Thermal comfort control method for vehicle cabin, controller and storage medium

Abstract

The invention relates to a thermal comfort control method for a carrier cabin, and the method comprises the steps: carrying out the thermal comfort control of the carrier cabin according to a target thermal comfort value, obtaining a current thermal comfort value corresponding to the carrier cabin, obtaining the feedback input of a cabin person for the thermal comfort of the carrier cabin, and obtaining a target thermal comfort value corresponding to the current thermal comfort value; and correcting the current thermal comfort value in response to the feedback input to determine the target thermal comfort value. The invention also relates to a related thermal comfort controller and a storage medium. By means of the method and device, the flexibility, universality, accuracy and effectiveness of determination of the target thermal comfort value can be ensured, and user dissatisfaction caused by a single and simple thermal comfort control process or the target thermal comfort value is avoided.

Description

Technical Field

[0001] This invention relates to a thermal comfort control method, controller, and storage medium for vehicle compartments. Background Technology

[0002] As is widely known, the rapid development of vehicles, especially new energy vehicles, has greatly stimulated people's attention to the comfort of vehicle cabins. Although ordinary air conditioning can provide good air quality for vehicle cabins, it requires users to manually adjust the settings, which is not intelligent enough.

[0003] Currently, some solutions provide intelligent air conditioning systems based on human thermal comfort rating indices. Existing technologies know to use Predicted Mean Vote (PMV) values ​​to represent human thermal comfort, based on the fundamental equations of human thermal balance and the levels of subjective thermal sensation in psychophysiology. The introduction of PMV values ​​enables intelligent air conditioning adjustment to meet user comfort requirements. However, PMV or other comfort rating indices are based on experimental data to represent the comfort level of most people in a specific region or category. Since comfort perception is relatively subjective, a universal PMV value obviously cannot guarantee and meet the comfort goals of all population groups. Therefore, it is necessary to find a solution that can adaptively adjust the PMV value calculation algorithm to meet personalized thermal comfort requirements, thereby further realizing the personalized requirements for thermal comfort in vehicle cabins.

[0004] Therefore, there is a need to improve the thermal comfort control methods used in vehicle compartments, aiming to achieve a more accurate and purposeful control process using precise, fast, and effective algorithms such as PMV to ensure control performance, accuracy, and precision. Furthermore, it is desirable to develop a thermal comfort control method that ensures flexibility, versatility, and effectiveness, avoiding user dissatisfaction caused by simplistic and repetitive thermal comfort control approaches. Summary of the Invention

[0005] According to a first aspect of the present invention, a thermal comfort control method for a vehicle compartment is provided, wherein the thermal comfort control method includes: performing thermal comfort control on the vehicle compartment toward a target thermal comfort value; obtaining a current thermal comfort value corresponding to the vehicle compartment; obtaining feedback input from compartment occupants regarding the thermal comfort of the vehicle compartment; and correcting the current thermal comfort value in response to the feedback input to determine it as the target thermal comfort value.

[0006] According to another aspect of the present invention, a thermal comfort controller for a vehicle compartment is provided, the thermal comfort controller being capable of implementing the thermal comfort control method as described above, the thermal comfort controller comprising: a storage unit for storing computer-readable instructions for implementing the thermal comfort control method as described above; and a processing unit comprising: a thermal comfort value acquisition module configured to acquire a current thermal comfort value corresponding to the vehicle compartment; a feedback input receiving module configured to acquire feedback input from the compartment occupants regarding the current thermal comfort value; and a target thermal comfort value determination module configured to modify the current thermal comfort value in response to the feedback input to determine it as a target thermal comfort value.

[0007] According to another aspect of the invention, a storage medium is provided that stores instructions, which, when executed, implement the thermal comfort control method for a vehicle compartment as described above. Attached Figure Description

[0008] Other notable features and advantages of the invention will become apparent from the following non-limiting description provided for illustrative purposes with reference to the accompanying drawings, wherein:

[0009] Figure 1 A general flowchart of a thermal comfort control method for a vehicle compartment according to an embodiment of the present invention is shown;

[0010] Figure 2 A detailed flowchart of the steps for determining a target thermal comfort value in a thermal comfort control method for a vehicle compartment according to an embodiment of the present invention is shown;

[0011] Figure 3 An exemplary detailed flowchart of machine learning training for a thermal comfort value model of a thermal comfort control method for a vehicle compartment according to an embodiment of the present invention is shown;

[0012] Figure 4 An exemplary detailed flowchart of the process for determining relevant current thermal comfort values ​​during the initiation of thermal comfort control in a thermal comfort control method for a vehicle compartment according to an embodiment of the present invention is shown;

[0013] Figure 5 A schematic diagram of various modules of a thermal comfort controller for a vehicle compartment according to an embodiment of the present invention is shown; and

[0014] Figure 6 A general schematic diagram of a thermal comfort controller for a vehicle compartment according to an embodiment of the present invention is shown. Detailed Implementation

[0015] The following description is merely exemplary and is not intended to limit the invention or its application or use. It will also be understood that in all the drawings, corresponding reference numerals denote the same or corresponding parts and features. The steps shown with reference to the disclosed methods are substantially exemplary and therefore not essential or critical. Throughout the drawings of this application, Y indicates a positive case or N indicates a negative case or no. Furthermore, throughout the drawings of this application, ST indicates the start or commencement of a step, method, or process, or a similar operation known to those skilled in the art, and ED indicates the end or termination of a step, method, or process, or a similar operation known to those skilled in the art.

[0016] The embodiments of this application will be further described below with reference to the accompanying drawings. Before further describing the embodiments of this application, the relevant concepts of PMV will be described in detail first. The PMV (Predicted Mean Vote) index is a comprehensive evaluation index that takes into account many factors related to human thermal comfort, based on the basic equation of human thermal balance and the levels of subjective thermal sensation in psychophysiology. The PMV index indicates the average index of a group's vote on seven levels of thermal sensation (+3 to -3). In other words, the PMV index from -3 to +3 corresponds to the degree of human body feeling from cold to hot. When PMV = 0, it indicates that the human body feels thermally neutral, which is the most comfortable value.

[0017] Figure 1 A general flowchart of a thermal comfort control method for a vehicle compartment according to an embodiment of the present invention is shown. Figure 1 As shown in the embodiments of this application, a thermal comfort control method for a vehicle compartment allows the actuators or manipulators of a vehicle compartment thermal conditioning device (which, as those skilled in the art will understand, can provide positive or negative heat) to react based on a thermal comfort model of the driver or other occupants, enabling the vehicle compartment thermal conditioning device to maintain the driver or other occupants in a comfortable thermal environment. It should be understood that in the field of vehicles, a vehicle may include one or more vehicle compartment thermal conditioning devices, such as the most common HVAC (Heating, Ventilation and Air Conditioning) systems. In subsequent embodiments of this application, although the specific process of the thermal comfort control method for a vehicle compartment is described using HVAC as an example, those skilled in the art will understand that this is merely exemplary, and they can envision using this thermal comfort control method for a vehicle compartment to control other vehicle compartment thermal conditioning devices without departing from the scope of this application.

[0018] In the embodiments of this application, the thermal comfort control method for a vehicle compartment (hereinafter referred to as the control method) includes at least the following steps:

[0019] At step S100, thermal comfort control is applied to the vehicle cabin towards a target thermal comfort value. It should be noted that, for methods such as thermal comfort control, this target thermal comfort value can be selected as an empirical or experimental thermal comfort value that is likely to be comfortable for a relatively large number of people, i.e., a default target thermal comfort value (this is particularly useful when a personalized target thermal comfort value, as described below, has not yet been determined); or it can be selected as a personalized target thermal comfort value corresponding to the cabin occupants, for example, determined or calculated by the thermal comfort control method of this application described below. Optionally, at step S100, target operating parameters of the vehicle cabin thermal conditioning device (e.g., HVAC) corresponding to the target thermal comfort value are determined based on the environmental conditions and / or the vehicle cabin conditions, and the vehicle cabin thermal conditioning device is activated with the determined target operating parameters.

[0020] Taking the HVAC system in a vehicle compartment thermal conditioning unit as an example, in step S100, the target operating parameters of the HVAC are determined based on the environment and / or the vehicle compartment status, and the HVAC is started with the determined target operating parameters. Further, determining the target operating parameters of the HVAC includes at least determining the damper positions of the compartment fan system within the HVAC. Of course, as those skilled in the art will understand, determining the target operating parameters of the HVAC also includes determining the operating temperature of the HVAC, etc., which will not be elaborated further here.

[0021] As an example and not a limitation, the environmental state and / or vehicle compartment state may optionally be represented by the outside temperature. Taking the HVAC in the vehicle compartment thermal regulation device as an example, therefore, at step S100, it can be determined whether the HVAC enters automatic cooling mode or automatic heating mode based on the outside temperature. That is, when the outside temperature is greater than T1, the ambient temperature is considered too hot, and the HVAC should enter cooling mode; when the outside temperature is less than T1 but greater than T2, the ambient temperature is considered moderate, and the HVAC should enter ventilation mode; when the outside temperature is less than T2, the ambient temperature is considered too cold, and the HVAC should enter heating mode, where T2... <T1。

[0022] As another example and without limitation, the environment and / or vehicle cabin status is represented by the cabin temperature. In the case of taking the HVAC in the vehicle cabin thermal regulation device as an example, therefore, at step S100, the initial operating parameters of the HVAC are determined based on whether the cabin temperature reaches a set threshold. For example, when the cabin temperature is greater than the set threshold T3, it is determined that the ambient temperature is too hot and the HVAC should enter the cooling mode. When the cabin temperature is less than T3 and greater than T4, it is considered that the ambient temperature is moderate and the HVAC should enter the ventilation mode. When the cabin temperature is less than T4, it is considered that the ambient temperature is too cold and the HVAC should enter the heating mode, where T4 < T3. As those skilled in the art of the present application can understand, the cabin temperature can be collected at any suitable position in the vehicle cabin. For example, various positions such as the inner side of the car door, the seat armrest, and the inside of the center console are within the scope of the present application without departing from it.

[0023] As another example and without limitation, the environment and / or vehicle cabin status is represented by the relative temperature between the outside temperature and the cabin temperature (outside temperature - cabin temperature). In the case of taking the HVAC in the vehicle cabin thermal regulation device as an example, therefore, at step S100, the target operating parameters of the HVAC are determined based on whether the relative temperature reaches a set threshold.

[0024] It can be envisioned that the above step S100 can be used in combination with a device or sensor for detecting the presence of people in the vehicle, such that when any person is detected in the vehicle, S100 is automatically executed. By means of this combined use, it is possible to achieve a fully automatic start and control of the thermal comfort control without the interference of the driver or any other occupant. Of course, this step S100 can also be started after being manually selected by the driver or any other occupant to avoid unnecessary energy consumption as much as possible.

[0025] At step S102, the current cabin thermal parameters of the vehicle cabin are obtained. Optionally, the current cabin thermal parameters at one or more control points within the vehicle cabin are obtained. As an example and without limitation, at step S102, the current cabin thermal parameters at the control point where thermal comfort control is desired are obtained, based at least on the average cabin temperature and thermodynamic parameters associated with the vehicle cabin, for example using a current cabin thermal parameter model. As an example, the aforementioned current cabin thermal parameter model is a cabin thermal parameter machine learning model, which is designed or trained to determine the current cabin thermal parameters at the control point where thermal comfort control is desired, based at least on the average cabin temperature and thermodynamic parameters associated with the vehicle cabin. As an example and alternatively, the current cabin thermal parameters can be directly measured using various electronic devices such as temperature, humidity, and wind direction sensors. As an example and without limitation, at step S102, the current control point temperature at the control point where thermal comfort control is desired is obtained, based at least on the average cabin temperature and thermodynamic parameters associated with the vehicle cabin, for example using a current control point temperature model (specifically, a machine learning model). It is conceivable that the thermodynamic parameters associated with the vehicle compartment include one or more of the following parameters: blower speed of the vehicle compartment thermal conditioning device, solar irradiance, output temperature of the vehicle compartment thermal conditioning device or outlet air temperature of the blower, outside temperature, position of the blower circulation damper of the vehicle compartment thermal conditioning device, and blower angle of the vehicle compartment thermal conditioning device. It is conceivable that the compartment thermal parameters at the control point include at least one or more of the following parameters: temperature, humidity, and wind speed. Furthermore, in this application, each control point is defined as corresponding to an associated area at or near a corresponding part of the driver or other occupant. It should be understood that, in the embodiments of this application, the current compartment thermal parameter model can present any possible mathematical model or fitting function that can identify the compartment thermal parameters of the current control point based at least on the average compartment temperature and the thermodynamic parameters associated with the vehicle compartment, without departing from the scope of this application. It should be noted that this step is only optional for the thermal comfort control method of this application. Furthermore, although the thermal comfort control method of this application is described in the embodiments using one or more control points in the vehicle compartment as examples, those skilled in the art should understand that this is merely exemplary and not restrictive. In fact, thermal comfort values ​​(the various thermal comfort values ​​involved in this application) can be calculated based on any relevant compartment thermal parameters of any possible range of the vehicle compartment (one or more control points, one or more control areas, the entire vehicle compartment, etc.) without departing from the scope of this application. In this step and thereafter, during the calculation process not based on control point compartment thermal parameters, it is possible to consider calculating thermal comfort values ​​based on relevant compartment thermal parameters corresponding to the vehicle compartment without departing from the scope of this application.

[0026] At step S104, the current thermal comfort value is calculated or estimated. Specifically, at step S104, the current thermal comfort value of the vehicle cabin is estimated based on thermal comfort value parameters, optionally using, for example, a thermal comfort value model. Alternatively, the current thermal comfort value at one or more control points of the vehicle cabin is estimated based on the thermal comfort value parameters. As will be known, the thermal comfort value model is constructed as a mathematical model for estimating the current thermal comfort value of the vehicle cabin based on thermal comfort value parameters. This model can be determined, for example, through machine learning algorithms or fitting, and reflects the mathematical relationship between the thermal comfort value parameters and the current thermal comfort value of the vehicle cabin. Its form can be of any known or unknown possible type without further limitation herein. Further, at step S104, the thermal comfort value model may optionally be a PMV model; the thermal comfort value is a PMV value and the thermal comfort value parameter is a PMV parameter. Optionally, in embodiments of this application, the thermal comfort value model is selected as a suitable combination of a general thermal comfort value sub-model (also known as a general PMV sub-model) and a personalized thermal comfort value sub-model (also known as a personalized PMV sub-model). Therefore, in step S104, the current PMV value at the control point is calculated based on the PMV parameters using the PMV model. Further, a first PMV value at the control point is calculated based on the PMV parameters using a combination of the general PMV sub-model and the personalized PMV sub-model. As those skilled in the art will understand, the thermal comfort value parameters include current cabin thermal parameters and may also include current personal thermal parameters (cabin occupant's clothing index, metabolic rate index, age, activity (e.g., sedentary / driving), and gender, etc.) and / or current external environmental thermal parameters (solar irradiance, cabin outdoor temperature, etc.). The acquisition of the current PMV value will be described in detail later and will not be repeated here. Furthermore, the details of the PMV model, including its machine learning training, will also be described in detail later and will not be repeated here. Furthermore, step S104 includes calculating or determining a target thermal comfort value, which represents a personalized target thermal comfort value that provides comfort for cabin occupants. Detailed steps for determining this target comfort value will be referenced. Figure 2 Detailed descriptions are omitted here. It is important to understand that, for different thermal comfort parameters, the same person can have different target thermal comfort values. These target thermal comfort values ​​can be selectively pre-calculated separately for different thermal comfort parameters without departing from the scope of this application. It should be noted that this step is also optional for the thermal comfort control method of this application.

[0027] In step S114, the target cabin thermal parameters of the vehicle cabin are determined at least in part based on the calculated or determined target thermal comfort value, such that the cabin thermal parameters of the vehicle cabin are adjusted to be at or near the target cabin thermal parameters and the current thermal comfort value is close to or at the target thermal comfort value. It should be explained here that step S114 can also be described as adjusting and updating the cabin thermal parameters of the vehicle cabin based on the target thermal comfort value.

[0028] As an example, step S114 can be interpreted as including sub-steps S106 and S108, wherein, in step S106, the target cabin thermal parameters of the vehicle cabin are determined based on the target thermal comfort value, and in step S108, the operating parameters of the vehicle cabin thermal adjustment device are adjusted based on the target cabin thermal parameters so that the current cabin thermal parameters are adjusted to or close to the target cabin thermal parameters and the current thermal comfort value is close to or at the target thermal comfort value.

[0029] Taking temperature from the cabin thermal parameters as an example, therefore, as an example and without limitation, in step S106, the target temperature of the vehicle cabin is determined at least based on the target thermal comfort value. In the case of the PMV model, in step S106, the target temperature is determined at least based on the target PMV value. As an example and further, the target temperature is determined based on the target PMV value so that the current temperature is adjusted to approach the target temperature via the vehicle cabin thermal conditioning device. Adjusting the temperature based on the target PMV value is beneficial for quickly achieving thermal comfort control. This is because the estimated current temperature may not be close to the temperature that makes the user feel thermally comfortable, thus causing the current PMV value to deviate significantly from the range that makes the user feel thermally comfortable. Therefore, when controlling the thermal comfort of the cabin, the combination of the real-time acquired current PMV value and the real-time acquired current temperature can significantly accelerate the efficiency and speed of temperature control (for example, when the absolute value of the difference between the current PMV value and the target thermal comfort value is large, a higher or lower HVAC output temperature can be used to quickly rise to the target control point temperature), making it easier to quickly reach the temperature range or value (such as the target control point temperature) that makes the driver or any other occupant feel comfortable. As an example and not a limitation, it can be understood that, for instance, if the current temperature obtained by the thermal comfort control method according to the present invention is 30 degrees Celsius, and the current PMV value estimated based on the PMV parameter indicates that the driver or any other occupant's subjective comfort level at this temperature is "heat + 1", then based on the target PMV value, the target temperature is determined to reduce the current temperature by M degrees Celsius to the target temperature to reduce the current temperature with the corresponding adjustment efficiency so that the current temperature approaches the target temperature; subsequently, as the current temperature decreases, the calculated current PMV value indicates that the subjective comfort level is "heat + 0.7", then based on this PMV value, the target temperature is determined to reduce the current temperature by N degrees Celsius (where N < M, and M and N are both positive numbers) to the target temperature to reduce the current temperature with the corresponding adjustment efficiency so that the current temperature further approaches the target temperature. It is foreseeable that as the current temperature continuously approaches the determined target temperature, the adjustment efficiency of the current temperature will gradually level off. Based on the above description, a PID control process can be envisioned that adjusts the current temperature based on the target PMV value to achieve the target. The current PMV value can be designed as the feedback value of the PID control process so that the temperature is appropriately adjusted with different adjustment efficiencies (or resolutions) according to the current first PMV value.Of course, those skilled in the art can consider any other possible control schemes based on the above teachings. While these control schemes are not detailed in this specification, they do not depart from the scope of this application. As those skilled in the art will understand, adjusting the temperature to the target temperature can be achieved in various ways. Taking the HVAC system in a vehicle cabin as an example, for instance, adjusting the outlet air temperature of the HVAC blower, etc., is not limited here. That is, any technical means capable of achieving the target temperature does not depart from the scope of this application. Although step S106 was described above using a target PMV value as an example, those skilled in the art will understand that the target PMV value can be replaced with the thermal comfort value output of any corresponding thermal comfort value model. Although the cabin thermal parameters were described above using temperature as an example, those skilled in the art will understand that this parameter can also be at least wind speed and / or humidity.

[0030] In step S108, the operating parameters of the vehicle cabin thermal regulation device are adjusted based on the target cabin thermal parameters so that the current cabin thermal parameters are adjusted to or close to the target cabin thermal parameters, and the corresponding current thermal comfort value is close to or at the target thermal comfort value. If the thermal comfort value is a PMV value, in step S108, the operating parameters of the vehicle cabin thermal regulation device are adjusted so that the current cabin thermal parameters are adjusted to or close to the target cabin thermal parameters, and the corresponding current PMV value is close to or at the target thermal comfort value. As an example, and further, the operating parameters of the vehicle cabin thermal regulation device are adjusted based on the current cabin thermal parameters, the target cabin thermal parameters, the target thermal comfort value (or the target PMV value in the case of a PMV model), solar irradiance, etc., so that the current cabin thermal parameters are adjusted to or close to the corresponding target cabin thermal parameters, and the current PMV value is close to or at the target thermal comfort value. As an example and further, the operating parameters of the vehicle cabin thermal control device are iteratively adjusted based on the current cabin thermal parameters, target cabin thermal parameters, thermal comfort value (or target PMV value in the case of a PMV model), solar radiation intensity, etc., so that the cabin thermal parameters are adjusted to or close to the corresponding target cabin thermal parameters (e.g., target temperature) and the current PMV value is close to or at the target thermal comfort value. As those skilled in the art will understand, the operating parameters of the vehicle cabin thermal control device include one or more of the following: the blower speed of the vehicle cabin thermal control device, the output temperature of the vehicle cabin thermal control device or the blower outlet air temperature, the blower blowing angle of the vehicle cabin thermal control device, and the position of the blower circulation damper of the vehicle cabin thermal control device. As an example and further, taking the HVAC in the vehicle cabin thermal conditioning device as an example and the thermal comfort value as the PMV value, in step S108, based on the current cabin thermal parameters, target cabin thermal parameters, target PMV value, solar irradiance, etc., one or more of the following are adjusted: blower speed, blower outlet air temperature, blower circulation damper position, blower blowing angle, blower air humidity of the HVAC in the vehicle cabin thermal conditioning device, so that the current cabin thermal parameters are adjusted to or close to the corresponding target cabin thermal parameters and the current PMV value is close to or at the target thermal comfort value. As an example and further, taking the HVA in the vehicle cabin thermal conditioning device as an example and the temperature in the cabin thermal parameters as an example, in step S108, one or more of the following parameters of the vehicle cabin thermal conditioning device are iteratively adjusted based on the current temperature, target temperature, current PMV value, solar irradiance, etc., such as blower speed, blower outlet air temperature, blower circulation damper position, blower blowing angle, and blower air humidity, so that the temperature is adjusted to or close to the corresponding target temperature and the current PMV value is finally at the target thermal comfort value.

[0031] Optionally, the thermal comfort control method for a vehicle compartment according to another embodiment of the present invention further includes steps S110 and S112, which involve a control process on the cooling side based on the above steps S100-S108. In step S110, the total energy demand is calculated. As an example and not a limitation, in step S110, the total energy demand for achieving the target thermal comfort value (as an example, the target PMV value) close to or at the target thermal comfort value is calculated based on an energy model.

[0032] At step S112, the heat exchanger temperature is calculated. As an example and without limitation, at step S112, the heat exchanger temperature is calculated using the total energy requirement.

[0033] As those skilled in the art will understand, in the case of HVAC in a vehicle cabin thermal conditioning system as an example, steps S110 and S112 mainly involve the quantitative calculation of the relationship between energy demand and heat exchange, which is used to control the operation of the cooling side (refrigeration unit), while steps S100-S108 mainly focus on the operational control of HVAC, especially the operational control of the HVAC blower. Therefore, the thermal comfort control method ensures to a certain extent that the thermal control of the cooling side and the cabin air system (i.e., the cabin fan system) can be decoupled, that is, developed, designed and applied independently, and that the two aspects of control can be easily combined to achieve the final thermal comfort control of the vehicle cabin.

[0034] The following will refer to Figure 2 The following is a detailed flowchart of the steps for determining or acquiring a target thermal comfort value in a thermal comfort control method for a vehicle compartment according to an embodiment of the present invention, wherein the thermal comfort control method is designed to perform thermal comfort control of the vehicle compartment based on the target thermal comfort value, i.e., step S104. As will be understood, the target thermal comfort value can be predetermined manually or determined empirically.

[0035] As mentioned above, step S104 includes step S1041, which calculates or obtains the current thermal comfort value corresponding to the vehicle compartment. Specifically, and as an example, in S1041, the current thermal comfort value of the vehicle compartment is estimated based on thermal comfort value parameters, for example, using a thermal comfort value model. This process has been described in detail above with reference to step S104 and will not be repeated here.

[0036] At step S1042, a feedback input is acquired, optionally acquired in real time. Specifically, this feedback input represents the cabin occupants' feedback on thermal comfort (which can correspond to the current cabin thermal parameters). Since thermal comfort is directly and closely related to the current thermal comfort value, this feedback input also represents the cabin occupants' feedback on the current thermal comfort value. It is conceivable that a target thermal comfort value, such as an empirical value, can be preset for this thermal comfort control method, so that the current thermal comfort value is first adjusted towards the target thermal comfort value. Therefore, naturally, for the current thermal comfort value that reaches the target thermal comfort value, this feedback input naturally represents the cabin occupants' feedback on the target thermal comfort value.

[0037] Then, in response to the feedback input, the current thermal comfort value is corrected to determine the target thermal comfort value. This process can be implemented, for example, with reference to steps S1043-S1048, which are described in detail below. It should be noted that these steps are exemplary and not limiting.

[0038] At step S1043, the type of feedback input is determined. Specifically, at this step, if the feedback input is determined to be comfort feedback SH, the process proceeds to step S1044, that is, maintaining the target thermal comfort value or directly selecting the current thermal comfort value as the target thermal comfort value for the cabin occupants. It should be noted that in various embodiments of this application, comfort feedback SH includes at least direct feedback from the user (e.g., the occupant directly inputs an input indicating that they feel comfortable) or default feedback from the user (e.g., if the occupant does not input any feedback on the current cabin thermal parameters after a predetermined time period, then the user is assumed to be in a comfortable state). As discussed previously, the target thermal comfort value represents the current thermal comfort value or the personalized thermal comfort value for which the current cabin thermal parameters are comfortable for the cabin occupants. This target thermal comfort value may optionally be used in the next thermal comfort control process, which will be described in detail later.

[0039] Conversely, if at step S1043, upon determining that the feedback input is cold feedback C or hot feedback H, it is necessary to further determine whether the pre-adjustment phase towards the target thermal comfort value for the vehicle cabin has been completed, and thus optionally proceed to step S1045. That is, when cabin occupants exhibit uncomfortable feedback of feeling cold or hot regarding the current thermal comfort value or the corresponding current cabin thermal parameters, it indicates that further adjustment and updating of the current cabin thermal parameters, i.e., the current thermal comfort value, is required; that is, correction of the current thermal comfort value, for example, determining an adjustment amount based on the feedback input as described above and correcting the current thermal comfort value based on that adjustment amount. However, it should be noted that for thermal comfort control of the vehicle cabin, there is a certain pre-adjustment phase towards the target thermal comfort value. For example, when thermal comfort control of the vehicle cabin is initiated, although the thermal comfort control moves towards the target thermal comfort value, it obviously takes a certain amount of time to reach the target thermal comfort value. Therefore, when occupants provide feedback too early, it only reflects the current thermal comfort value, which may be far from the target thermal comfort value. This feedback lacks accuracy and is distorted, complicating the determination or adjustment of the target thermal comfort value for occupants, especially when occupants begin providing feedback as soon as thermal comfort control is activated. Therefore, before correcting the current thermal comfort value, it is necessary to ensure that the pre-adjustment phase towards the target thermal comfort value for the vehicle cabin has been completed. In other words, it is necessary to prevent premature feedback from occupants from interfering with or distorting the determination of the target thermal comfort value. As an example, the completion of this pre-adjustment phase towards the target thermal comfort value will be determined below based on step S1045.

[0040] At step S1045, it is determined whether the pre-adjustment phase for the target thermal comfort value of the vehicle cabin has been completed, that is, whether the pre-adjustment phase for thermal comfort control of the vehicle cabin towards the target thermal comfort value has been completed. The completion of the pre-adjustment phase indicates that the current thermal comfort value is sufficiently close to the target thermal comfort value, and the cabin occupants' perception of the current thermal comfort value accurately reflects their evaluation of the current thermal comfort value. Therefore, based on the current thermal comfort value and evaluation feedback, the target comfort value can be accurately determined, avoiding distortion. As an example and optionally, at step S1045, the current thermal comfort value is compared with a predetermined expected thermal comfort value range or the target thermal comfort value. Specifically, at step S1045, it is compared whether the current thermal comfort value is within the expected thermal comfort value range. This expected thermal comfort value range represents a pre-defined empirical value (or range) or predetermined value (or range), which represents a range, especially a sufficiently close range, from the target thermal comfort value for the average person. For example, with the target thermal comfort value set to a default of 0, the expected thermal comfort value range can be selected as (-1, 1), indicating that the distance to the target thermal comfort value is relatively close and sufficiently large, thus signifying that the pre-adjustment phase towards the target thermal comfort value has been completed. Of course, while the expected thermal comfort value range is selected as (-1, 1) here, the boundaries of this range can be modified without departing from the scope of this application. For example, it can also be selected as (-1, 0), (0, 1), (-0.8, 0.8), and other symmetrical or asymmetrical boundaries, and the boundary values ​​of these ranges can be predetermined based on appropriate experience or models. Alternatively, the expected thermal comfort value range can be optionally replaced with a fixed value rather than a range; such embodiments are obviously also included in the scope of this application. The expected thermal comfort value range can be represented as a value within a range of a fixed distance from the target thermal comfort value; that is, the expected thermal comfort value range can be selected to be continuously updated as the target thermal comfort value changes.

[0041] Further, at S1045, if it is determined that the pre-adjustment phase has not yet been completed, proceed to step S1046, where the step of correcting the target thermal comfort value is not performed, i.e., the target thermal comfort value remains unchanged. More specifically, and as an example, at step S1045, if it is determined that the current thermal comfort value is not within the desired thermal comfort value range, then it is determined that the pre-adjustment phase towards the target thermal comfort value has not yet been completed, and therefore proceed to step S1046, where the step of correcting the target thermal comfort value is not performed, i.e., the current thermal comfort value continues to be adjusted towards the target thermal comfort value independently of the feedback input. This step can also be described as adjusting the current cabin thermal parameters independently of the feedback input in a direction closer to the target thermal comfort value or the desired thermal comfort value range. Obviously, if the current thermal comfort value is not yet within the expected thermal comfort value range (for example, the HVAC system has not been started for a sufficient period of time, i.e., the pre-adjustment phase has not been completed) and there is feedback input from users regarding heat or cold, it can be determined that the cabin personnel are not satisfied with the current thermal comfort value, and therefore the current cabin thermal parameters should be adjusted towards the expected thermal comfort value range first, and the relevant current thermal comfort value should be updated in real time or accordingly. It is important to note that in step S1046, the process of adjusting the current cabin thermal parameters towards the desired thermal comfort range or the target thermal comfort value is independent of the feedback input. For example, if the current thermal comfort value is determined to be 1.5, regardless of whether the user's feedback input is cold feedback C (e.g., the user expects to further increase the thermal comfort value) or hot feedback H (i.e., the user expects to further decrease the thermal comfort value), the current cabin thermal parameters are first adjusted towards the desired thermal comfort range or the target thermal comfort value until the current thermal comfort value reaches the range or is at the target thermal comfort value. Then, the user's feedback input is further collected, and an adjustment amount is determined based on the feedback input. The current thermal comfort value is then corrected based on this adjustment amount, thereby avoiding invalid input from the user (inputs that may interfere with the determination of the target thermal comfort value).

[0042] Further, at S1045, if it is determined that the pre-adjustment phase has been completed, proceed to step S1047, where a step to correct the target thermal comfort value is performed. More specifically, and as an example, at step S1045, if it is determined that the current thermal comfort value is within or reaches the desired thermal comfort value range or is within the target thermal comfort value, then proceed to step S1047. For step S1047, as an example and specifically, an adjustment amount is determined based on the feedback input, and the current thermal comfort value is corrected based on this adjustment amount to determine the target thermal comfort value for thermal comfort control of the vehicle cabin. Optionally, if the feedback input is cold feedback C, the current thermal comfort value is adjusted by a certain amount in the direction of heat; if the feedback input is heat feedback H, the current thermal comfort value is adjusted by a certain amount in the direction of cold. Optionally, the vehicle compartment can adjust and update the current compartment thermal parameters based on feedback input (thus adjusting the current thermal comfort value) to approach the target thermal comfort value until the feedback input for the current compartment thermal parameters corresponding to the target thermal comfort value is comfort feedback SH. At this point, it can be determined that the user is satisfied with the current thermal comfort value corresponding to the target thermal comfort value. Therefore, the obtained target comfort value is further selected as the target thermal comfort value for the compartment occupants. Additionally, this target thermal comfort value is maintained and recorded as described above.

[0043] By incorporating user feedback input, this application enables a more accurate and targeted thermal comfort control and personalized target thermal comfort value determination through a precise, rapid, and effective process, ensuring control performance, precision, and accuracy. Furthermore, the above embodiments ensure flexibility, versatility, accuracy, and effectiveness in thermal comfort control, avoiding user dissatisfaction caused by single and simplistic thermal comfort control methods.

[0044] Optionally, step S1045 can be selectively terminated after the pre-adjustment phase toward the target thermal comfort value has been completed. That is, once S1045 determines that the pre-adjustment phase toward the target thermal comfort value has been completed, any subsequent determination of the target thermal comfort value can selectively omit the step of determining whether the pre-adjustment phase has been completed, i.e., step S1045 is omitted thereafter.

[0045] Further, after determining the type of feedback input in step S1043, a digital conversion step S1048 may be optionally included, wherein the feedback input is converted into a digital increment, which may include a positive increment, a zero increment, or a negative increment. As previously mentioned, a positive increment corresponds to the thermal feedback H of the cabin occupants, i.e., feeling too hot at the current thermal comfort value and desiring a cooler current thermal comfort value; correspondingly, a negative increment corresponds to the cold feedback C of the driver or occupants, i.e., feeling too cold at the current thermal comfort value and desiring a warmer current thermal comfort value. Correspondingly, a zero increment corresponds to the comfort feedback SH of the cabin occupants. In embodiments of this application, it can be envisioned that a positive increment is a positive value associated with the user's feedback input and a negative increment is a negative value associated with the user's feedback input. Furthermore, it can be envisioned that the magnitude of the positive or negative increment represents the subjective gap or degree between the user's current thermal comfort value and the thermal comfort value they feel comfortable with. For example, if the user feels particularly hot at the current thermal comfort value, they would like the current thermal comfort value to be much smaller, so they input a larger degree of cold feedback C, and therefore this cold feedback C is digitized as a negative value with a larger magnitude.

[0046] As examples, some examples of user feedback input are given here, but it should be understood that these examples are merely illustrative and not restrictive. For example, a user can provide feedback input via voice interaction, such as recognizing the user's thermal feedback H if the user's feedback input is heat or heat-related content and digitizing the feedback input as a positive increment, such as 1 (this can be set according to the required accuracy of the calculation, location data, current thermal parameters, vehicle external environment, etc.). The selection of this increment is, for example, variable; for example, for a vehicle in winter, the digitized positive increment of the user's thermal feedback H can be chosen to be a smaller value, while in summer, the digitized positive increment of the user's thermal feedback H can be chosen to be a larger value, etc. Optionally, the degree of feedback input can also be determined based on the intensity of the customer's voice and digitized accordingly into different positive increments. For example, if the user's feedback input is cold, the feedback input can be digitized as a negative increment -1, and conversely, if the user's feedback input is very cold, the feedback input can be digitized as a negative increment -2. Any suitable process for digitizing feedback input is available and included within the scope of this application. In addition to the aforementioned voice interaction, graphical user interface interaction (e.g., touchscreen), button interaction, etc., may also be selected. Any appropriate means of obtaining user feedback input is possible and does not depart from the scope of this application.

[0047] Further, in step S1047, the adjustment amount is determined based on the current thermal comfort value, the feedback input, and the order of the feedback input. As an example, in step S1047, the adjustment amount for the current thermal comfort value is calculated based on the aforementioned positive or negative increment. As an example, in this step, the adjustment amount is selected as half the sum of the current thermal comfort value and the corresponding positive or negative increment. This method of selecting the adjustment amount is merely exemplary; as another example, the adjustment amount is directly selected as half of the corresponding positive or negative increment. In fact, as those skilled in the art will understand, any possible method of selecting the adjustment amount for the current thermal comfort value based on the positive or negative increment of the user's feedback input is possible and is included within the scope of this application. Of course, as mentioned above, the corresponding thermal comfort value parameters corresponding to the current thermal comfort value are also recorded accordingly. These parameters have been described above and will not be repeated here.

[0048] Furthermore, step S1047 includes using the difference between the current thermal comfort value and the adjustment amount as a target thermal comfort value. In other words, here, the current thermal comfort value of the vehicle compartment is adjusted in a direction equal to the target thermal comfort value equal to the difference between the current thermal comfort value and the adjustment amount, so that the current thermal comfort value can be adjusted in the desired direction (heating direction or cooling direction) according to the user's feedback input. This method of adjusting the current thermal comfort value based on positive or negative feedback takes into account the degree of user feedback input and the range of the current thermal comfort value, thus ensuring that the updated current thermal comfort value is more likely to reach a level that makes the user feel comfortable, without increasing the overall adjustment time due to large fluctuations in the current thermal comfort value. This step also includes transmitting the calculated target thermal comfort value to step S114 for thermal comfort control of the vehicle compartment.

[0049] Figure 3 An exemplary detailed flowchart of machine learning training for a thermal comfort value model of a thermal comfort control method for a vehicle compartment according to an embodiment of the present invention is shown. The thermal comfort model includes a general thermal comfort value sub-model and a personalized thermal comfort value sub-model.

[0050] In this application Figure 3In the exemplary embodiment shown, the thermal comfort value model is selected as an appropriate combination of a general thermal comfort value sub-model and a personalized thermal comfort value sub-model. As an example, this combination can be selected by employing different weights for the thermal comfort value inputs of the corresponding thermal comfort value model. As will be understood, in the embodiments of this application, the general thermal comfort model is selected as a model trained solely on a basic thermal comfort value machine learning training database under standardized conditions collected in a laboratory or standard vehicle. That is, the data in the basic thermal comfort value machine learning training database consists of measurements or literature data under ideal conditions for specific situations. This data, for example, can come from open-source data from various experimental platforms, which typically excludes interference from differences between personnel and obtains relevant experimental data only through theoretical experiments. Therefore, at step 302, the basic thermal comfort value machine learning training database is obtained. Further, at step 304, the general thermal comfort value sub-model is trained based on the obtained basic thermal comfort value machine learning training database.

[0051] Conversely, the personalized thermal comfort value sub-model is selected as a model trained on a personalized thermal comfort value machine learning training database actually collected by cabin personnel during operation, including feedback inputs. That is, this personalized thermal comfort value machine learning training database originates from data related to the user's actual choices during operation. This data includes various parameters related to thermal comfort values ​​as described above, so that the personalized thermal comfort value sub-model can more closely reflect actual conditions. Therefore, in step 306, the personalized thermal comfort value machine learning training database related to the individual user is obtained. Further, in step 308, the personalized thermal comfort value sub-model is trained based on the obtained personalized thermal comfort value machine learning training database.

[0052] Because the total amount of data related to individual users may be relatively scarce, the personalized thermal comfort value machine learning training database can be selectively derived from the relevant thermal comfort control data (data related to the selection of thermodynamic parameters in the vehicle cabin) of all cabin occupants located within a certain range, such as a certain city. This is because cabin occupants in a certain area generally have a more similar thermal comfort experience. Optionally, in the embodiments of this application, the untrained original models of the general thermal comfort value sub-model and the personalized thermal comfort value sub-model can be selected as the same, similar, or related models, and then trained using the aforementioned untrained database.

[0053] Optionally, for the personalized thermal comfort value machine learning training database, it is partially updated (due to the continuous recording of thermal comfort data related to individual users), but its total capacity (the nominal number of data entries in the database) is selected as a predetermined value. This is because, due to the continuous accumulation of data related to users' actual operations regarding thermal comfort values, the data volume of the personalized thermal comfort value machine learning training database will become enormous and difficult to use for model training. Therefore, a portion of the continuously accumulating data can usually be selected as data in the training database. Of course, the continuous increase of the latest data will eventually overload the current capacity (the nominal number of data entries in the database), that is, exceed the predetermined value. At this point, the oldest data in the personalized thermal comfort value machine learning training database can be optionally deleted, and the latest data can be saved to the personalized thermal comfort value machine learning training database to update the database. Therefore, before training the personalized thermal comfort value sub-model with the acquired personalized thermal comfort value machine learning training database, it is necessary to determine the current capacity and update status of the personalized thermal comfort value machine learning training database. To this end, in step 310, it is determined whether the current number of data entries in the personalized thermal comfort value machine learning training database exceeds the fixed capacity. If the number exceeds the limit, in step 312, the oldest data in the personalized thermal comfort value machine learning training database is deleted, and the latest data from individual users is saved to the personalized thermal comfort value machine learning training data, so that the database is updated for training the personalized thermal comfort value sub-model. If the number does not exceed the total capacity, the latest data from individual users can be directly saved to the personalized thermal comfort value machine learning training data and used to train the personalized thermal comfort value sub-model.

[0054] Optionally, the thermal comfort value models, including both general and personalized thermal comfort value sub-models, are selected for offline or cloud training. This is because on-vehicle computing resources are typically limited, while the training databases for different thermal comfort value models are relatively large. To ensure training efficiency, these thermal comfort models are usually trained on suitable computing power devices. Therefore, relevant user data during actual operation can be wirelessly or wired, for example via Wi-Fi or other wireless networks, to the personalized thermal comfort value machine learning training database for updates. This personalized thermal comfort value machine learning training database can optionally be stored in a cloud database or a dedicated remote data center.

[0055] Furthermore, in step 314, the trained general thermal comfort value sub-model and the personalized thermal comfort value sub-model are combined to form a thermal comfort value model. As an example, but not a limitation, the trained general thermal comfort value sub-model and the personalized thermal comfort value sub-model can be linearly combined with corresponding weights to form a thermal comfort value model. Of course, it is also conceivable that the trained general thermal comfort value sub-model and the personalized thermal comfort value sub-model can be combined in any non-linear form without departing from the scope of this application.

[0056] For the vehicle, optionally, it can be envisioned that the thermal comfort value model, especially the continuously updated thermal comfort value model formed by appropriately combining a personalized thermal comfort value sub-model trained via a continuously updated personalized thermal comfort value machine learning training database with a trained general thermal comfort value sub-model, is selected to be updated into the vehicle based on an over-the-air (OTA) update request for user vehicle thermal comfort control. This method of loading an offline or cloud-trained model into the vehicle based on an OTA update is obvious to those skilled in the art. Therefore, at step 316, it is determined whether an OTA update request has been received. If a relevant OTA request is received, then at step 318, the updated thermal comfort value model is updated into the vehicle. Optionally, at step 318, only the updated personalized thermal comfort value sub-model and the general thermal comfort model can be updated into the vehicle, and the thermal comfort value model can be updated in the vehicle by combining them based on a defined relationship between the personalized thermal comfort value sub-model and the general thermal comfort value sub-model. In this case, step 314 can be omitted without departing from the scope of this application. Figure 3 The embodiment shows step 314, but it is clear that it is only optional and not required.

[0057] Of course, for the distributed thermal comfort control method, the thermal comfort value model is selected to be stored in the cloud and not loaded into the vehicle. In this case, step 318, etc., can be omitted. In this case, the calculation of thermal comfort values ​​used for the thermal comfort control method is also completed in the cloud or distributedly, enabling the relevant thermal comfort value data in the vehicle to be remotely transmitted to the distributed thermal comfort value model and the calculation results to be transmitted back to the vehicle via the thermal comfort value model for use in thermal comfort control.

[0058] Optionally, the basic thermal comfort value machine learning training database and / or the personalized thermal comfort value machine learning training database are selected to be updated in real time or periodically. The update mode or update frequency of these training databases can be selected according to actual needs without departing from the scope of this application.

[0059] Figure 4An exemplary detailed flowchart illustrates the process of determining a relevant current thermal comfort value during the initiation of thermal comfort control in a thermal comfort control method for a vehicle compartment according to an embodiment of the present invention. In fact, in Figure 4 The document illustrates a possible implementation of how to combine a trained general thermal comfort value sub-model with a personalized thermal comfort value sub-model to form a thermal comfort value model.

[0060] In fact, Figure 4 In the embodiments described, when cabin occupants initiate thermal comfort control, the influence of the personalized thermal comfort value sub-model and the general thermal comfort value sub-model on the judgment of the current thermal comfort value is obviously different. As will be understood, the personalized thermal comfort value sub-model will obviously obtain a current thermal comfort value that is more in line with the user's experience as its training database gradually expands (meaning that the current thermal comfort value output will be more in line with the user's actual experience rather than being detached from the actual user experience as the input of only the general thermal comfort value sub-model), thus minimizing excessive thermal comfort dissatisfaction among users. Based on this, optionally and alternatively, in the embodiments of this application, the thermal comfort value model calculates the current thermal comfort value differently based on the ratio of the current capacity to the total capacity of the personalized thermal comfort value machine learning training database. In other words, in the embodiments of this application, the current thermal comfort value is calculated as the sum of the corresponding weights of the estimation results of the general thermal comfort value sub-model and the personalized thermal comfort value sub-model for the thermal comfort value parameters during this thermal comfort control period under different capacity ratios (such as the ratio of the current capacity to the total capacity of the personalized thermal comfort value machine learning training database mentioned above). In other words, the current thermal comfort value is calculated as the thermal comfort value parameter estimation result of the thermal comfort value model formed by the general thermal comfort value sub-model and the personalized thermal comfort value sub-model with their respective weights under different capacity ratios (such as the ratio of the current capacity of the personalized thermal comfort value machine learning training database to the total capacity as mentioned above).

[0061] Furthermore, in Figure 4In this embodiment, the adjustment of the current thermal comfort value during the thermal comfort control process is also involved. As previously described, after a target thermal comfort value is determined in the thermal comfort control method, it is associated with the cabin occupants and maintained and recorded such that when the cabin occupants reactivate thermal comfort control, the current thermal comfort value is adjusted based on the target thermal comfort value, and the current cabin thermal parameters are updated to reach or approach the target thermal comfort value. Specifically, if the thermal comfort value model has been updated or is being used for the first time, the target thermal comfort value is selected as a predetermined value, such as 0 or another value; otherwise, the target thermal comfort value is selected as the target thermal comfort value determined during the previous thermal comfort control. In effect, this step determines the direction in which the thermal comfort control method should adjust to approximate the user's actual thermal comfort experience for the current thermal comfort value output by the thermal comfort value model. If the thermal comfort value model is updated, previously recorded target thermal comfort values ​​may become invalid or deviate from the actual target thermal comfort value, thus requiring re-determination. Therefore, in this re-determination, for a given thermal comfort value model, it is natural to consider using predetermined values ​​for adjustment during the initial run in order to determine the actual target thermal comfort value.

[0062] Based on the above, optionally, in Figure 4In an exemplary embodiment, in step 402, it is determined whether the current thermal comfort value model has been updated, for example, based on an OTA request. If it is determined that the thermal comfort value model has been updated, then in step 404, the target thermal comfort value is selected as a predetermined value and the current cabin thermal parameters are updated to reach or approach the target thermal comfort value, such as 0 or any other value in a predetermined range of desired thermal comfort values. If it is determined that the thermal comfort value model has not been updated, then in step 406, the target thermal comfort value is selected as the target thermal comfort value determined during the previous thermal comfort control, and the current cabin thermal parameters are updated to reach or approach the target thermal comfort value. In both of the above-mentioned scenarios, the current thermal comfort value is selected for calculation as follows: At step 408, it is determined that the number of current data entries in the personalized thermal comfort value machine learning training database is less than a first threshold percentage of its total capacity. If so, at step 412, the current thermal comfort value can be selected as the estimation result of the personalized thermal comfort value sub-model with the first weight and the estimation result of the general thermal parameter value model with the fourth weight. At step 410, it is determined that the number of current data entries in the personalized thermal comfort value machine learning training database is not less than a first threshold percentage of its total capacity but less than a threshold percentage. If so, at step 414, the current thermal comfort value can be selected as the estimation result of the personalized thermal comfort value sub-model with the third weight and the estimation result of the general thermal parameter value model with the fourth weight. When it is determined that the number of current data entries in the personalized thermal comfort value machine learning training database is not less than a threshold percentage of its total capacity, at step 416, the current thermal comfort value can be selected as the estimation result of the personalized thermal comfort value sub-model with the fifth weight and the estimation result of the general thermal parameter value model with the sixth weight. It should be understood that as the proportion of personalized data increases, the first, third, and fifth weights also gradually increase. Of course, the fifth weight can be chosen, for example, to be fifty percent. Then, in step 418, the current thermal comfort value calculated above is adjusted towards the target thermal comfort value.

[0063] Subsequently, although not directly shown, it is conceivable and optional to refer to the current thermal comfort value that has been adjusted to the target thermal comfort value. Figure 1 The method for controlling thermal comfort in vehicle compartments is used to obtain target thermal comfort values ​​for compartment occupants.

[0064] Although the thermal comfort control method and its various corresponding embodiments are described in a certain order in the embodiments of this application, it should be understood that one or more of these steps can be implemented in reverse order or in parallel; and multiple steps can be implemented by a single step, while one of these steps can be implemented by multiple steps together, which obviously does not depart from the scope of this application. Furthermore, one or more steps in the thermal comfort control method in the above embodiments can be omitted or removed without departing from the scope of this application.

[0065] Figure 5 A schematic diagram of various modules of a thermal comfort controller 500 for a vehicle cabin according to an embodiment of the present invention is shown. In embodiments of this application, the thermal comfort controller 500 for a vehicle cabin is capable of implementing the thermal comfort control method as described above. As an example, the thermal comfort controller 500 includes: a storage unit 502 for storing computer-readable instructions; and a processing unit 506 that executes the computer-readable instructions to implement the relevant thermal comfort control method. The processing unit includes: an optional vehicle cabin thermal conditioning device activation module 5061 configured to perform thermal comfort control of the vehicle cabin towards a target thermal comfort value; an optional current cabin thermal parameter estimation module 5062 configured to acquire the current cabin thermal parameters of the vehicle cabin, wherein the cabin thermal parameters include at least one or more of the following parameters: temperature, humidity, and wind speed; and an optional thermal comfort value calculation module 5063 configured to estimate the thermal comfort value of the vehicle cabin based on the thermal comfort value parameters. The system includes: a current thermal comfort value, particularly utilizing a thermal comfort value model; a thermal comfort value acquisition module 5068 configured to acquire the current thermal comfort value corresponding to the vehicle compartment; a feedback input receiving module 5064 configured to acquire feedback input from compartment occupants regarding the thermal comfort of the vehicle compartment; an optional thermal comfort value determination module 5065 configured to determine whether a pre-adjustment phase has been completed; an optional operating parameter adjustment module 5066 configured to adjust and update the current compartment thermal parameters, thereby adjusting the current thermal comfort value accordingly; and a target thermal comfort value determination module 5067 configured to correct the current thermal comfort value in response to the feedback input to determine it as the target thermal comfort value.

[0066] Figure 6 A general schematic diagram of a thermal comfort controller 600 for a vehicle compartment according to an embodiment of the present invention is shown. Figure 6As shown, based on the same inventive concept, the thermal comfort controller generally includes at least the following components: a processor 601, a memory 602, a communication interface 603, and a bus 604; wherein, the processor 601, the memory 602, and the communication interface 603 communicate with each other through the bus 604; the communication interface 603 is used to realize information interaction communication of the thermal comfort controller and information transmission with other software or hardware; the processor 601 is used to call the computer program or computer-readable instructions in the memory 602, and when the processor executes the program, it implements the flow of the thermal comfort control method as described above in this application.

[0067] Based on the same inventive concept, another embodiment of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the flow of the thermal comfort control method as described previously in this application, which will not be repeated here.

[0068] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the process executed by the monitoring system 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, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0069] The working principle and beneficial effects of the computer program stored on the computer-readable storage medium provided in the embodiments of the present invention are similar to those of the thermal comfort control method provided in the above embodiments. For details, please refer to the description of the above embodiments. The embodiments of the present invention will not describe it in detail here.

[0070] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 network units. Some or all of the modules can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0071] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute various embodiments or some parts of embodiments.

[0072] It should also be understood that various modifications can be made depending on specific requirements. For example, custom hardware can also be used, and / or specific components can be implemented using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. For example, some or all of the system of the disclosed monitoring system and the processes it executes can be implemented by programming the hardware (e.g., programmable logic circuits including field-programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs)) using the logic and algorithms according to this disclosure in assembly language or hardware programming languages ​​(such as Verilog, VHDL, C++).

[0073] It should also be understood that the process executed by the aforementioned thermal comfort control method can be implemented using a server-client model. For example, the client can receive data and send it to the server. Alternatively, the client can receive data, perform a portion of the processing in the aforementioned thermal comfort control method, and send the resulting data to the server. The server can receive data from the client and execute either the process or another portion of the aforementioned thermal comfort control method, returning the execution result to the client. The client can receive the execution result of the thermal comfort control method from the server and, for example, present it to the user via an output device.

[0074] It should also be understood that the components of a monitoring system can be distributed across a network. For example, some processing can be performed by one processor, while other processing can be performed by another processor located far away from that one processor. Other components of the monitoring system can also be distributed similarly. In this way, the monitoring system can be interpreted as a distributed computing system that performs processing in multiple locations.

[0075] Although embodiments of this application have been specifically described above in conjunction with the accompanying drawings, those skilled in the art can make various modifications or substitutions to the above embodiments based on the teachings of this application without departing from the scope of the application.

Claims

1. A method for thermal comfort control in a vehicle compartment, wherein, The thermal comfort control method includes: Thermal comfort control of the vehicle cabin is implemented towards the target thermal comfort value. Obtain the current thermal comfort value corresponding to the vehicle compartment. Obtain feedback input from cabin occupants regarding the thermal comfort of the vehicle cabin. The current thermal comfort value is corrected in response to the feedback input to determine the target thermal comfort value.

2. The thermal comfort control method according to claim 1, wherein, The current thermal comfort value is obtained through the following steps: Obtain the current thermal parameters of the vehicle compartment, wherein the thermal parameters include at least one or more of the following parameters: temperature, humidity, and wind speed; and The current thermal comfort value of the vehicle compartment is estimated based on thermal comfort value parameters, wherein the thermal comfort value parameters include the current compartment thermal parameters, current personal thermal parameters, and / or current external environmental thermal parameters.

3. The thermal comfort control method according to claim 1, wherein, Determine whether the pre-adjustment phase of controlling the thermal comfort of the vehicle cabin towards the target thermal comfort value has been completed. If it is determined that it has not been completed, the step of correcting the target thermal comfort value will not be performed. If it is determined that it has been completed, the step of correcting the target thermal comfort value will be performed.

4. The thermal comfort control method according to claim 3, wherein, Whether the pre-adjustment phase for thermal comfort control of the vehicle cabin based on the determined target thermal comfort value has been completed includes: - If the current thermal comfort value is not within the predetermined desired thermal comfort value range, then the pre-adjustment phase is determined to be incomplete; and Otherwise, it is determined that the pre-adjustment phase has been completed.

5. The thermal comfort control method according to claim 2, wherein, The thermal comfort control of the vehicle cabin based on the target thermal comfort value includes adjusting the current cabin thermal parameters based on the target thermal comfort value to reach or approach the target thermal comfort value. And / or Specifically, the current thermal comfort value is estimated based on thermal comfort value parameters using a thermal comfort value model, which is constructed as a mathematical model.

6. The thermal comfort control method according to claim 1, wherein, The method includes: - Convert the feedback input into a digital increment, which includes a positive increment, a zero increment, or a negative increment, wherein the positive increment corresponds to the thermal feedback of the cabin occupants and the negative increment corresponds to the cold feedback of the driver or crew member, and the zero increment corresponds to the comfort feedback of the cabin occupants. The adjustment amount is determined based on the current thermal comfort value, the feedback input, and the order of the feedback input, and the difference between the current thermal comfort value and the adjustment amount is taken as the target thermal comfort value.

7. The thermal comfort control method according to claim 5, wherein, The thermal comfort value model was selected as a combination of a general thermal comfort value sub-model and a personalized thermal comfort value sub-model. The general thermal comfort value sub-model is selected as a model trained solely on a machine learning training database of basic thermal comfort values ​​under standardized conditions collected in a laboratory or standard vehicle; and The personalized thermal comfort value sub-model is selected as a model trained by a machine learning training database of personalized thermal comfort values, which is actually collected by the cabin personnel during operation and includes the feedback input. The thermal comfort value model is selected to achieve at least one of the following: The total capacity of the personalized thermal comfort value machine learning training database is selected as a predetermined value. If the addition of the latest data causes the capacity to be overloaded, the earliest data in the personalized thermal comfort value machine learning training database is deleted and the latest data is saved to the personalized thermal comfort value machine learning training database. The thermal comfort value model was selected to be trained offline or in the cloud. The thermal comfort value model was selected to be updated in the vehicle based on an over-the-air (OTA) update request. The thermal comfort value model is selected to be stored in the cloud and not loaded into the vehicle; or The basic thermal comfort value machine learning training database and / or the personalized thermal comfort value machine learning training database are selected to be updated in real time or periodically.

8. The thermal comfort control method according to claim 7, wherein, When the cabin occupants initiate thermal comfort control, the thermal comfort value model calculates the current thermal comfort value differently based on the ratio of the current capacity to the total capacity of the personalized thermal comfort value machine learning training database. The current thermal comfort value is calculated as the sum of the weighted estimates of thermal comfort value parameters for the current thermal comfort control period from the general thermal comfort value sub-model and the personalized thermal comfort value sub-model, considering their respective weights within the different capacity proportions of the personalized thermal comfort value machine learning training database; and / or The target thermal comfort value is associated with the cabin occupants; and when the same or similar cabin occupants reactivate thermal comfort control, the current thermal comfort value is adjusted based on the target thermal comfort value to reach or approach the target thermal comfort value, wherein if the thermal comfort value model has been updated or the thermal comfort value model is used for the first time, the target thermal comfort value is selected as a predetermined value; otherwise, the target thermal comfort value is selected as the target thermal comfort value determined during the previous thermal comfort control period.

9. A thermal comfort controller for a vehicle compartment, the thermal comfort controller being capable of implementing the thermal comfort control method as described in any one of claims 1-8, the thermal comfort controller comprising: Storage unit for storing computer-readable instructions for implementing the thermal comfort control method as described in any one of claims 1-8; Processing unit, the processing unit comprising at least: A thermal comfort value receiving module is configured to receive the current thermal comfort value corresponding to the vehicle compartment; A feedback input receiving module is configured to acquire feedback input from the cabin occupants regarding the current thermal comfort value; A target thermal comfort value determination module is configured to determine the target thermal comfort value based on the feedback input.

10. A storage medium storing instructions that, when executed, implement a thermal comfort control method for a vehicle compartment according to any one of claims 1-8.

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