Automatic control of heating elements in a vehicle steering wheel

Abstract

The present disclosure provides "automatic control of heating elements in a vehicle steering wheel". A method of controlling a heating element within a steering wheel of a vehicle, comprising: collecting data relating to a particular identifiable condition; determining whether the collected data satisfies a rule of a pre-built predictive enablement model governing enablement of the heating element by comparing the collected data to the rule of the pre-built predictive enablement model, the rule varying with data relating to a particular identifiable condition; and automatically enabling the heating element within the steering wheel if the collected data satisfies the rule.

Description

Technical Field

[0001] This invention generally relates to heating of vehicle steering wheels. Background Technology

[0002] Vehicles typically include a steering wheel designed for the operator / passenger to control the vehicle's direction. The steering wheel sometimes includes a heating element that provides heat to the outer surface of the wheel, keeping the operator / passenger's hands warm and improving comfort. The operator / passenger usually has to manually activate and deactivate the heating element via buttons or a dial, which can be confusing, expensive, and distracting. Summary of the Invention

[0003] According to a first aspect of the invention, a method for controlling a heating element within a steering wheel of a vehicle includes: providing a vehicle, the vehicle including: a steering wheel including a heating element; a controller communicating with the heating element, the controller including a pre-built predictive activation model listing rules governing the activation of the heating element, the rules varying with data relating to a specific identifiable condition; and a user interface configured to allow manual activation or deactivation of the heating element; collecting data relating to the specific identifiable condition; determining whether the collected data satisfies the rules of the pre-built predictive activation model by comparing the collected data with the rules of the pre-built predictive activation model in order to initially and automatically activate the heating element; and automatically activating the heating element.

[0004] Embodiments of the first aspect of the present invention may include any one or a combination of the following features:

[0005] • The pre-built predictive activation model is formed based on classification and regression tree analysis of input data related to the specific identifiable condition collected from other operators of other vehicles;

[0006] • The pre-built prediction-enabled model establishes rules that vary with at least the following specific identifiable conditions: the temperature inside the vehicle and the temperature outside the vehicle;

[0007] • The pre-built predictive enable model establishes rules that vary with at least the following specific identifiable conditions: the level of air being blown by the climate control system in the vehicle and the front user-side temperature setpoint.

[0008] • The pre-built prediction-enabled model establishes rules that vary with at least the following additional specific identifiable conditions: time of day; and the temperature difference between the temperature inside the vehicle and the temperature outside the vehicle;

[0009] • When the temperature inside the vehicle is below a certain temperature, the controller automatically activates the heating element according to the rules of the pre-built prediction activation model based on data changes related to at least one other specific identifiable condition besides the temperature inside the vehicle.

[0010] • When the occupant-side temperature setpoint is greater than a specific value, the controller automatically activates the heating element; however, when the occupant-side temperature setpoint is less than the specific value, the controller does not automatically activate the heating element.

[0011] According to the rules of the pre-built prediction activation model, if the heated seat function has not been activated, the controller activates the heating element; however, if the heated seat function has been activated, the controller does not activate the heating element.

[0012] • According to the rules of the pre-built prediction activation model, if the level of air being blown by the climate control system in the vehicle is greater than a certain level, then the controller activates the heating element;

[0013] • According to the rules of the pre-built prediction activation model, if the level of air being blown by the climate control system in the vehicle is lower than a certain level, then the controller does not activate the heating element.

[0014] • After the heating element is initially and automatically activated according to the pre-built prediction activation model, if the collected data related to the specific identifiable condition collected after the initial automatic activation of the heating element satisfies the rule of the pre-built prediction activation model for deactivating the heating element, then the heating element is automatically deactivated according to the pre-built prediction activation model.

[0015] • After the heating element is automatically deactivated according to the pre-built predictive activation model, if the collected data related to the specific identifiable condition collected after the heating element is deactivated again satisfies the activation rule according to the pre-built predictive activation model, then the heating element is automatically reactivated according to the pre-built predictive activation model.

[0016] • The first passenger in the vehicle can manually disable the heating element via the user interface;

[0017] • When the first passenger manually deactivates the heating element via the user interface, the pre-built predictive activation model is recalibrated to a new predictive activation model, thereby taking into account the collected data related to the specific identifiable conditions present when the first passenger manually deactivates the heating element, and establishing new rules for enabling and / or deactivating the heating element.

[0018] The first passenger manually activates the heating element via the user interface;

[0019] • When the first passenger manually activates the heating element via the user interface, the new prediction activation model is recalibrated to an updated prediction activation model, thereby taking into account the collected data related to the specific identifiable conditions present when the passenger manually activates the heating element, and establishing new rules for activating and / or deactivating the heating element;

[0020] The heating element can be adjusted to several different heating levels;

[0021] The controller also includes a pre-built prediction level model, which establishes rules governing which of the several different heating levels the controller will initially and automatically set for the heating element, the rules of the pre-built prediction level model varying with one or more of the specific identifiable conditions;

[0022] The user interface is further configured to allow the first passenger to manually select one of the several different heating levels;

[0023] • By comparing the collected data with the rules of the pre-built predictive level model, it is determined which of the several different heating levels the controller will initially and automatically set for the heating element;

[0024] • Initially and automatically set the heating element to a predetermined level;

[0025] • The pre-built prediction level model is formed based on multilayer perceptron classifier analysis of input data collected from other vehicles related to the specific identifiable condition;

[0026] The first passenger manually changes the heating level of the heating element via the user interface;

[0027] • When the first passenger manually changes the heating level of the heating element via the user interface, the pre-built prediction level model is recalibrated to a new prediction level model, thereby taking into account the collected data related to the specific identifiable conditions present when the first passenger manually changes the heating level, and establishing new rules for managing the heating level of the heating element when the heating element is automatically activated.

[0028] • By comparing the collected data with the rules of the new prediction level model, it is determined which of the several different heating levels the controller will initially and automatically set for the heating element;

[0029] • To exempt the first passenger from using the steering wheel; to allow a second passenger who is using the steering wheel to occupy the vehicle; to identify that the second passenger is different from the first passenger; and to collect data related to the identifiable condition while the second passenger is using the steering wheel;

[0030] • By comparing the data collected only when the second passenger is using the steering wheel, and not when the first passenger is using the steering wheel, with the rules of the pre-built predictive activation model, it is determined whether the collected data meets the rules of the pre-built predictive activation model in order to initially and automatically activate the heating element; and

[0031] • The heating element is initially and automatically activated when the second passenger is using the steering wheel.

[0032] By studying the following specification, claims and drawings, those skilled in the art will understand and appreciate these and other aspects, objectives and features of the invention. Attached Figure Description

[0033] In the attached diagram:

[0034] Figure 1 It is a perspective view of the vehicle interior, showing the steering wheel, controllers, and user interface. The steering wheel includes heating elements for providing heat to the outer surface of the steering wheel.

[0035] Figure 2 yes Figure 1 The schematic diagram of the controller shows that the controller accepts input from various data sources and user interfaces, and uses these input sources to automatically control the activation / deactivation of the heating element and the level of heat transfer by the heating element;

[0036] Figure 3A yes Figure 1The front view of an exemplary user interface shows a touchscreen display that provides notification that the controller has automatically enabled the heating element and the heating level, and provides a touchable “off” button that allows passengers to manually deactivate the heating element, as well as touchable buttons for manually changing the heating level;

[0037] Figure 3B yes Figure 1 The front view of an exemplary user interface shows a touchscreen display that provides a notification that the controller has not automatically enabled the heating element and provides a touchable "on" button that allows passengers to manually enable the heating element;

[0038] Figure 4 This is a process diagram illustrating that, following a triggering event (such as starting the vehicle engine), the controller collects data from various data sources relating to specific identifiable conditions (accepting data as input), compares the data with rules governing the activation and deactivation of the heating element established by a pre-built predictive activation model, and activates or deactivates / deactivates the heating element according to the rules; and

[0039] Figures 5A to 5G This is a schematic diagram illustrating the rules of a first exemplary pre-built predictive activation model, which provides data related to specific identifiable conditions that must exist for the controller to automatically activate the heating element to transfer heat and to deactivate / automatically deactivate the heating element. Detailed Implementation

[0040] It should be understood that the specific apparatus and processes shown in the accompanying drawings and described in the following specification are merely exemplary embodiments of the inventive concept defined in the appended claims. Therefore, unless otherwise expressly stated in the claims, the specific dimensions and other physical characteristics relating to the embodiments disclosed herein should not be considered limiting.

[0041] refer to Figure 1 The vehicle 10 includes an interior 12. A steering wheel 14 is disposed within the interior 12. The steering wheel 14 allows the operator of the vehicle 10 (not shown) to control the direction of movement of the vehicle 10 during use, such as when seated in the seat assembly 16. The steering wheel 14 includes a heating element 18 and an outer surface 20. The heating element 18 can be any element that raises the temperature of the outer surface 20 of the steering wheel 14 upon command. The heating element 18 may be disposed below the outer surface 20 of the steering wheel 14. The heating element 18 can be a heating mechanism for transferring heat, such as a wire that impedes current and generates heat, or a Peltier thermoelectric device capable of generating heat, and other options. The heating element 18 can be adjustable to provide several different heating levels. For example, the heating element 18 can provide relatively high, medium, or low levels of heating.

[0042] Now for reference Figure 2 The vehicle 10 also includes a controller 22. The controller 22 communicates with the heating element 18. The controller 22 controls whether the heating element 18 is activated (i.e., to transfer heat to the outer surface 20 of the steering wheel 14) and the intensity (i.e., level, such as high, medium, or low) of heat transfer by the heating element 18. The controller 22 may include a microprocessor 24 to execute programs stored in a memory 26, such as those for controlling the heating element 18.

[0043] The controller 22 includes a pre-built predictive activation model that lists rules governing whether the controller 22 will initially and automatically activate the heating element 18 to transfer heat to the outer surface 20 of the steering wheel 14 in the absence of input or instructions from the operator or other passengers of the vehicle 10. One or more pre-built predictive activation models may be stored in memory 26. The pre-built predictive activation model is generated as a result of analyzing data collected from users of other vehicles (hereinafter referred to as "test vehicles") and the conditions existing when those other users manually activate or deactivate the heating element 18 installed in the steering wheel 14 of those vehicles. The analysis generally involves the problem of the conditions existing when users of the test vehicles activate the heating element 18 of the steering wheel 14 to transfer heat. Identification of these conditions and monitoring of the current conditions experienced by the vehicle 10 can be used to predict when the user of the steering wheel 14 of the vehicle 10 wants to activate the heating element 18 of the steering wheel 14 to transfer heat, and then automatically activate the heating element 18 to transfer heat without the user manually instructing the controller 22 to activate the heating element 18. In other words, the pre-built predictive enable model is formed based on the analysis of input data collected from test vehicles relating to a number of conditions (hereinafter referred to as "specific identifiable conditions"). The pre-built predictive enable model varies with those specific identifiable conditions. The pre-built predictive enable model and specific identifiable conditions are discussed in more detail below.

[0044] The controller 22 also includes a pre-built predictive level model for heating stored in memory 26. The pre-built predictive level model establishes rules governing which of several different heating levels the controller 22 will initially and automatically set for the heating element 18. In other words, the pre-built predictive level model establishes rules governing the heating level (i.e., the intensity of heating) that the controller 22 will automatically set for the heating element 18 when the controller 22 automatically activates the heating element 18 to transfer heat. The rules of the pre-built predictive level model vary with specific identifiable conditions. In other words, the level assigned by the controller 22 according to the rules of the pre-built predictive level model depends on the specific identifiable conditions that the vehicle 10 is currently experiencing. The pre-built predictive level model is further discussed below.

[0045] The controller 22 receives input about specific identifiable conditions from one or more data sources 28 within the vehicle 10. The one or more data sources 28 can be sensors and / or settings, etc. For example, one data source 28 can be a temperature sensor that provides the controller 22 with ambient (external) temperature. Another data source 28 can be a clock that provides the controller 22 with time data. The data source 28 can be external to the vehicle 10, such as location data via a Global Positioning System (GPS). As further discussed below, the controller 22 uses the data about the specific identifiable conditions to control the heating element 18 based on a pre-built predictive enable model, a pre-built predictive level model, and subsequent improvements caused by the user's manual overdrive / control of the heating element 18 (as discussed below).

[0046] Now for reference Figure 3A and Figure 3BTherefore, vehicle 10 also includes a user interface 30 that communicates with controller 22. User interface 30 may be located within vehicle 10, allowing the user of steering wheel 14 (a person who may occupy seat assembly 16) to interact with it. For example, user interface 30 may be a touchscreen display 32, a knob, a switch, and / or a voice-operable user interface, etc. User interface 30 is configured to allow the user of steering wheel 14 to manually activate heating element 18 if controller 22 has not yet activated heating element 18 to transfer heat as desired by the user. Additionally, user interface 30 is configured to manually deactivate heating element 18 if controller 22 has already activated heating element 18 to transfer heat contrary to the user's expectations. For example, user interface 30 may be a touchscreen display 32 with options (e.g., a button 34 labeled "Off") that allow the user of steering wheel 14 or another passenger of vehicle 10 to deactivate the heating element 18 of steering wheel 14 that controller 22 has automatically activated to transfer heat according to a pre-built predictive activation model. If the user presses the "Off" button 34, the controller 22 accepts the interaction as input and deactivates the heating element 18 to prevent heat transfer. Similarly, the touchscreen display 32 may include an option (e.g., a button 36 labeled "On") that allows the user of the steering wheel 14 to activate the heating element 18 of the steering wheel 14 if the controller 22 has not automatically activated it according to rules of a pre-built predictive activation model (or its subsequent revised iterations, as discussed below). If the user presses the button 36 labeled "On," the controller 22 accepts the interaction as input and activates the heating element 18. For example, if the user wants the heating element 18 to transfer heat to the steering wheel 14, but the controller 22 has not automatically enabled the heating element 18 to do so according to a pre-built predictive activation model, the user can navigate to the appropriate options screen on the touchscreen display 32 and press the button 36 labeled "On," and the controller 22 will then activate the heating element 18 to transfer cooling. The controller 22 may cause the touchscreen display 32 to provide the user with a notification 38 regarding the current status of the automatically controlled heating element 18 of the steering wheel 14. For example, as Figure 3A As shown, when notification 38 informs the user that heating element 18 is currently automatically disabled (not enabled), the "On" button 36 can be presented to the user. Similarly, as... Figure 3B As shown, when notification 38 informs the user that heating element 18 is currently automatically enabled, the "Off" button 34 can be presented to the user as a touchable option.

[0047] Additionally, the user interface 30 is configured to allow the passenger of seat assembly 16 to manually select one of several different temperature change levels. For example, the touchscreen display 32 may have options that allow the user of steering wheel 14 to manually select a relatively high heating level (e.g., button 40 labeled "High"), a relatively low temperature change level (e.g., button 42 labeled "Low"), or a temperature change level between high and low (e.g., button 44 labeled "Medium"). Instead of "High," "Medium," and "Low," the options on touchscreen display 32 may be "3," "2," and "1," respectively. If the user presses one of buttons 40, 42, or 44, and the heating element 18 is not yet heated at that level, then controller 22 accepts the interaction as input and adjusts the level of heating element 18 accordingly, thereby automatically setting the level for heating element 18 based on a pre-built predictive level model. The user interacts with the user interface 30 in this way, influencing the automatic control of the heating element 18 by the controller 22, which then automatically controls the heating element 18, as discussed in more detail below.

[0048] Now for reference Figure 4The vehicle 10, which includes a steering wheel 14 with a heating element 18, a controller 22, and a user interface 30, can be used to perform a novel method of controlling the heating element 18. This novel method (at step 46) may include a triggering event that causes the controller 22 to begin the remainder of the method (e.g., start a routine). The triggering event may be the power activation of the vehicle 10 (e.g., engine ignition), a preset time of day (i.e., five minutes before the most common user begins their morning commute), the vehicle 10 detecting the presence of a user in the seat assembly 16 (e.g., via a capacitive or weight sensor), the vehicle 10 detecting one or both of the user's hands on the steering wheel 14 (e.g., via a capacitive or other sensor), and other such possible triggering events. The novel method includes: (at step 48) collecting data related to specific identifiable conditions (from data source 28); (at step 50) determining whether the collected data meets the rules of the pre-built predictive activation model for initially and automatically activating the heating element 18 (by comparing the collected data with the management activation rules of the pre-built predictive activation model); and if so, (at step 52) initially and automatically activating the heating element 18. If comparing the collected data with the pre-built predictive activation model indicates that the rules for activating the heating element 18 are not met, then (at step 54) the heating element 18 is not activated (or, if already automatically activated, deactivated), and the method can return to step 48 and continue data collection. In other words, even if the collected data meets the rules of the pre-built predictive activation model for activating the heating element 18 at a point in time, the method may also include returning to step 48 to continue data collection and subsequently determining whether the collected data meets the rules of the pre-built predictive activation model for deactivating the heating element 18, thereby causing the heating element 18 to be deactivated at step 54. This cycle is continuous, and the controller 22 continuously collects data to determine whether to enable or disable / deactivate the heating element 18 based on the rules of the pre-built predictive enable model.

[0049] We now further discuss the test vehicles and the data collected from them related to specific identifiable conditions, the analysis of data forming pre-built predictive enable models and pre-built predictive level models (and their subsequent recalibration or iteration). The data was collected from several test vehicles equipped with heating elements 18 for transferring heat to the steering wheel 14.

[0050] The data was reduced to several conditions relating to specific identifiable conditions, which were considered to be related to user decisions regarding when to activate (and deactivate) the heating element 18 and at what level (aggressiveness) the heating element 18 would transfer heat. These specific identifiable conditions included: whether the user had activated the heating of the seat assembly 16 (“RCCM_DF_Cond_Seat_Req”); the front user / operator side temperature setpoint (“Front_Left_Temp_Setpt”); the front passenger side temperature setpoint (“Front_Rt_Temp_Setpt”); (the last two temperature setpoints refer to the setpoint temperature of the air blown toward the seat assembly 16 and the seat assemblies adjacent to the seat assembly 16 in the first row, respectively); whether the air conditioning had been activated (“AC_Request”); whether the rear window defroster had been activated (“Overriding_ModeReq”); and the external temperature of the vehicle 10, i.e., the ambient temperature. Ambient temperature (“AirAmb_Te_Actl”, “AirAmb_Te_Actl_UB”); the level of air being blown by the climate control system in vehicle 10 (“RCCM_Fr_Rr_Blower_Req”); the temperature of the interior 12 of vehicle 10 (“InCarTemp”, “InCarTempQF”); the speed of vehicle 10 (“Veh_V_ActlEng_UB”, “Veh_V_ActlEng”); engine speed (e.g., revolutions per minute) (“EngAout_N_Actl”, “EngAout_N_Actl_UB”); and the time of day, which can be expressed as the time of day (“hour”). (The front occupant-side temperature setpoint (“Front_Rt_Temp_Setpt”) and front operator-side temperature setpoint (“Front_Left_Temp_Setpt”) used in the following examples are unitless and range between 119 and 171. This range is linearly correlated with 60 degrees Fahrenheit and 85 degrees Fahrenheit, respectively. Therefore, a value of “Front_Rt_Temp_Setpt <= 154.9836” roughly corresponds to 77.3 degrees Fahrenheit.)

[0051] The identifiers indicated in parentheses above are provided to aid in interpreting the exemplary pre-built predictive-enabled model reproduced below. Several identifiers may relate to the same concept. For example, both “AirAmb_Te_Actl” and “AirAmb_Te_Actl_UB” relate to the temperature outside vehicle 10 (i.e., ambient temperature). It may be advantageous to combine several identifiers into a single identifier before analyzing data related to specific identifiable conditions to generate the pre-built predictive-enabled model and the pre-built predictive-level model. For example, data with the identifier “AirAmb_Te_Act_UB” may substantially duplicate “AirAmb_Te_Act” and may be completely removed from the data before analysis to generate the pre-built predictive-enabled model and the pre-built predictive-level model.

[0052] Further analysis of data from test vehicles or other test vehicles may result in specific identifiable conditions including sunshine levels and trip-related statistics (such as trip length, trip frequency, trip characteristics (such as commuting vs. leisure)), GPS positioning (such as latitude and longitude), road gradient, altitude, urban vs. rural driving, highway vs. urban roads, torque, braking, idling time, and other time-related concepts (such as minutes, seconds, date, weekday (Monday, Tuesday, etc.) and season).

[0053] Data related to certain identifiable conditions can be manipulated to make the data more predictive for pre-built prediction-enabled models and pre-built prediction-level models. In this regard, some of the specific identifiable conditions can be derived from other specific identifiable conditions and further analyzed for the predictive power of pre-built prediction-enabled models and pre-built prediction-level models. For example, specific identifiable conditions for the temperature inside the vehicle (“InCarTemp”) and the ambient temperature (“AirAmb_Te_Actl”) can be used to calculate specific identifiable conditions for the temperature difference (“tempDiff”) between these two temperatures.

[0054] Similarly, one or more specific identifiable conditions can be calculated to attempt to capture generalized conditions for "cold" days (ambient temperatures below 60 degrees Fahrenheit) in which the interior temperature of vehicle 10 has not yet "warmed up," and the degrees to which the interior temperature of vehicle 10 has not yet "warmed up." For example, in the pre-built prediction-enabled model provided below, the specific identifiable conditions "coldInside40," "coldInside30," "coldInside20," and "coldInside10" are all constrained as true or false conditions generated from other specific identifiable conditions. The specific identifiable condition "coldInside40" is TRUE when the ambient temperature ("AirAmb_Te_Actl") is less than 60 degrees Fahrenheit and the temperature difference ("tempDiff" as explained above) is less than 40 degrees Fahrenheit (in the exemplary pre-built prediction-enabled model below, TRUE is given a value of 1, i.e., ">0"). Otherwise, “coldInside40” is FALSE (in the exemplary pre-built prediction enabled model below, FALSE is given a value of 0, i.e., “<= 0”). When the ambient temperature (“AirAmb_Te_Actl”) is less than 60 degrees Fahrenheit and the temperature difference (“tempDiff” as explained above) is less than 30 degrees Fahrenheit, the specific identifiable condition “coldInside30” is TRUE (in the exemplary pre-built prediction enabled model below, TRUE is given a value of 1, i.e., "> 0”). Otherwise, “coldInside30” is FALSE (in the exemplary pre-built prediction enabled model below, FALSE is given a value of 0, i.e., “<= 0”). When the ambient temperature (“AirAmb_Te_Actl”) is less than 60 degrees Fahrenheit and the temperature difference (“tempDiff” as explained above) is less than 20 degrees Fahrenheit, the specific identifiable condition “coldInside20” is TRUE (in the exemplary pre-built prediction enabled model below, TRUE is given a value of 1, i.e., ">0”). Otherwise, “coldInside20” is FALSE (in the exemplary pre-built prediction enabled model below, FALSE is given a value of 0, i.e., “<=0”). When the ambient temperature (“AirAmb_Te_Actl”) is less than 60 degrees Fahrenheit and the temperature difference (“tempDiff” as explained above) is less than 10 degrees Fahrenheit, the specific identifiable condition “coldInside10” is TRUE (in the exemplary pre-built prediction enabled model below, TRUE is given a value of 1, i.e., ">0”). Otherwise, “coldInside10” is FALSE (in the following example pre-built prediction enabled model, FALSE is given a value of 0, i.e., “<=0”).Therefore, all these constraints will be FALSE when the ambient temperature (“AirAmb_Te_Actl”) is 60 degrees Fahrenheit or higher (i.e., a “warm” day) (i.e., a day when the user might not want to activate the heating element 18). Furthermore, “coldInside10” (TRUE, ">0”) may capture a generalized scenario where the interior 12 of vehicle 10 has not yet “warmed up” (and therefore, “tempDiff” is less than 10 degrees Fahrenheit), which is a scenario where the user might want to activate the heating element 18. Although "coldInside20", "coldInside30", and "coldInside40" would also be TRUE (">0) under the same generalized scenario, when the interior 12 of vehicle 10 "heats up," causing the temperature difference ("tempDiff") between the interior temperature of vehicle 10 ("InCarTemp") and the ambient temperature ("AirAmb_Te_Actl") to increase, the conditions "coldInside20", "coldInside30", and "coldInside40" will sequentially begin to become FALSE ("<=0"). For example, when the interior 12 "heats up," the temperature difference ("tempDiff") may rise from 9 degrees to 21 degrees. In this case, "coldInside10" and "coldInside20" will change from TRUE to FALSE because 21 degrees is greater than both 10 degrees and 20 degrees.

[0055] Furthermore, the specific identifiable condition indicating whether the user requests vehicle 10 to heat the interior 12 at low, medium, or high fan speeds (“turnOnHeat1”, “turnOnHeat2”, and “turnOnHeat3”, respectively) when the heating element 18 is activated is derived from the specific identifiable conditions of the operator-side temperature setting (“Front_Left_Temp_Setpt”) and the level of air being blown by the vehicle's climate control system (“RCCM_Fr_Rr_Blower_Req”). Finally, in the following illustrative example, the specific identifiable condition of whether it is morning (“isMorning”) is derived from time data, just as the specific identifiable condition of whether the season is summer (“isSummerx”) is derived.

[0056] In general, by analyzing data from the test vehicle related to specific identifiable conditions, the specific identifiable conditions under which users of the test vehicle decide to activate the heating element 18 of the steering wheel 14 and deactivate the heating element 18 can be determined. A pre-built predictive activation model and a pre-built predictive level model can then be developed to establish rules that vary with the data relating to specific identifiable conditions that satisfy a specific percentage of situations where users of the test vehicle decide to activate / deactivate the heating element 18 as appropriate. In other words, by modeling past user behavior demonstrated in the test vehicle, the pre-built predictive activation model and the pre-built predictive level model can be used to predict future user expectations regarding the activation / deactivation of the heating element 18 (and the level of temperature change) in vehicle 10 and automatically control the activation / deactivation and level management of the heating element 18.

[0057] The pre-built prediction activation model and the pre-built prediction level model can be derived from data related to specific identifiable conditions collected from all test vehicles. Alternatively, the data related to specific identifiable conditions collected from the test vehicles can first be segmented (e.g., divided into 3 segments, hereinafter referred to as "segments") based on criteria such as operator type (e.g., major city driver, major highway driver, "aggressive" operator, etc.). A separate pre-built prediction activation model and pre-built prediction level model can be generated for each segment (i.e., for each operator type) using data generated only by the test vehicles in that specific segment. In other words, for the major city driver segment, the pre-built prediction activation model and pre-built prediction level model will be generated using only data from the test vehicles in the major city driver segment. If the user of vehicle 10 is identified as belonging to the major city driver segment, then controller 22 will use the pre-built prediction activation model and pre-built prediction level model generated based on the major city driver segment of the test vehicles to manage the future automatic activation / deactivation of the heating element 18 of vehicle 10. This assumes that one type of operator will exhibit different patterns of enabling and disabling the heating element 18 compared to another type. For example, a pre-built predictive enable model can be derived for one type of operator, and another pre-built predictive enable model can be derived for another type of operator, and so on. Criteria that can be used to segment the data collected from test vehicles include: the average distance traveled per test vehicle, the standard deviation of the distance traveled, the average number of distances traveled per unit time (such as per day), the number of distances that might be considered “short-distance” (such as less than two miles), the number of highway miles already traveled by the test vehicle, the number of off-highway miles already traveled by the test vehicle, and the ratio between the latter two. Other criteria that can be used to segment the data collected from test vehicles include those related to the “aggressiveness” of a particular test vehicle’s travel, such as torque, load, vehicle speed, engine speed per minute, fuel economy, or the frequency of driver coasting (i.e., the frequency with which the vehicle moves without the operator accelerating or decelerating it by braking). The data collected from test vehicles can be segmented to form segments using a k-means clustering algorithm.

[0058] Controller 22 may initially include a pre-built predictive enable model and a pre-built predictive level model generated for each segment; however, by default, only the pre-built predictive enable model and pre-built predictive level model for a specific segment are used for enabling / disabling (and level control) the heating element 18. Data related to specific identifiable conditions can then be collected when the user begins operating vehicle 10 for a specific time period. This data can then be compared to the segments to determine which segment the user of vehicle 10 is most similar to. For example, one segment might be data from a subset where the test vehicle primarily travels on public roads, and the user might also primarily drive vehicle 10 on public roads. The pre-built predictive enable model and pre-built predictive level model derived from that specific segment can then be the pre-built predictive enable model and pre-built predictive level model used by controller 22 thereafter.

[0059] Pre-built predictive enable models can be formed based on classification and regression tree (CART) analysis of data collected from all test vehicles or segmented data related to specific identifiable conditions (the data is segmented, thus generating one or more pre-built predictive enable models for each segment), as explained above. A variety of CART analyses exist that can provide useful results, including: the C.50 program (version 2.07 GPL, available from...). www.rulequest.com (obtained), such as the M5P classifier implemented in Weka (available from...) http: / / weka.sourceforge.net / doc.stable / weka / classifiers / trees / M5P.html (obtained), and random tree classifiers such as those implemented in Weka (available from...) http: / / weka.sourceforge.net / doc.dev / weka / classifiers / trees / RandomTree.html (Available). Other CART analyses exist and this is not intended to be an exhaustive list.

[0060] The following is an exemplary pre-built predictive enable model formed according to the C.50 procedure CART analysis. This exemplary pre-built predictive enable model lists rules for enabling / disabling the heating element 18 for transferring heat to the outer surface 20 of the heating element 18, and these rules vary with data related to specific identifiable conditions.

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] Those skilled in the art will understand how to interpret the above pre-built predictive enablement model. Each line includes an identifier associated with a specific, identifiable condition. For example, the first line “InCarTemp<=20.94798:” includes the identifier “InCarTemp”, which, as stated above, refers to the temperature of the interior 12 of vehicle 10. Each line includes a value associated with the preceding specific identifiable condition. For example, the first line “InCarTemp<=20.94798:” includes the value “20.94798”, which refers to 20.94798 degrees Celsius. Each line includes a conditional statement. For example, the first line “InCarTemp<=20.94798:” can be understood as “If the temperature of the interior 12 of vehicle 10 is less than or equal to 20.94798 degrees Celsius, and…”. The second line, which is indented and otherwise identified as subordinate to the first line, will then be understood. The second line, “RCCM_Fr_Rr_Blower_Req<=0.04545455:”, can be understood as “If the level of air being blown by the climate control system in vehicle 10 is less than or equal to a specific level (in this case, the specific level is represented by the value “0.4545455”), and…”. The third line, which is subordinate to the second, can then be understood. The third line, “coldInside20<=0”, can be understood as “If the ambient temperature is above 60 degrees Fahrenheit or if the temperature difference between the interior 12 of vehicle 10 and the ambient temperature is greater than 20 degrees Fahrenheit, and…”. The fourth and fifth lines, which are subordinate to the third, can then be understood. The fourth line states “InCarTempQF<=2.763158:0”, while the fifth line states “InCarTempQF>2.763158:1”. The fourth and fifth lines, as before, include the identifier, the value, and also include a conclusion represented by “0” or “1” after the colon “:”. The "0" in the fourth line indicates that the heating element 18 for heat transfer is disabled / not activated. Conversely, the fifth line includes ":1", indicating that the heating element 18 for heat transfer is activated. Therefore, the fourth line is interpreted as "If the temperature inside the vehicle 10 12 is less than or equal to 2.736158 degrees Celsius, then the heating element 18 is disabled / not activated." Conversely, the fifth line is interpreted as "If the temperature inside the vehicle 12 is greater than 2.736158 degrees Celsius, then the heating element 18 is activated."

[0067] Therefore, rows one through five are together.

[0068]

[0069] This can be understood as follows: if the temperature inside the vehicle 10 12 is less than or equal to 20.94798 degrees Celsius, and if the level of air being blown by the climate control system in the vehicle 10 is less than or equal to a certain level, and if the ambient temperature is higher than 60 degrees Fahrenheit or if the temperature difference between the inside temperature of the vehicle 10 12 and the ambient temperature is greater than 20 degrees Fahrenheit, and if the temperature inside the vehicle 10 12 is less than or equal to 2.736158 degrees Celsius, then the heating element 18 is not activated / deactivated; however, if the temperature inside the vehicle 10 12 is greater than 2.736158 degrees Celsius, then the heating element 18 is activated.

[0070] The above example is a relatively simple pre-built predictive enablement model for heating, because the pre-built predictive enablement model establishes rules that vary with only a few of the specific identifiable conditions, namely the temperature of the interior 12 of vehicle 10 (“InCarTemp” and “InCarTempQF”), the level of air being blown by the climate control system in vehicle 10 (“RCCM_Fr_Rr_Blower_Req”), and whether the ambient temperature is higher than 60 degrees Fahrenheit or whether the temperature difference between the interior 12 of vehicle 10 and the ambient temperature is greater than 20 degrees Fahrenheit (“coldInside20<=0”).

[0071] More specifically, see now Figures 5A to 5G At step 56, controller 22 begins the process of comparing the collected data with the pre-built prediction-enabled model. Figure 4Step 50 of the entire process is shown. After step 56, controller 22 proceeds to step 58, where controller 22 determines whether the temperature of the interior 12 of vehicle 10 (“InCarTemp”) is greater than or less than or equal to 20.94798 degrees Celsius. If controller 22 determines that the temperature of the interior 12 of vehicle 10 is less than or equal to 20.94798 degrees Celsius (“InCarTemp<=20.94798”), then controller 22 proceeds to step 60. At step 60, controller 22 determines whether the level of air being blown by the climate control system in vehicle 10 (“RCCM_Fr_Rr_Blower_Req”) is greater than or less than or equal to a specific value. If controller 22 determines that the level of air being blown by the climate control system in vehicle 10 (“RCCM_Fr_Rr_Blower_Req”) is less than or equal to a specific value (“RCCM_Fr_Rr_Blower_Req<=0.04545455”), then controller 22 proceeds to step 62. At step 62, controller 22 determines whether the aforementioned limiting condition “ColdInside20” is true (“>0”) or false (“<=0”). If controller 22 determines that the limiting condition “coldInside20” is false (“coldInside20<=0”), indicating that the temperature is above 60 degrees Fahrenheit or the temperature difference (“tempDiff” as explained above) is greater than 20 degrees Fahrenheit, then controller 22 proceeds to step 64. At step 64, controller 22 determines whether the temperature of the interior 12 of vehicle 10 (“InCarTempQF”) is greater than or less than or equal to a specific value (2.763158 degrees Celsius). If, at step 64, the controller 22 determines that the temperature of the interior 12 of the vehicle 10 is less than or equal to a specific value (“InCarTempQF<=2.763158”), then the controller 22 proceeds to step 66 and does not activate the heating element 18, or deactivates the heating element 18 if it has previously been automatically activated by the controller 22. However, if, at step 64, the controller 22 determines that the temperature of the interior 12 of the vehicle 10 is greater than the specific value (“InCarTempQF>2.763158”), then the controller 22 proceeds to step 68 and activates the heating element 18. As shown, then, when the temperature of the vehicle's interior is below the specific temperature, the controller automatically activates the heating element 18 according to a pre-built activation model based on data changes related to at least one other specific identifiable condition besides the temperature of the vehicle's interior among the specific identifiable conditions.

[0072] Returning to step 62, if controller 22 determines that the limiting condition “ColdInside20” is true (“coldInside20>0”), indicating that the temperature is below 60 degrees Fahrenheit and the temperature difference (“tempDiff” as explained above) is less than 20 degrees Fahrenheit, then controller 22 proceeds to step 70. At step 70, controller 22 determines whether the temperature of the interior 12 of vehicle 10 (“InCarTemp”) is less than or equal to a specific temperature (“10” degrees Celsius) or greater than a specific temperature. If controller 22 determines that the temperature of the interior 12 of vehicle 10 is less than or equal to the specific value (“InCarTemp<=10”), then controller 22 proceeds to step 72 and does not activate heating element 18, or deactivates heating element 18 if it has previously been automatically activated by controller 22. However, at step 70, if controller 22 determines that the temperature of the interior 12 of vehicle 10 is greater than the specific value (“InCarTemp>10”), then controller 22 proceeds to step 74. At step 74, controller 22 determines whether the interior temperature 12 of vehicle 10 (“InCarTemp”) is less than or equal to a specific temperature (“12.08333” degrees Celsius) or greater than a specific temperature. If controller 22 determines that the interior temperature 12 of vehicle 10 is greater than the specific value (“InCarTemp>12.08333”), then controller 22 proceeds to step 76 and does not activate heating element 18, or deactivates heating element 18 if it has previously been automatically activated by controller 22. However, at step 74, if controller 22 determines that the interior temperature 12 of vehicle 10 is less than or equal to the specific value (“InCarTemp<=12.08333”), then controller 22 proceeds to step 78. At step 78, controller 22 determines whether the front occupant side temperature setpoint (“Front_Rt_Temp_Setpt”) is less than or equal to or greater than a specific value (“142.95”). If controller 22 determines that the front passenger side temperature setpoint is less than or equal to a specific value ("Front_Rt_Temp_Setpt<=142.95"), then controller 22 proceeds to step 80 and enables heating element 18. However, if controller 22 determines that the front passenger side temperature setpoint is greater than the specific value ("Front_Rt_Temp_Setpt>142.95"), then controller 22 proceeds to step 82 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22.Therefore, based on the above, when the temperature inside the interior 12 of the vehicle 10 is lower than a certain value, the controller 22, which applies the rules of the pre-built prediction model, will enable or disable / deactivate the heating element 18 according to other specific identifiable conditions (such as the front occupant side temperature setpoint), so that when the occupant side temperature setpoint is greater than the certain value, the controller automatically enables the heating element 18, but when the occupant side temperature setpoint is less than the certain value, the controller 22 does not automatically enable the heating element 18.

[0073] Returning to step 60, if controller 22 determines that the level of air being blown by the climate control system in vehicle 10 is greater than a specific value (“RCCM_Fr_Rr_Blower_Req>0.04545455”), then controller 22 proceeds to step 84. At step 84, controller 22 determines whether the temperature of the interior 12 of vehicle 10 (“InCarTemp”) is less than, equal to, or greater than a specific value (“19.7485”). If controller 22 determines that the temperature of the interior 12 of vehicle 10 is less than or equal to the specific value (“InCarTemp<=19.7485”), then controller 22 proceeds to step 86 and activates the heating element 18. However, if controller 22 determines that the temperature of the interior 12 of vehicle 10 is greater than the specific value (“InCarTemp>19.7485”), then controller 22 proceeds to step 88. At step 88, controller 22 determines whether the ambient temperature (“AirAmb_Te_Actl”) is less than, equal to, or greater than a specific value (“4.716667”). If controller 22 determines that the ambient temperature is less than or equal to the specific value (“AirAmb_Te_Actl<=4.716667”), then controller 22 proceeds to step 90. At step 90, controller 22 determines whether the front passenger side temperature setpoint (“Front_Rt_Temp_Setpt”) is greater than, less than, or equal to a specific value. If controller 22 determines that the front passenger side temperature setpoint is greater than the specific value (“Front_Rt_Temp_Setpt>138.1818”), then controller 22 activates heating element 18 at step 92. However, if controller 22 determines that the front passenger side temperature setpoint is less than or equal to the specific value (“Front_Rt_Temp_Setpt<=138.1818”), then controller 22 proceeds to step 94. At step 94, controller 22 determines whether the current time is morning (“isMorning”). If controller 22 determines that the current time is morning (“isMorning>0”, meaning “TRUE”), then controller 22 proceeds to step 96 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the current time is not morning (“isMorning<=0”), then controller 22 proceeds to step 98. At step 98, controller 22 determines whether the user has enabled heating of seat assembly 16 (“RCCM_DF_Cond_Seat_Req”). If controller 22 determines that the user has not enabled heating of seat assembly 16 (“RCCM_DF_Cond_Seat_Req<=0”, meaning “FALSE”), then controller 22 proceeds to step 100 and enables heating element 18.However, if controller 22 determines that the user has already enabled heating of seat assembly 16 (“RCCM_DF_Cond_Seat_Req>0”), then controller 22 proceeds to step 102 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. Therefore, according to the above rules of the pre-built predictive activation model, assuming other specific identifiable conditions exist, controller 22 enables heating element 18 if heating of seat assembly 16 has not yet been enabled, but does not enable heating element 18 if heating of seat assembly 16 has already been enabled.

[0074] Returning to step 88, if controller 22 determines that the ambient temperature is higher than a specific value (“AirAmb_Te_Actl>4.716667”), then controller 22 proceeds to step 104. At step 104, controller 22 determines whether the front user / operator side temperature setpoint (“Front_Left_Temp_Setpt”) is less than, equal to, or greater than a specific value (“140”). If controller 22 determines that the front user / operator side temperature setpoint is less than or equal to the specific value (“Front_Left_Temp_Setpt<=140”), then controller 22 proceeds to step 106. At step 106, controller 22 determines whether the current time is morning (“isMorning”). If controller 22 determines that the current time is morning (“isMorning>0”, meaning “TRUE”), then controller 22 proceeds to step 108 and either disables heating element 18 or deactivates it if heating element 18 has previously been automatically enabled by controller 22. If controller 22 determines that the current time is not morning ("isMorning<=0"), then controller 22 proceeds to step 110. At step 110, controller 22 determines whether the interior temperature 12 of vehicle 10 ("InCarTemp") is less than, equal to, or greater than a specific value ("20.29802"). If controller 22 determines that the interior temperature 12 of vehicle 10 is less than or equal to the specific value ("InCarTemp<=20.29802"), then controller 22 proceeds to step 112 and activates heating element 18. If controller 22 determines that the interior temperature 12 of vehicle 10 is greater than the specific value ("InCarTemp>20.29802"), then controller 22 proceeds to step 114. At step 114, controller 22 determines whether the interior temperature 12 of vehicle 10 ("InCarTemp") is less than, equal to, or greater than the specific value ("20.93529"). If controller 22 determines that the temperature of the interior 12 of vehicle 10 is less than or equal to a specific value ("InCarTemp <= 20.93529"), then controller 22 proceeds to step 116 and does not activate heating element 18, or deactivates heating element 18 if it has previously been automatically activated by controller 22. However, if controller 22 determines that the temperature of the interior 12 of vehicle 10 is greater than the specific value ("InCarTemp > 20.93529"), then controller 22 proceeds to step 118 and activates heating element 18.

[0075] Returning to step 104, if controller 22 determines that the front user / operator side temperature setpoint is greater than a specific value (“Front_Left_Temp_Setpt>140”), then controller 22 proceeds to step 120. At step 120, controller 22 determines whether the condition “coldInside10” is TRUE or FALSE. If controller 22 determines that the condition “coldInside10” is FALSE (“coldInside10<=0”), then controller 22 proceeds to step 122 and activates heating element 18. However, if controller 22 determines that the condition “coldInside10” is TRUE (“coldInside10>0”), then controller 22 proceeds to step 124. At step 124, controller 22 determines whether the air conditioning in vehicle 10 has been activated (“AC_Request”). If controller 22 determines that the air conditioning in vehicle 10 is already activated (“AC_Request>1.727273, meaning “TRUE”), then controller 22 proceeds to step 126. At step 126, controller 22 determines whether the front passenger-side temperature setpoint (“Front_Rt_Temp_Setpt”) is greater than, less than, or equal to a specific value (“138”). If controller 22 determines that the front passenger-side temperature setpoint is less than or equal to the specific value (“Front_Rt_Temp_Setpt<=138”), then controller 22 proceeds to step 128 and does not activate heating element 18, or deactivates heating element 18 if it has previously been automatically activated by controller 22. However, if controller 22 determines that the front passenger-side temperature setpoint is greater than the specific value (“Front_Rt_Temp_Setpt>138”), then controller 22 proceeds to step 130 and activates heating element 18.

[0076] Returning to step 124, if controller 22 determines that the air conditioning in vehicle 10 is not yet activated (“AC_Request<=1.727273, meaning “FALSE”), then controller 22 proceeds to step 132. At step 132, controller 22 determines whether the temperature inside the interior 12 of vehicle 10 (“InCarTemp”) is greater than, less than, or equal to a specific value (“20.11021”). If controller 22 determines that the temperature inside the interior 12 of vehicle 10 is greater than the specific value (“InCarTemp>20.11”), then... If the temperature inside the vehicle 10 is less than or equal to a specific value ("InCarTemp <= 20.11021"), then the controller 22 proceeds to step 134 and disables the heating element 18, or disables the heating element 18 if it has previously been automatically enabled by the controller 22. However, if the controller 22 determines that the temperature inside the vehicle 10 is less than or equal to a specific value ("InCarTemp <= 20.11021"), then the controller 22 proceeds to step 136. At step 136, the controller 22 determines the level of air being blown by the climate control system in the vehicle 10 ("RCCM_Fr_Rr_Bl"). If controller 22 determines that the level of air being blown by the climate control system in vehicle 10 is less than or equal to a specific level ("RCCM_Fr_Rr_Blower_Req<=0.9581395"), then controller 22 proceeds to step 138 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the level of air being blown by the climate control system in vehicle 10 is greater than a specific level ("Rr_Rr_Blower_Req<=0.9581395"), then controller 22 proceeds to step 138 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. If CCM_Fr_Rr_Blower_Req>0.9581395”, then controller 22 proceeds to step 140 and activates heating element 18. Therefore, according to the rules of the pre-built prediction activation model, the following situations may occur: (a) if the level of air being blown by the climate control system in vehicle 10 is greater than a certain level, controller 22 activates heating element 18; but (b) if the level of air being blown by the climate control system in vehicle 10 is less than a certain level, controller 22 either does not activate heating element 18 or disables heating element 18.

[0077] Returning to step 58, if controller 22 determines that the temperature inside the interior 12 of vehicle 10 is greater than a specific value (“InCarTemp>20.94798”), then controller 22 proceeds to step 142. At step 142, controller 22 determines whether the ambient temperature (“AirAmb_Te_Actl”) is less than, equal to, or greater than a specific value (“1.487569”). If controller 22 determines that the ambient temperature is less than or equal to the specific value (“AirAmb_Te_Actl<=1.487569”), then controller 22 proceeds to step 144. At step 144, controller 22 determines whether the temperature inside the interior 12 of vehicle 10 (“InCarTemp”) is less than, equal to, or greater than a specific value (“23.36164”). If controller 22 determines that the temperature inside the interior 12 of vehicle 10 is less than or equal to the specific value (“InCarTemp<=23.36164”), then controller 22 proceeds to step 146. At step 146, controller 22 determines whether the front occupant-side temperature setpoint (“Front_Rt_Temp_Setpt”) is greater than, less than, or equal to a specific value (“140.9863”). If controller 22 determines that the front occupant-side temperature setpoint is less than or equal to the specific value (“Front_Rt_Temp_Setpt<=140.9863”), then controller 22 proceeds to step 148. At step 148, controller 22 determines whether the ambient temperature (“AirAmb_Te_Actl”) is less than, equal to, or greater than a specific value (“-6.584821”). If controller 22 determines that the ambient temperature is less than or equal to the specific value (“AirAmb_Te_Actl<=-6.584821”), then controller 22 proceeds to step 150 and activates heating element 18. If controller 22 determines that the ambient temperature is greater than a specific temperature (“AirAmb_Te_Actl>-6.584821”), then controller 22 proceeds to step 152. At step 152, controller 22 determines whether the vehicle 10 speed (“Veh_V_ActlEng_UB”) is greater than, less than, or equal to a specific value. If controller 22 determines that the vehicle 10 speed is greater than the specific value (“Veh_V_ActlEng_UB>85.87412”, which can be in miles per hour), then controller 22 proceeds to step 154 ​​and either disables the heating element 18 or deactivates it if the heating element 18 has previously been automatically enabled by controller 22. However, if controller 22 determines that the vehicle 10 speed is less than or equal to the specific value (“Veh_V_ActlEng_UB<=85.87412”), then controller 22 proceeds to step 156.At step 156, controller 22 determines whether the front user / operator side temperature setpoint (“Front_Left_Temp_Setpt”) is less than, equal to, or greater than a specific value (“140.9863”). If controller 22 determines that the front user / operator side temperature setpoint is greater than the specific value (“Front_Left_Temp_Setpt>140.9863”), then controller 22 proceeds to step 158 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the front user / operator side temperature setpoint is less than or equal to the specific value (“Front_Left_Temp_Setpt<=140.9863”), then controller 22 proceeds to step 160. At step 160, controller 22 determines whether the temperature in the interior 12 of vehicle 10 (“InCarTemp”) is less than, equal to, or greater than a specific value (“22.23913”). If controller 22 determines that the temperature inside 12 is less than or equal to a specific value ("InCarTemp <= 22.23913"), then controller 22 proceeds to step 162 and activates heating element 18. However, if controller 22 determines that the temperature inside 12 is greater than the specific value ("InCarTemp > 22.23913"), then controller 22 proceeds to step 164 and does not activate heating element 18, or deactivates heating element 18 if it has previously been automatically activated by controller 22. Therefore, there may be a situation where the determination of activating or deactivating / deactivating heating element 18 can vary with the speed of vehicle 10.

[0078] Returning to step 146, if controller 22 determines that the front occupant side temperature setpoint is greater than a specific value (“Front_Rt_Temp_Setpt>140.9863”), then controller 22 proceeds to step 166. At step 166, controller 22 determines whether the internal temperature 12 (“InCarTemp”) is less than, equal to, or greater than a specific value (“22.33793”). If controller 22 determines that the internal temperature 12 is less than or equal to the specific value (“InCarTemp<=22.33793”), then controller 22 proceeds to step 168 and activates heating element 18. However, if controller 22 determines that the internal temperature 12 is greater than the specific value (“InCarTemp>22.33793”), then controller 22 proceeds to step 170. At step 170, controller 22 determines whether the ambient temperature (“AirAmb_Te_Actl”) is less than, equal to, or greater than a specific value (“-1.482906”). If controller 22 determines that the ambient temperature is less than or equal to a specific temperature (“AirAmb_Te_Actl<=-1.482906”), then controller 22 proceeds to step 172 and activates heating element 18. However, if controller 22 determines that the ambient temperature is greater than a specific value (“AirAmb_Te_Actl>-1.482906”), then controller 22 proceeds to step 174. At step 174, controller 22 determines whether the temperature of interior 12 (“InCarTemp”) is greater than or less than a specific value (“22.92803”). If controller 22 determines that the temperature of interior 12 is greater than a specific value (“InCarTemp>22.92803”), then controller 22 proceeds to step 176. At step 176, controller 22 determines whether the front occupant side temperature setpoint (“Front_Rt_Temp_Setpt”) is less than or equal to or greater than a specific value (“142.95”). If controller 22 determines that the front passenger side temperature setpoint is less than or equal to a specific value (“Front_Rt_Temp_Setpt<=142.95”), then controller 22 proceeds to step 178 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the front passenger side temperature setpoint is greater than the specific value (“Front_Rt_Temp_Setpt>142.95”), then controller 22 proceeds to step 180 and enables heating element 18.

[0079] Returning to step 174, if controller 22 determines that the temperature of internal 12 is less than or equal to a specific value (“InCarTemp<=22.92803”), then the controller proceeds to step 182. At step 182, controller 22 determines whether the current time is morning (“isMorning”). If controller 22 determines that the current time is not morning (“isMorning<=0”, meaning “FALSE”), then the controller proceeds to step 184 and activates heating element 18. However, if controller 22 determines that the current time is morning (“isMorning>0”, meaning “TRUE”), then controller 22 proceeds to step 186. At step 186, controller 22 determines whether the ambient temperature (“AirAmb_Te_Actl”) is less than, equal to, or greater than a specific value (“0.2857143”). If controller 22 determines that the ambient temperature is less than or equal to a specific value ("AirAmb_Te_Actl <= 0.2857143"), then the controller proceeds to step 188 and enables heating element 18. However, if controller 22 determines that the ambient temperature is greater than the specific temperature ("AirAmb_Te_Actl > 0.2857143"), then controller 22 proceeds to step 190 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22.

[0080] Returning to step 144, if controller 22 determines that the internal temperature 12 is greater than a specific temperature (“InCarTemp>23.36164”), then controller 22 proceeds to step 192. At step 192, controller 22 determines whether the ambient temperature (“AirAmb_Te_Actl”) is less than, equal to, or greater than a specific value (“-8.155629”). If controller 22 determines that the ambient temperature is less than or equal to the specific value (“AirAmb_Te_Actl<=-8.155629”), then controller 22 proceeds to step 194. At step 194, controller 22 determines whether the internal temperature 12 (“InCarTemp”) is greater than, less than, or equal to a specific value (“25.19261”). If controller 22 determines that the internal temperature 12 is greater than the specific value (“InCarTemp>25.19261”), then controller 22 proceeds to step 196. At step 196, controller 22 determines whether the ambient temperature (“AirAmb_Te_Actl”) is greater than, less than, or equal to a specific value (“-20.89634”). If controller 22 determines that the ambient temperature is greater than the specific temperature (“AirAmb_Te_Actl>-20.89634”), then controller 22 proceeds to step 198 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the ambient temperature is less than or equal to the specific value (“AirAmb_Te_Actl<=-20.89634”), then controller 22 proceeds to step 200. At step 200, controller 22 determines whether the temperature of the interior 12 (“InCarTemp”) is less than, equal to, or greater than the specific value (“26.63714”). If controller 22 determines that the temperature of internal 12 is less than or equal to a specific value ("InCarTemp <= 26.63714"), then the controller proceeds to step 202 and activates heating element 18. However, if controller 22 determines that the temperature of internal 12 is greater than the specific value ("InCarTemp > 26.63714"), then the controller proceeds to step 204 and does not activate heating element 18, or deactivates heating element 18 if it has previously been automatically activated by controller 22.

[0081] Returning to step 194, if controller 22 determines that the temperature of interior 12 is less than or equal to a specific value (“InCarTemp<=25.19261”), then controller 22 proceeds to step 206. At step 206, controller 22 determines whether the front passenger-side temperature setpoint (“Front_Rt_Temp_Setpt”) is less than or equal to or greater than a specific value (“140.9863”). If controller 22 determines that the front passenger-side temperature setpoint is greater than the specific value (“Front_Rt_Temp_Setpt>140.9863”), then controller 22 proceeds to step 208 and activates heating element 18. However, if controller 22 determines that the front passenger-side temperature setpoint is less than or equal to the specific value (“Front_Rt_Temp_Setpt<=140.9863”), then controller 22 proceeds to step 210. At step 210, controller 22 determines the level of air being blown by the climate control system in vehicle 10 (“RCCM_Fr_Rr_Blower_Req”). If controller 22 determines that the climate control system in vehicle 10 is blowing air at a level less than or equal to a specific value (“RCCM_Fr_Rr_Blower_Req<=0.6914893”), then controller 22 proceeds to step 212 and activates heating element 18. However, if controller 22 determines that the climate control system in vehicle 10 is blowing air at a level greater than the specific value (“RCCM_Fr_Rr_Blower_Req>0.6914893”), then controller 22 proceeds to step 214. At step 214, controller 22 determines whether the ambient temperature (“AirAmb_Te_Actl”) is greater than or less than or equal to a specific value (“-11.27286”). If controller 22 determines that the ambient temperature is greater than a specific value (“AirAmb_Te_Actl>-11.27286”), then controller 22 proceeds to step 216 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the ambient temperature is less than or equal to a specific temperature (“AirAmb_Te_Actl<=-11.27286”), then controller 22 proceeds to step 218. At step 218, controller 22 determines whether the temperature of the interior 12 (“InCarTemp”) is less than or equal to or greater than a specific value (“24.19364”). If controller 22 determines that the temperature of the interior 12 is less than or equal to the specific value (“InCarTemp<=24.19364”), then controller proceeds to step 220 and enables heating element 18.However, if the controller 22 determines that the temperature of the internal 12 is greater than a certain value (“InCarTemp>24.19364”), then the controller 22 proceeds to step 222 and does not enable the heating element 18, or disables the heating element 18 if it has been previously enabled automatically by the controller 22.

[0082] Returning to step 192, if controller 22 determines that the ambient temperature is greater than a specific value (“AirAmb_Te_Actl>-8.155629”), then controller 22 proceeds to step 224. At step 224, controller 22 determines whether the temperature of the interior 12 (“InCarTemp”) is greater than, less than, or equal to a specific value (“24.11828”). If controller 22 determines that the temperature of the interior 12 is greater than the specific value (“InCarTemp>24.11828”), then controller 22 proceeds to step 226 and does not enable heating element 18, or disables heating element 18 if it was previously automatically enabled by controller 22. However, if controller 22 determines that the temperature of the interior 12 is less than or equal to the specific value (“InCarTemp<=24.11828”), then controller 22 proceeds to step 228. At step 228, controller 22 determines whether the temperature difference (“tempDiff”) between the ambient temperature and the internal temperature 12 is less than, equal to, or greater than a specific value (“26.80822”). If controller 22 determines that the temperature difference is less than or equal to the specific value (“tempDiff<=26.80822”), then controller 22 proceeds to step 230 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the temperature difference is greater than the specific value (“tempDiff>26.80822”), then controller 22 proceeds to step 232. At step 232, controller 22 determines whether the front user / operator side temperature setpoint (“Front_Left_Temp_Setpt”) is less than, equal to, or greater than a specific value (“140.9863”). If controller 22 determines that the front user / operator side temperature setpoint is less than or equal to a specific value (“Front_Left_Temp_Setpt<=140.9863”), then controller 22 proceeds to step 234. At step 234, controller 22 determines whether the engine speed (“EngAout_N_Actl”) is less than or equal to or greater than a specific value (“856.9707”). If controller 22 determines that the engine speed is less than or equal to the specific value (“EngAout_N_Actl<=856.9707”), then controller 22 proceeds to step 236 and enables heating element 18. However, if controller 22 determines that the engine speed is greater than the specific value (“EngAout_N_Actl>856.9707”), then controller 22 proceeds to step 238 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22.

[0083] Returning to step 232, if controller 22 determines that the front user / operator side temperature setpoint is greater than a specific value (“Front_Left_Temp_Setpt>140.9863”), then controller 22 proceeds to step 240. At step 240, controller 22 determines whether the internal temperature 12 (“InCarTemp”) is less than, equal to, or greater than a specific value (“InCarTemp<=23.54177”). If controller 22 determines that the internal temperature 12 is less than or equal to the specific value (“InCarTemp<=23.54177”), then controller 22 proceeds to step 242 and activates heating element 18. If controller 22 determines that the internal temperature 12 is greater than the specific value (“InCarTemp>23.54177”), then controller 22 proceeds to step 244. At step 244, controller 22 determines whether the constraint “coldInside30” is met. If controller 22 determines that the condition "coldInside30 <= 0" (meaning "FALSE") is not met, then controller 22 proceeds to step 246 and activates heating element 18. However, if controller 22 determines that the condition "coldInside30 > 0" (meaning "TRUE") is met, then controller 22 proceeds to step 248. At step 248, controller 22 determines whether the air conditioning in vehicle 10 has been activated ("AC_Request"). If controller 22 determines that the air conditioning in vehicle 10 has not been activated ("AC_Request <= 1.795455" (meaning "FALSE"), then controller 22 proceeds to step 250 and activates heating element 18. However, if controller 22 determines that the air conditioning in vehicle 10 has been activated ("AC_Request > 1.795455"), then controller 22 proceeds to step 252 and does not activate heating element 18, or deactivates heating element 18 if it has previously been automatically activated by controller 22.

[0084] Returning to step 142, if controller 22 determines that the ambient temperature is greater than a specific value (“AirAmb_Te_Actl>1.487569”), then controller 22 proceeds to step 254. At step 254, controller 22 determines whether the internal temperature of 12 (“InCarTemp”) is greater than, less than, or equal to a specific value (“22.96875”). If controller 22 determines that the internal temperature of 12 is greater than the specific value (“InCarTemp>22.96875”), then controller 22 proceeds to step 256 and either disables heating element 18 or deactivates it if heating element 18 has previously been automatically enabled by controller 22. However, if controller 22 determines that the internal temperature of 12 is less than or equal to the specific value (“InCarTemp<=22.96875”), then controller 22 proceeds to step 258. At step 258, controller 22 determines whether the ambient temperature (“AirAmb_Te_Actl”) is greater than, less than, or equal to a specific value (“6.652516”). If controller 22 determines that the ambient temperature is greater than the specific value (“AirAmb_Te_Actl>6.652516”), then the controller proceeds to step 260. At step 260, controller 22 determines whether the front user / operator side temperature setpoint (“Front_Left_Temp_Setpt”) is greater than, less than, or equal to a specific value (“143.1795”). If controller 22 determines that the front user / operator side temperature setpoint is greater than the specific value (“Front_Left_Temp_Setpt>143.1795”), then controller 22 proceeds to step 262. At step 262, controller 22 determines whether the constraint “coldInside10” is met. If controller 22 determines that the limiting condition ("coldInside10>0", meaning "TRUE") is met, then the controller proceeds to step 264 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the limiting condition ("coldInside10<=0") is not met, then the controller proceeds to step 266. At step 266, controller 22 determines whether the level of air being blown by the climate control system in vehicle 10 ("RCCM_Fr_Rr_Blower_Req") is true. If controller 22 determines that the level of air being blown by the climate control system in vehicle 10 is less than or equal to a specific level ("RCCM_Fr_Rr_Blower_Req<=0.9583333"), then controller 22 proceeds to step 268 and enables heating element 18.However, if the controller 22 determines that the climate control system in the vehicle 10 is blowing air at a certain level (“RCCM_Fr_Rr_Blower_Req>0.9583333”), then the controller 22 proceeds to step 270 and does not enable the heating element 18, or disables the heating element 18 if it has previously been automatically enabled by the controller 22.

[0085] Returning to step 260, if controller 22 determines that the front user / operator side temperature setpoint is less than or equal to a specific value (“Front_Left_Temp_Setpt<=143.1795”), then controller 22 proceeds to step 272. At step 272, the controller determines whether the ambient temperature (“AirAmb_Te_Actl”) is greater than or less than or equal to a specific value (“9.283654”). If controller 22 determines that the ambient temperature is greater than the specific value (“AirAmb_Te_Actl>9.283654”), then controller 22 proceeds to step 274 and either does not enable heating element 18, or disables heating element 18 if it was previously automatically enabled by controller 22. However, if controller 22 determines that the ambient temperature is less than or equal to the specific value (“AirAmb_Te_Actl<=9.283654”), then controller 22 proceeds to step 276. At step 276, controller 22 determines whether the temperature of interior 12 (“InCarTemp”) is greater than, less than, or equal to a specific value (“21.20414”). If controller 22 determines that the temperature is greater than the specific value (“InCarTemp>21.20414”), then controller 22 proceeds to step 278 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the interior temperature is less than or equal to the specific value (“InCarTemp<=21.20414”), then controller 22 proceeds to step 280. At step 280, controller 22 determines whether the front occupant side temperature setpoint (“Front_Rt_Temp_Setpt”) is less than, equal to, or greater than the specific value (“140.9863”). If controller 22 determines that the front passenger side temperature setpoint is less than or equal to a specific value (“Front_Rt_Temp_Setpt<=140.9863”), then controller 22 proceeds to step 282 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the front passenger side temperature setpoint is greater than the specific value (“Front_Rt_Temp_Setpt>140.9863”), then controller 22 proceeds to step 284. At step 284, controller 22 determines whether rear window defrosting (“Overriding_ModeReq”) has been enabled. If controller 22 determines that rear window defrosting has not been enabled (“Overriding_ModeReq<=1.898305”, meaning “FALSE”), then controller 22 proceeds to step 286 and does not enable heating element 18, or disables heating element 18 if heating element 18 has previously been automatically enabled by controller 22.However, if controller 22 determines that rear window defrosting has been enabled (“Overriding_ModeReq>1.898305”, meaning “TRUE”), then controller 22 proceeds to step 288 and enables heating element 18.

[0086] Returning to step 258, if controller 22 determines that the ambient temperature is less than or equal to a specific temperature (“AirAmb_Te_Actl<=6.652516”), then controller 22 proceeds to step 290. At step 290, controller 22 determines whether the internal temperature 12 (“InCarTemp”) is greater than or less than or equal to a specific value (“21.99206”). If controller 22 determines that the internal temperature 12 is greater than the specific value (“InCarTemp>21.99206”), then controller 22 proceeds to step 292. At step 292, controller 22 determines whether the front occupant side temperature setpoint (“Front_Rt_Temp_Setpt”) is less than or equal to or greater than a specific value (“140.9863”). If controller 22 determines that the front occupant-side temperature setpoint is less than or equal to a specific value (“Front_Rt_Temp_Setpt<=140.9863”), then controller 22 proceeds to step 294 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the front occupant-side temperature setpoint is greater than the specific value (“Front_Rt_Temp_Setpt>140.9863”), then controller 22 proceeds to step 296. At step 296, controller 22 determines whether the ambient temperature (“AirAmb_Te_Actl”) is greater than or less than or equal to the specific value (“5.568714”). If controller 22 determines that the ambient temperature is greater than a specific value (“AirAmb_Te_Actl>5.568714”), then controller 22 proceeds to step 298 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the ambient temperature is less than or equal to the specific temperature (“AirAmb_Te_Actl<=5.568714”), then controller proceeds to step 300. At step 300, controller 22 determines whether the current time is morning (“isMorning”). If controller 22 determines that the current time is not morning (“isMorning<=0”, meaning FALSE), then controller 22 proceeds to step 302. At step 302, controller 22 determines whether the internal temperature of 12 (“InCarTemp”) is less than or equal to or greater than a specific value (“22.41667”). If the controller 22 determines that the temperature of the internal 12 is less than or equal to a specific value (“InCarTemp<=22.41667”), then the controller 22 proceeds to step 304 and enables the heating element 18.However, if the controller 22 determines that the temperature of the internal 12 is greater than a certain value (“InCarTemp>22.41667”), then the controller 22 proceeds to step 306 and does not enable the heating element 18, or disables the heating element 18 if it has been previously enabled automatically by the controller 22.

[0087] Returning to step 300, if controller 22 determines that the current time is morning ("isMorning>0", meaning "TRUE"), then controller 22 proceeds to step 308. At step 308, controller 22 determines whether the vehicle 10 speed ("Veh_V_ActlEng_UB") is less than, equal to, or greater than a specific value. If controller 22 determines that the vehicle 10 speed is less than or equal to the specific value ("Veh_V_ActlEng_UB<=106.6641"), then controller 22 proceeds to step 310 and does not activate heating element 18, or deactivates heating element 18 if it was previously automatically activated by controller 22. However, if controller 22 determines that the vehicle 10 speed is greater than the specific value ("Veh_V_ActlEng_UB>106.6641"), then controller 22 proceeds to step 312 and activates heating element 18.

[0088] Returning to step 290, if controller 22 determines that the temperature of interior 12 is less than or equal to a specific value (“InCarTemp<=21.99206”), then controller 22 proceeds to step 314. At step 314, controller 22 determines whether the front passenger side temperature setpoint (“Front_Rt_Temp_Setpt”) is less than, equal to, or greater than a specific value (“140.1316”). If controller 22 determines that the front passenger side temperature setpoint is greater than the specific value (“Front_Rt_Temp_Setpt>140.1316”), then controller 22 proceeds to step 316. At step 316, controller 22 determines whether rear window defrosting (“Overriding_ModeReq”) has been enabled. If controller 22 determines that rear window defrosting is not enabled (“Overriding_ModeReq<=1.428571”, meaning “FALSE”), then controller 22 proceeds to step 318 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that rear window defrosting is enabled (“Overriding_ModeReq>1.428571”, meaning “TRUE”), then controller 22 proceeds to step 320 and enables heating element 18.

[0089] Returning to step 314, if controller 22 determines that the front occupant side temperature setpoint is less than or equal to a specific value (“Front_Rt_Temp_Setpt<=140.1316”), then controller 22 proceeds to step 322. At step 322, controller 22 determines whether the vehicle 10 speed (“Veh_V_ActlEng”) is less than, equal to, or greater than a specific value (“0.4081633”). If controller 22 determines that the vehicle 10 speed is less than or equal to the specific value (“Veh_V_ActlEng<=0.4081633”), then controller 22 proceeds to step 324 and activates the heating element 18. However, if controller 22 determines that the vehicle 10 speed is greater than the specific value (“Veh_V_ActlEng>0.4081633”), then controller 22 proceeds to step 326. At step 326, controller 22 determines whether the current time is morning (“isMorning”). If controller 22 determines that the time of day is morning ("isMorning>0"), then controller 22 proceeds to step 328 and does not activate heating element 18, or deactivates heating element 18 if it has previously been automatically activated by controller 22. However, if controller 22 determines that the time of day is not morning ("isMorning<=0"), then controller 22 proceeds to step 330. At step 330, controller 22 determines that the climate control system in vehicle 10 is blowing air at a level less than, equal to, or greater than a specific value ("0.9537572") ("RCCM_Fr_Rr_Blower_Req"). If controller 22 determines that the climate control system in vehicle 10 is blowing air at a level less than or equal to a specific value (“RCCM_Fr_Rr_Blower_Req<=0.9537572”), then controller 22 proceeds to step 332 and does not enable heating element 18, or disables heating element 18 if it has previously been automatically enabled by controller 22. However, if controller 22 determines that the climate control system in vehicle 10 is blowing air at a level greater than the specific value (“RCCM_Fr_Rr_Blower_Req>0.9537572”), then controller 22 proceeds to step 334. At step 334, controller 22 determines whether the ambient temperature (“AirAmb_Te_Actl”) is less than or equal to, or greater than, the specific value (“5.457317”). If controller 22 determines that the ambient temperature is less than or equal to a specific value ("AirAmb_Te_Actl<=5.457317"), then controller 22 proceeds to step 336 and enables heating element 18.However, if the controller 22 determines that the ambient temperature is greater than a specific value ("AirAmb_Te_Actl>5.457317"), then the controller 22 proceeds to step 338 and does not enable the heating element 18, or disables the heating element 18 if it has been previously enabled automatically by the controller 22.

[0090] One or more pre-built predictive level models that establish rules and manage the heating level of heating element 18 can be formed based on neural network analysis or multilayer perceptron classifier analysis of input data related to specific identifiable conditions, which is collected from the whole test vehicle or segmented, as explained above. A variety of analyses exist that can provide useful results, including: R (version 3.2.5) statistical programming software and Multilayer Perceptron (available for classification via Weka)... http: / / weka.sourceforge.net / doc.stable / weka / classifiers / functions / MultilayerPerceptron.html (See below). Other analyses are available, and this is not intended to be an exhaustive list. Then, rules from one or more pre-built predictive level models are used to manage the level of heat transfer from the heating element 18 to the outer surface 20 of the steering wheel 14 via the controller 22, based on input data to the controller 22 relating to specific identifiable conditions present in the vehicle 10.

[0091] The method may further include: after the heating element 18 is initially and automatically activated according to the pre-built prediction activation model, if the collected data related to a specific identifiable condition collected after the initial automatic activation of the heating element 18 satisfies the rules of the pre-built prediction activation model for deactivating the heating element 18, then the heating element 18 is automatically deactivated according to the pre-built prediction activation model. For example, using the pre-built predictive activation model formed according to the C.50 procedure stated above, if the temperature inside 12 is less than or equal to a specific value (“InCarTemp<=20.94798”), the climate control system in vehicle 10 is blowing air at a level less than or equal to a specific value (“RCCM_Fr_Rr_Blower_Req<=0.04545455”), the “coldInside20” condition (“coldInside20<=0”, meaning “FALSE”) is not met, and the temperature inside 12 is greater than a specific value (“InCarTempQF>2.763158”), then controller 22 will initially and automatically activate heating element 18. However, if controller 22 receives an input that still satisfies all other conditions, but the temperature inside 12 is now less than or equal to a specific value (“InCarTempQF<=2.763158”), then controller 22 will deactivate heating element 18.

[0092] The method for controlling the heating element 18 may further include: after automatically deactivating the heating element 18 according to a pre-built predictive activation model, if collected data related to a specific identifiable condition collected after deactivating the heating element 18 again satisfies the activation rules of one or more pre-built predictive activation models, then automatically reactivating the heating element 18 according to one or more pre-built predictive activation models. The controller 22 may continue to collect data related to the specific identifiable condition while the triggering event (see step 46 above) remains active (i.e., the vehicle 10 is still moving), and compare the collected data with the rules of the pre-built predictive activation model. If the collected data again satisfies the activation rules of the pre-built predictive activation model for the heating element 18, then the controller 22 may reactivate the heating element 18 accordingly. For example, using the exemplary pre-built predictive enable model described above again, if controller 22 subsequently receives input data related to a specific identifiable condition that satisfies the rules of the pre-built predictive enable model for enabling heating element 18, such as the temperature of internal 12 being greater than a specific value again (“InCarTempQF>2.763158”), then controller 22 re-enables heating element 18. In other words, controller 22 accepts input data related to a specific identifiable condition “in real time” and dynamically considers whether the input data satisfies any rules of one or more pre-built predictive enable models for enabling or disabling heating element 18, and controls the enabling / disabling of heating element 18 accordingly.

[0093] The method of controlling the heating element 18 may also include: passengers of the vehicle 10 (such as passengers of the seat assembly 16) controlling the heating element 18 via a user interface 30 (see...). Figure 3A The heating element 18 can be manually deactivated. For example, an occupant of seat assembly 16 can press the button 34 marked "Off" on touchscreen display 32 to manually deactivate the heating element 18, which was previously automatically activated by controller 22 based on a pre-built predictive activation model. Controller 22 accepts this interaction as input and thus deactivates the heating element 18.

[0094] The method for controlling the heating element 18 may further include: when a passenger of vehicle 10 manually deactivates the heating element 18 via user interface 30, recalibrating a pre-built predictive activation model to a new predictive activation model, thereby taking into account collected data related to specific identifiable conditions present when the passenger manually deactivates the heating element 18, and thus establishing new rules for enabling and deactivating the heating element 18. For this purpose, controller 22 may be pre-loaded with software that performs classification and regression tree analysis, such as the C.50 program mentioned above. The passenger's manual deactivation of the heating element 18, which has already been automatically activated by controller 22 according to the pre-built predictive activation model, constitutes a rejection by the passenger of the rules (and thus the specific identifiable conditions satisfying the rules) that controller 22 relies on to automatically activate the heating element 18. For example, using the pre-built predictive activation model rules stated above—

[0095]

[0096] —If controller 22 receives an input that the temperature of interior 12 is less than or equal to a specific value (“InCarTemp<=20.94798”), the climate control system in vehicle 10 is blowing air at a level less than or equal to a specific value (“RCCM_Fr_Rr_Blower_Req<=0.04545455”), the “coldInside20” condition (“coldInside20<=0”, meaning “FALSE”) is not met, and the temperature of interior 12 is greater than a specific value (“InCarTempQF>2.763158”), then controller 22 will automatically activate heating element 18. However, if controller 22 automatically activates heating element 18 and the passenger refuses the activation by manually deactivating heating element 18 via user interface 30, then controller 22 will recalibrate the pre-built predictive activation model into a new predictive activation model, thereby taking into account data related to the specific identifiable conditions present when the passenger manually deactivates heating element 18. The new predictive activation model can impose greater weight on passenger decisions, such that controller 22 will not activate heating element 18 when the specific identifiable conditions present when the passenger manually deactivates heating element 18 reappear, and will automatically deactivate heating element 18 when it is activated. In other words, the new predictive activation model can include rules for deactivating or not activating heating element 18 that vary with those specific identifiable conditions. Alternatively, the new predictive activation model can impose the same weights on data related to the specific identifiable conditions present when the passenger manually deactivates heating element 18 as on data collected from the test vehicle from which the pre-built predictive activation model was originally derived. In any case, the new predictive activation model will continue to utilize data collected from the test vehicle as well as data collected from vehicle 10 related to the specific identifiable conditions present when the passenger manually deactivates heating element 18.

[0097] The method of controlling the heating element 18 may also include: the passenger manually activating the heating element 18 via the user interface 30. For example, a passenger of vehicle 10 may press the touchscreen display 32 (see...). Figure 3B The button 36 marked "On" is pressed to activate the heating element 18. The controller 22 accepts this interaction as input and thus activates the heating element 18 that was previously deactivated or disabled by the controller 22 according to a pre-built prediction activation model (or a new prediction activation model).

[0098] The method of controlling the heating element 18 may further include: when a passenger manually activates the heating element 18 via the user interface 30, recalibrating a new predictive activation model to an updated predictive activation model, thereby taking into account collected data related to specific identifiable conditions present when the passenger manually activates the heating element 18, and establishing new rules for activating and / or deactivating the heating element 18. The controller 22 records data related to specific identifiable conditions present when the passenger manually activates the heating element 18, and uses said data to prepare an updated predictive activation model with new rules for activation. Similarly, the updated predictive activation model may assign greater weight to the data related to specific identifiable conditions present when the passenger manually activates the heating element 18 according to the new rules, such that the controller 22 automatically activates the heating element 18 when those specific identifiable conditions reappear. Alternatively, the updated predictive activation model may assign the same weight to the data related to specific identifiable conditions present when the passenger manually activates the heating element 18 as to other data previously relied upon to derive the pre-built predictive activation model. Overall, whenever a passenger in vehicle 10 manually enables or disables the heating element 18, controller 22 continues to improve predictive modeling by performing new CART analysis based on data related to specific identifiable conditions (pre-built predictive enable model and its subsequent recalibration). Finally, predictive modeling will be improved according to passenger preferences, and passengers will no longer need to manually enable or disable the heating element 18—predictive modeling will automatically enable or disable the heating element 18 to meet passenger preferences.

[0099] The refinement of the pre-built predictive activation model into a new predictive activation model, the updated predictive activation model, and subsequent improvements will identify passenger preferences, including scenarios where passengers wish to activate the heating element 18 for reasons other than the temperature inside vehicle 10 or the ambient temperature. For example, a passenger might want the heating element 18 to provide heat to their hands during the first few minutes of their commute for medical reasons. As another example, during a spring weekend when the current occupant-side setpoint temperature is below a specific temperature (used to ensure passenger comfort), passengers might want the heating element 18 to provide heat (to ensure passenger comfort) as a compensatory effect against the blowing cold air, thus attempting to meet the previous occupant-side setpoint temperature. CART analysis of the collected data in relation to specific identifiable conditions will learn this behavior and ultimately enable and disable the heating element 18 automatically accordingly. Therefore, CART analysis is a learning algorithm that provides high accuracy because it takes into account specific identifiable conditions throughout the history of vehicle 10. Other possible non-learning methods (such as those involving weighted averages) would not be as accurate, nor would they take into account time / day / seasonal correlations.

[0100] The method of controlling the heating element 18 may further include: determining, by comparing collected data with the rules of a pre-built predictive level model, which of several different heating levels the controller 22 will initially and automatically set for the heating element 18, and initially and automatically setting the heating element 18 to the determined level. In other words, when the controller 22 determines to automatically activate the heating element 18 based on a pre-built predictive activation model (or a new predictive activation model or an updated predictive activation model), the controller 22 additionally determines which level (e.g., low, medium, or high) to set the heating element 18 to based on the pre-built predictive level model and data relating to specific identifiable conditions. While the heating element 18 remains activated, the controller 22 dynamically compares the collected data with the rules of the pre-built predictive level model and adjusts the level of the heating element 18 accordingly. If the rules of the pre-built predictive level model indicate a change in the heating level of the heating element 18 based on data collected after the heating element 18 has been activated, then the controller 22 therefore causes the heating element 18 to transfer heat according to the level specified by the pre-built predictive level model.

[0101] The method of controlling the heating element 18 may also include: a passenger of the vehicle 10 manually changing the heating level of the heating element 18 via a user interface 30. For example, based on a pre-built predictive level model, the controller 22 may have initially set the heating element 18 to heat at level 3 (high), and the passenger may then press a button 42 marked "low" on the touchscreen display 32 to cause the heating element 18 to deliver heat at a relatively low level. The controller 22 accepts this interaction as input and thus causes the heating element 18 to heat at this relatively low level.

[0102] The method of controlling the heating element 18 may further include: recalibrating a pre-built predictive level model to a new predictive level model when the passenger manually changes the heating level of the heating element 18 via the user interface 30, thereby taking into account the collected data related to the specific identifiable conditions present when the passenger manually changes the heating level, and establishing new rules for managing the heating level of the heating element 18 when the heating element 18 is automatically activated. The controller 22 records data related to the specific identifiable conditions present when the passenger manually changes the heating level and prepares a new predictive level model to take these specific identifiable conditions into account. Overall, whenever the passenger manually changes the heating level of the heating element 18, the controller 22 continues to improve the predictive modeling for managing the temperature change level by performing new neural network analysis or multilayer perceptron classifier analysis, including the collected data related to the specific identifiable conditions. Like CART analysis, multilayer perceptron classifier analysis is therefore a learning algorithm that provides high accuracy because it takes into account the specific identifiable conditions throughout the history of the vehicle 10. Other possible non-learning methods (such as those involving weighted averages) would not be as accurate.

[0103] The method of controlling the heating element 18 may further include: automatically deactivating the heating element 18 after a passenger manually changes the heating level, and then automatically reactivating the heating element 18. When the controller 22 automatically reactivates the heating element 18 according to a pre-built predictive activation model (or a recalibrated version thereof), the method may further include: determining which of several different heating levels the controller 22 will initially and automatically set for the heating element 18 by comparing collected data with a new predictive level model, and automatically setting the heating element 18 to the determined level. In other words, during subsequent automatic activation periods of the heating element 18, the controller 22 uses the rules of the new predictive level model to determine which heating level the heating element 18 will be set to.

[0104] The method of controlling the heating element 18 may further include: freeing the passenger from using the steering wheel 14 and allowing a second passenger who is using or will use the steering wheel 14 to occupy the seat assembly 16, and identifying the second passenger as different from the first passenger. The controller 22 may determine that the second passenger, different from the first passenger, is occupying the seat assembly 16 and using the steering wheel 14 in various ways, such as by comparing the weight of the second passenger measured by the seat assembly 16 with the weight of the first passenger. Alternatively, the second passenger may indicate to the controller 22 via the user interface 30 (such as by selecting a user profile specific to the second passenger) that it is the second passenger, not the first passenger, who is occupying the seat assembly 16 and will be using or is using the steering wheel 14 with the heating element 18.

[0105] The method of controlling the heating element 18 may further include: collecting data related to specific identifiable conditions when the second passenger is using the steering wheel 14, and determining whether the collected data meets the rules of the pre-built predictive activation model in order to initially and automatically activate the heating element 18 by comparing only the data collected when the second passenger occupies the vehicle 10 (and uses the steering wheel 14), and not the data collected when the first passenger occupies the vehicle 10 (and uses the steering wheel 14), with the rules of the pre-built predictive activation model. In other words, the controller 22 recognizes that the second passenger is using the steering wheel 14 and restarts using the pre-built predictive activation model instead of a predictive activation model (such as a new predictive activation model or its subsequent recalibrated version) that takes into account the first passenger's manual activation or deactivation of the heating element 18. Therefore, only the second passenger's manual activation and deactivation of the heating element 18 will result in the pre-built predictive activation model being recalibrated to the subsequent predictive model. The method of controlling the heating element 18 may further include: initially and automatically activating the heating element 18 when the second passenger is using the steering wheel 14. In other words, the controller 22 compares the collected data related to specific identifiable conditions with the rules of the pre-built predictive activation model, and can then activate the heating element 18 to transfer heat as indicated by the collected data and the rules of the pre-built predictive activation model when the second passenger uses the steering wheel 14.

[0106] The method of automatically controlling the activation / deactivation of the heating element 18 based on a pre-built predictive activation model (and its subsequent recalibration iterations) and automatically controlling the heating level based on a pre-built predictive level model (and its subsequent recalibration iterations) offers advantages over other methods that control all temperature control means in the vehicle, such as fan level, temperature setpoint, etc. For example, the user of the steering wheel 14 may only want the controller 22 to automatically control the heating element 18 in the steering wheel 14, but not the entire climate in the interior 12 of the vehicle 10.

[0107] It should be understood that changes and modifications can be made to the above structure without departing from the concept of the invention, and it should also be understood that these concepts are intended to be covered by the following claims unless expressly stated otherwise in their language.

[0108] According to the present invention, a method for controlling a heating element in a steering wheel of a vehicle includes: providing a vehicle, the vehicle including: a steering wheel including a heating element; a controller communicating with the heating element, the controller including a pre-built predictive activation model listing rules governing the activation of the heating element, the rules varying with data relating to a specific identifiable condition; and a user interface configured to allow manual activation or deactivation of the heating element; collecting data relating to the specific identifiable condition; determining whether the collected data satisfies the rules of the pre-built predictive activation model by comparing the collected data with the rules of the pre-built predictive activation model in order to initially and automatically activate the heating element; and automatically activating the heating element.

[0109] According to one embodiment, the pre-built predictive enablement model is formed based on classification and regression tree analysis of input data related to the specific identifiable condition collected from other operators of other vehicles.

[0110] According to one embodiment, the pre-built predictive enablement model establishes rules that vary with at least the following specific identifiable conditions: the temperature inside the vehicle and the temperature outside the vehicle.

[0111] According to one embodiment, the pre-built predictive enablement model establishes rules that vary with at least the following specific identifiable conditions: the level of air being blown by the climate control system in the vehicle and the front user-side temperature setpoint.

[0112] According to one embodiment, the pre-built prediction-enabled model establishes rules that vary with at least the following additional specific identifiable conditions: the time of day; and the temperature difference between the temperature inside the vehicle and the temperature outside the vehicle.

[0113] According to one embodiment, when the temperature inside the vehicle is below a certain temperature, the controller automatically activates the heating element based on the rules of the pre-built prediction activation model that vary with data related to at least one other specific identifiable condition besides the temperature inside the vehicle.

[0114] According to one embodiment, when the occupant-side temperature setpoint is greater than a specific value, the controller automatically activates the heating element; however, when the occupant-side temperature setpoint is less than the specific value, the controller does not automatically activate the heating element.

[0115] According to one embodiment, based on the rules of the pre-built prediction activation model, if the heated seat function has not yet been activated, the controller activates the heating element; however, if the heated seat function has already been activated, the controller does not activate the heating element.

[0116] According to one embodiment, the controller activates the heating element if the level of air being blown by the climate control system in the vehicle is greater than a certain level, according to the rules of the pre-built predictive activation model; and wherein, according to the rules of the pre-built predictive activation model, the controller does not activate the heating element if the level of air being blown by the climate control system in the vehicle is greater than a certain level.

[0117] According to one embodiment, a further feature of the invention is that, after the heating element is initially and automatically activated according to the pre-built predictive activation model, if the collected data related to the specific identifiable condition collected after the initial and automatic activation of the heating element satisfies the rule of the pre-built predictive activation model for deactivating the heating element, then the heating element is automatically deactivated according to the pre-built predictive activation model.

[0118] According to one embodiment, a further feature of the invention is that, after the heating element is automatically deactivated according to the pre-built predictive activation model, if the collected data related to the specific identifiable condition collected after the heating element is deactivated again satisfies the activation rule of the pre-built predictive activation model, the heating element is automatically reactivated according to the pre-built predictive activation model.

[0119] According to one embodiment, a further feature of the invention is that the first passenger of the vehicle manually deactivates the heating element via the user interface.

[0120] According to one embodiment, a further feature of the invention is that when the first passenger manually disables the heating element via the user interface, the pre-built predictive enable model is recalibrated to a new predictive enable model, thereby taking into account the collected data related to the specific identifiable conditions present when the first passenger manually disables the heating element, and establishing new rules for enabling and / or disabling the heating element.

[0121] According to one embodiment, a further feature of the invention is that the first passenger manually activates the heating element via the user interface.

[0122] According to one embodiment, a further feature of the invention is that when the first passenger manually activates the heating element via the user interface, the new predictive activation model is recalibrated to an updated predictive activation model, thereby taking into account the collected data related to the specific identifiable conditions present when the first passenger manually activates the heating element, and establishing new rules for activating and / or deactivating the heating element.

[0123] According to one embodiment, a further feature of the invention is that the heating element can be adjusted to several different heating levels; the controller further includes a pre-built predictive level model, the pre-built predictive level model establishing rules governing which of the several different heating levels the controller will initially and automatically set for the heating element, the rules of the pre-built predictive level model varying with one or more of the specific identifiable conditions; and the user interface is further configured to allow the first passenger to manually select the level of the several different heating levels; the method further includes: determining which of the several different heating levels the controller will initially and automatically set for the heating element by comparing collected data with the rules of the pre-built predictive level model; and initially and automatically setting the heating element to the determined level.

[0124] According to one embodiment, the pre-built prediction level model is formed based on multilayer perceptron classifier analysis of input data collected from other vehicles in relation to the specific identifiable condition.

[0125] According to one embodiment, a further feature of the invention is that the first passenger manually changes the heating level of the heating element via the user interface.

[0126] According to one embodiment, a further feature of the invention is that, when the first passenger manually changes the heating level of the heating element via the user interface, the pre-built predictive level model is recalibrated to a new predictive level model, thereby taking into account collected data related to the specific identifiable conditions present when the first passenger manually changes the heating level, and establishing new rules for managing the heating level of the heating element when the heating element is automatically activated; automatically deactivating the heating element; automatically reactivating the heating element; determining which of the several different heating levels the controller will initially and automatically set for the heating element by comparing the collected data with the rules of the new predictive level model; and automatically setting the heating element to the determined level.

[0127] According to one embodiment, a further feature of the invention is that the first passenger is exempted from using the steering wheel; a second passenger using the steering wheel occupies the vehicle; the second passenger is identified as different from the first passenger; data related to the identifiable condition is collected while the second passenger is using the steering wheel; whether the collected data satisfies the rules of the pre-built predictive activation model by comparing only the data collected while the second passenger is using the steering wheel, and not the data collected while the first passenger is using the steering wheel, with the rules of the pre-built predictive activation model in order to initially and automatically activate the heating element; and the heating element is initially and automatically activated while the second passenger is using the steering wheel.

Claims

1. A method for controlling a heating element within a vehicle's steering wheel, comprising: Provide a vehicle, said vehicle comprising: Steering wheel, the steering wheel including a heating element; A controller, communicating with the heating element, includes a pre-built predictive activation model that lists rules governing the activation of the heating element, the rules varying with data related to specific identifiable conditions; and A user interface configured to allow manual activation or deactivation of the heating element; Collect data related to the specific identifiable condition; By comparing the collected data with the rules of the pre-built predictive activation model, it is determined whether the collected data meets the rules of the pre-built predictive activation model in order to initially and automatically activate the heating element; and The heating element is activated automatically; The pre-built predictive activation model is formed based on classification and regression tree analysis of input data related to the specific identifiable conditions collected from other operators of other vehicles.

2. The method as described in claim 1, in, The pre-built prediction-enabled model establishes rules that vary with at least the following specific identifiable conditions: the temperature inside the vehicle and the temperature outside the vehicle.

3. The method as described in claim 1 or 2, in, The pre-built predictive enablement model establishes rules that vary with at least the following specific identifiable conditions: the level of air being blown by the climate control system in the vehicle and the front user-side temperature setpoint.

4. The method as described in claim 1 or 2, in, The pre-built prediction-enabled model establishes rules that vary with at least the following additional specific identifiable conditions: the time of day; and the temperature difference between the temperature inside the vehicle and the temperature outside the vehicle.

5. The method as described in claim 2, in, When the temperature inside the vehicle is below a certain temperature, the controller automatically activates the heating element according to the rules of the pre-built prediction activation model that vary with data related to at least one other specific identifiable condition besides the temperature inside the vehicle.

6. The method as described in claim 1, in, When the occupant-side temperature setpoint is greater than a specific value, the controller automatically activates the heating element; however, when the occupant-side temperature setpoint is less than the specific value, the controller does not automatically activate the heating element.

7. The method as described in claim 3, in, According to the rules of the pre-built prediction activation model, if the level of air being blown by the climate control system in the vehicle is greater than a certain level, then the controller activates the heating element. and Specifically, according to the rules of the pre-built prediction activation model, if the level of air being blown by the climate control system in the vehicle is lower than a certain level, then the controller does not activate the heating element.

8. The method of claim 1, further comprising: After the heating element is initially and automatically activated according to the pre-built predictive activation model, if the collected data related to the specific identifiable condition collected after the initial automatic activation of the heating element satisfies the rule of the pre-built predictive activation model for deactivating the heating element, then the heating element is automatically deactivated according to the pre-built predictive activation model.

9. The method of claim 8, further comprising: After the heating element is automatically deactivated according to the pre-built predictive activation model, if the collected data related to the specific identifiable condition collected after the heating element is deactivated again satisfies the activation rule according to the pre-built predictive activation model, the heating element is automatically reactivated according to the pre-built predictive activation model.

10. The method of claim 1, further comprising: The first passenger in the vehicle manually deactivates the heating element via the user interface; as well as When the first passenger manually disables the heating element via the user interface, the pre-built predictive enable model is recalibrated to a new predictive enable model, thereby taking into account the collected data related to the specific identifiable conditions present when the first passenger manually disables the heating element, and establishing new rules for enabling and / or disabling the heating element.

11. The method of claim 10, further comprising: The first passenger manually activates the heating element via the user interface; as well as When the first passenger manually activates the heating element via the user interface, the new predictive activation model is recalibrated to an updated predictive activation model, thereby taking into account the collected data related to the specific identifiable conditions present when the first passenger manually activates the heating element, and establishing new rules for activating and / or deactivating the heating element.

12. The method as described in claim 10, The heating element can be adjusted to several different heating levels; The controller also includes a pre-built prediction level model, which establishes rules governing which of the several different heating levels the controller will initially and automatically set for the heating element, and these rules vary with one or more of the specific identifiable conditions; and The user interface is further configured to allow the first passenger to manually select one of the several different heating levels; The method further includes: By comparing the collected data with the rules of the pre-built predictive level model, it is determined which of the several different heating levels the controller will initially and automatically set for the heating element; and The heating element is initially and automatically set to a predetermined level.

13. The method of claim 12, further comprising: The first passenger manually changes the heating level of the heating element via the user interface; When the first passenger manually changes the heating level of the heating element via the user interface, the pre-built prediction level model is recalibrated to a new prediction level model, thereby taking into account the collected data related to the specific identifiable conditions present when the first passenger manually changes the heating level, and establishing new rules for managing the heating level of the heating element when the heating element is automatically activated. The heating element is automatically deactivated; The heating element is automatically reactivated; By comparing the collected data with the rules of the new prediction level model, it is determined which of the several different heating levels the controller will initially and automatically set for the heating element; as well as The heating element is automatically set to the predetermined level.

14. The method of any one of claims 10-13, further comprising: This frees the first passenger from using the steering wheel; The vehicle is occupied by a second passenger who is using the steering wheel; The second passenger was identified as different from the first passenger; Data related to the identifiable conditions is collected while the second passenger is using the steering wheel; The collected data is compared with the rules of the pre-built predictive activation model only when the second passenger is using the steering wheel, and not when the first passenger is using the steering wheel, to determine whether the collected data meets the rules of the pre-built predictive activation model in order to initially and automatically activate the heating element. as well as The heating element is initially and automatically activated when the second passenger is using the steering wheel.

Images