• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    Method for automatically controlling a heating system (12) of a seat (13) of a vehicle (1), comprising the steps of i) training an algorithm based on at least one manual operation of the heating system (12) by the user (14), where training the algorithm comprises generating a function based on an activation temperature (31) of the heating system (12) and an operating time (45) of the heating system (12); ii) complete the training of the algorithm based on a plurality of manual actions by the user (14); and iii) automatically control the heating system (12) of the seat (13) based on the function generated, in order to reach a situation of autonomous operation of the intensity or power of the heating in the seat (13) of a vehicle (1). (Machine-translation by Google Translate, not legally binding)
    公开号:ES2728150A1
    申请号:ES201830386
    申请日:2018-04-20
    公开日:2019-10-22
    发明作者:Del Cotillo Alberto Díez；Becerra Hernan Ccorimanya；Saavedra Tania Moreno；Roca Mariona Farell；Maldonado Sergio Garcia；Roca Marc Tena；Echave Iñigo Catalan；La Gala Fernandez Marcos De
    申请人:SEAT SA；
    IPC主号:
    专利说明:

    [0001]
    [0002]
    [0003]
    [0004] OBJECT OF THE INVENTION
    [0005]
    [0006] The object of the present patent application is a method for automatically controlling a heating system of a vehicle seat, according to claim 1, as well as the associated system according to claim 15, incorporating both notable innovations and advantages.
    [0007]
    [0008] BACKGROUND OF THE INVENTION
    [0009]
    [0010] The current trend in the automobile sector is to reduce the physical actuators present in the console and in the instrument panel of the vehicle, and to control these functions through the touch screen or interface present in the dashboard. Among these functions would be the control of the temperature of the cabin, and more specifically the temperature of the seats of the occupants.
    [0011]
    [0012] In this regard, it is known from the state of the art, as described in DE102014201545, a control unit for controlling a temperature regulating device, in particular a heating element. The activation of the temperature regulating device is automatic, and is a function of a minimum temperature defined by the user. In addition, its deactivation is also automatic, depending on a maximum temperature defined again by the user.
    [0013]
    [0014] Said invention would allow the user to preselect values through the console screen, or man-machine interface, albeit in a completely manual way. From this document it is observed that it is known to pursue the objective of automatically regulating a heating element located in a seat of a vehicle. Even so, the margins in which the heating element is activated must be defined manually, it is difficult and not intuitive for the user to set and adjust a minimum temperature and a maximum temperature of use of the system, being difficult to adapt to the different needs of the user in terms of the interior temperature of the vehicle.
    [0015]
    [0016] Thus, and in view of the above, it is seen that there is still a need to reach a situation of autonomous operation of regulation of the intensity or power of the heating in the seat of a vehicle occupant. By an autonomous operation it is understood to regulate and anticipate the circumstance in which the user feels heat, reducing before this the intensity or power of the heating passes.
    [0017]
    [0018] DESCRIPTION OF THE INVENTION
    [0019]
    [0020] The present invention consists of a method for automating the operation of seat heating. It is thus understood to activate the heating element, regulate the intensity of the heating element and deactivate said heating element. For this, the present invention is based on a previous phase of learning the behavior of the client regarding the handling of the heating. During this previous phase a set of algorithms are established and a set of parameters are established, which learn from the user's behavior, in order to achieve autonomous operation of the heating system adapted to the particular needs of each user.
    [0021]
    [0022] The elimination of the physical actuators currently arranged to regulate the seat's air conditioning and heating system accentuates the need for autonomous operation of said systems. Instead, the drive will be through the console screen or interface. Predictive and intelligent functions will be increasingly necessary, because it is desired to minimize user interaction and attention.
    [0023]
    [0024] It is additionally object of the present invention not to increase the technical complexity of current heating systems, avoiding incorporating new sensors and / or actuators, such as a temperature sensor from the outer surface of the seat or contact surface with the user.
    [0025]
    [0026] Specifically, in the present invention, the user seeks to establish an algorithm that allows predicting the behavior of said particular user by means of daily learning of the handling of seats in the seats.
    [0027] To achieve this objective, an algorithm must be defined, which without added hardware can predict the operation of seat heating for a given user. This is possible since it has been observed, through a series of measurements and tests, a relationship between the time of use of the heating and the initial temperature of the blanket or heating element, which allows to predict the surface temperature of the seat, without the need for a sensor that measures it directly.
    [0028]
    [0029] Point out that the system of the present invention works in continuous learning. Thus, after a first learning phase, in which the system operates in manual mode and is operated by the user, in order to take measurements and meet the particular needs of said user, an algorithm is defined, so that the System can work automatically. However, during said automatic operation mode, in the event that the user manually anticipates the deactivation of the heating, or the reduction and / or extension of an intensity of the heating element, the system saves and learns from said modifications. The manual changes saved are used to periodically review whether the algorithm should be modified and adapted to the user's new needs.
    [0030]
    [0031] Specifically and in the case of a heating that has different predefined intensity levels, the present invention seeks to predict the moment in which the user would make the change of the heating power or intensity, for example of a level 3 (intensity level maximum) at level 2 (medium intensity level), from level 2 to level 1 (low intensity level), and from level 1 to level 0 (heating element deactivation). Thus, the system of the present invention establishes a plurality of algorithms and variables that allow predicting the moment in which the user would make a change in intensity between the intensity levels mentioned above.
    [0032]
    [0033] On the other hand, specify that the seat heating or heating device has the function of heating the seats of the motor vehicle, and in this way increase the comfort of the user in his driving work. Said seat heater, according to a certain embodiment, comprises a series of heating cables, which are incorporated in the cover of a seat for the motor vehicle. It is electrically operated and guarantees a pleasant and comfortable driving sensation in terms of temperature. The heating elements are composed of flexible electric cables that pass through the seat cover, seat cheeks and backrest coil. Heat is produced by means of resistance, and through the Joule effect. Depending on the vehicle manufacturer, the heating capacity can be set in several stages or intensity levels. The temperature sensor may preferably be of the NTC type, that is, it has a negative temperature coefficient. Said temperature sensor allows to know the temperature of the resistors. It should be noted that it is not a sensor arranged in the cover, which allows the temperature of the contact surface to be measured with the seat occupant.
    [0034]
    [0035] More specifically, the method of automatically controlling a heating system of a vehicle seat comprises the steps of:
    [0036] i) training an algorithm based on at least one manual operation of the heating system by the user, where training the algorithm comprises generating a function based on a heating system activation temperature and a heating system operating time ;
    [0037] ii) complete the training of the algorithm based on a plurality of manual actions by the user; Y
    [0038] iii) automatically control the seat heating system based on the function generated.
    [0039]
    [0040] In said heating system you can have several levels of power or caloric intensity, or a single level. The method of the present invention is not limited to a specific configuration of the heating system.
    [0041]
    [0042] Specify that the activation temperature must be understood as the determined temperature when the heating system is activated manually. This temperature is obtained by means of a sensor arranged in the heating element of the heating system. Said sensor does not necessarily have to be an additional sensor, preferably the sensor that measures the temperature of the resistance of the heating element.
    [0043]
    [0044] On the other hand, by operation time it is to be understood as the time interval from the activation of the heating system until another manual instruction is determined. The instruction may be a decrease in the intensity or power level of the heating, or a deactivation of the heating system.
    [0045] Advantageously, the method of the present invention comprises an additional step of identifying the user sitting in the seat, where the step of training the algorithm is additionally based on the identified user and where the stage of automatically controlling the seat heating system It is additionally based on the identified user. In this way, the system is able to adapt to the specific needs of each user of the vehicle, generating particular functions for each of the users of the vehicle.
    [0046]
    [0047] More specifically, the function generated is suitable for calculating an estimated time, where the estimated time is equivalent to the automatic activation time of the heating system, where the estimated time is based on an initial temperature and the function generated. In this way the system is able to automatically regulate the period of time between the activation of the heating system until the next instruction to be executed.
    [0048]
    [0049] Specify that the estimated time is the time calculated based on the function. That is, the period of time that, according to the algorithm, it is estimated that the user will want to keep the heating system activated. It can be the time interval of keeping a certain heating level activated, as well as the time interval that has elapsed until the heating system is turned off.
    [0050]
    [0051] On the other hand, initial temperature means the temperature determined at the moment when the heating system is automatically activated. Said temperature is obtained by means of a sensor arranged in the heating element of the heating system, not being, as already mentioned, an additional sensor.
    [0052]
    [0053] More specifically, the step of training the algorithm comprises the steps of determining a manual ignition of the heating system by the user, obtaining the activation temperature of the heating system, determining a manual instruction in the heating system by the user, calculate the operating time of the heating system from the determined manual ignition to the determined manual instruction, memorize the activation temperature obtained and the calculated operating time, and modify the function based on the activation temperature obtained and the operating time calculated. In this way, the algorithm learns the activation times of the heating system, depending on the operation of the vehicle user.
    [0054] A manual instruction means any manipulation of the heating system by the user. Said manual instruction can be both to turn off the heating system and to decrease the level of intensity or heating power.
    [0055]
    [0056] Therefore, in an embodiment where the heating system comprises a single intensity level, the manual instruction amounts to a deactivation of the heating system by the user.
    [0057]
    [0058] In contrast, in an embodiment where the heating system comprises a plurality of intensity levels, there will be an algorithm for each of the levels. Thus, in an initial phase, the manual instruction is equivalent to a decrease in the intensity of the heating system, so that the operating time of the heating system is comprised between manual ignition until the determined intensity decrease. Subsequently, in an intermediate phase, the manual instruction is equivalent again to a decrease in the intensity of the heating system, so that the operating time of the heating system is between the activation of an intermediate intensity until the decrease of said determined intensity . Finally, the manual instruction is equivalent to the deactivation of the heating system, so that the operating time of the heating system is between the activation of a low intensity until the deactivation of the determined heating system.
    [0059]
    [0060] In a preferred embodiment of the invention, the function is a regression line. Said regression line is defined by the approximation of a plurality of measurements, where each measurement comprises an activation temperature and an operating time. In the control phase of the heating system automatically, the regression line will allow obtaining an estimated time value based solely on a value of the initial temperature.
    [0061]
    [0062] More particularly, the step of training the algorithm comprises an additional step of discarding the activation temperature obtained and the calculated operating time if the calculated operating time is greater than a predefined maximum value, or if the operating time is less than a predefined minimum value, where the predefined maximum value is based on the estimated time and where the minimum predefined value is based on the estimated time. The objective of discarding the values of the activation temperature obtained and / or of the operation time lies in eliminating measures far from the average values obtained. Such remote measures would significantly modify the determined function, and may make it not adapt to the user's needs. As an example, in the event that the operating time is outside the usual values, for example, forgetting to deactivate the heating by the user, this measure would lead to distortion and errors in the training of the algorithm. .
    [0063]
    [0064] On the other hand, the step of finishing the training of the algorithm comprises determining a dispersion in the plurality of activation temperatures of the heating system determined during the step of training the algorithm. Dispersion means a sufficiently wide range of determined activation temperatures. This does not mean having a high number of activation temperatures, for example 100. It means that the system has been trained in a range of different temperatures within the range of possible activation temperatures. For example, an ideal training would be 30 measurements, with a difference of 1 degree between the plurality of activation temperatures. Thus, avoid unnecessarily prolonging the training stage of the algorithm, introducing values that would lead to loss of precision and reliability in the work of simulating user management behavior.
    [0065]
    [0066] According to another aspect of the invention, the step of automatically controlling the heating system comprises the steps of determining a presence of a user in the seat, obtaining the initial temperature, calculating the estimated operating time of the heating system, where the Estimated operating time is based on the function generated, and activate the heating system for the estimated estimated time. In this way, and after the estimated time has elapsed, the heating system proceeds with the following automatic memorized instruction, which can be both to deactivate the heating system and decrease the heating intensity level. Therefore, it will proceed to an automatic and autonomous control of the heating system from its activation to its deactivation.
    [0067]
    [0068] More specifically, the method of the invention comprises an additional step of adjusting the estimated estimated time, where adjusting the estimated time comprises reducing the estimated estimated time value between 1% and 10%. Thus, the method of the present invention takes precedence over user manual actions. The method understands that if the user performs a manual action it is because his comfort is not at its optimum level. Of this mode, the method takes precedence, during the control phase of the heating system, to situations in which the level of control is no longer optimal, improving the user's sense of comfort.
    [0069]
    [0070] According to another aspect of the invention, the step of automatically controlling the heating system comprises activating the heating system if the initial temperature obtained is lower than a limit temperature, where the limit temperature is based on the function generated. In this way the automatic heating system is activated for suitable initial temperatures, preventing its activation when the initial temperatures are determined to be high.
    [0071]
    [0072] Limit temperature means the maximum activation temperature of the heating system. If a given initial temperature value is greater than the maximum activation temperature, the stage of automatically controlling the heating system decides not to activate said system. On the contrary, if a given initial temperature value is lower than the maximum activation temperature, the stage of automatically controlling the heating system decides to activate said heating system.
    [0073]
    [0074] In a preferred embodiment of the invention, the method comprises an additional step of updating the trained algorithm, so that if a manual instruction in the heating system is performed by the user during the automatic control of the heating system, the algorithm is updated based on the determined manual instruction. In this way the algorithm is capable of being updated and / or permanently trained, during the operation of the heating system and its handling by the vehicle user.
    [0075]
    [0076] More specifically, the step of updating the algorithm comprises determining a manual instruction in the heating system by the user, obtaining the initial temperature, calculating the operating time of the heating system from the activation of the heating system to the determined manual instruction , memorize the initial temperature obtained and the calculated operating time, and modify the function based on the initial temperature obtained and the calculated operating time. Thus, the function is updated based on the new values that reflect the user management of the heating system, either adapting to new preferences of the same, or correcting previous errors or inaccuracies that may exist in said generated function. It is about because of a continuous training and optimization of the algorithm, in case of manual instructions detected by the user that do not equal the values established by the function generated.
    [0077]
    [0078] Specify that the initial temperature has been memorized during the stage of automatically controlling the heating system.
    [0079]
    [0080] It should be mentioned that the heating system comprises a plurality of intensity levels, or power, where the step of training the algorithm comprises generating a plurality of functions, where each function is based on the activated intensity level, the system activation temperature heating and operating time, where the operating time of the heating system corresponds to the time interval where each intensity level is activated. In a non-limiting example of the invention, if the heating system comprises three different intensity or power levels, the method for automatically controlling the heating system comprises training three different algorithms, where each algorithm comprises generating an independent function and where Each function allows calculating an independent estimated time for each intensity level. It is noted that each algorithm has been trained based on manual actions by the user at the corresponding intensity level.
    [0081]
    [0082] In a preferred embodiment of the invention, the step of automatically controlling the heating system comprises activating a maximum intensity level in the heating system and reducing the intensity level based on the plurality of functions generated. In this way the automatic system regulates the heating level according to the values on which previously each of the vehicle users has been handling the heating.
    [0083]
    [0084] The object of the invention is also a heating system of a seat of a vehicle, where the heating system comprises a control unit configured to execute the method as mentioned above. Said automatic heating system thus benefits from the above-mentioned advantages for each specific embodiment of the method of the present invention. Thus, the heating system comprises at least one heating cable, where the at least one heating cable is disposed between the cover and the foaming of the seat, where the at least one heating cable generates heat by Joule effect.
    [0085] Additionally, the system comprises at least one temperature sensor, where the temperature sensor is preferably of the NTC type. Said temperature sensor determines a temperature of at least one heating cable. Note that it is not a sensor that measures the temperature of the roof but directly the temperature of the heating cable itself. Additionally, the heating system comprises a control unit, where the control unit is in connection with the at least one temperature sensor and with at least one clock, where the clock calculates the operating time of the heating system and allows control The estimated operating time of the heating system.
    [0086]
    [0087] The attached drawings show, by way of non-limiting example, a method for automatically controlling a heating system, and associated system, constituted according to the invention. Other features and advantages of said method for automatically controlling a heating system, and associated system, object of the present invention, will be apparent from the description of a preferred but not exclusive embodiment, which is illustrated by way of example. non-limiting in the accompanying drawings, in which:
    [0088]
    [0089] BRIEF DESCRIPTION OF THE DRAWINGS
    [0090]
    [0091] Figure 1.- It is a perspective view of the driving position of a vehicle, in accordance with the present invention.
    [0092] Figure 2.- It is a perspective view of the seat of a vehicle, showing its internal elements, which incorporates any heating system.
    [0093] Figure 3.- It is a graph that represents the intensity or heating power as a function of time, and the successive level decreases of said intensity or heating power, according to an embodiment of the present invention.
    [0094] Figure 4.- It is a graph that represents the evolution of the temperature as a function of time, with successive decreases in the level of said intensity or heating power, according to an embodiment of the present invention.
    [0095] Figure 5.- Represents the training phase of an algorithm according to the present invention.
    [0096] Figure 6.- They are a series of graphs that represent a series of different values measured during a manual operation of a vehicle user, in accordance with the present invention.
    [0097] Figure 7.- It is a graph that represents a plurality of different generated algorithms based on a plurality of different users, in accordance with the present invention. Figure 8.- Represents the phase of updating the trained algorithm according to at least one user manual action, in accordance with the present invention.
    [0098]
    [0099] DESCRIPTION OF A PREFERRED EMBODIMENT
    [0100]
    [0101] In view of the aforementioned figures and, according to the numbering adopted, an example of a preferred embodiment of the invention can be observed therein, which comprises the parts and elements indicated and described in detail below.
    [0102]
    [0103] Figure 1 shows, in an illustrative way, a perspective view of the driving position of a vehicle 1. The presence in the vehicle 1 of a control unit 11, and of a heating system 12 can be seen more specifically. arranged in the seat 13. The seat 13 is occupied by a user 14 of said vehicle 1. The user 1 has the possibility of operating the heating system 12 through the control unit 11, within reach once seated in the seat 13.
    [0104]
    [0105] The control unit 11 comprises a processing unit, intended to process the information received and act on the heating system 12 of the seat 13. In addition, said control unit 11 comprises an interface for interacting with the user 14 being, in this case, , a touch screen arranged on the dashboard. Said interface may alternatively be a set of actuators arranged in the seat 13.
    [0106]
    [0107] The heating system 12 arranged in the seat 13 has the function of heating the at least one seat 13 of the vehicle 1 in order to increase the user's driving comfort, preferably, in low temperature conditions.
    [0108]
    [0109] In Fig. 2 an illustrative perspective view of the driving seat 13 of a vehicle 1 can be seen illustratively, showing its internal elements. For example, it can be seen more specifically, the heating system 12 in the cushion part of said seat 13, where the user body 14. mainly rests and supports said heating system 12 comprises a heating element, preferably a heating blanket or flexible electrical cables arranged between the foam element of the cushion and the outer covering of said seat 13 which, by Joule effect, generates heat in order to increase the temperature of the seat 13. In addition, said heating system 12 comprises a temperature sensor of the heating element, preferably an NTC sensor that allows to know the temperature of the heating element. Note that it is not a sensor that measures the temperature of the contact surface between the seat 13 and the occupant 14. Additionally, the heating system 12 is in communication with the control unit 11.
    [0110]
    [0111] The technology in heating blankets starts from the principle of heating an electrical resistance, and its difficulty lies in the distribution and isolation of the resistive filaments so that they fulfill their heating mission and in parallel they have a high durability. According to one embodiment, said filaments are sewn directly onto a fabric support of the cushion or backrest to which it must be attached, and is directly adhered on the foams immediately below the seat upholstery 13.
    [0112]
    [0113] More in detail, in figure 3 it can be seen, illustratively, a graph representing the intensity or heating power as a function of time, and the successive level decreases of said intensity 2 or heating power. The variables of intensity level 2, maximum intensity level 21, lower intensity level 22 and operating time 45 can be seen more concretely. It is also seen that the intensity or heating power is set at levels (power level set represented as steps in the graph) where level 3 is equivalent to the maximum operating power of the heating element, level 2 is equivalent to the average operating power of the heating element, level 1 is equivalent to the minimum or low operating power of the heating element and level 0 is equivalent to zero power or deactivation of the heating element. Specifically, there are three graphs that could correspond to three different users 14, or three operations of the heating system 12 for the same user 14, in which a successive decrease in intensity level 2 is seen. For each of the different graphs, it is observed that the user 14 has activated each of the different intensity levels 2 although a different period of time has remained in each of the intensity levels 2.
    [0114]
    [0115] More in detail, the behavior of a heating system 12 comprising a plurality of preset intensity levels 2 is analyzed. Specifically three. He Normal operation of a heating system 12 such as the one described above and without the automatic control described in the present invention is as follows:
    [0116] - Activation by the user 14 of the heating system 12,
    [0117] - Initiation with a maximum intensity level 21,
    [0118] - Manual instruction by the user 14 to reduce the intensity from the maximum intensity level to a medium intensity level,
    [0119] - Manual instruction by the user 14 to reduce the intensity from the medium intensity level to a low intensity level, and
    [0120] - Manual instruction by the user 14 to deactivate the heating system 12.
    [0121] Note that at any time, the user 14 can increase the heating system 12 again or deactivate the heating system 12 without the need to have activated each of the intensity levels 2.
    [0122]
    [0123] Additionally, there are heating systems 12 that comprise a single intensity level or a variable regulation within power within each of the predetermined intensity levels. The method of the present invention is capable of automatically controlling any of the heating systems 12 described above.
    [0124]
    [0125] In figure 4 it can be seen, illustratively, a graph that represents the evolution of the temperature as a function of time, with successive decreases in level of said intensity 2 or heating power before a manual operation of the heating system 12 by a user 14 anyone.
    [0126]
    [0127] It is noted that the user 14 activates the heating system 12 at a given activation temperature 31. During a first operation time 45, it maintains the maximum intensity level 21 activated. Next, the user 14 performs a manual operation of the heating system 12 to reduce the intensity level. During a second operating time 45, it keeps the average intensity level activated. Next, the user 14 performs a manual operation of the heating system 12 to reduce the intensity level. During a third operation time 45, it keeps the low intensity level activated. Finally, the user 14 performs a manual operation of the heating system 12 to deactivate the heating system 12.
    [0128] Detail that the temperature variable is represented on the vertical axis of the graph, while the time variable is represented on the horizontal axis. Below the horizontal axis there are graphic elements that represent the intensity levels 2 of the heating system 12. Thus, the small horizontal bars that are observed, start from a complete ignition, and from above they go down with scales representing the successive levels of intensity 2 of the heating system 12 described (3, 2, 1, 0).
    [0129]
    [0130] During a manual operation of the heating system 12 as shown in Figure 4, the method of the present invention learns from said operation and from the particular needs of the user 14. Thus, and more particularly, the method of the present invention for Automatically control a heating system 12 of a seat 13 of a vehicle 1, comprises the steps of:
    [0131] i) training an algorithm based on at least one manual operation of the heating system 12 by the user 14, where training the algorithm comprises generating a function based on an activation temperature 31 of the heating system 12 and an operating time 45 of the heating system 12. It is required that for each of the intensity levels 2 above, a particular function is generated, which will allow to control and regulate each of the intensity levels 2 individually.
    [0132] ii) complete the training of the algorithm based on a plurality of manual actions by the user 14. That is, the training phase ends when there are a plurality of user actions 14 such as those shown in Figure 4.
    [0133] iii) automatically control the heating system 12 of the seat 13 based on the function generated. Once the needs and particularities of the user are known and learned, the function that controls each of the intensity levels 2 is robust enough so that the control of the heating system 12 is automatic.
    [0134]
    [0135] The objective of the generated function is to allow to estimate a time in which each intensity level 2 is maintained. Thus, by means of the function, the estimated time 46 that the control method expects to keep the intensity level 2 activated will be calculated. As can be seen, the calculation is very simplified, since the estimated time 46 will depend solely on the initial temperature 32 with the one that has started the heating system 12.
    [0136]
    [0137] It is emphasized that, in a system composed of a plurality of intensity levels 12, an independent function will be generated that will estimate the time in which each level should be maintained.
    [0138] In order that each algorithm adapts to the particular temperature needs of each user 14, the initial phase or stage of learning, or training of the algorithm, is individualized for each user 14. Thus, by means of facial recognition, or recognition of the key that activates the vehicle ... the user occupying the seat 13 is identified. Thus, in a first phase of the method, the specific needs of the user are learned 14. Subsequently, in a third phase of the method, controls the heating system 12 according to the specific needs learned from the identified user 14. Preferably, the time of year is taken into account, in order to establish the appropriate operating temperatures, both in the training phase and in the training completion phase.
    [0139]
    [0140] More in detail, as can be seen in Figure 4, the step of training the algorithm comprises the steps of:
    [0141] - determine a manual ignition of the heating system 12 by the user 14,
    [0142] - obtain the activation temperature 31 of the heating system 12,
    [0143] - determine a manual instruction in the heating system 12 by the user 14,
    [0144] - calculate the operating time 45 of the heating system 12 from the determined manual ignition to the determined manual instruction,
    [0145] - memorize the activation temperature 31 obtained and the operating time 45 calculated, and
    [0146] - modify the function based on the activation temperature 31 obtained and the operating time 45 calculated.
    [0147]
    [0148] Figure 5 shows, illustratively, the training phase of an algorithm for any intensity level 2. To simplify the explanation, it will be the maximum intensity level 22. In this graph that represents the temperature on the vertical axis and on the horizontal axis the time.
    [0149]
    [0150] During the training phase, a point cloud is determined in the previous graph. Each point is determined by the activation temperature 31 with which the heating system 12 has started and the operating time 45, represented as t32, at which the heating system 12 has been maintained at the maximum intensity level 21. A The second measurement is determined, and represented in the graph by the point generated by a new activation temperature 31 'and an operating time 45', represented as t32 '. By means of these points, a function is generated, in particular, a regression line 5. Said regression line 5 is updated and modified each time a new point or measure is determined. It is emphasized that only two specific variables are necessary to generate the algorithm of the present invention: the activation temperature 31 and the operating time 45.
    [0151]
    [0152] By means of the regression line 5 presented in Figure 5, the method will be able to calculate the estimated time 46 during which an intensity level 22 is maintained activated. Thus, and already in the automatic control phase of the heating system 12, The method of the present invention determines an initial temperature 32 with which the heating system 12 starts. By means of the regression line 5 generated during the learning stage and said initial temperature 32, the estimated system operation time 46 is calculated. of heating 12. After this estimated time 46 has elapsed, the heating system 12 will be deactivated or the intensity level 2 decreased, depending on the control phase in which the system is located.
    [0153]
    [0154] Additionally, and as can be seen in Figure 5, an activation temperature 31 is visible on the vertical axis, from which the heating system begins to operate, as long as we are below a limit temperature 33. According to the function of the graph, which in particular is a regression line 5, an operating time 45 corresponds to each activation temperature 31, the time after which the heating system 12 is deactivated. Said temperature limit 33 is determined during the learning phase and based on user preferences 14.
    [0155]
    [0156] Specify that, in a first embodiment, all the intensity levels 2 calculated are calculated based on a regression line 5, where each regression line 5 is different for each intensity level 2.
    [0157]
    [0158] In a second embodiment, only the first maximum intensity level 21 is calculated by regression line 5, while the remaining intensity levels 2 have a constant value of the estimated time 46 for each intensity level 2. In relation to the changes of lower intensity level 22, it has been observed, through experimentation, that the time at which user 14 remains at a certain intensity level 2 is approximately constant Therefore, the determined function is a constant time for said lower intensity levels 22, said constant value being determined during the training phase of the algorithm. Optionally, said constant time value is based on the identified user 14. Thus, the computational cost is reduced but high comfort is maintained for the user 14.
    [0159]
    [0160] In a third embodiment, the heating system 12 comprises a single intensity level, where the operating time 45 corresponds to the interval in which the heating system 12 is activated.
    [0161]
    [0162] Figure 6 shows, in an illustrative way, a series of graphs that represent in the vertical axis and in the horizontal axis the time in which a disconnection occurs, or a decrease in intensity level 2 or power. Each graph also represents a regression line 5 generated from a plurality of measures determined during the learning stage of the algorithm.
    [0163]
    [0164] As mentioned, the algorithm on which the present invention is based, is subjected to a process of learning and creating a regression line 5. Thus, knowing the activation temperature 31 of the heating system 12 with which The process starts, and the activation time 45 in which the user remains at the maximum intensity level 21 of the heating, each point of the mentioned regression line 5 is obtained.
    [0165]
    [0166] The two upper graphs shown in Figure 6 go deeper into the stage of completing the algorithm training. Specifically, the graph at the top left shows a plurality of measurements. Each measurement is formed by an activation temperature 31 and an activation time 45. Thus, it is observed that the activation temperatures 31 are grouped, that is, they comprise a low dispersion, whereby the regression line 5 may comprise inaccuracies. On the contrary, the graph at the top right shows a plurality of measurements, where the activation temperatures 31 comprise a high dispersion with each other, the regression line 5 being more precise. Thus, it is shown how the prediction will be much more reliable if there are values at different initial temperatures 32, and not with most of these values concentrated in the same area or in the same range.
    [0167] Therefore, the algorithm training process of the present invention ends when there is sufficient dispersion between the activation temperatures 31 that have been determined during said training phase.
    [0168]
    [0169] Preferably, and in order to take into account a good dispersion of the activation temperatures 31 of the heating system 12, the time of the day of the manual activation and the season of the year of said manual activation must be taken into account, since , depending on when, the data or measurements of the manual actions influence the generation of the function and the calculation of the estimated time 46.
    [0170]
    [0171] Additionally, the two graphs arranged in the middle zone of Figure 6 deepen the stage of training the algorithm. In detail, there is a discard of the measurements obtained during the training phase if the operating time 45 obtained is greater than a predetermined maximum value 42, or if the obtained operating time 45 is less than a predetermined minimum value 43. As You can see in the figure on the left, an operating time value 45 for a given activation temperature 31 is less than the predetermined minimum value 43, so that measurement will be discarded and will not modify the function or regression line 5. On the contrary, if an operating time value 45 is within the predetermined maximum value range 42 and predetermined minimum value 43, the measurement will be memorized and will modify the function based on said new values.
    [0172]
    [0173] Said values of predetermined maximum value 42 and predetermined minimum value 43 are obtained based on the operating time 45. Thus, said values are determined by multiplying by a fixed value the operating time 45 that would be obtained for a given initial temperature 32. The fixed value can be, for example, 20%.
    [0174]
    [0175] Another example of discarding a measurement taken is shown in the lower graphs of Figure 6, where it is observed that the points marked with an 'X' are outside predetermined margins, so the activation temperature values 31 and time of operation 45 do not update the regression line 5. This discard is done once enough values have been obtained to generate a regression line 5.
    [0176]
    [0177] Figure 7 shows, in an illustrative way, a series of functions, preferably straight regression generated during the learning stage. More in In detail, different regression lines 5 are observed, where each comprises a different inclination and a different temperature limit 33. Each of the regression lines is a function of independent learning for each user 14.
    [0178]
    [0179] More in detail, on the vertical axis the temperature is represented and on the horizontal axis the time, where each regression line 5 shows the relationship between the initial temperature 32 and the estimated time 46 for each user 14.
    [0180]
    [0181] Thus, according to Figure 7, it is shown that the step of automatically controlling the heating system 12 comprises:
    [0182] - identify a user 14 in the seat 13, either by means of image analysis or by means of the key inserted in the vehicle or by manual indication by the user 14 himself,
    [0183] - measure the initial temperature 32 at the time the vehicle 1 is activated,
    [0184] - calculate the estimated time 46 in which the intensity level 2 of the heating system 12 will remain activated. Said estimated time 46 is obtained by means of the regression line 5, based on the initial temperature 32.
    [0185] - Activate heating system 12 for the estimated time 46 calculated.
    [0186]
    [0187] Note that during the process of learning or training the algorithm, a temperature limit 33 is established for each of the different users 14 at which the heating system 12 will no longer be activated. Said border or upper limit is defined for each user 14 based on the measurements obtained in the training stage of the algorithm.
    [0188]
    [0189] Note that, as shown in Figure 7, for a given initial temperature value 32 and during the stage of automatically controlling the heating system 12, the estimated time 46 of activating the system will be different for each of the users 14.
    [0190]
    [0191] One of the objectives of the present invention is to perform an automatic control that anticipates situations where user comfort 14 is no longer optimal. For this, the method of the present invention comprises an additional step of adjusting the estimated time 46 calculated, where adjusting the estimated time 46 comprises reducing the estimated time value 46 calculated between 1% and 10%. That is, move a certain value the regression line 5 to the left. Thus, for a given initial temperature 32, the system will obtain a lower estimated time value 46, anticipating the moment when the user 14 has the need to reduce the intensity level 2 of the heating system 12.
    [0192]
    [0193] Thus, the heating system 12 will make the intensity level changes 2 constantly between 10 and 1% before, preferably 5%, in order to anticipate the sensation of heat, improving user comfort 14.
    [0194]
    [0195] Figure 8 shows an update stage of the algorithm obtained during the training phase based on different user manual manipulations 14 (the black dots) that are performed during the automatic control stage. More in detail, during the automatic control of the heating system 12, there is a possibility that the user 14 contradicts the automatic actions performed by means of manual actions. The method will train the algorithm again based on these manual actions.
    [0196]
    [0197] Thus, as shown in Figure 8, during the training phase a regression line 5 has been generated. During the automatic control stage two manual actions have been determined:
    [0198] - On the one hand, before an activation temperature 31, the user 14 has anticipated the estimated time 46 dictated by the regression line 5, so that the activation of the heating system 12 has remained an operating time 45 less than the time estimated 46.
    [0199] - On the other hand, before an activation temperature 31 ', the user 14 has anticipated the estimated time 46 dictated by the regression line 5, so that the activation of the heating system 12 has remained an operating time 45' longer at the estimated time 46.
    [0200]
    [0201] Before these two measures, the method memorizes the initial temperatures 32 and 32 'with which the activation of the heating system 12 and the calculated operating time 45 and 45' have occurred, and the regression line 5 is modified until generating a new 5 'regression line based on these new measures.
    [0202]
    [0203] The details, shapes, dimensions and other accessory elements, as well as the components used in the implementation of the method to control in a way automatic heating system, and associated system, may be conveniently replaced by others that are technically equivalent, and do not depart from the essentiality of the invention or the scope defined by the claims that are included after the following list.
    [0204]
    [0205] List references:
    [0206]
    [0207] 1 vehicle
    [0208] 11 control unit
    [0209] 12 heating system
    [0210] 13 seat
    [0211] 14 user
    [0212] 2 intensity level
    [0213] 21 maximum intensity level
    [0214] 22 lower intensity level
    [0215] 31 activation temperature
    [0216] 32 initial temperature
    [0217] 33 limit temperature
    [0218] 41 default average value
    [0219] 42 default maximum value
    [0220] 43 default minimum value
    [0221] 45 operating time
    [0222] 46 estimated time
    [0223] 5 regression line
    权利要求:
    Claims (1)
    [0001]
    1- Method for automatically controlling a heating system (12) of a seat (13) of a vehicle (1), comprising the steps of:
    i) training an algorithm based on at least one manual operation of the heating system (12) by the user (14), where training the algorithm comprises generating a function based on an activation temperature (31) of the heating system (12) and an operating time (45) of the heating system (12);
    ii) complete the training of the algorithm based on a plurality of manual actions by the user (14); Y
    iii) automatically control the heating system (12) of the seat (13) based on the function generated.
    2- Method according to claim 1, comprising an additional step of identifying the user (14) sitting in the seat (13), where the step of training the algorithm is additionally based on the user (14) identified and where the stage of Automatically controlling the heating system (12) of the seat (13) is additionally based on the user (14) identified.
    3- Method according to any of the preceding claims, wherein the function generated is suitable for calculating an estimated time (46), where the estimated time (46) is equivalent to the automatic activation time of the heating system (12), where the estimated time (46) is based on an initial temperature (32) and the function generated.
    4- Method according to claim 3, wherein the step of training the algorithm comprises the steps of:
    - determine a manual ignition of the heating system (12) by the user (14),
    - obtain the activation temperature (31) of the heating system (12),
    - determine a manual instruction in the heating system (12) by the user (14),
    - calculate the operating time (45) of the heating system (12) from the determined manual ignition to the determined manual instruction,
    - memorize the activation temperature (31) obtained and the operating time (45) calculated, and
    - modify the function based on the activation temperature (31) obtained and the operating time (45) calculated.
    5- Method according to claim 4, wherein the function is a regression line (5).
    6- Method according to any of claims 4 to 5, wherein the step of training the algorithm comprises an additional step of discarding the activation temperature (31) obtained and the operating time (45) calculated if the operating time (45) calculated is greater than a predefined maximum value (42), or if the operating time (45) is less than a predefined minimum value (43), where the maximum predefined value (42) is based on the estimated time (46) and where the minimum predefined value (43) is based on the estimated time (46).
    7- Method according to any of the preceding claims, wherein the step of completing the algorithm training comprises determining a dispersion in the plurality of activation temperatures (31) of the heating system (12) determined during the step of training the algorithm.
    8- Method according to claim 3, wherein the step of automatically controlling the heating system (12) comprises the steps of:
    - determine a presence of a user (14) in the seat (13),
    - obtain the initial temperature (32),
    - calculate the estimated time (46) of operation of the heating system (12), where the estimated time (46) of operation is based on the function generated, and
    - activate the heating system (12) for the estimated time (46) calculated.
    Method according to claim 8, which comprises an additional step of adjusting the estimated time (46) calculated, where adjusting the estimated time (46) comprises reducing the estimated estimated value (46) from 1% to 10%.
    10. Method according to any of claims 8 or 9, wherein the step of automatically controlling the heating system (12) comprises activating the heating system. (12) if the initial temperature (32) obtained is lower than a limit temperature (33), where the limit temperature (33) is based on the function generated.
    11. Method according to any of claims 8 or 10, comprising an additional step of updating the trained algorithm, so that if a manual instruction in the heating system (12) is performed by the user (14) during the control Automatic heating system (12), the algorithm is updated based on the determined manual instruction.
    12- Method according to claim 11, wherein the step of updating the algorithm comprises: - determining a manual instruction in the heating system (12) by the user (14),
    - obtain the initial temperature (32),
    - calculate the operating time (45) of the heating system (12) from the activation of the heating system (12) to the determined manual instruction,
    - memorize the initial temperature (32) obtained and the operating time (45) calculated, and
    - modify the function based on the initial temperature (32) obtained and the operating time (45) calculated.
    13. Method according to any of the preceding claims, wherein the heating system (12) comprises a plurality of intensity levels (2), wherein the step of training the algorithm comprises generating a plurality of functions, where each function is based on the intensity level (2) activated, the activation temperature (31) of the heating system (12) and the operating time (45), where the operating time (45) of the heating system (12) corresponds to the interval of time where each intensity level (2) is activated.
    14. Method according to claim 13, wherein the step of automatically controlling the heating system (12) comprises activating a maximum intensity level (21) in the heating system (12) and reducing the intensity level (2) based on the plurality of functions generated.
    15- Heating system (12) of a seat (13) of a vehicle (1), wherein the heating system (12) comprises a control unit (11) configured to execute the method according to any of the preceding claims.
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    同族专利:
    公开号 | 公开日
    ES2728150B2|2020-03-27|
    EP3557366B1|2021-07-07|
    EP3557366A1|2019-10-23|
    引用文献:
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    EP1060943A2|1999-06-14|2000-12-20|Ford Motor Company|Automatic temperature controlled seat for vehicles|
    GB2427039A|2003-08-21|2006-12-13|Ford Global Tech Llc|A method for pre-heating a seat for a vehicle based on the available power of a battery|
    DE102014201545A1|2014-01-29|2015-07-30|Bayerische Motoren Werke Aktiengesellschaft|Control unit and operating device for controlling a temperature control device for a vehicle component|
    US20150232006A1|2014-02-20|2015-08-20|Hyundai Motor Company|Heating apparatus for automobile seat and control method thereof|
    US20160325656A1|2015-05-04|2016-11-10|Hyundai America Technical Center, Inc|Thermal wave-based seat heating|
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    法律状态:
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    ES201830386A|ES2728150B2|2018-04-20|2018-04-20|METHOD TO AUTOMATICALLY CONTROL A HEATING SYSTEM, AND ASSOCIATED SYSTEM|ES201830386A| ES2728150B2|2018-04-20|2018-04-20|METHOD TO AUTOMATICALLY CONTROL A HEATING SYSTEM, AND ASSOCIATED SYSTEM|
    EP19168363.0A| EP3557366B1|2018-04-20|2019-04-10|Method for the automatic control of a heating system, and associated system|
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