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

Abstract

The invention relates to a thermal comfort control method for a vehicle cabin. The thermal comfort control method comprises the steps that current control point thermal comfort parameters at one or more control points are estimated through a current control point thermal comfort parameter machine learning model at least based on the cabin average temperature and thermodynamic parameters related to the vehicle cabin; an operating parameter of the vehicle cabin thermal conditioning device is adjusted based at least in part on the current control point thermal comfort parameter to improve thermal comfort at the control point. The invention further relates to a controller for implementing the thermal comfort control method and a storage medium. By means of the method, the measurement cost can be saved, a personalized thermal comfort value model is realized, decoupling from the control design of the refrigeration side is realized, the control computing power requirement is reduced, and the expected state is quickly realized.

Description

Technical Field

[0001] The invention relates to a thermal comfort control method, a controller and a storage medium for a vehicle cabin. Background Art

[0002] Range anxiety, or energy range, is one of the most important issues for vehicle users. As we all know, the air conditioning system is one of the most important parts of an electric vehicle, but it is also an important energy consumption auxiliary system. Therefore, how to reduce the energy consumption of the air conditioning system has received more and more attention.

[0003] Currently, relevant technical concepts focus on improving the efficiency of the refrigeration side (i.e., the refrigerator side), but few concepts focus on the efficiency of the cabin air system. In other words, the current so-called "automatic" air-conditioning system still requires the driver to manually input the target temperature on the panel. On the one hand, this may cause safety problems by distracting the driver's attention. On the other hand, driving conditions are diverse, so a constant target temperature may not always make the driver feel comfortable, which will further increase the burden on the driver to make manual adjustments.

[0004] In order to make the vehicle more suitable for the driver's comfort and to automatically adjust the cabin temperature more accurately and appropriately, it is known in the prior art to calculate the PMV (Predicted Mean Vote) value to express the thermal comfort evaluation of the human body. The value is based on the basic equation of human thermal balance and the level of subjective thermal sensation in psychophysiology, and is a comprehensive evaluation index that takes into account many relevant factors of human thermal comfort. However, the thermal comfort theory based on the PMV value is not applicable to people in different regions, different body shapes, different genders, and ages, which also limits the use of this theory in vehicle air conditioning and therefore usually only has some applications in the construction industry.

[0005] Furthermore, although it is conceivable to apply the PMV value to the automatic control process of the vehicle air conditioning, the control strategy of the existing vehicle air conditioning control system based on the PMV value is relatively simple, so the efficiency, performance and accuracy of the control are not satisfactory and difficult to apply to the actual vehicle cabin thermal comfort control process. In addition, the current control method for the air conditioning of the vehicle cabin is usually unable to be decoupled from the control method for the adjustment of the refrigeration side for separate design or processing (that is, in the prior art, the purpose of air conditioning the vehicle cabin is usually achieved by designing the parameters of the refrigeration side. For example, the parameter design of a control method for the air conditioning of the vehicle cabin will be significantly related to the parameters of the refrigeration side used, and replacing the parameters of the refrigeration side will cause the control method previously used for air conditioning to fail). Therefore, this inability to decouple the control method for the air conditioning of the vehicle cabin and the control method for the adjustment of the refrigeration side separately increases the design cost of the control method and the effect is not satisfactory.

[0006] Therefore, a thermal comfort controller for a vehicle cabin is needed, which can use a more accurate and effective control process to achieve thermal comfort control to ensure control performance and control accuracy. Further, it is expected that the thermal comfort controller can achieve automatic control of the thermal comfort of the vehicle cabin to reduce the dependence of the control process on the driver. Summary of the invention

[0007] According to one aspect of the present disclosure, the present disclosure relates to a thermal comfort control method for a vehicle cabin, wherein it includes at least the following steps: estimating current control point thermal comfort parameters Pactual at one or more control points where thermal comfort control is desired based at least on the cabin average temperature and thermodynamic parameters associated with the vehicle cabin using a current control point thermal comfort parameter Pactual machine learning model, wherein each control point corresponds to an associated area at or near a corresponding part of a driver or other occupant, wherein the control point thermal comfort parameters include at least one or more of the following parameters: temperature, humidity, and wind speed, so as to adjust the operating parameters of the vehicle cabin thermal adjustment device at least partially based on the current control point thermal comfort parameters Pactual to improve the thermal comfort at the control point.

[0008] According to another aspect of the present disclosure, the present disclosure relates to a thermal comfort controller for a vehicle cabin, which can implement the thermal comfort control method as mentioned above, and the thermal comfort controller includes: a storage unit for storing a computer program for implementing the thermal comfort control method; an acquisition unit, which is constructed to acquire various parameters for implementing the thermal comfort control method; and a processing unit, the processing unit including: a vehicle cabin thermal adjustment device startup module, which is constructed to determine the initial operating parameters of the vehicle cabin thermal adjustment device based on the environmental state and / or the vehicle cabin state and start the vehicle cabin thermal adjustment device with the initial operating parameters; a current control point thermal comfort parameter estimation module, which is constructed to estimate the current control point thermal comfort parameters at one or more control points where thermal comfort control is desired based on at least the cabin average temperature and thermodynamic parameters associated with the vehicle cabin using a current control point thermal comfort parameter machine learning model, wherein each control point corresponds to an associated area at or near a corresponding part of the driver or other occupants, wherein the control point thermal comfort parameters include at least one or more of the following parameters: temperature, humidity and wind speed; thermal comfort parameters of the vehicle cabin thermal adjustment device startup module. A comfort value calculation module, which is constructed to estimate the thermal comfort value at the control point based on the thermal comfort value parameters using a thermal comfort value model or both the thermal comfort value model and personalized thermal value data; a target control point thermal comfort parameter determination module, which is constructed to determine the target control point thermal comfort parameter at the control point based at least in part on the thermal comfort value; an operating parameter adjustment module, which is constructed to adjust the operating parameters of the vehicle cabin thermal adjustment device based on the current control point thermal comfort parameter, the target control point thermal comfort parameter, the thermal comfort value, and the solar radiation so that the thermal comfort parameter at the control point is adjusted to or close to the target control point thermal comfort parameter and the thermal comfort value at the adjusted control point is close to or within the thermal comfort range or value; an energy demand calculation module, which is constructed to calculate the sum of the stable part energy demand and the transition part energy demand using an energy model to obtain the overall energy demand for achieving the thermal comfort value at the control point close to or within the thermal comfort value or range; and a heat exchanger temperature calculation module, which is constructed to calculate the temperature of the heat exchanger based on the overall energy demand so that the heat exchanger of the vehicle cabin thermal adjustment device is adjusted to the temperature.

[0009] According to yet another aspect of the present disclosure, the present disclosure relates to a storage medium storing instructions, which, when executed, implement the thermal comfort control method for a vehicle cabin as described above.

[0010] With the aid of the thermal comfort control method, controller and storage medium for the vehicle cabin of the present application, it is possible to realize automatic control of the air condition of the vehicle cabin by the control method of feedforward and feedback PID, and by adopting a combination with machine learning, it is possible to ensure that the air condition of the vehicle cabin is more in line with the comfort of the driver or other members in a way that is more adapted to the needs of the user, while reducing the calculation of various real-time parameters in a complex environment to reduce computing power requirements. In addition, with the aid of the thermal comfort control method, controller and storage medium for the vehicle cabin of the present application, it is possible to achieve a certain degree of decoupling control and control method design between the refrigeration side and the cabin air system. With the aid of the thermal comfort control method, controller and storage medium for the vehicle cabin of the present application, it is possible to reduce the demand for measuring devices and thus reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Other notable features and advantages of the invention emerge from the following non-limiting description provided for illustrative purposes with reference to the following drawings, in which:

[0012] Figure 1a An overall flow chart of a thermal comfort control method for a vehicle cabin according to an embodiment of the present invention is shown;

[0013] Figure 1b shows an overall flow chart of a thermal comfort control method for a vehicle cabin according to another embodiment of the present invention;

[0014] Figure 2 A detailed flow chart showing the steps for estimating the current control point temperature Tactual of the thermal comfort control method for a vehicle cabin according to an embodiment of the present invention;

[0015] Figure 3 A detailed flow chart showing the steps of calculating PMV values ​​and optionally combining personalized PMV data for a thermal comfort control method for a vehicle cabin according to an embodiment of the present invention;

[0016] Figure 4 A detailed flow chart showing the steps of adjusting the wind speed of a blower in a thermal comfort control method for a vehicle cabin according to an embodiment of the present invention;

[0017] Figure 5 A diagram showing an exemplary solar radiation level classification for a thermal comfort control method for a vehicle cabin according to an embodiment of the present invention;

[0018] Figure 6 shows an overall control flow chart of an energy model according to an embodiment of the present invention;

[0019] Figure 7An overall schematic diagram of various parts in an energy model according to an embodiment of the present invention is shown;

[0020] Figure 8 A schematic diagram showing various modules of a thermal comfort controller for a vehicle cabin according to an embodiment of the present invention; and

[0021] Fig. 9 A general schematic diagram of a thermal comfort controller for a vehicle cabin according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0022] The embodiments of the present application will be further described below in conjunction with the accompanying drawings.

[0023] Before further describing the embodiments of the present application, the relevant concepts about PMV will first be described in detail.

[0024] PMV (Predicted Mean Vote), estimated average evaluation, is a comprehensive evaluation index based on the basic equation of human thermal balance and the level of subjective thermal sensation in psychophysiology, taking into account many factors related to human thermal comfort. The PMV index shows the average index of the group's votes for seven levels of thermal sensation (+3 to -3). In other words, the PMV value index from -3 to +3 corresponds to the degree of human body feeling from cold to hot. When PMV = 0, it means that the human body feels thermal neutrality, which is the comfort value.

[0025] Figure 1a FIG. 2 shows an overall flow chart of a thermal comfort control method for a vehicle cabin according to an embodiment of the present invention. Figure 1a As shown, in an embodiment of the present application, a thermal comfort control method for a vehicle cabin is provided, and the control method allows the vehicle cabin thermal adjustment device (as those skilled in the art will understand, the heat source can provide positive heat or negative heat) actuator to automatically react based on the thermal comfort model of the driver or other occupants so that the vehicle cabin thermal adjustment device can keep 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 cabin thermal adjustment devices, such as the most common HVAC (heating, ventilation and air conditioning system), seat temperature adjustment system, etc. In subsequent embodiments of the present application, although the specific process of the thermal comfort control method for the vehicle cabin is described using HVAC as an example, those skilled in the art will understand that this is only exemplary, and those skilled in the art can envision using the thermal comfort control method for the vehicle cabin for the control of other vehicle cabin thermal adjustment devices of the vehicle without departing from the scope of the present application. In an embodiment of the present application, the thermal comfort control method for the vehicle cabin (hereinafter referred to as the control method) includes at least the following steps:

[0026] At step S100, based on the environmental state and / or the vehicle cabin state, determine the initial operating parameters of the vehicle cabin thermal regulation device and start the vehicle cabin thermal regulation device with the determined initial operating parameters. As those skilled in the art will understand, this step S100 is only optional and can be omitted in some cases. For this reason, in Figure 1a and 1b this step is represented by a dashed box. As an alternative to this step, in S100, the initial operating parameters of the vehicle cabin thermal regulation device can be manually determined based on the driver's subjective feeling.

[0027] Taking the HVAC in the vehicle cabin thermal regulation device as an example, in this step S100, use the HVAC initial operating parameter judgment model to determine the initial operating parameters of the HVAC based on the environment and / or the vehicle cabin state and start the HVAC with the determined initial operating parameters. Further, determining the initial operating parameters of the HVAC at least includes determining the damper position of the cabin fan system in the HVAC. Of course, as those skilled in the art can understand, determining the initial operating parameters of the HVAC also includes determining aspects such as the operating temperature of the HVAC, which will not be elaborated additionally here.

[0028] As an example and not by way of limitation, the environmental state and / or the vehicle cabin state is represented as the outside vehicle temperature. Taking the HVAC in the vehicle cabin thermal regulation device as an example, therefore, at step S100, it is possible to determine that the HVAC enters the automatic cooling mode or the automatic heating mode based on the outside vehicle temperature. That is, when the outside vehicle temperature is greater than T1, it is considered that the environmental temperature is on the hot side and the HVAC should enter the cooling mode. When the outside vehicle temperature is less than T1 and greater than T2, it is considered that the environmental temperature is moderate and the HVAC should enter the ventilation mode. When the outside vehicle temperature is less than T2, it is considered that the environmental temperature is on the cold side and the HVAC should enter the heating mode, where T2 < T1.

[0029] As another example and not by way of limitation, the environment and / or the vehicle cabin state is represented as the cabin temperature. Taking the HVAC in the vehicle cabin thermal regulation device as an example, therefore, at step S100, determine the initial operating parameters of the HVAC based on whether the cabin temperature reaches the set threshold. For example, when the cabin temperature is greater than the set threshold T3, it is considered that the environmental temperature is on the hot side 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 environmental temperature is moderate and the HVAC should enter the ventilation mode. When the cabin temperature is less than T4, it is considered that the environmental temperature is on the cold side and the HVAC should enter the heating mode, where T4 < T3. As those skilled in the art of this application can understand, the cabin temperature can be collected at any suitable position in the vehicle cabin. For example, various positions such as the inside of the car door, the armrest of the seat, and the inside of the center console are within the scope of this application without departing from it.

[0030] As another example and not limiting, the environment and / or vehicle cabin state is represented as a relative temperature between the vehicle outside temperature and the cabin temperature (vehicle outside temperature - cabin temperature). In the case of HVAC in the vehicle cabin thermal conditioning device as an example, therefore, at step S100, the initial operating parameters of the HVAC are determined based on whether the relative temperature reaches a set threshold.

[0031] It is conceivable that the above step S100 can be used in combination with a vehicle occupant presence detection device or sensor, so that when any person is detected in the vehicle, S100 is automatically executed. With this combination, it is possible to achieve fully automatic start and control of thermal comfort control without the intervention of the driver or any other occupant. Of course, the step S100 can also be started after the driver or any other occupant manually selects it to avoid unnecessary energy loss as much as possible.

[0032] At step S102, the current control point thermal comfort parameter Pactual at the control point is estimated. As an example and not limitation, at step S102, the current control point thermal comfort parameter Pactual model is used to estimate the current control point thermal comfort parameter Pactual at the control point where thermal comfort control is desired based at least on the cabin average temperature and thermodynamic parameters associated with the vehicle cabin, so as to adjust the operating parameters of the vehicle cabin thermal adjustment device at least in part based on the current control point thermal comfort parameter Pactual to improve the thermal comfort at the control point. As an example, the above thermal comfort parameter model is a thermal comfort parameter machine learning model. As an example and not limitation, at step S102, the current control point temperature Tactual model (in particular, a machine learning model) is used to estimate the current control point temperature Tactual at the control point where thermal comfort control is desired based at least on the cabin average temperature and thermodynamic parameters associated with the vehicle cabin, so as to adjust the operating parameters of the vehicle cabin thermal adjustment device at least in part based on the current control point temperature Tactual to improve the thermal comfort at the control point. It can be imagined that the thermodynamic parameters associated with the vehicle cabin include one or more of the following parameters: the blower wind speed Vblower of the vehicle cabin thermal conditioning device, the solar radiation R, the output temperature of the vehicle cabin thermal conditioning device or the blower outlet air temperature Toutlet, the vehicle outside temperature Tambient, the blower circulation damper position P of the vehicle cabin thermal conditioning device, the blower blowing angle of the vehicle cabin thermal conditioning device It can be assumed that the thermal comfort parameters of the control point include at least one or more of the following parameters: temperature, humidity and wind speed. The detailed process and description of estimating the current control point temperature Tactual in the current control point thermal comfort parameter Pactual will refer to Figure 2In addition, it is defined in the present application that each control point corresponds to an associated area at or near a corresponding part of the driver or other passengers.

[0033] At step S104, the thermal comfort value is calculated. As an example and specifically, at step S104, the thermal comfort value at the control point is estimated based on the thermal comfort value parameters using a thermal comfort value model or both the thermal comfort value model and the personalized thermal value data. Further, at step S104, the thermal comfort value model is a PMV machine learning model, the personalized thermal value data is personalized PMV value data, and the thermal comfort value is a PMV value. Therefore, at step S104, the PMV value at the control point is calculated based on the PMV parameters using the PMV machine learning model or both the PMV model and the personalized PMV value data. Further, the PMV value at the control point is calculated based on the PMV parameters using the PMV machine learning model and optional personalized PMV data. For a detailed process and description of calculating the PMV value at the control point using the PMV machine learning model as an example, please refer to Figure 3 As will be understood by those skilled in the art, the thermal comfort value parameter includes the thermal comfort parameter Pactual of the current control point and may also include one or more of the following: solar irradiance R, humidity RH, blower wind speed Vblower, clothing Cloth, age Age, activity (e.g., sitting / driving, etc.) Act, and gender Gen.

[0034] At step S114, a target control point thermal comfort parameter Ptarget at the control point is determined based at least in part on the thermal comfort value to adjust the operating parameters of the vehicle cabin thermal conditioning device based on the target control point thermal comfort parameter Ptarget so that the thermal comfort parameter at the control point is adjusted to or close to the target control point thermal comfort parameter Ptarget and the thermal comfort value at the control point after adjustment is close to or in the thermal comfort range or value. As an example, step S114 can be interpreted as including sub-steps S106 and S108, wherein at step S106, the target control point thermal comfort parameter Ptarget at the control point is determined based on the thermal comfort value and at step S108, the operating parameters of the vehicle cabin thermal conditioning device are adjusted based on the target control point thermal comfort parameter Ptarget so that the thermal comfort parameter at the control point is adjusted to or close to the target control point thermal comfort parameter Ptarget and the thermal comfort value at the control point after adjustment is close to or in the thermal comfort range or value.

[0035] Taking the temperature in the thermal comfort parameter as an example, therefore, as an example and not limiting, at step S106, the target control point temperature Ttarget at the control point is determined at least based on the thermal comfort value. In the case of the PMV machine learning model, at step S106, the target control point temperature Ttarget at the control point is determined at least based on the PMV value. As an example and further, the target control point temperature Ttarget is determined based on the PMV value to adjust the current control point temperature Tactual at the control point close to the target control point temperature Ttarget via the vehicle cabin thermal adjustment device. As mentioned above, the index of the PMV value from -3 to +3 corresponds to the degree of human body from feeling cold to hot. When PMV = 0, it means that the human body feels thermal neutrality, that is, the comfort value. As an example, when the estimated PMV value is 1 to 3, it means that the current temperature is hot and the current control point temperature Tactual needs to be lowered; if the estimated PMV is -1 to -3, it means that the current temperature is cold and the current control point temperature Tactual needs to be increased. Adjusting the temperature at the control point based on the PMV value is beneficial for quickly achieving thermal comfort control: this is because the current control point temperature Tactual usually estimated may not be close to the temperature that makes the user feel thermally comfortable, so the PMV value at one or more control points is not within the range that makes the user thermally comfortable and deviates greatly. Therefore, when performing thermal comfort control on the cabin, the combination of the real-time calculated PMV value at the one or more control points and the real-time estimated current control point temperature Tactual can significantly speed up the efficiency and speed of temperature control, so that the one or more control points can more quickly reach the temperature range or value (such as the target control point temperature Ttarget) that makes the driver or any other occupant feel comfortable.As an example and not limiting, it can be understood that, for example, when the current control point temperature Tactual of a certain control point estimated by the thermal comfort control method of 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 feeling of control point comfort at this temperature is heat +1, then based on the PMV value, the target control point temperature Ttarget is determined so that the current control point temperature Tactual is reduced by M degrees Celsius to the target control point temperature so as to reduce the current control point temperature Tactual according to the corresponding regulation efficiency so that the current control point temperature Tactual is reduced by M degrees Celsius to the target control point temperature. ual approaches the target control point temperature Ttarget; thereafter, as the current control point temperature Tactual decreases, the calculated PMV value indicates that the subjective feeling of comfort is hot + 0.7, then the target control point temperature Ttarget is determined based on the PMV value so that the current control point temperature Tactual is reduced by N (where N < M, where M and N are both positive numbers) degrees Celsius to the target control point temperature Ttarget so as to reduce the current control point temperature Tactual according to the corresponding adjustment efficiency so that the current control point temperature Tactual is further close to the target control point temperature Ttarget. It can be foreseen that as the current control point temperature Tactual continues to approach the determined target control point temperature Ttarget, the amplitude efficiency of the adjustment of the current control point temperature Tactual will gradually slow down. Based on the above description, a PID control process can be envisioned in which the current control point temperature Tactual is adjusted based on the PMV value at the control point to reach the target control point, wherein the PMV value can be designed as the feedback value of the PID control process so that the control point temperature is appropriately adjusted with different adjustment efficiencies (or resolutions) according to the PMV value. Of course, those skilled in the art can consider any other possible control schemes based on the above enlightenment, but these control schemes, although not listed in detail in this specification, still do not depart from the scope of the present application. As those skilled in the art will understand, the method for adjusting the temperature at the control point to the target control point temperature Ttarget can be implemented in various ways. In the case of taking HVAC in the vehicle cabin thermal conditioning device as an example, for example, by adjusting the air outlet temperature of the blower of HVAC, etc., it is not limited here. That is, any technical means that can achieve the target control point temperature Ttarget at the control point does not depart from the scope of the present application. Although step S106 is described above using the PMV value as an example, those skilled in the art will understand that the PMV value can be replaced by the thermal comfort value output of any corresponding thermal comfort value model. Although the control point thermal comfort parameter is described above using temperature as an example, those skilled in the art will understand that the parameter can also be at least wind speed and / or humidity.

[0036] At step S108, the operating parameters of the vehicle cabin thermal adjustment device are adjusted based on the target control point thermal comfort parameter Ptarget so that the thermal comfort parameter at the control point is adjusted to or close to the target control point thermal comfort parameter Ptarget and the thermal comfort value at the control point after adjustment is close to or in the thermal comfort range or value. In the case where the thermal comfort value is PMV, at step S108, the operating parameters of the vehicle cabin thermal adjustment device are adjusted so that the thermal comfort parameter at the control point is adjusted to or close to the target control point thermal comfort parameter Ptarget and the PMV value at the control point after adjustment is close to or in the thermal comfort range or value. As an example and further, based on the current control point thermal comfort parameter Pactual, the target control point thermal comfort parameter Ptarget, the thermal comfort value (PMV value in the case of the PMV model), and the solar radiation, the operating parameters of the vehicle cabin thermal adjustment device are adjusted so that the thermal comfort parameter at the control point is adjusted to or close to the corresponding target control point thermal comfort parameter Ptarget and the PMV value at the control point after adjustment is close to or in the corresponding thermal comfort value or range. As an example and further, the operating parameters of the vehicle cabin thermal conditioning device are iteratively adjusted based on the current control point thermal comfort parameter Pactual, the target control point thermal comfort parameter Ptarget, the thermal comfort value (PMV value in the case of the PMV model), and the solar radiation, so that the thermal comfort parameter at the control point is adjusted to or close to the corresponding target control point thermal comfort parameter Ptarget (for example, the target control point temperature Ttarget) and the PMV value at the adjusted control point is close to or within the corresponding thermal comfort value or range. As will be understood by those skilled in the art, the operating parameters of the vehicle cabin thermal conditioning device include one or more of the following: the blower wind speed of the vehicle cabin thermal conditioning device, the output temperature of the vehicle cabin thermal conditioning device or the blower outlet air temperature, the blower blowing angle of the vehicle cabin thermal conditioning device, and the blower circulation damper position of the vehicle cabin thermal conditioning device. As an example and further, in the case where the HVAC in the vehicle cabin thermal conditioning device is taken as an example and the thermal comfort value is the PMV value, at step S108, the blower wind speed Vblower, the blower outlet air temperature Toutlet, the blower circulation damper position P, the blower blowing angle of the HVAC in the vehicle cabin thermal conditioning device are adjusted based on the current control point thermal comfort parameter Pactual, the target control point thermal comfort parameter Ptarget, the PMV value, and the solar radiation R. One or more of the blower air humidity RHoutlet so that the thermal comfort parameter at the control point is adjusted to or close to the corresponding target control point thermal comfort parameter Ptarget and the PMV value at the control point after adjustment is close to or in the thermal comfort range or value. As an example and further, taking the HVAC in the vehicle cabin thermal conditioning device as an example and the temperature in the thermal comfort parameter as an example, at step S108, based on the current control point temperature Tactual, the target control point temperature Ttarget, the PMV value, and the solar radiation, the blower wind speed, the blower outlet air temperature, the blower circulation damper position, the blower blowing angle, and the blower air humidity of the vehicle cabin thermal conditioning device are iteratively adjusted so that the temperature at the control point is adjusted to or close to the corresponding target control point temperature Ttarget and the PMV value at the control point is finally in the thermal comfort value or range. As an example, taking the HVAC in the vehicle cabin thermal conditioning device as an example and the thermal comfort value is the PMV value, the detailed process of adjusting the blower wind speed as an example will be referred to. Figure 4 Although this step is described as an example for the blower speed of the HVAC in the vehicle cabin thermal conditioning device, the adjustment process for the circulating damper position and the blowing angle is similar. Of course, for other components in the vehicle cabin thermal conditioning device, such as the seat thermal conditioning system, corresponding processes can be adopted to adjust the operating parameters, which will not be described in detail here.

[0037] Figure 1b The overall flow chart of the thermal comfort control method for a vehicle cabin according to another embodiment of the present invention is shown. Figure 1a The difference is that this embodiment further includes steps S110 and S112, and these two steps involve further control process of the refrigeration side based on the above steps S100-S108.

[0038] At step S110, the total energy demand Qtotal is calculated. As an example and not a limitation, at step S110, the sum of the stable energy demand Qs and the transitional energy demand Qt is calculated based on the energy model to obtain the total energy demand Qtotal that achieves the thermal comfort value (for example, PMV value) at the control point close to or within the thermal comfort value or range. The detailed process and description of calculating the energy demand using the energy model will be referred to. Figure 6 and 7 The explanation is given and no further elaboration is given here.

[0039] At step S112, the heat exchanger temperature is calculated. As an example and not limiting, at step S112, the heat exchanger temperature is calculated using the total energy demand Qtotal. The details of this step will be elaborated in the description of calculating the energy demand below and will not be further elaborated here.

[0040] As will be understood by those skilled in the art, taking the HVAC in the vehicle cabin thermal conditioning device as an example, steps S110 and S112 are mainly manifested as quantitative calculations between energy demand and heat exchange, which are used to control the operation of the refrigeration side (refrigerator), while the main goal of steps S100-S108 is to focus on the operational control of the HVAC, especially the operational control of the HVAC blower. Therefore, the above thermal comfort control method ensures to a certain extent that the thermal control of the refrigeration side and the cabin air system (that is, the cabin fan system) can be decoupled, that is, they can be developed, designed and applied independently of each other and the two aspects of control can be easily combined to achieve the ultimate thermal comfort control of the vehicle cabin. More generally, for the vehicle cabin thermal conditioning device, steps S110 and S112 are mainly manifested as quantitative calculations between energy demand and heat exchange, which are used to control the operation of the refrigeration side (refrigerator) of the vehicle cabin thermal conditioning device, while the main goal of steps S100-S108 focuses on the operational control of the output side of the vehicle cabin thermal conditioning device (such as the blower of HVAC, the heat output part of the seat temperature control system, etc.). Therefore, the above thermal comfort control method ensures to a certain extent that the thermal control of the refrigeration side and the heat output side of the vehicle cabin thermal conditioning device can be decoupled, that is, they must be developed, designed and applied independently of each other and the two aspects of control can be easily combined to achieve the final thermal comfort control of the control point of the vehicle cabin.

[0041] The above steps will be explained in further detail below with reference to other drawings.

[0042] Figure 2 A detailed flow chart of step S102 of the thermal comfort control method for a vehicle cabin according to an embodiment of the present invention is shown, taking estimating the current control point temperature TactualTestimate as an example.

[0043] First, as will be understood by those skilled in the art, as described above, each control point corresponds to an associated area at or near a corresponding part of the driver or other occupants, so the control point temperature can represent the temperature of any position (i.e., control point) in the cabin related to a human body part, for example, in order to better control the comfort of the driver or any other occupant, the control point temperature can represent the face temperature, chest temperature, arm temperature, hand temperature, body temperature, foot temperature, back temperature, hip temperature, lip temperature, etc. Further, the current control point temperature Tactual is defined as the actual temperature at any position (control point) related to a human body part in the vehicle cabin. For example, for the hand temperature, there is usually an appropriate temperature (and / or temperature range) that makes, for example, the driver's hand neither sweat nor freeze, and the appropriate temperature is defined as the current control point temperature Tactual of the hand, and the definition and explanation of the current control point temperature Tactual of other parts are similar. In the embodiment of the present application, unless otherwise specified, the thermal comfort control method for the vehicle cabin of the present application refers only to the thermal comfort control at a single control point.

[0044] Further, as mentioned above, at step S102, the current control point temperature Tactual model (particularly, a machine learning model) is used to estimate the current control point temperature Tactual 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, so as to adjust the operating parameters of the vehicle cabin thermal adjustment device based at least in part on the current control point temperature Tactual to improve the thermal comfort at the control point. In the following, a possible model example of how to calculate the average cabin temperature is first given. Of course, for those skilled in the art, the average cabin temperature may not be obtained by calculation using a predetermined model as described below, but may also be obtained by measuring or by a corresponding number and arrangement of temperature sensors. As an example and not limiting, a stable partial energy demand Qs is calculated based on various heat loads of the vehicle cabin. As defined in the present application, the stable partial energy demand Qs represents the heat load demand inherent to the vehicle cabin, that is, the energy demand required to maintain the current vehicle cabin. As an example and as known to those skilled in the art, the thermal load of the vehicle cabin (i.e., the inherent thermal load of the vehicle cabin, which of course can be calculated as a positive or negative value to indicate that the vehicle cabin currently needs cooling or heating) can be expressed as:

[0045] Qsteady=Q1+Q2+Q3+Q4+Q5+Q6

[0046] in:

[0047] Q1 is the heat load from the outside air and the cabin shell: Q1 = K*A*(Tcabin-Tambient), where K is the heat transfer parameter and A is the area of ​​the vehicle cabin shell (or in some cases, the contact area between the vehicle cabin and the external environment (such as air)), which includes the doors, roof, windows, floor, fire wall, etc. The K value of the door is 7w / (m 2 *℃), the K value of the roof is 4.3w / (m 2 *℃), the K value of the car window is 6.4w / (m 2 *℃), the K value of the fire wall is 4.9w / (m 2 *℃). Tcabin represents the average cabin temperature of the vehicle cabin. Tambient represents the outside temperature of the vehicle cabin and can be obtained, for example, by one or more sensors disposed outside the vehicle cabin (the outside temperature can also represent the average temperature measured by one or more temperature sensors);

[0048] Q2 is the solar radiation heat load, which represents the heat load caused by solar heat radiation to the vehicle cabin shell;

[0049] Q3 is the human body heat load, which represents the heat load on the cabin environment of the vehicle cabin due to the human body;

[0050] Q4 is the mechanical and instrument heat load, which represents the heat load on the cabin environment of the vehicle cabin due to the mechanical movement of the vehicle and the operation of the instrument itself;

[0051] Q5 is other heat loads in the vehicle, which, for example, represents the heat loads of other heat sources inside the vehicle cabin;

[0052] Q6 is the heat load caused by ventilation, specifically, Q6 = qm*Pfresh*(hinlet-houlet), wherein Pfresh is the proportion of fresh air, which is determined, for example, by the circulating damper position P, which can be quantitatively measured by means of a posture sensor arranged at the circulating damper position, or can be directly obtained by calling and querying the control signal of the servo motor that controls the circulating damper position; hinlet and houlet represent the enthalpy of the air at the inlet and outlet of the blower, which can be obtained by table lookup from the air temperature T and humidity RH (outlet air temperature Toulet and RHoutput and outlet air temperature Tinlet and RHinlet) at these two places (therefore obtained by temperature and humidity sensors of the air respectively arranged at the inlet and outlet of the blower); qm is the air flow rate flowing through the blower, which can be obtained, for example, by table lookup based on the terminal voltage of the blower (and / or the blower wind speed Vblower). As will be appreciated by those skilled in the art, tables for looking up enthalpy values ​​corresponding to temperature T and humidity RH and air flow corresponding to blower terminal voltage (and / or blower wind speed) can be obtained through prior art and can be pre-recorded.

[0053] The above-mentioned structure is only exemplary, and those skilled in the art can add more components related to the stable partial energy demand Qs or further simplify the calculation components according to the requirements of calculation accuracy.

[0054] Further, according to the general design of the vehicle, the heat compensation for the vehicle cabin is basically provided by the vehicle cabin thermal conditioning device. In the case where the vehicle cabin thermal conditioning device is only HVAC, the heat load provided by the HVAC as a whole is expressed as Qp=qm*(hinlet-houlet), where qm, hinlet, and houlet have the same meanings as described above with respect to Q6. Of course, those skilled in the art will understand that in the case where the vehicle cabin thermal conditioning device also includes a seat temperature control system, the Qp may additionally include the heat load of other vehicle cabin thermal conditioning devices such as the seat temperature control system.

[0055] According to the energy balance principle, if it is desired to keep a certain temperature state of the vehicle cabin unchanged, then when all the energy is provided by the vehicle cabin thermal conditioning device, Qp = Qs. Through this equation, the only unknown quantity Tcabin in the above equation can be solved, that is, the average cabin temperature.

[0056] Then, in the case of HVAC in the vehicle cabin thermal conditioning device, the current control point temperature Tactual is estimated with the help of a model (especially a machine learning model) based on the obtained cabin average temperature and thermodynamic parameters associated with the vehicle cabin, wherein the thermodynamic parameters associated with the vehicle cabin include one or more of the following parameters: blower wind speed Vblower (and / or blower terminal voltage), solar radiation, blower inlet air temperature, blower outlet air temperature, vehicle outside temperature, blower circulation damper position, blower blowing angle, etc. Of course, for other vehicle cabin thermal conditioning devices including seat temperature control systems, the control process is similar, but the estimation of the current control point temperature Tactual based on a model (especially a machine learning model) may also involve parameters related to, for example, the output temperature of the heat output part of the seat temperature control system, which will not be elaborated here.

[0057] With respect to the machine learning model, in the case of HVAC in the vehicle cabin thermal conditioning device, the machine learning model for estimating the current control point temperature Tactual is first trained using a machine learning training database including the above-mentioned related variables (and / or inputs) and corresponding results so that the machine learning model can output the actual temperature at the control point that is desired to be adjusted. Of course, it is conceivable that in a training environment, more sensors may be used to monitor temperature or other related signals, but no matter how the sensors are involved or how large the training database is, in the case of HVAC in the vehicle cabin thermal conditioning device, the above-mentioned sensor data or training database includes at least one or more of the following parameters: actual cabin average temperature and current control point temperature Tactual, blower wind speed Vblower (and / or blower terminal voltage), solar radiation R, blower outlet air temperature Toutlet, outside temperature Tambient, circulating damper position P, blowing angle This database is then used to train a machine learning model that estimates the current control point temperature Tactual.

[0058] For the thermal comfort control method of the vehicle cabin, the current control point thermal comfort parameter Pactual may not be directly obtained through measurement. Therefore, the current control point thermal comfort parameter Pactual is estimated based on the cabin average temperature and thermodynamic parameters associated with the vehicle cabin through the current control point temperature Tactual model (especially, the machine learning model), which can significantly save the cost of specific devices required for direct measurement of the current control point thermal comfort parameter Pactual (such as temperature). In addition, with the help of this current control point thermal comfort parameter Pactual model, the current control point thermal comfort parameter Pactual of different control points in the driver or other occupants' body can be calculated with a low-cost solution, so as to adjust the thermal comfort parameters of different control points to achieve precise comfort control, improve user experience and save energy.

[0059] Figure 3 A detailed flow chart of the steps of calculating the PMV value and optionally combining personalized PMV data for the thermal comfort control method for the vehicle cabin according to an embodiment of the present invention is shown. As mentioned above, the steps are also described for the case of HVAC in the vehicle cabin thermal conditioning device. For other thermal conditioning systems in the vehicle cabin thermal conditioning device, such as seat thermal conditioning systems, similar steps related to the calculation of PMV values ​​of other thermal conditioning systems such as seat thermal conditioning systems can be taken. Figure 3 The dashed lines in represent optional processes related to personalizing PMV data.

[0060] As mentioned above, at step S104, the PMV value is estimated based on the PMV parameters using the PMV machine learning model and optional personalized PMV data. The specific definition of the PMV value will not be further elaborated here, please refer to the above description.

[0061] First, personalized PMV data is defined here. As those skilled in the art will understand, for different people, although the thermal comfort temperature of a certain control point is generally maintained in a relatively stable temperature range, for some individuals, the thermal comfort temperature range applicable to most people is not applicable. For example, for some heavy-weight people, the thermal comfort temperature they require is significantly lower than that of most people of uniform weight. In this case, there are a few people whose thermal comfort temperature range is different from that of most people. In order to ensure the universality of the thermal comfort control method for the vehicle cabin, the methods adopted in this application are designed based on the general conditions of most people. Therefore, for a few people, the above method is not applicable, and therefore it is necessary to supplement personalized PMV data or feedback to correct the PMV obtained by the above general method to adapt to specific people. Of course, as understood by those skilled in the art, the personalized PMV data can be manually supplemented or input by the personnel involved for appropriate direct correction (that is, direct correction or modification of the PMV obtained by the general method), or the PMV machine learning model in the above method can be supplemented with actual machine training through the feedback of the personnel involved on the PMV obtained by the general method, so that the supplemented PMV machine learning model can be adapted to the specific population (that is, the PMV machine learning model is independently trained based on the data fed back by the specific population to obtain a dedicated PMV machine learning model that can generate PMV values ​​that meet their thermal comfort requirements). Of course, it is also possible to collect various information such as the corresponding human body and age from a small number of people in multiple similar situations and conduct special machine learning training on them, so that users can directly adopt the control method that matches their own situation by choice when using the thermal comfort control method of the present application.

[0062] Then, refer to Figure 3The process of estimating the PMV value based on the PMV parameters using the PMV machine learning model and optional personalized PMV data is described in detail. As will be understood, the PMV parameters include the current control point temperature Tactual and may also include one or more of the following: solar irradiance R, humidity RH, blower wind speed Vblower, clothing Cloth, age Age, activity Act (e.g., sitting / driving, etc.), and gender Gen. Therefore, the machine learning model uses a PMV machine learning training database DB including the current control point temperature Tactual, solar irradiance R, humidity RH, blower wind speed Vblower, clothing Cloth, age Age, activity Act (e.g., sitting / driving, etc.), gender Gen, and the PMV machine learning training database DB for determining the PMV value corresponding to the above parameters to train the PMV machine learning model. As will be understood, the PMV machine learning training database may include a basic PMV machine learning training database DBbase collected by a laboratory or a standard vehicle and a user feedback PMV machine learning training database DBfb composed of data of user feedback USerfb. Of course, in order to avoid the user feedback PMV machine learning training database DBfb from rudely interfering with the trained PMV machine learning model, causing the PMV machine learning model to be interfered with by certain specific population types so that the PMV machine learning model can output a PMV value within a generally reasonable range, the maximum proportion of the user feedback PMV machine learning training database DBfb can be limited, for example, not more than fifty percent.

[0063] Advantageously, for the user feedback PMV machine learning training database DBfb, the relevant data of the PMV values ​​selected by many users under different conditions can be collected and recorded online and added to the PMV machine learning training database DB in real time and / or non-real time for subsequent training of the PMV machine learning model. It will be understood that the data collected for the relevant data of the PMV values ​​selected by many users under different conditions does not necessarily have to be the vehicle that adopts the thermal comfort control method of the present application, it can be a vehicle that adopts other PMV value thermal comfort control methods. Of course, any method of collecting the various parameters mentioned above is possible, as long as the collected data can be fully included for training the PMV machine learning model such as the present application.

[0064] Of course, as mentioned above, for a certain minority of people, the above-mentioned PMV machine learning model can be additionally trained separately by using the feedback data of these minority of people to achieve matching, or the PMV value obtained by using the above-mentioned PMV machine learning model can be corrected by using personalized PMV data. For example, for a person whose PMV value comfort temperature is 1 degree Celsius higher than the usual PMV value comfort temperature, the 1 degree Celsius can be automatically added to the usual PMV value to achieve correction. Those skilled in the art can conceive of any other method for customized correction of the PMV value obtained by the PMV machine learning model for an individual, and these methods are also included in the scope of the present application, even if not described in detail herein.

[0065] Although the above text describes the use of personalized PMV data to correct the PMV value obtained by the PMV machine learning model using a few people as an example, in fact, this personalized PMV data can be freely adopted based on each person’s different subjective feelings about the comfortable temperature. Of course, if the PMV value obtained by the PMV machine learning model happens to match the individual’s thermal comfort temperature, no additional personalized PMV data is required. Although the PMV value is described above in terms of thermal comfort temperature, in fact, a similar process or thermal comfort control method can also be used for other human feelings in PMV.

[0066] Further, the personalized PMV data can be generated, for example, based on the user's feedback input via a personalized PMV conversion model. As an example, if the user does not feel comfortable with a certain PMV comfort temperature and adjusts the panel temperature (if possible), the PMV conversion model compares the adjusted panel temperature with the current control point temperature Tactual, and then adjusts the current comfortable PMV value based on the difference between the panel temperature and the current control point temperature Tactual (for example, a corresponding adjustment table between the difference between the panel temperature and the current control point temperature Tactual and the PMV adjustment change can be queried). Or when the user only indicates "cold" or "hot" for the temperature range in which the comfortable PMV value is located (for example, through voice input), the current comfortable PMV value can be adjusted according to any appropriate method (for example, isometric traversal or dichotomy, etc.).

[0067] Of course, ID codes can be set for user feedback data of different users, so that the vehicle can train the PMV machine learning model differently according to different IDs and the user can directly obtain the corresponding trained PMV value by inputting the ID. Furthermore, it can be imagined that the PMV machine learning model trained for a specific person with the help of personalized PMV data is stored in a cloud storage platform or server, so that the user can directly download the trained PMV machine learning model suitable for the specific person from the cloud storage platform or server by entering his / her person-specific ID code, so that the specific person can still obtain the PMV machine learning model adapted to him / her when changing the vehicle. That is, the PMV machine learning model trained based on personalized PMV data is constructed to be downloaded from the cloud storage platform or server to the vehicle based on the user's specific ID code to execute the thermal comfort control method so that the user can change the vehicle freely.

[0068] Figure 4 A detailed flowchart of the steps of adjusting the blower wind speed Vblower of the vehicle cabin thermal conditioning device based on the target control point temperature Ttarget, the current control point temperature Tactual, the PMV value and the solar radiation of the thermal comfort control method for the vehicle cabin according to an embodiment of the present invention is shown. In the case of the HVAC in the vehicle cabin thermal conditioning device as an example, step S108 adjusts the blower wind speed Vblower based on the current control point temperature Tactual, the target control point temperature Ttarget, the PMV value and the solar radiation R. As an example or preferably, the blower wind speed adopts feedforward control and feedback control to obtain the feedforward blower wind speed FF and the feedback blower wind speed FB respectively. Specifically, first, the feedforward blower wind speed FF is determined based on the current target control point temperature Ttarget, the current control point temperature Tactual and the solar radiation. As an example, the relationship between the blower wind speed levels based on the difference between the target control point temperature Ttarget and the current control point temperature Tactual and the different levels of solar radiation is shown as follows (Table 1).

[0069] Table 1

[0070]

[0071] It should be made clear here that the above relationship table is only exemplary, and ordinary technicians in this field can design different blower wind speed levels according to different temperature differences and different solar irradiances. In the above relationship table, the higher the solar irradiance level, the stronger the solar irradiance; the higher the blower wind speed level, the higher the blower wind speed. Based on the above relationship table, it can be roughly determined that for the target control point temperature Ttarget and the current control point temperature Tactual exceeding a certain difference, regardless of the solar irradiance, the maximum wind speed is directly adopted in order to reach the target control point temperature Ttarget as soon as possible. Further, for the current target control point temperature Ttarget and the current control point temperature Tactual with a small range of difference, different blower wind speeds can be used for different solar irradiances. Since the determination of the blower wind speed does not involve any feedback control, but only involves the difference between the target control point temperature Ttarget and the current current control point temperature Tactual and the solar irradiance, the blower wind speed obtained by querying the above relationship table is called the feedforward blower wind speed FF. Further, regarding the classification of different solar irradiance levels, for example, see Figure 5 , which is 200w / m 2 Different solar irradiance levels are divided for the step length. Of course, this division is only exemplary and those skilled in the art can make various modifications.

[0072] Then, the description is given of obtaining the feedback blower wind speed FB based on the current PMV value. In fact, in this step, it is more accurately described as obtaining the feedback blower wind speed FB based on the current PMV value and the target PMV value. However, for conventional situations, the target PMV value usually pursued is 0. Of course, the target PMV value can represent a certain range such as [-0.5, +0.5] or any determined value within the range or any subset of the range. In the case where the target PMV value is a determined value, the target PMV value can be considered to be a constant value without additional elaboration. The following describes an example of how to obtain the feedback blower wind speed FB based on the current PMV value. Here, the PID feedback control law is used as an example for description: when the thermal comfort control method for the vehicle cabin as in the present application is running, the current PMV at any moment can be used to adjust the PMV value of the control point (for example, the design current control point temperature Tactual) at a certain blower wind speed, and the PMV value of the control point is closer to the target PMV value (for example, the current control point temperature Tactual is closer to the target control point temperature Ttarget) due to the aforementioned adjustment, so that the PMV value at the next moment changes, and this PMV value change is naturally feedback controlled to further adjust the PMV value with another blower wind speed, thus constituting PID feedback control. Of course, the above-mentioned PID feedback control is only exemplary, and those skilled in the art can adopt other types of feedback control to obtain the feedback blower wind speed FB based on the current PMV value and the target PMV value according to their needs, such as any known or currently unknown but later known possible feedback control such as fuzzy control.

[0073] Although the thermal comfort control method of the present application is described above using temperature as an example, the process is similar for other aspects of thermal comfort parameters and will not be further elaborated here.

[0074] Figure 6 The overall control flow chart of the energy model according to an embodiment of the present invention is shown and Figure 7 1 shows a schematic diagram of the change of the transition part energy demand Qt according to an embodiment of the present invention. As mentioned above, at step S110, the energy demand is calculated. As an example and not limiting, at step S110, the energy demand Qs of the stable part and the energy demand Qt of the transition part are calculated using the energy model. The specific process will be referred to in the Figure 6 and Figure 7 Give a description.

[0075] As mentioned above, the stable part of the energy demand Qs represents the inherent heat load demand of the vehicle cabin, and in contrast, the transitional part of the energy demand Qt represents the energy transition change demand Qt from a certain vehicle cabin state to another vehicle cabin state. As an example, in the case where the vehicle cabin thermal adjustment device only includes an HVAC, for the process of changing from a certain cabin temperature to the current control point temperature Tactual in the unit of thermal comfort PMV value, it can be determined that for the vehicle cabin, in order to ensure the current state of the vehicle cabin, the energy QP provided by the HVAC as a whole should be at least equal to the inherent heat load Qs of the vehicle cabin = Q1 + Q2 + Q3 + Q4 + Q5 + Q6, where the meanings of Q1-Q6 refer to the definition above. In this case, in order to achieve rapid and smooth adjustment of the temperature of the vehicle cabin, the transitional part of the energy demand Qt is introduced. As Figure 6 As shown, the filtered energy demand Qt indicates the amount of additional energy required to adjust the thermal load of the vehicle cabin at different times. That is, the transitional energy demand Qt actually indicates the non-fixed energy demand provided by the refrigeration side to achieve the speed of thermal load adjustment. Figure 6 It can be determined that the total energy demand Qtotal can be determined as the sum of the transition part energy demand Qt and the stable part energy demand Qs. Based on the overall demand, the temperature of the refrigeration side, such as the temperature of the heat exchanger, can be further calculated. Furthermore, the refrigeration utility or refrigeration temperature (such as the temperature of the heat exchanger) of the refrigeration side is adjusted based on the temperature of the heat exchanger to meet the requirements of the above-mentioned overall demand. As those skilled in the art will understand, in the technical field of HVAC, the temperature of the heat exchanger can be achieved by various known or future-produced appropriate means, such as by adjusting the speed of the compressor and other methods, which are not limited here. That is, any means that can achieve the corresponding temperature of the heat exchanger does not depart from the scope of the present application. Of course, those skilled in the art can imagine that a similar process is used to calculate the heat load demand of other vehicle cabin thermal adjustment devices such as seat temperature adjustment systems.

[0076] like Figure 7As shown, it is assumed that the process of changing from a certain cabin temperature to the current control point temperature Tactual in the unit of thermal comfort PMV value is realized between t1-t4, wherein, at the beginning, due to the large difference with the current control point temperature Tactual, it is expected that the temperature is adjusted as soon as possible. At this time, a sufficiently high transition part energy demand Qt is selected so that the internal heat load of the vehicle cabin can be adjusted by the sum of the transition part energy demand Qt and the stable part energy demand Qs. As an example, the filtered part energy demand can be set by those skilled in the art according to the needs, for example, the transition part energy demand Qt can be a certain proportion or multiple of the stable part energy demand Qs, the transition part energy demand Qt can also be a certain value, and other settings. As the vehicle cabin temperature gradually approaches the current control point temperature Tactual (t2-t3), the transition part energy demand Qt that gradually decreases with the first slope over time can be supplemented to the stable part energy demand Qs to adjust the heat load of the vehicle cabin. As the vehicle cabin temperature gradually approaches the current control point temperature Tactual (t3-t4), the transitional partial energy demand Qt which gradually decreases with the second slope over time can be used to supplement the stable partial energy demand Qs to adjust the thermal load of the vehicle cabin, wherein the absolute value of the second slope is less than the absolute value of the second slope. Of course, Figure 7 The transition energy demand Qt for heating the vehicle cabin is shown in the figure. For the embodiment of cooling the vehicle cabin, the transition energy demand Qt is a negative value and the absolute value of the negative value varies with Figure 7 The example shown in corresponds to .

[0077] As mentioned above, this transitional energy demand Qt that changes at any time to supplement the stable energy demand Qs enables the thermal comfort control method to achieve a vehicle cabin heat load that matches the current control point temperature Tactual in a faster and more stable manner. On the other hand, this calculation based on energy demand only involves the control of the refrigeration side, and will not significantly affect the control of the output of the vehicle cabin thermal conditioning device (such as the HVAC blower, etc.). Therefore, it can achieve separation and decoupling between the refrigeration side and the output side to a certain extent, thereby improving control efficiency and availability.

[0078] Although, in most of the embodiments of the present application, the thermal comfort control method for a vehicle cabin is described using the PMV machine learning model as an example, a person skilled in the art can conceive, based on the above disclosure, that the PMV machine learning model is only exemplary, and the scope of the present application includes any thermal comfort value model that considers thermal comfort values, including those thermal comfort value models that have been developed or will be developed in the future.

[0079] Although a thermal comfort control method for a vehicle cabin is described above, those skilled in the art may conceive of other control methods for simultaneously realizing thermal comfort control for multiple control points based on the above method. As an example, a possible method for simultaneously realizing thermal comfort control for multiple control points based on the above method is briefly described herein. In the case where the thermal comfort value is a PMV value, the main difference between this thermal comfort control method for multiple control points and the above thermal comfort control method is that, at step S104, a machine learning model and optional personalized PMV data are used to estimate the joint PMV value at multiple control points based on PMV parameters. Here, as an example, the joint PMV value can be expressed as a weighted value, an average value, etc. of the corresponding PMV values ​​at multiple control points without any restriction as long as the joint PMV value can appropriately represent the overall thermal comfort at the multiple control points. At step S106, the corresponding target control point temperatures Ttarget at the multiple control points are determined based on the corresponding PMV values ​​at the multiple control points and the combined PMV values; and at step S108, the operating parameters of the vehicle cabin thermal adjustment device are adjusted based on the corresponding current control point temperatures Tactual, the corresponding target control point temperatures Ttarget, the corresponding PMV values, the solar radiation and the combined PMV values ​​of the multiple control points so that the temperatures at the multiple control points are adjusted to or close to the corresponding target control point temperatures Ttarget and the PMV values ​​at the adjusted control points are close to or in the corresponding thermal comfort values ​​or ranges. The other steps are substantially the same. Of course, it is possible to perform the thermal comfort control method for a single control point as described above for each control point, and then perform a certain degree of joint calculation on the target control point temperature Ttarget and the blower wind speed obtained by each individually executed control method as the overall output. Of course, it is also conceivable to use different thermal comfort devices (such as corresponding air outlets, etc.) at different control points and respectively use the above-mentioned thermal comfort control method for a single control point for separate control and other designs. However, those skilled in the art should understand that these assumptions do not deviate from the scope of the present application.

[0080] Although the thermal comfort control method for a vehicle cabin is described above in a certain order, it should be understood by those skilled in the art that this order is not limiting but exemplary. Those skilled in the art will understand that one or more of these steps can be performed in a different order or simultaneously with each other. Those skilled in the art may also understand that one or more of these steps can be omitted or merged into one or more other steps.

[0081] Figure 8A schematic diagram of various modules of a thermal comfort controller 800 for a vehicle cabin according to an embodiment of the present invention is shown. As mentioned above, the thermal comfort controller 800 for a vehicle cabin can implement the thermal comfort control method for a vehicle cabin as mentioned above. Therefore, the thermal comfort controller 800 at least includes: a storage unit 802 for storing a computer program for implementing the above-mentioned thermal comfort control method; an acquisition unit 804, which is constructed to acquire various parameters for implementing the above-mentioned thermal comfort control method; and a processing unit 806, which includes: a vehicle cabin thermal adjustment device start-up module 8060, which is constructed to determine the initial operating parameters of the vehicle cabin thermal adjustment device based on the environmental state and / or the vehicle cabin state and start the vehicle cabin thermal adjustment device with the initial operating parameters; a current control point thermal comfort parameter Pactual estimation module 8061, which is constructed to estimate the current control point thermal comfort parameter Pactual at one or more control points where thermal comfort control is desired based on at least the average cabin temperature and thermodynamic parameters associated with the vehicle cabin using the current control point thermal comfort parameter Pactual machine learning model, wherein each control point corresponds to an associated area at or near a corresponding part of the driver or other occupants, wherein the control point thermal comfort parameter includes at least one or more of the following parameters: temperature, humidity and wind speed; a thermal comfort value calculation module 8062, which is constructed to estimate the thermal comfort value at the control point based on the thermal comfort value parameter using the thermal comfort value model or both the thermal comfort value model and personalized thermal numerical data; a target control point thermal comfort parameter Ptarget determination module 8063, which is constructed to determine the target control point thermal comfort parameter Ptarget at the control point at least partially based on the thermal comfort value; an operating parameter adjustment module 8064, which is constructed to adjust the operating parameters of the vehicle cabin thermal adjustment device based on the current control point thermal comfort parameter Pactual, the target control point thermal comfort parameter Ptarget, the thermal comfort value, and solar radiation. so that the thermal comfort parameter at the control point is adjusted to or close to the target control point thermal comfort parameter Ptarget and the thermal comfort value at the adjusted control point is close to or in the thermal comfort range or value; a total energy demand calculation module 8065, which is constructed to calculate the sum of the stable part energy demand Qs and the transition part energy demand Qt using the energy model to obtain the total energy demand Qtotal; and a heat exchanger temperature calculation module 8066, which is constructed to calculate the heat exchanger temperature on the cooling side using the total energy demand Qtotal to control the temperature of the heat exchanger. As will be understood by those skilled in the art, the thermal comfort controller for multiple control points includes a similar structure as above, but the function of each module will be changed accordingly for the case of multiple control points, for example, refer to the thermal comfort control method for multiple control points above.

[0082] It should be understood that although the above description of the modules that can implement the various steps of the thermal comfort control method for the vehicle cabin described above is for reference, those skilled in the art should be aware that this is merely exemplary and not restrictive, and it is conceivable that a single module can implement multiple steps in the description of the thermal comfort control method for the vehicle cabin or there may be multiple modules that can implement a single step in the description of the thermal comfort control method for the vehicle cabin. Although it is described herein that the thermal comfort controller includes a storage unit, this is not restrictive, and it is conceivable that the storage unit can be separated from the thermal comfort controller to form a distributed structure or can be in various structures such as a cloud platform, a cloud server, etc., which does not deviate from the scope of the present invention.

[0083] Advantageously, the thermal comfort controller for a vehicle cabin within the scope of the present application further comprises a human-machine interface, wherein the human-machine interface is used to receive information input by a user, such as hot and cold information, comfort level information, etc. The human-machine interface is configured to receive user initial information in any form that can be implemented by the user (e.g., voice input, text input, image recognition). As an example and not a limitation, the human-machine interface can be implemented as any hardware or combination thereof that can receive the initial information input by the user, such as a keyboard, a mouse, a touch screen, a joystick, a microphone, etc.

[0084] Advantageously, the thermal comfort controller for a vehicle cabin within the scope of the present application further comprises a display interface, wherein the display interface is used to display the parameters currently acquired by the acquisition unit and the various results calculated related to the above-mentioned thermal comfort control method. Advantageously, the display interface is preferably a screen, such as a liquid crystal display, an organic light emitting diode, etc. However, it can be foreseen that, as an example and not a limitation, the display interface can also be an output device hardware such as a voice broadcast device, a projection device, or a combination thereof.

[0085] More advantageously, the human-machine interface and the display interface in the thermal comfort controller for the vehicle cabin within the scope of the present application can be integrated together. As an example and not limitation, for example, a touch screen can be an example of an integrated human-machine interface and display interface. However, it is foreseeable that other human-machine interfaces including screens can also be integrated with the display interface to achieve the functions of both, such as a combination of a display and a keyboard (or other physical input device), etc.

[0086] Advantageously, the storage unit of the thermal comfort controller for the vehicle cabin within the scope of the present application may include, for example, a memory, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other hardware storage capable of storing data. Further, the storage unit according to the present invention may include software storage such as a database and a cloud storage. Further, the storage unit may include any software program that can also store the method for implementing the present application.

[0087] like Fig. 9 As shown, Fig. 9 The overall structure diagram of the device for assisting disease reasoning according to an embodiment of the present invention is shown, wherein, based on the same inventive concept, the device for assisting disease reasoning includes at least the following units: a processor 901, a memory 902, a communication interface 903 and a bus 904;

[0088] Among them, the processor 901, memory 902, and communication interface 903 communicate with each other through the bus 904; the communication interface 903 is used to realize information interaction communication of the thermal comfort controller for the vehicle cabin and information transmission with other software or hardware; the processor 901 is used to call the computer program in the memory 902, and when the processor executes the program, it implements the thermal comfort method described before in this application.

[0089] For the thermal comfort controller for a vehicle cabin provided in an embodiment of the present invention, its working principle and beneficial effects are similar to the thermal comfort control method for a vehicle cabin provided in the above-mentioned embodiment. For specific contents, please refer to the introduction of the above-mentioned embodiment, and the embodiment of the present invention will not describe it in detail.

[0090] Based on the same inventive concept, another embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described previously in this application is implemented, which will not be repeated here.

[0091] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0092] For the computer-readable storage medium provided in an embodiment of the present invention, the working principle and beneficial effects of the computer program stored thereon are similar to the thermal comfort control method for the vehicle cabin provided in the above-mentioned embodiment. Please refer to the introduction of the above-mentioned embodiment for the specific contents, and the embodiment of the present invention will not describe them in detail.

[0093] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention. Those of ordinary skill in the art may understand and implement them without creative effort.

[0094] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the test report automatic generation method described in each embodiment or some parts of the embodiment.

[0095] It should also be understood that various modifications may be made according to specific requirements. For example, custom hardware may also be used, and / or specific elements may be implemented in hardware, software, firmware, middleware, microcode, hardware description language, or any combination thereof. For example, some or all of the disclosed methods and controllers may be implemented by programming hardware (e.g., programmable logic circuits including field programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs)) in assembly language or hardware programming languages ​​(such as VERILOG, VHDL, C++) using logic and algorithms according to the present disclosure.

[0096] It should also be understood that the foregoing method can be implemented through a server-client model. For example, the client can receive data input by the user and send the data to the server. The client can also receive data input by the user, perform a portion of the processing in the foregoing method, and send the processed data to the server. The server can receive data from the client, and execute the foregoing method or another portion of the foregoing method, and return the execution result to the client. The client can receive the execution result of the method from the server, and present it to the user, for example, through an output device.

[0097] It should also be understood that the components of the thermal comfort controller for a vehicle cabin can be distributed over a network. For example, some processing can be performed using one processor, while other processing can be performed by another processor remote from the one processor. Other components of the thermal comfort controller for a vehicle cabin can also be similarly distributed. In this way, the thermal comfort controller for a vehicle cabin can be interpreted as a distributed computing system that performs processing at multiple locations.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A thermal comfort control method for a vehicle cabin, wherein: The control method comprises at least the following steps: A current control point thermal comfort parameter model is used to estimate current control point thermal comfort parameters at one or more control points where thermal comfort control is desired based at least on an average cabin temperature and thermodynamic parameters associated with the vehicle cabin, wherein each control point corresponds to an associated area at or near a corresponding part of a driver or other occupant, and wherein the control point thermal comfort parameters include at least one or more of the following parameters: temperature, humidity, and wind speed, so as to adjust the operating parameters of the vehicle cabin thermal adjustment device at least partially based on the current control point thermal comfort parameters to improve the thermal comfort at the control point.

2. The thermal comfort control method according to claim 1, wherein: The control method further comprises: Calculate the sum of the stable energy demand and the transition energy demand based on the energy model to obtain the overall energy demand to achieve that the thermal comfort values ​​at the control points are close to or within the thermal comfort value or range; and A temperature of a heat exchanger of the vehicle cabin thermal conditioning device is calculated based on the overall energy demand so that the heat exchanger of the vehicle cabin thermal conditioning device is regulated to the temperature.

3. The thermal comfort control method according to claim 1 or 2, wherein: The control method further comprises: estimating the thermal comfort value at the control point based on thermal comfort value parameters using a thermal comfort value model or both the thermal comfort value model and personalized thermal value data, wherein the thermal comfort value parameters include the thermal comfort parameters of the current control point; and A target control point thermal comfort parameter at the control point is determined at least in part based on the thermal comfort value to adjust the operating parameters of the vehicle cabin thermal adjustment device at least in part based on the target control point thermal comfort parameter so that the thermal comfort parameter at the control point is adjusted to or close to the target control point thermal comfort parameter.

4. The thermal comfort control method according to claim 3, wherein: The thermal comfort value model is a PMV machine learning model, the personalized thermal numerical value data is personalized PMV value data, and the thermal comfort value is a PMV value.

5. The thermal comfort control method according to claim 4, wherein: The operating parameters of the vehicle cabin thermal adjustment device are adjusted based on the thermal comfort parameters of the current control point, the thermal comfort parameters of the target control point, the PMV value, and the solar radiation, so that the PMV value at the control point after adjustment is close to or within the corresponding thermal comfort value or range.

6. The thermal comfort control method according to claim 1 or 2, wherein: The operating parameters of the vehicle cabin thermal conditioning device include one or more of the following: the blower wind speed of the vehicle cabin thermal conditioning device, the output temperature of the vehicle cabin thermal conditioning device or the blower outlet air temperature, the blower blowing angle of the vehicle cabin thermal conditioning device, the blower air humidity and the blower circulation damper position of the vehicle cabin thermal conditioning device.

7. The thermal comfort control method according to claim 1, wherein: Thermodynamic parameters associated with the vehicle cabin include one or more of the following parameters: the blower wind speed of the vehicle cabin thermal conditioning device, the solar radiation, the blower outlet air temperature of the vehicle cabin thermal conditioning device, the vehicle outside temperature, the blower circulation damper position of the vehicle cabin thermal conditioning device, and the blower blowing angle of the vehicle cabin thermal conditioning device.

8. The thermal comfort control method according to claim 1, wherein: The thermal comfort parameters include one or more of the following: solar irradiance, humidity, wind speed, clothing, age, activity and gender.

9. A thermal comfort controller for a vehicle cabin, the thermal comfort controller being capable of implementing the thermal comfort control method according to any one of claims 1 to 8, the thermal comfort controller comprising: A storage unit, used for storing a computer program for implementing the thermal comfort control method according to any one of claims 1 to 8; An acquisition unit, configured to acquire various parameters for implementing the thermal comfort control method according to any one of claims 1 to 8; as well as A processing unit, the processing unit comprising: A vehicle cabin thermal conditioning device start-up module, which is configured to determine initial operating parameters of the vehicle cabin thermal conditioning device based on an environmental state and / or a vehicle cabin state and start the vehicle cabin thermal conditioning device with the initial operating parameters; a current control point thermal comfort parameter estimation module, which is configured to estimate current control point thermal comfort parameters at one or more control points where thermal comfort control is desired based on at least an average cabin temperature and thermodynamic parameters associated with the vehicle cabin using a current control point thermal comfort parameter machine learning model, wherein each control point corresponds to an associated area at or near a corresponding part of a driver or other occupant, wherein the control point thermal comfort parameters include at least one or more of the following parameters: temperature, humidity, and wind speed; a thermal comfort value calculation module, which is configured to estimate the thermal comfort value at the control point based on thermal comfort value parameters using a thermal comfort value model or both the thermal comfort value model and personalized thermal value data; a target control point thermal comfort parameter determination module, configured to determine a target control point thermal comfort parameter at the control point based at least in part on the thermal comfort value; An operating parameter adjustment module, which is configured to adjust the operating parameters of the vehicle cabin thermal adjustment device based on the current control point thermal comfort parameter, the target control point thermal comfort parameter, the thermal comfort value, and the solar radiation so that the thermal comfort parameter at the control point is adjusted to or close to the target control point thermal comfort parameter and the thermal comfort value at the adjusted control point is close to or within a thermal comfort range or value; an energy demand calculation module, configured to calculate the sum of a stable energy demand and a transitional energy demand by using an energy model to obtain an overall energy demand for achieving the thermal comfort value at the control point being close to or within a thermal comfort value or range; and A heat exchanger temperature calculation module is configured to calculate a temperature of a heat exchanger based on the overall energy demand so that the heat exchanger of the vehicle cabin thermal conditioning device is regulated to the temperature.

10. A storage medium storing instructions, when the instructions are executed, the instructions implement R.407719 A thermal comfort control method for a vehicle cabin is provided according to any one of claims 1-8.

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