system is reduced, thereby reducing unnecessary energy consumption and improving the overall performance and reliability of the vehicle. In the above steps, by dynamically adjusting the operating range of the engine and generator, combined with the operation control of the thermal management system, the temperature fluctuation of key components can be effectively reduced, the service life of these components can be extended, and maintenance costs can be reduced.

[0072] For example, when a vehicle is climbing a hill and the driving intention indicates a need for higher power output, multiple demand prediction models are activated, including an engine demand prediction module and a generator demand prediction module. The engine demand prediction module analyzes the climbing condition and driving intention, predicting that the engine's first predicted demand in the current scenario is higher power output. Similarly, the generator demand prediction module, based on the same information, predicts that the generator's second predicted demand is to provide additional power. Subsequently, the control system can fuse the first and second predicted demands to obtain an initial power demand corresponding to a single demand prediction model. This means that both the engine and generator need to operate in a higher power range to meet the vehicle's power requirements during hill climbing.

[0073] Furthermore, the preset modes include: a first preset mode corresponding to the engine and a second preset mode corresponding to the generator. The mode prediction model includes: a feature extraction module, a first prediction module, and a second prediction module. Inputting the target power demand into the mode prediction model and using the mode prediction model to predict the preset modes includes: using the feature extraction module to extract features from the target power demand to obtain demand features; inputting the demand features into the first prediction module and using the first prediction module to predict the first preset mode; and inputting the demand features into the second prediction module and using the second prediction module to predict the second preset mode.

[0074] The aforementioned first preset mode can be an operating mode set for the engine, which includes the engine's optimal operating parameters under different operating conditions, so that when the engine is running in this mode, it can meet the vehicle's power requirements while maintaining a low energy consumption and good heat dissipation state.

[0075] The aforementioned second preset mode can be an operating mode set for the generator, which includes the generator's optimal operating parameters under different operating conditions to balance energy consumption, ensure stable power supply, and prevent the generator from overheating.

[0076] The aforementioned feature extraction module can be an algorithm or software component used to extract key attributes or features from target dynamic requirements.

[0077] The aforementioned first prediction module can be an algorithm part used to predict the preset mode of the engine. It receives demand characteristics as input, analyzes and processes these data, and outputs the suggested operating mode of the engine under current or future operating conditions.

[0078] The aforementioned second prediction module can be an algorithm part used to predict the preset mode of the generator. Also based on demand characteristics, it calculates the most effective action that the generator should take, such as energy recovery or auxiliary power supply, to meet the needs of the vehicle and improve the overall performance of the vehicle.

[0079] The aforementioned demand characteristics can be specific indicators reflecting the vehicle's power demand, including but not limited to driver operation, vehicle speed, acceleration, road conditions, ambient temperature, etc. These characteristics together describe the vehicle's immediate power and thermal management needs.

[0080] In one alternative embodiment, considering that engine efficiency is not constant but varies with operating points, there exists one or a series of optimal operating points where engine efficiency is high, emissions are low, and heat generation is easily managed. Similar to engines, generators also have a relatively efficient operating point range. By setting the generator to operate within this range, energy recovery efficiency can be improved, energy waste reduced, and the additional heat generated by high power consumption decreased, thus alleviating the burden on the thermal management system. Therefore, the control system needs to determine a first preset mode corresponding to the optimal operating point of the engine, and a second preset mode corresponding to the optimal operating point of the engine, to facilitate the subsequent determination of more accurate equipment control parameters and ensure that both the engine and generator operate in their optimal modes. Based on this, the control system can extract features of the target power demand using a mode prediction model. This process is completed by the feature extraction module in the mode prediction model, and the obtained demand features will be used to predict the preset modes of the engine and generator. Subsequently, the control system can input the aforementioned demand characteristics into the first prediction module and the second prediction module respectively, so as to predict the first preset mode corresponding to the engine and the second preset mode corresponding to the generator, so as to dynamically adjust the working mode of the engine and generator. At the same time, combined with the thermal management system, multi-objective optimization is achieved, that is, while meeting the vehicle's power demand, the system energy consumption is kept low, the temperature of the engine and generator is kept within a suitable range, the life of the generator and engine is extended, and maintenance needs are reduced.

[0081] For example, suppose a vehicle is cruising at a constant speed on a highway. At this time, the vehicle's power demand is low and stable at a fixed value. The control system can input this power demand into a mode prediction model. First, the feature extraction module analyzes the vehicle's operating characteristics under this demand, such as speed and load, to obtain the demand characteristics. Then, the control system can input these demand characteristics into a first prediction module and a second prediction module. The first prediction module predicts a first preset mode for the engine based on the demand characteristics, which may be operating under low load and high efficiency. The second prediction module predicts a second preset mode for the generator based on the same demand characteristics, which may be operating in energy recovery mode. In this way, the system can accurately predict and select the most suitable engine and generator operating modes for the current operating conditions based on the vehicle's actual power demand, thereby achieving efficient power distribution and energy utilization.

[0082] According to an embodiment of the present invention, a control device for a thermal management system is provided. It should be noted that this device can be used to execute the control method of the aforementioned thermal management system. Specific implementation methods and application scenarios are the same as those described above, and will not be repeated here. Figure 2 This is a schematic diagram of a control device for a thermal management system according to an embodiment of the present invention, such as... Figure 2 As shown, the device includes:

[0083] The mode monitoring module 202 is used to monitor the working mode of the power equipment on the vehicle, wherein the power equipment is used to provide power to the vehicle.

[0084] The demand acquisition module 204 is used to acquire the vehicle's thermal management demand when the working mode is a preset mode. The power equipment can meet the vehicle's target power demand when running in the preset mode, and the thermal management demand is used to characterize the heat demand of different equipment on the vehicle.

[0085] The parameter construction module 206 is used to construct the target control parameters of the vehicle's thermal management system based on thermal management requirements.

[0086] The operation control module 208 is used to control the operation of the thermal management system based on the target control parameters.

[0087] Furthermore, the thermal management system includes: multiple thermal management devices, including power equipment; target control parameters include: equipment control parameters corresponding to any one of the thermal management devices; the parameter construction module is also used to: obtain temperature influence parameters of the current environment of the vehicle, wherein the temperature influence parameters are used to characterize the parameters in the environment that are related to the temperature of different devices on the vehicle; determine the target temperature range of any one of the thermal management devices based on thermal management requirements; input the target temperature range and temperature influence parameters into the parameter construction model, and use the parameter construction model to construct the equipment control parameters corresponding to the thermal management device.

[0088] Furthermore, the parameter construction module is also used to: obtain the degree of correlation between any temperature-affecting parameter and the thermal management equipment; determine the target influencing parameter from the temperature-affecting parameters based on the degree of correlation, wherein the degree of correlation corresponding to the target influencing parameter is greater than the degree of correlation corresponding to other parameters; input the target influencing parameter and the target temperature range into the parameter construction model, and use the parameter construction model to construct the equipment control parameters.

[0089] Furthermore, the device also includes: a status acquisition module for acquiring the vehicle's driving status and the driver's driving intention; a demand determination module for determining the target power demand based on the driving status and driving intention; and a mode prediction module for inputting the target power demand into a mode prediction model and using the mode prediction model to predict a preset mode.

[0090] Furthermore, the demand determination module is also used to: input driving status and driving intention into multiple demand prediction models, use multiple demand prediction models to predict the vehicle's power demand to obtain multiple initial power demands, wherein different demand prediction models use different computational logic; and perform weighted processing on the multiple initial power demands to obtain the target power demand.

[0091] Furthermore, the power equipment includes an engine and a generator, and any demand forecasting model includes an engine demand forecasting module and a generator demand forecasting module; the demand determination module is also used to: in the process of forecasting the power demand of the vehicle using any demand forecasting model, process the driving state and driving intention using the engine demand forecasting module to obtain the first predicted demand of the engine; process the driving state and driving intention using the generator demand forecasting module to obtain the second predicted demand of the generator; and fuse the first predicted demand and the second predicted demand to obtain the initial power demand corresponding to the demand forecasting model.

[0092] Furthermore, the preset modes include: a first preset mode corresponding to the engine and a second preset mode corresponding to the generator. The mode prediction model includes: a feature extraction module, a first prediction module, and a second prediction module. The mode prediction module is also used to: extract features of the target power demand using the feature extraction module to obtain demand features; input the demand features into the first prediction module to predict the first preset mode; and input the demand features into the second prediction module to predict the second preset mode.

[0093] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0094] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0095] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0096] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of the present invention.

[0097] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of the present invention.

[0098] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0103] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A control method for a thermal management system, characterized in that, include: The operating mode of the power equipment on the vehicle is monitored, wherein the power equipment is used to provide power to the vehicle; When the working mode is a preset mode, the thermal management requirements of the vehicle are obtained, wherein the power equipment can meet the target power requirements of the vehicle when operating in the preset mode, and the thermal management requirements are used to characterize the heat requirements of different equipment on the vehicle. Based on the aforementioned thermal management requirements, the target control parameters for the vehicle's thermal management system are constructed. The thermal management system is controlled to operate based on the target control parameters.

2. The method according to claim 1, characterized in that, The thermal management system includes: multiple thermal management devices, the multiple thermal management devices including the power equipment; the target control parameters include: device control parameters corresponding to any one of the thermal management devices; based on the thermal management requirements, the target control parameters of the vehicle's thermal management system are constructed, including: The temperature influence parameters of the current environment of the vehicle are obtained, wherein the temperature influence parameters are used to characterize the parameters in the environment that are related to the temperature of different devices on the vehicle; Based on the aforementioned thermal management requirements, determine the target temperature range for any thermal management device. The target temperature range and the temperature influence parameters are input into the parameter construction model, and the equipment control parameters corresponding to the thermal management equipment are constructed using the parameter construction model.

3. The method according to claim 2, characterized in that, The target temperature range and the temperature influence parameters are input into the parameter construction model, and the device control parameters corresponding to the thermal management device are constructed using the parameter construction model, including: Obtain the correlation between any temperature-affecting parameter and the thermal management device; Based on the degree of correlation, a target influence parameter is determined from the temperature influence parameters, wherein the degree of correlation corresponding to the target influence parameter is greater than the degree of correlation corresponding to other parameters; The target influence parameters and the target temperature range are input into the parameter construction model, and the device control parameters are constructed using the parameter construction model.

4. The method according to claim 1, characterized in that, The method further includes: The driving status of the vehicle and the driving intention of the driver in the vehicle are obtained. The target power requirement is determined based on the driving status and the driving intention. The target power demand is input into the pattern prediction model, and the pattern prediction model is used to predict the preset pattern.

5. The method according to claim 4, characterized in that, Determining the target power requirement based on the driving state and the driving intention includes: The driving state and driving intention are input into multiple demand prediction models, and the power demand of the vehicle is predicted by the multiple demand prediction models to obtain multiple initial power demands. The different demand prediction models use different computational logic. The target power demand is obtained by weighting the multiple initial power demands.

6. The method according to claim 5, characterized in that, The power equipment includes an engine and a generator, and each demand prediction model includes an engine demand prediction module and a generator demand prediction module. The driving state and driving intention are input into multiple demand prediction models, and the power demand of the vehicle is predicted using each of these models to obtain multiple initial power demands, including: In the process of predicting the power demand of the vehicle using any demand prediction model, the engine demand prediction module processes the driving state and the driving intention to obtain the first predicted demand of the engine. The generator demand prediction module processes the driving state and the driving intention to obtain the second predicted demand for the generator. The first predicted demand and the second predicted demand are fused to obtain the initial power demand corresponding to the demand prediction model.

7. The method according to claim 4, characterized in that, The preset modes include: a first preset mode corresponding to the engine and a second preset mode corresponding to the generator. The mode prediction model includes: a feature extraction module, a first prediction module, and a second prediction module. Inputting the target power demand into the mode prediction model and using the mode prediction model to predict the preset modes includes: The feature extraction module is used to extract features from the target power demand to obtain demand features; The demand characteristics are input into the first prediction module, and the first prediction module is used to predict the first preset pattern. The demand characteristics are input into the second prediction module, and the second prediction module is used to predict the second preset pattern.

8. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

Images