Automatically control heating and cooling of vehicle seat assemblies based on predictive modeling

Abstract

The present disclosure provides "Automatic Control of Heating and Cooling of a Vehicle Seat Assembly Based on Predictive Modeling." A method of controlling a temperature change element in a seat assembly of a vehicle includes: having a first occupant occupy a seat assembly having a temperature change element; collecting data related to a specific identifiable condition while the first occupant is occupying the seat assembly; determining whether the collected data satisfies rules of a pre-established predictive activation model to activate the temperature change element by comparing the collected data to rules of a pre-established predictive activation model, the pre-established predictive activation model prescribing rules governing activation of the temperature change element based on the data related to the specific identifiable condition; and activating the temperature change element.

Description

Technical Field

[0001] The present invention generally relates to heating and cooling of vehicle seat assemblies. Background Art

[0002] Vehicles typically include a seat assembly designated for a driver-occupant of the vehicle. The seat assembly sometimes includes a temperature change element that can selectively provide heat to or remove heat from the seat assembly (i.e., cooling), which improves the comfort of the seat assembly occupant. The seat assembly occupant typically must manually activate and deactivate the temperature change element via a user interface. Summary of the Invention

[0003] According to one aspect of the present invention, a method for controlling a temperature change element within a seat assembly of a vehicle comprises: providing a vehicle, the vehicle comprising: a seat assembly, the seat assembly including a temperature change element; a controller, the controller communicating with the temperature change element, the controller comprising a pre-established predictive activation model, the model prescribing rules governing activation of the temperature change element based on data relating to a specific identifiable condition; and a user interface configured to allow manual activation or deactivation of the temperature change element; having a first occupant occupy the seat assembly; collecting data relating to the specific identifiable condition while the first occupant is occupying the seat assembly; determining whether the collected data satisfies the rules of the pre-established predictive activation model by comparing the collected data with the rules of the pre-established predictive activation model so as to automatically activate the temperature change element first; and automatically activating the temperature change element.

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

[0005] Pre-established predictive engagement models are formed based on classification and regression tree analysis of input data related to specific identifiable conditions collected from other drivers in other vehicles;

[0006] A pre-established predictive engagement model establishes rules that are a function of at least the following specific identifiable conditions: ambient temperature; a temperature set point for the vehicle's interior; time of day; whether an occupant has requested the vehicle to heat the interior with the blower at a specific blower speed; the temperature of the vehicle's interior; and a temperature difference between the ambient temperature and the temperature in the vehicle;

[0007] A pre-established predictive activation model establishes rules that are a function of at least the following specific identifiable conditions: whether the windshield wipers are activated; whether the air conditioning is activated; the vehicle's interior temperature set point; the ambient temperature; the level of air blown by the vehicle's climate control system; the engine speed; the vehicle's speed; and the vehicle's interior temperature;

[0008] A pre-established predictive activation model establishes rules that are a function of at least the following specific identifiable conditions: vehicle interior temperature; ambient temperature; the level of air blown by the vehicle's climate control system; whether rear window defrost is activated; vehicle speed; whether air conditioning is activated; engine speed; and whether windshield wipers are activated;

[0009] When the ambient temperature is greater than a specified temperature, the controller automatically activates the temperature changing element based on data related to at least one other specified identifiable condition other than the ambient temperature according to a pre-established predictive activation model;

[0010] When the ambient temperature is less than a specified temperature, the controller automatically activates the temperature changing element based on data related to at least one other specified identifiable condition other than the ambient temperature according to a pre-established predictive activation model;

[0011] When the windshield wipers are already activated, the controller does not automatically activate the temperature changing element based on a pre-established predictive activation model;

[0012] when the windshield wipers are not already activated, the controller automatically activates the temperature changing element based on data related to at least one other specific identifiable condition other than whether the windshield wipers are already activated in accordance with a pre-established predictive activation model;

[0013] When the temperature in the vehicle is below a specified temperature, the controller does not automatically activate the temperature changing element to provide cooling based on a pre-established predictive activation model;

[0014] When the temperature in the vehicle exceeds a certain temperature, the controller automatically activates the temperature change element to provide cooling based on a pre-established predictive activation model;

[0015] a pre-established predictive activation model establishing rules for activating the temperature changing element to provide cooling, the rules being a function of data related to at least the following specific identifiable conditions: ambient temperature; temperature in the vehicle; whether rear window defrost has been activated; and a temperature set point for the interior of the vehicle;

[0016] When the ambient temperature is less than a specified temperature and the temperature in the vehicle is greater than another specified temperature, the controller automatically activates the temperature changing element to apply cooling according to rules of a pre-established predictive activation model based on data related to at least one other specified identifiable condition including vehicle speed;

[0017] When the ambient temperature is greater than a specified temperature, the controller automatically activates the temperature changing element to apply cooling according to rules of a pre-established predictive activation model based on data related to at least one other specified identifiable condition;

[0018] automatically deactivating the temperature change element according to the pre-established predictive activation model after the temperature change element is first automatically activated according to the pre-established predictive activation model if collected data related to the particular identifiable condition collected after the temperature change element was first automatically activated satisfies a rule of the pre-established predictive activation model regarding deactivation of the temperature change element;

[0019] After automatically deactivating the temperature change element according to the pre-established predictive activation model, automatically reactivating the temperature change element according to the pre-established predictive activation model if collected data related to the specific identifiable condition collected after deactivation of the temperature change element again satisfies an activation rule according to the pre-established predictive activation model;

[0020] An occupant of the seat assembly manually deactivates the temperature changing element via the user interface;

[0021] Upon manual deactivation of the temperature change element by the occupant via the user interface, recalibrating the pre-established predictive activation model to a new predictive activation model to account for the collected data related to the specific identifiable conditions that existed when the occupant manually deactivated the temperature change element and establishing new rules regarding activation and / or deactivation of the temperature change element;

[0022] The occupant manually activates the temperature changing element via the user interface;

[0023] Upon manual activation of the temperature change element by the occupant via the user interface, recalibrating the new predictive activation model to an updated predictive activation model to account for the collected data related to the specific identifiable conditions that existed when the occupant manually activated the temperature change element and establishing new rules regarding activation and / or deactivation of the temperature change element;

[0024] The temperature change element can be adjusted to several different temperature change levels;

[0025] the controller further comprising a pre-established predictive level model that establishes rules governing which of several different temperature change levels the controller will automatically set for the temperature changing element first, the rules of the pre-established predictive level model being a function of one or more of the specific identifiable conditions;

[0026] The user interface is further configured to allow the occupant to manually select one of several different levels of temperature change;

[0027] Determining which of several different temperature change levels the controller will automatically set for the temperature change element first by comparing the collected data to the rules of a pre-established predictive level model;

[0028] First, the temperature changing element is automatically set to a determined level;

[0029] The pre-established predictive level model is formed based on a multi-layer perceptron classifier analysis of input data related to specific identifiable conditions collected from other vehicles;

[0030] An occupant of the seat assembly manually changes the temperature change level of the associated temperature changing element via the user interface;

[0031] recalibrating the pre-established predictive level model to a new predictive level model after the occupant manually changes the temperature change level associated with the temperature change element via the user interface, thereby taking into account the collected data related to the specific identifiable conditions that existed when the occupant manually changed the temperature change level and establishing new rules governing the temperature change level associated with the temperature change element when the temperature change element is automatically activated;

[0032] Automatically deactivating the temperature changing element;

[0033] Automatically reactivate the temperature changing element;

[0034] Determining which of several different temperature change levels the controller will automatically set for the temperature change element first by comparing the collected data to the rules of the new predictive level model;

[0035] Automatically set the temperature changing element to a determined level;

[0036] Remove the occupant from the seat assembly;

[0037] having a second occupant occupy the seat assembly;

[0038] recognizing that the second occupant is different from the first occupant;

[0039] collecting data related to the identifiable condition while the second occupant is occupying the seat assembly;

[0040] determining whether the collected data satisfies the rules of the pre-established predictive engagement model to automatically engage the temperature change element first by comparing the data collected only when the second occupant is occupying the seat assembly, but not when the occupant is occupying the seat assembly, to the rules of the pre-established predictive engagement model; and

[0041] The temperature changing element is automatically activated first while the second occupant is occupying the seat assembly.

[0042] Those skilled in the art will understand and appreciate these and other aspects, objects and features of the present invention after studying the following specification, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In the attached figure:

[0044] Figure 1 is a side plan view of an interior of a vehicle illustrating a first seat assembly including a temperature change element for providing selective heating or cooling, a controller, and a user interface;

[0045] Figure 2 yes Figure 1 A schematic diagram of a controller illustrating that the controller accepts inputs from various data sources and a user interface and uses these input sources to automatically control activation / deactivation of a temperature changing element and the level at which the temperature changing element changes temperature;

[0046] Figure 3A yes Figure 1 a front view of an exemplary user interface showing a touchscreen display that provides notification that the controller has automatically enabled the temperature change element and the level of temperature change, and provides a touchable "off" button to allow an occupant of the first seat assembly to manually deactivate the temperature change element;

[0047] Figure 3B yes Figure 1 a front view of an exemplary user interface of FIG. 1 showing a touchscreen display providing notification that the controller has not automatically enabled the temperature change element and providing a touchable “ON” button to allow an occupant of the first seat assembly to manually enable the temperature change element;

[0048] Figure 4is a flow chart illustrating the controller collecting (accepting as input) data from various data sources related to a particular identifiable condition after an occupant occupies the first seat assembly, comparing the data to rules governing activation and deactivation of a temperature change element established by a pre-established predictive activation model, and activating the temperature change element or not activating / deactivating the temperature change element in accordance with the rules;

[0049] Figure 5 is a schematic diagram illustrating rules of a first exemplary pre-established predictive activation model (relating to heating) that provides data relating to specific identifiable conditions that must exist for a controller to automatically activate a temperature change element to apply heat and to not activate / automatically deactivate the temperature change element;

[0050] Figure 6A and Figure 6B is a schematic diagram illustrating rules of a second exemplary pre-established predictive activation model (also relating to heating) that provides data relating to specific identifiable conditions that must exist for the controller to automatically activate the temperature change element to apply heat and not activate / automatically deactivate the temperature change element; and

[0051] Figure 7A and Figure 7B is a schematic diagram illustrating rules for a third exemplary pre-established predictive activation model (this time relating to cooling) that provides data relating to specific identifiable conditions that must exist for a controller to automatically activate a temperature changing element to impart cooling and not activate / automatically deactivate the temperature changing element. DETAILED DESCRIPTION

[0052] For the purpose of this description, the term "backward" shall refer to Figure 1 The disclosure herein is oriented as herein described. However, it should be understood that the disclosure may assume various alternative orientations unless expressly indicated to the contrary. It should also be understood that the specific devices and processes shown in the drawings and described in the following specification are merely exemplary embodiments of the inventive concepts defined in the appended claims. Accordingly, specific dimensions and other physical characteristics related to the embodiments disclosed herein are not to be construed as limiting unless the claims expressly state otherwise.

[0053] refer to Figure 1, the vehicle 10 includes an interior 12. A first seat assembly 14 and a second seat assembly 16 are positioned within the interior 12 and form a first row of seats 18. The vehicle 10 may also include a second row of seats 20 positioned behind the first row of seats 18, a third row of seats 22 positioned behind the second row of seats 20, and so on. The first seat assembly 14 may be designated for an occupant who is driving the vehicle 10. The second seat assembly 16 may be designated for an occupant who is a passenger in the vehicle 10. Because the second seat assembly 16, the second row of seats 20, and the third row of seats 22 may be the same as the first seat assembly 14 for purposes of this disclosure, only the first seat assembly 14 will be discussed in detail herein.

[0054] The first seat assembly 14 includes a temperature change element 24. The temperature change element 24 can be any element that raises or lowers the temperature of the first seat assembly 14 on command. The temperature change element 24 can be a heating mechanism for applying heat, such as a wire that resists an electric current and generates heat, a cooling mechanism such as cooling air that removes heat (i.e., applies cooling), or a Peltier thermoelectric device that can generate cooling or heating. The first seat assembly 14 can include a temperature change element 24 dedicated to heating and another temperature change element 24 dedicated to cooling. The temperature change element 24 can be adjusted to provide several different levels of temperature change. For example, the temperature change element 24 can provide a relatively high, medium, or low level of temperature change.

[0055] Now also refer to Figure 2 The vehicle 10 also includes a controller 26. The controller 26 communicates with the temperature changing element 24. The controller 26 controls whether the temperature changing element 24 is enabled (i.e., providing heat or providing cooling) and the intensity of the temperature change caused by the temperature changing element 24 (i.e., which level, such as high, medium, or low). The controller 26 may include a microprocessor 28 to execute programs stored in the memory 30, such as those for controlling the temperature changing element 24.

[0056] The controller 26 includes a pre-established predictive activation model for heating and / or a pre-established predictive activation model for cooling that governs whether the controller 26 will automatically activate the temperature change element 24 to apply heat or cooling, respectively, to the first seat assembly 14 without requiring input or instruction from an occupant of the first seat assembly 14. The pre-established predictive activation model may be stored in the memory 30. The pre-established predictive activation model is formed by analyzing data collected from occupants of seat assemblies in other vehicles (hereinafter referred to as "test vehicles"). The analysis generally addresses the conditions that exist when occupants of the seat assemblies of the test vehicles activate the temperature change elements of those seat assemblies to apply heat and cooling. Identifying these conditions can be used to predict when an occupant of the first seat assembly 14 desires to activate the temperature change element 24 of the first seat assembly 14 to apply heat (or cooling) and then automatically activate the temperature change element 24 to perform this operation without requiring the occupant to manually instruct the controller 26 to activate the temperature change element 24. In other words, the pre-established predictive enabling model is formed based on analysis of input data collected from test vehicles related to a plurality of conditions (hereinafter referred to as "specific identifiable conditions"). The pre-established predictive enabling model is a function of these specific identifiable conditions. The pre-established predictive enabling model and specific identifiable conditions are discussed in more detail below.

[0057] The controller 26 also includes a pre-established predictive level model for heating and / or a pre-established predictive level model for cooling stored in the memory 30. The pre-established predictive level model establishes rules governing which of several different temperature change levels the controller 26 will automatically set for the temperature change element 24 first. For example, the pre-established predictive level model for heating establishes rules governing which heating level (i.e., heating intensity) the controller 26 will automatically set for the temperature change element 24 when the controller 26 automatically enables the temperature change element 24 to apply heat. Similarly, the pre-established predictive level model for cooling establishes rules governing which cooling level (i.e., cooling intensity) the controller 26 will automatically set for the temperature change element 24 when the controller 26 automatically enables the temperature change element 24 to apply cooling. The rules of the pre-established predictive level model are a function of specific identifiable conditions. The pre-established predictive level model will also be discussed further below.

[0058] The controller 26 receives input regarding the specific identifiable conditions from one or more data sources 46 within the vehicle 10. The one or more data sources 46 may be, among other things, sensors and / or settings. As discussed further below, the controller 26 utilizes the data regarding the specific identifiable conditions to control the temperature changing element 24 according to the pre-established predictive model and the second pre-established predictive model (and subsequent refinements thereof).

[0059] Now also refer to Figure 3A and Figure 3B The vehicle 10 also includes a user interface 32 in communication with the controller 26. The user interface 32 can be located in the vehicle 10 so that an occupant of the first seat assembly 14 can interact with the user interface 32. For example, the user interface 32 can be, in particular, a touchscreen display 34, a knob, a switch, and / or a voice-operated user interface. The user interface 32 is configured to allow an occupant to manually activate the temperature change element 24 if the controller 26 has not yet activated it, to apply heating or cooling according to the occupant's needs. Alternatively, the user interface 32 is configured to manually deactivate the temperature change element 24 if the controller 26 has already activated it, to apply heating or cooling contrary to the occupant's needs. For example, the user interface 32 can be a touchscreen display 34 with an option (e.g., a button 36 labeled "Off") that allows an occupant of the first seat assembly 14 to deactivate the temperature change element 24 of the first seat assembly 14 that the controller 26 has automatically activated to apply heat based on a pre-established predictive activation model. If the occupant presses the "Off" button 36, the controller 26 accepts the interaction as input and deactivates the temperature change element 24 to prevent it from applying heat. The same applies if the controller 26 has already automatically activated the temperature change element 24 to apply cooling based on a pre-established predictive activation model. Similarly, the touchscreen display 34 can include an option (e.g., a button 44 labeled "On") that allows an occupant of the first seat assembly 14 to activate a temperature change element 24 of the first seat assembly 14 that the controller 26 has not already automatically activated based on a pre-established predictive activation model. If the occupant presses the button 44 labeled "On," the controller 26 accepts the interaction as input and activates the temperature change element 24. For example, if the occupant desires that the temperature change element 24 apply heat to the first seat assembly 14, but the controller 26 has not yet automatically caused the temperature change element 24 to do so based on a pre-established predictive activation model for cooling, the occupant can navigate the touchscreen display 34 to the cooling options screen and press the button 44 labeled "On," and the controller 26 then activates the temperature change element 24 to apply cooling.

[0060] Additionally, the user interface 32 is configured to allow an occupant of the first seat assembly 14 to manually select one of several different temperature change levels. For example, the touchscreen display 34 may have options that allow the occupant of the first seat assembly 14 to manually select a relatively high temperature change level (e.g., button 38 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 40 labeled "Medium"). Instead of "High," "Medium," and "Low," the touchscreen display options may be "3," "2," and "1," respectively. If the occupant presses one of the buttons 38, 40, or 42, the controller 26 accepts the interaction as input and adjusts the level of the temperature change element 24 accordingly, thereby overriding the level that the controller 26 automatically sets for the temperature change element 24 based on a pre-established predictive level model. Occupant interaction with the user interface 32 in this manner to override the controller 26's automatic control of the temperature change element 24 affects subsequent automatic control by the controller 26, as discussed in more detail below.

[0061] Now refer to Figure 4 The novel method of controlling the temperature change element 24 can be performed using the above vehicle 10 including the first seat assembly 14 having the temperature change element 24, the controller 26, and the user interface 32. The novel method includes (at step 48) having an occupant occupy the first seat assembly 14, (at step 50) collecting data related to a specific identifiable condition (from the data source 46) while the occupant is occupying the first seat assembly 14, (at step 52) determining (by comparing the collected data with the rules of the pre-established predictive activation model governing activation) whether the collected data satisfies the rules of the pre-established predictive activation model to automatically activate the temperature change element 24 first, and if so, (at step 54) automatically activating the temperature change element 24 first. If the comparison of the collected data with the pre-established predictive activation model indicates that the activation rules for the temperature change element 24 are not satisfied, the method can return to step 50 and continue data collection. Even if the collected data satisfies the rules of the pre-established predictive activation model regarding activation of the temperature changing element 24, the method may further include returning to step 50 to continue data collection and subsequently determining whether the collected data satisfies the rules of the pre-established predictive activation model regarding deactivation of the temperature changing element 24, thereby resulting in deactivation of the temperature changing element 24 at step 56.

[0062] We now further discuss the test vehicles and the data collected therefrom relating to specific identifiable conditions, analyzing which data formed the previously established predictive activation model and the previously established predictive level model (and subsequent iterations thereof). Data was collected from 700 test vehicles. The data was narrowed down to less than 60 conditions that formed specific identifiable conditions that were believed to be relevant to the occupant's decision to activate the temperature change element 24 and the level (intensity) of temperature change that the temperature change element would cause. These specific identifiable conditions include: whether the windshield wipers are enabled due to sensing rain or other factors, i.e., whether they are wiping ("Smart_Wiper_Motor_Stat"); the front passenger side temperature set point ("Front_Rt_Temp_Setpt"); the front driver side temperature set point ("Front_Left_Temp_Setpt") (these last two temperature set points represent the set point temperatures for air blown toward the first seat assembly 14 and the second seat assembly 16, respectively); whether the rear window defrost is enabled ("Overriding_ModeReq", "Rear_Defrost_Soft_Bttn_Stt"); the level of air blown by the vehicle's climate control system ("Fr ont_Rear_Blower_Req”); outside / ambient temperature (“AirAmb_Te_ActlFilt”, “AirAmb_Te_ActlFilt_UB”, “AirAmb_Te_Actl”, “AirAmb_Te_Actl_UB”); the temperature of the interior of the vehicle (“InCarTemp”, “InCarTempQF”); engine speed (such as revolutions per minute) (“EngAout_N_Actl”, “EngAout_N_Actl_UB”); whether the driver has enabled the interior air recirculation function (“Recirc_Request”); and the time of day, which can be expressed as a time of day (“hour”). Other specific identifiable conditions include: whether the driver has requested steering wheel heating (“CC_HtdStrWhl_Req_Binary”, “CC_HtdStrWhl_Req”); whether the driver has enabled the front window defrost function (“Front_Defrost_Sft_Btn_Stt”); whether air conditioning should be enabled (“AC_Request”); vehicle 10 speed (“Veh_V_ActlEng_UB”, “Veh_V_ActlEng”); whether the passenger has enabled the temperature change element 24 for the second seat assembly 16, the passenger seat assembly (“Pass_Fr_Cond_Seat_Req”); and if so, to what level (“Pass_Fr_Cond_Seat_Lvl”).Still other specific identifiable conditions may include: the general state of the defrost control (“Default_Defrost_State”); whether the driver has enabled a function to defrost the side mirrors (“RrDefrost_HtdMirrReq”); the defrost state of the side mirrors (“RrDefrost_HtdMirrState”); whether the driver has manually overridden the automatic defrost function of the side mirrors (“Mirror_Manual_Override”); the horizontal and vertical positioning of the passenger mirror (“Pass_Mirror_Sw_UD_Stat” and “Pass_Mirror_Sw_LR_Stat”); whether the passenger in the second row seat 20 on the passenger side has enabled the temperature change element (“Pass_Rr_Cond_Seat_Req”), and if so, to what level (“Pass_Rr_Cond_Seat_Lvl”); whether the passenger in the second row seat 20 on the driver's side has enabled the temperature change element (“Drvr_Rr_Cond_Seat_Req”). Still other specific identifiable conditions relate to time, which can include minutes, seconds, date and day of the week (Monday, Tuesday, etc.), and season. Still other specific identifiable conditions include sunlight levels and trip-related statistics, such as trip length, trip frequency, trip characteristics (such as commuting versus sightseeing), GPS location (such as latitude and longitude), road grade, altitude, city versus rural driving, highway versus urban roads, torque, braking, and idling time.

[0063] The identifiers in parenthetical quotes above are provided to aid in interpreting the exemplary pre-established predictive enabling model reproduced below. Several identifiers may relate to the same concept. For example, "AirAmb_Te_ActlFilt," "AirAmb_Te_ActlFilt_UB," "AirAmb_Te_Actl," and "AirAmb_Te_Actl_UB" all relate to the temperature of the ambient air. Before analyzing data related to a particular identifiable condition to generate a pre-established predictive enabling model and a pre-established predictive level model, it may be advantageous to combine several identifiers into one identifier. For example, data with the identifier "AirAmb_Te_Act_UB" may be essentially a copy of "AirAmb_Te_Act" and may be completely removed from the data before analysis to generate the pre-established predictive enabling model and the pre-established predictive level model. As another example, "AirAmb_Te_ActlFilt" may be a version of "AirAmb_Te_Act" to filter out short-term fluctuations in the data using the "AirAmb_Te_Act" identifier. Therefore, only data with "AirAmb_Te_Act" may be included in the analysis to generate a pre-established predictive activation model and a pre-established predictive level model.

[0064] Generally speaking, by analyzing data related to specific identifiable conditions from a test vehicle, it is possible to determine what the specific identifiable conditions are when an occupant of the test vehicle decides to activate the temperature change element 24 (for both heating and cooling the first seat assembly 14) and when an occupant of the test vehicle decides to deactivate the temperature change element 24. When an occupant of the test vehicle decides to activate / deactivate the temperature change element 24, a pre-established predictive activation model and a pre-established predictive level model can then be developed to establish rules based on the data related to the specific identifiable conditions being met a certain percentage of the time. In other words, by modeling past occupant behavior exhibited in the test vehicle, the pre-established predictive activation model and the pre-established predictive level model can be used to predict future occupant expectations in the vehicle 10 regarding activation / deactivation of the temperature change element 24 (and temperature change levels), and automatically control activation / deactivation and level management of the temperature change element 24.

[0065] Prior to analyzing the data, some of the data may be processed to generate a pre-established predictive activation model and a pre-established predictive level model. For example, data related to a specific identifiable condition of whether the windshield wipers were activated due to sensing rain may be processed to simply reflect the wiper state as on or off (and assigned a value of 1 or 0) ("Smart_Wiper_Motor_Stat_V1") rather than initial data including a numeric value between 0 and 1 to reflect the speed of the wipers ("Smart_Wiper_Motor_Stat"). Data related to other specific identifiable conditions may be processed in the same manner to enable the pre-established predictive activation model and the pre-established predictive level model to more meaningfully predict the data. As another example, some of the specific identifiable conditions may be derived from other specific identifiable conditions and may be further analyzed to achieve predictive capabilities with respect to the pre-established predictive activation model and the pre-established predictive level model. For example, when the temperature changing element 24 is activated ("turnOnHeat1", "turnOnHeat2", "turnOnHeat3"), the specific identifiable condition of whether the occupant has requested that the vehicle 10 heat the interior 12 at low, medium, or high blower speed is derived from the specific identifiable conditions of the driver's side temperature setting ("Front_Left_Temp_Setpt") and the level of air blown by the vehicle's climate control system ("Front_Rear_Blower_Req", "RCCM_Fr_Rr_Blower_Req"). As another example, the specific identifiable conditions of the ambient temperature ("AirAmb_Te_Act1") and the temperature in the vehicle 10 ("InCarTemp") can be used to calculate the temperature difference between the two temperatures ("tempDiff"). As another example, the specific identifiable condition of the time of day ("hour") can be divided, such as whether it is morning ("isMorning"). As another example, a particular identifiable condition, time of day ("hour"), or another time related to a particular identifiable condition, may be divided into months ("month") or seasons, such as whether it is spring, summer, fall, or winter ("isSummerx").

[0066] The pre-established predictive engagement model and the pre-established predictive level model can be derived from data related to specific identifiable conditions collected from the test vehicle as a whole. Alternatively, the data related to specific identifiable conditions collected from the test vehicle can first be divided (e.g., into three segments, hereinafter referred to as "segments") based on criteria such as driver type (e.g., primarily city drivers, primarily highway drivers, "aggressive" drivers). The pre-established predictive engagement model and the pre-established predictive level model for each segment (i.e., each driver type) are separated. It is assumed that one type of operator will exhibit a different pattern of engaging the temperature changing element 24 than another type of operator. For example, one pre-established predictive engagement model for heating can be derived for one type of driver, another pre-established predictive engagement model for heating can be derived for another type of driver, and so on. Criteria that can be used to partition the data collected from over 700 vehicles include: the average trip length of each of the test vehicles; the standard deviation of the trip lengths; the average number of trips per unit time, such as per day; the number of trips that can be considered "short," such as two miles or less; the number of highway miles the test vehicles have traveled; the number of off-highway miles the vehicles have traveled; and the ratio between the latter two. Other criteria that can be used to partition the data collected from the test vehicles include: those that may relate to how "aggressively" a particular test vehicle has been driven, such as torque, load, vehicle speed, engine rpm, fuel economy, and how often the driver coasts (that is, how often the vehicle moves without the driver causing the vehicle to accelerate or decelerate via braking). Partitioning the data collected from the test vehicles to form segments can be performed via a k-means cluster algorithm.

[0067] The controller 26 may initially include pre-established predictive activation models and pre-established predictive level models generated from each segment, but by default, only use the pre-established predictive activation model and pre-established predictive level model for activation / deactivation (and level control) of the temperature changing element 24 for a particular segment. Then, as the vehicle 10 begins operating for a period of time, data related to specific identifiable conditions may be collected. This data may then be compared to the segments to determine which of the segments the vehicle 10 most resembles. For example, one of the segments may be data from a subset of test vehicles that primarily travel on highways, and the vehicle 10 may also primarily travel on highways. The pre-established predictive activation model and pre-established predictive level model derived from that particular segment may then be the pre-established predictive activation model and pre-established predictive level model that are subsequently utilized by the controller 26.

[0068] The pre-established predictive enabling model can be formed based on a classification and regression tree ("CART") analysis of data related to specific identifiable conditions collected from the test vehicle as a whole or partitioned as explained above (resulting in a pre-established predictive enabling model for each segment). There are a variety of CART analyses that can provide useful results, including the C.50 program (available from www.rulequest.com 2.07 GPL); as implemented in Weka (available from http: / / weka.sourceforge.net / doc.stable / weka / classifiers / trees / M5P.html ); and the random tree classifier as implemented in Weka (available from http: / / weka.sourceforge.net / doc.dev / weka / classifiers / trees / RandomTree.html There are other CART assays available, and this is not an exhaustive list.

[0069] An exemplary pre-established predictive activation model for heating developed according to the C.50 program CART analysis is set forth below. The exemplary pre-established predictive activation model sets forth rules for activating / deactivating the temperature change element 24 to apply heat to the first seat assembly 14 based on data associated with specific identifiable conditions.

[0070]

[0071] Those skilled in the art will understand how to interpret the above pre-established predictive activation model. Each row includes an identifier associated with a specific, identifiable condition. For example, the first row, "AirAmb_Te_Actl > 12.17466:" includes the identifier "AirAmb_Te_Actl," which, as described above, represents the ambient temperature. Each row includes a value associated with the preceding specific, identifiable condition. For example, the first line ""AirAmb_Te_Actl>12.17466:" includes the value "12.17466", which represents 12.17466 degrees Celsius. Each line includes a conditional statement. For example, the first line "AirAmb_Te_Actl>12.17466:" can be understood to mean "if the ambient temperature is greater than 12.17466 and". The reading will then proceed to the second and third lines, which are indented and otherwise considered subordinate to the first line. The second line includes the same identifiers and values as the previous ones, and also includes a conclusion indicated by adding a "0" after the colon ":". The "0" indicates that the temperature change element is disabled / not enabled. Element 24 is enabled to apply heat. Conversely, the third row includes ":1", indicating that the temperature changing element 24 is enabled to apply heat. (As used herein, the values for the front passenger side temperature set point ("Front_Rt_Temp_Setpt") and the front driver side temperature set point ("Front_Left_Temp_Setpt") are unitless and range between 119 and 171. This range is linearly related to 60 degrees Fahrenheit and 85 degrees Fahrenheit, respectively. Therefore, the value in the second row "Front_Rt_Temp_Setpt<=154.9836" would be roughly equal to 77.3 degrees Fahrenheit.) Therefore, the first, second, and third rows

[0072] AirAmb_Te_Actl>12.17466:

[0073] :...Front_Rt_Temp_Setpt<=154.9836:0(288)

[0074] :Front_Rt_Temp_Setpt>154.9836:1(18 / 1)

[0075] Together, this can be understood as follows: if the ambient temperature is greater than 12.17466 degrees Celsius and the front passenger side set point temperature is less than or equal to 154.9836 (77.3 degrees Fahrenheit), the temperature changing element 24 is not enabled to apply heat (or is disabled if heat is already being applied); but if the ambient temperature is greater than 12.17466 degrees Celsius and the front passenger side set point temperature is greater than 154.9836 (77.3 degrees Fahrenheit), the temperature changing element 24 is enabled to apply heat.

[0076] The above example is a relatively simple pre-established predictive engagement model for heating because the rules established by the pre-established predictive engagement model are functions of only a few of the specific identifiable conditions, namely, the ambient temperature (“AirAmb_Te_Actl” and “AirAmb_Te_ActlFilt”), the front passenger side temperature set point (“Front_Rt_Temp_Setpt”), the time of day divided into whether it is morning (“isMorning”), the temperature in the vehicle (“InCarTemp”), the temperature difference between the ambient temperature and the temperature in the vehicle (“tempDiff”), whether the occupant has requested the vehicle to heat the interior at a high blower speed (“turnOnHeat3”), and the front driver side temperature set point (“Front_Left_Temp_Setpt”).

[0077] More specifically, now refer to Figure 5 At step 60 , the controller 26 determines whether the ambient temperature is greater than 12.17466 degrees Celsius, or less than or equal to 12.17466 degrees Celsius. If the controller 26 determines that the ambient temperature is greater than 12.17466 degrees Celsius (“AirAmb_Te_Actl>12.17466”), then at step 62 , the controller 26 determines whether the front passenger-side temperature set point (“Front_Rt_Temp_Setpt”) is greater than a specific temperature (“Front_Rt_Temp_Setpt>154.9836”), or less than or equal to a specific temperature (“Front_Rt_Temp_Setpt<=154.9836”). If the controller 26 determines that the front passenger-side temperature set point (“Front_Rt_Temp_Setpt”) is greater than the specified temperature (“Front_Rt_Temp_Setpt>154.9836”), the controller 26 activates the temperature change element 24 to apply heat at step 64. Conversely, if the controller 26 determines that the front passenger-side temperature set point (“Front_Rt_Temp_Setpt”) is less than or equal to the specified temperature (“Front_Rt_Temp_Setpt<=154.9836”), the controller 26 does not activate the temperature change element 24 to apply heat at step 66, or deactivates the temperature change element 24 if the controller 26 has already activated the temperature change element 24 to apply heat. In other words, when the ambient temperature is greater than a certain temperature (“AirAmb_Te_Actl>12.17466”), the pre-established predictive model will automatically activate the temperature changing element 24 based on the front passenger side temperature set point (“Front_Rt_Temp_Setpt”) first at steps 62 and 64 .

[0078] If, on the other hand, the controller 26 determines at step 60 that the ambient temperature is less than or equal to the specified temperature ("AirAmb_Te_Actl<=12.17466"), the controller 26 first determines at step 68 whether the time of day is classified as morning ("isMorning"). If, on step 68, the controller 26 determines that it is not morning ("isMorning<=0"), the controller 26 does not activate the temperature change element 24 to apply heat at step 70, or deactivates the temperature change element 24 if it has already been activated. If, on the other hand, on step 68, the controller 26 determines that it is morning ("isMorning>0"), the controller 26 proceeds to step 72, where the controller 26 determines whether the occupant has requested that the vehicle 10 heat the interior 12 at a high blower speed ("turnOnHeat3"). If, at step 72, the controller 26 determines that the occupant has not requested the vehicle 10 to heat the interior 12 at a high blower speed ("turnOnHeat3 <= 0"), the controller 26 determines, at step 74, whether the temperature difference between the ambient temperature and the temperature in the vehicle 10 ("tempDiff") is greater than or less than a specified amount. If, at step 74, the controller 26 determines that the temperature difference between the ambient temperature and the temperature in the vehicle 10 is less than or equal to a specified amount ("tempDiff <= 3.670543") (3.67 degrees Celsius), at step 76, the controller 26 does not activate the temperature change element 24 to generate heat, or, if the controller 26 has already activated the temperature change element 24 to generate heat, deactivates the temperature change element 24. However, if, at step 74, the controller 26 determines that the temperature difference between the ambient temperature and the temperature in the vehicle 10 is greater than a specified amount ("tempDiff > 3.670543"), the controller 26 activates the temperature change element 24 to generate heat at step 78.

[0079] If, at step 72, the controller 26 determines that the occupant has requested that the vehicle 10 heat the interior 12 at a high blower speed ("turnOnHeat3>0"), the controller 26 determines, at step 80, whether the front driver's side temperature set point ("Front_Left_Temp_Setpt") is greater than or less than a specified amount. If, at step 80, the controller 26 determines that the front driver's side temperature set point is greater than the specified temperature ("Front_Left_Temp_Setpt>152"), the controller 26 does not activate the temperature change element 24 to generate heat, or, if the controller 26 has already activated the temperature change element 24 to generate heat, deactivates the temperature change element 24. However, if, at step 80, the controller 26 determines that the front operator's side temperature set point is less than or equal to the specified temperature ("Front_Left_Temp_Setpt<=152"), the controller 26 proceeds to step 84. At step 84, the controller 26 determines whether the ambient temperature ("AirAmb_Te_Actl") is greater than or less than a specific temperature. If, at step 84, the controller 26 determines that the ambient temperature is less than or equal to the specific temperature ("AirAmb_Te_Actl<=5.594171"), the controller 26 proceeds to step 86. At step 86, the controller 26 again determines whether the ambient temperature ("AirAmb_Te_ActlFilt") is greater than or less than the specific temperature. If, at step 86, the controller 26 determines that the ambient temperature is less than or equal to the specific temperature ("AirAmb_Te_ActlFilt<=-2.632576"), the controller 26 activates the temperature change element 24 to generate heat at step 88. However, if the controller 26 determines at step 86 that the ambient temperature is greater than the specified temperature (“AirAmb_Te_ActlFilt>-2.632576”), then at step 90 the controller 26 does not enable the temperature change element 24 to generate heat, or deactivates the temperature change element 24 if the controller 26 has already enabled the temperature change element 24 to generate heat.

[0080] Returning to step 84, if the controller 26 determines that the ambient temperature is greater than the specified temperature ("AirAmb_Te_Actl>5.594171"), the controller 26 proceeds to step 92. At step 92, the controller 26 determines whether the temperature in the vehicle 10 ("InCarTemp") is greater than or less than the specified temperature. If the controller 26 determines at step 92 that the temperature in the vehicle 10 is greater than the specified temperature ("InCarTemp>26.7017"), the controller 26 activates the temperature changing element 24 to apply heat at step 94. However, if the controller 26 determines at step 92 that the temperature in the vehicle 10 is less than or equal to the specified temperature ("InCarTemp<=26.7017"), the controller 26 proceeds to step 96 to again determine whether the temperature in the vehicle 10 ("InCarTemp") is greater than or less than the specified temperature. At step 96, if the controller 26 determines that the temperature in the vehicle 10 ("InCarTemp") is less than or equal to the specified temperature ("InCarTemp<=24.46211"), the controller 26 proceeds to step 98. At step 98, the controller 26 again determines whether the temperature in the vehicle 10 ("InCarTemp") is greater than or less than the specified temperature. At step 98, if the controller 26 determines that the temperature in the vehicle 10 is less than or equal to the specified temperature ("InCarTemp<=18.35714"), then at step 100, the controller 26 does not activate the temperature change element 24 to generate heat, or deactivates the temperature change element 24 if the controller 26 has already activated the temperature change element 24 to generate heat. However, at step 98, if the controller 26 determines that the temperature in the vehicle 10 is greater than the specified temperature ("InCarTemp>18.35714"), then at step 102, the controller 26 activates the temperature change element 24 to apply heat. Referring back to step 96, if the controller 26 determines that the temperature in the vehicle 10 is greater than the specified temperature ("InCarTemp>24.46211"), the controller 26 proceeds to step 104. At step 104, the controller 26 determines whether the ambient temperature ("AirAmb_Te_ActlFilt") is greater than or less than the specified temperature. At step 104, if the controller 26 determines that the ambient temperature is less than or equal to the specified temperature ("AirAmb_Te_ActlFilt<=5.649194"), the controller 26 proceeds to step 106 and activates the temperature change element 24 to apply heat. However, at step 104, if the controller 26 determines that the ambient temperature is greater than the specified temperature ("AirAmb_Te_ActlFilt>5.649194"), the controller 26 proceeds to step 108 and does not activate the temperature change element 24 to generate heat, or, if the controller 26 has already activated the temperature change element 24 to generate heat, deactivates the temperature change element.

[0081] It should be noted that according to the rules of the pre-established predictive activation model formed based on this CART analysis, when the ambient temperature is determined to be less than a certain temperature ("AirAmb_Te_Actl <= 12.17466"), the pre-established predictive model initially automatically activates the temperature change element 24 based on the time of day (whether it is morning or not) ("isMorning"). It can even be said that this relatively simple pre-established predictive activation model (regarding heating) demonstrates the advantages of such a model. According to the rules of the following model, although the ambient temperature may be considered cold enough ("less than or equal to 21.17466 degrees Celsius") to assume that the occupant of the first seat assembly 14 will desire the temperature change element 24 to apply heat, if the time of day is not considered to be morning ("isMorning <= 0:0"), the last "0" indicating no activation / deactivation, and the rules of the pre-established predictive activation model will not activate / deactivate the temperature change element 24.

[0082] AirAmb_Te_Actl<=12.17466:

[0083] :...isMorning<=0:0(120)

[0084] An automatic control system based solely on ambient temperature may automatically activate the activation temperature changing element 24 to apply heat against the occupant's desire, thereby overriding the automatic control system. The pre-established predictive activation model developed via CART analysis of the present disclosure identifies and forms rules to cover this potentially counter-intuitive situation.

[0085] Another exemplary pre-established predictive activation model for heating, developed from a C.50 program CART analysis of data obtained from a test vehicle associated with a specific identifiable condition, is set forth below. The pre-established predictive activation model for heating sets forth rules governing when and whether the controller 26 activates / deactivates the temperature change element 24 to apply heat based on input data associated with a specific identifiable condition associated with the vehicle 10. The exemplary pre-established predictive activation model for heating is:

[0086]

[0087] In the following, with the help of Figure 6A and Figure 6BThe rules of this exemplary pre-established predictive activation model for heating are further explained. The controller 26 determines at step 110 whether the automatic windshield wipers are wiping due to sensing rain ("Smart_Wiper_Motor_Stat"). At step 110, if the controller 26 determines that the automatic windshield wipers are wiping ("Smart_Wiper_Motor_Stat>0"), the controller 26 does not activate the temperature change element 24 to generate heat at step 112, or deactivates the temperature change element 24 if the controller 26 has already activated the temperature change element 24 to generate heat. However, at step 110, if the controller 26 determines that the automatic windshield wipers are not wiping ("Smart_Wiper_Motor_Stat<=0"), the controller 26 proceeds to step 114. At step 114, the controller 26 determines whether the driver has activated the air conditioning function ("AC_Request"). At step 114, if the controller 26 determines that the driver has enabled the air conditioning function ("AC_Request <= 1.358025"), the controller 26 does not enable the temperature change element 24 to generate heat at step 116, or, if the controller 26 has already enabled the temperature change element 24 to generate heat, deactivates the temperature change element 24. However, at step 114, if the controller 26 determines that the driver has not enabled the air conditioning function ("AC_Request > 1.358025"), the controller 26 proceeds to step 118. At step 118, the controller 26 determines whether the front passenger-side temperature set point ("Front_Rt_Temp_Setpt") is greater than or less than a specified temperature. At step 118, if the controller 26 determines that the front passenger-side temperature set point is greater than the specified temperature ("Front_Rt_Temp_Setpt > 148"), the controller 26 proceeds to step 120. At step 120, if the controller 26 determines that the front passenger-side temperature set point is greater than a specific temperature ("Front_Rt_Temp_Setpt>154.9836"), the controller 26 activates the temperature changing element 24 to generate heat at step 122. However, at step 120, if the controller 26 determines that the front passenger-side temperature set point is less than or equal to the specific temperature ("Front_Rt_Temp_Setpt<=154.9836"), the controller 26 proceeds to step 124. At step 124, the controller 26 determines whether the ambient air temperature ("AirAmb_Te_ActlFilt") is greater than or less than the specific temperature.At step 124, if the controller 26 determines that the ambient air temperature is greater than the specified temperature ("AirAmb_Te_ActlFilt>7.580769"), the controller 26 does not activate the temperature change element 24 to generate heat at step 126, or, if the controller 26 has already activated the temperature change element 24 to generate heat, deactivates the temperature change element 24. However, if at step 124 the controller 26 determines that the ambient air temperature is less than or equal to the specified temperature ("AirAmb_Te_ActlFilt<=7.580769"), the controller 26 proceeds to step 128. At step 128, the controller 26 again determines whether the ambient temperature ("AirAmb_Te_ActlFilt") is greater than or less than the specified temperature. At step 128, if the controller 26 determines that the ambient temperature is less than or equal to a specific temperature ("AirAmb_Te_ActlFilt<=-2.632576"), the controller 26 activates the temperature changing element 24 to apply heat at step 130. However, at step 128, if the controller 26 determines that the ambient temperature is greater than the specific temperature ("AirAmb_Te_ActlFilt>-2.632576"), the controller 26 proceeds to step 132. At step 132, the controller 26 determines whether the vehicle's climate control system is blowing air at a level greater than or less than a specific level ("Front_Rear_Blower_Req"). At step 132, if the controller 26 determines that the climate control system of the vehicle 10 is blowing air at a level less than or equal to a specific level ("Front_Rear_Blower_Req<=20.3375"), the controller 26 does not activate the temperature change element 24 to generate heat at step 134, or if the controller 26 has already activated the temperature change element 24 to generate heat, deactivates the temperature change element 24. However, if the controller 26 determines at step 132 that the climate control system of the vehicle 10 is blowing air at a level greater than the specific level ("Front_Rear_Blower_Req>20.3375"), the controller 26 proceeds to step 136. At step 136, the controller 26 determines whether the ambient temperature ("AirAmb_Te_ActlFilt") is greater than or less than the specific temperature. At step 136 , if the controller 26 determines that the ambient temperature is less than or equal to the specified temperature (“AirAmb_Te_ActlFilt<=5.283582”), then at step 138 , the controller 26 does not enable the temperature change element 24 to generate heat, or deactivates the temperature change element 24 if the controller 26 has already enabled the temperature change element 24 to generate heat.However, at step 136 , if the controller 26 determines that the ambient temperature is greater than a certain temperature (“AirAmb_Te_ActlFilt>5.283582”), the controller 26 enables the temperature changing element 24 to apply heat at step 140 .

[0088] If, at step 118, the controller 26 determines that the front passenger-side temperature set point is less than or equal to a specific temperature ("Front_Rt_Temp_Setpt<=148"), the controller 26 proceeds to step 142. At step 142, the controller 26 determines whether the front driver-side temperature set point ("Front_Left_Temp_Setpt") is greater than or less than the specific temperature. If, at step 142, the controller 26 determines that the front driver-side temperature set point is greater than the specific temperature ("Front_Left_Temp_Setpt>150"), the controller 26 does not activate the temperature change element 24 to generate heat, or deactivates the temperature change element 24 if the controller 26 has already activated the temperature change element 24 to generate heat. However, if, at step 142, the controller 26 determines that the front driver-side temperature set point is less than or equal to the specific temperature ("Front_Left_Temp_Setpt<=150"), the controller 26 proceeds to step 146. At step 146, the controller 26 determines whether the ambient temperature ("AirAmb_Te_Act1") is greater than or less than a specific temperature. At step 146, if the controller 26 determines that the ambient temperature is greater than the specific temperature ("AirAmb_Te_Act1>12.2037"), the controller 26 does not activate the temperature change element 24 to generate heat at step 147, or deactivates the temperature change element 24 if the controller 26 has already activated the temperature change element 24 to generate heat. However, at step 146, if the controller 26 determines that the ambient temperature is less than or equal to the specific temperature ("AirAmb_Te_Act1<=12.2037"), the controller 26 proceeds to step 148. At step 148, the controller 26 again determines whether the ambient temperature ("AirAmb_Te_Act1") is greater than or less than the specific temperature. At step 148, if the controller 26 determines that the ambient temperature is less than or equal to a specific temperature ("AirAmb_Te_Actl<=6.929124"), the controller 26 proceeds to step 150. At step 150, the controller 26 determines whether the speed of the vehicle 10 is greater than or less than a specific value. At step 150, if the controller 26 determines that the speed of the vehicle 10 is greater than the specific value ("Veh_V_ActlEng>19.2492"), the controller 26 does not activate the temperature change element 24 to generate heat at step 152, or deactivates the temperature change element 24 if the controller 26 has already activated the temperature change element 24 to generate heat. However, at step 150, if the controller 26 determines that the speed of the vehicle 10 is less than or equal to the specific value ("Veh_V_ActlEng<=19.2492"), the controller 26 proceeds to step 154.At step 154, controller 26 determines whether the engine speed ("EngAout_N_Actl") is greater than or less than a specified value. If, at step 154, controller 26 determines that the engine speed is less than or equal to the specified value ("EngAout_N_Actl <= 893.8228"), controller 26 does not activate temperature change element 24 to generate heat at step 156, or, if controller 26 has already activated temperature change element 24 to generate heat, deactivates temperature change element 24. However, if, at step 154, controller 26 determines that the engine speed is greater than the specified value ("EngAout_N_Actl > 893.8228"), controller 26 activates temperature change element 24 to apply heat at step 158. Referring back to step 148, if controller 26 determines that the ambient temperature is greater than the specified temperature ("AirAmb_Te_Actl > 6.929124"), controller 26 proceeds to step 160. At step 160, the controller 26 determines whether the temperature in the vehicle 10 ("InCarTemp") is greater than or less than a specific temperature. At step 160, if the controller 26 determines that the temperature in the vehicle 10 is greater than a specific value ("InCarTemp>24.47753"), the controller 26 proceeds to step 162. At step 162, the controller 26 again determines whether the ambient temperature ("AirAmb_Te_ActlFilt") is greater than or less than a specific temperature. At step 162, if the controller 26 determines that the ambient temperature is less than or equal to the specified temperature ("AirAmb_Te_ActlFilt<=6.353571"), the controller 26 activates the temperature change element 24 to apply heat at step 164. However, if the controller 26 determines at step 162 that the ambient temperature is greater than the specified temperature ("AirAmb_Te_ActlFilt>6.353571"), the controller 26 does not activate the temperature change element 24 to generate heat at step 166, or deactivates the temperature change element 24 if the controller 26 has already activated the temperature change element 24 to generate heat. Referring back to step 160, if the controller 26 determines that the temperature in the vehicle 10 is less than or equal to the specified temperature ("InCarTemp<=24.47753"), the controller 26 proceeds to step 168. At step 168, the controller 26 determines whether the vehicle 10 speed ("Veh_V_ActlEng") is greater than or less than the specified speed. At step 168, if the controller 26 determines that the vehicle 10 speed is greater than the specified speed ("Veh_V_ActlEng>29.94641"), the controller 26 activates the temperature changing element to apply heat at step 170.However, at step 168, if the controller 26 determines that the vehicle 10 speed is less than or equal to the specified speed ("Veh_V_ActlEng<=29.94641"), the controller 26 proceeds to step 172. At step 172, the controller 26 determines whether the automatic windshield wipers are wiping due to sensing rain ("Smart_Wiper_Motor_Stat_UB"). At step 172, if the controller 26 determines that the automatic windshield wipers are not wiping due to sensing rain (Smart_Wiper_Motor_Stat_UB<=0.9605263), the controller 26 activates the temperature change element 24 to apply heat at step 174. However, at step 172 , if the controller 26 determines that the automatic windshield wipers are wiping due to sensing rain (“Smart_Wiper_Motor_Stat_UB>0.9605263”), the controller 26 does not enable the temperature change element 24 to generate heat at step 176 , or deactivates the temperature change element 24 if the controller 26 has already enabled the temperature change element 24 to generate heat.

[0089] The following describes an exemplary pre-established predictive activation model for cooling that is developed based on a C.50 program CART analysis of data obtained from a test vehicle associated with a specific identifiable condition. The pre-established predictive activation model for cooling describes the rules governing when and whether the controller 26 activates / deactivates the temperature changing element 24 to apply cooling based on input data associated with a specific identifiable condition associated with the vehicle 10. The exemplary pre-established predictive activation model for cooling is:

[0090]

[0091] In the following, with the help of Figure 7A and Figure 7BThe rules of this exemplary pre-established predictive activation model for cooling are further explained. At step 178, the controller 26 first determines whether the temperature in the vehicle 10 ("InCarTemp") is greater than or less than a specified temperature. At step 178, if the controller 26 determines that the temperature in the vehicle 10 is less than or equal to the specified temperature ("InCarTemp<=24.07895"), then at step 180, the controller 26 does not activate the temperature change element 24 to provide cooling, or deactivates the temperature change element 24 if the controller 26 has already activated the temperature change element 24 to provide cooling. However, at step 178, if the controller 26 determines that the temperature in the vehicle 10 is greater than the specified temperature ("InCarTemp>24.07895"), then the controller 26 proceeds to step 182. At step 182, the controller 26 determines whether the ambient temperature ("AirAmb_Te_ActlFilt") is greater than or less than the specified temperature. At step 182, if the controller 26 determines that the ambient temperature is less than or equal to a specific temperature ("AirAmb_Te_ActlFilt<=20.35959"), then at step 184, the controller 26 does not activate the temperature change element 24 to provide cooling, or if the controller 26 has already activated the temperature change element 24 to provide cooling, then the controller 26 deactivates the temperature change element 24. However, at step 182, if the controller 26 determines that the ambient temperature is greater than the specific temperature ("AirAmb_Te_ActlFilt>20.35959"), then the controller 26 proceeds to step 186. At step 186, the controller 26 determines whether the level of air blown by the climate control system of the vehicle 10 ("Front_Rear_Blower_Req") is greater than or less than a specific level. At step 186, if the controller 26 determines that the climate control system of the vehicle 10 is blowing air at a level less than or equal to a certain level ("Front_Rear_Blower_Req<=1.785714"), the controller 26 proceeds to step 188. At step 188, the controller 26 determines whether the driver has enabled the rear window defrost function ("Overriding_ModeReq"). At step 188, if the controller 26 determines that the driver has enabled the rear window defrost function ("Overriding_ModeReq>0.4810127"), the controller 26 enables the temperature changing element 24 to provide cooling at step 190. However, at step 188, if the controller 26 determines that the driver has not enabled the rear window defrost function ("Overriding_ModeReq<=0.4810127"), the controller 26 proceeds to step 192. At step 192 , the controller 26 determines whether the ambient temperature (“AirAmb_Te_ActlFilt”) is greater than or less than a certain temperature.At step 192, if the controller 26 determines that the ambient temperature is less than or equal to a specific temperature ("AirAmb_Te_ActlFilt<=21.76103"), the controller 26 activates the temperature changing element 24 to provide cooling at step 194. However, at step 192, if the controller 26 determines that the ambient temperature is greater than the specific temperature ("AirAmb_Te_ActlFilt>21.76103"), the controller 26 proceeds to step 196. At step 196, the controller 26 determines whether the temperature in the vehicle 10 is greater than or less than a specific temperature ("InCarTemp"). At step 196, if the temperature in the vehicle is less than or equal to the specific temperature ("InCarTemp<=26.8012"), the controller 26 proceeds to step 198. At step 198, the controller 26 again determines whether the temperature in the vehicle 10 ("InCarTemp") is greater than or less than the specific temperature. At step 198, if the controller 26 determines that the temperature in the vehicle 10 is less than or equal to a specified temperature ("InCarTemp <= 24.73298"), the controller 26 proceeds to step 200. At step 200, the controller 26 determines whether the ambient temperature ("AirAmb_Te_Actl_UB") is above or below the specified temperature. At step 200, if the controller 26 determines that the ambient temperature is less than or equal to the specified temperature ("AirAmb_Te_Actl_UB <= 0.9703704"), then at step 202, the controller 26 does not activate the temperature change element 24 to provide cooling, or if the controller 26 has already activated the temperature change element 24 to provide cooling, then the controller 26 deactivates the temperature change element 24. However, at step 200, if the controller 26 determines that the ambient temperature is greater than the specified temperature ("AirAmb_Te_Actl_UB > 0.9703704"), then at step 204, the controller 26 activates the temperature change element 24 to provide cooling. Referring back to step 198, if the controller 26 determines that the temperature in the vehicle 10 is greater than a specific temperature ("InCarTemp>24.73298"), the controller 26 proceeds to step 206. At step 206, the controller 26 determines whether the speed of the vehicle 10 ("Veh_V_ActlEng") is greater than or less than a specific value. At step 206, if the controller 26 determines that the speed of the vehicle 10 is less than or equal to the specific value ("Veh_V_ActlEng<=67.21477"), the controller 26 activates the temperature changing element 24 to provide cooling at step 208.However, at step 206 , if the controller 26 determines that the vehicle 10 speed is greater than a certain value (“Veh_V_ActlEng>67.21477”), the controller 26 does not enable the temperature changing element 24 to provide cooling at step 210 , or deactivates the temperature changing element 24 if the controller 26 has already enabled the temperature changing element 24 to provide cooling.

[0092] Referring back to step 196, if the controller 26 determines that the temperature in the vehicle 10 is greater than a specified value ("InCarTemp>26.8012"), the controller 26 proceeds to step 214. At step 214, the controller 26 determines whether the driver has enabled the air conditioning function ("AC_Request"). At step 214, if the controller 26 determines that the driver has enabled the air conditioning function ("AC_Request>0.375"), the controller 26 does not enable the temperature change element 24 to provide cooling at step 216, or deactivates the temperature change element 24 if the controller 26 has enabled the temperature change element 24 to provide cooling. However, at step 214, if the controller 26 determines that the driver has not enabled the air conditioning function ("AC_Request<=0.375"), the controller 26 proceeds to step 218. At step 218, the controller 26 determines whether the ambient temperature is greater than or less than a specified temperature. At step 218, if the controller 26 determines that the ambient temperature is less than or equal to the specified temperature ("AirAmb_Te_Actl<=22.88243"), then at step 220, the controller 26 does not activate the temperature change element 24 to provide cooling, or if the controller 26 has already activated the temperature change element 24 to provide cooling, deactivates the temperature change element 24. However, at step 218, if the controller 26 determines that the ambient temperature is greater than the specified temperature ("AirAmb_Te_Actl>22.88243"), then the controller 26 proceeds to step 222. At step 222, the controller 26 determines whether the ambient temperature ("AirAmb_Te_ActlFilt") is still greater than or less than the specified temperature. At step 222, if the controller 26 determines that the ambient temperature is less than or equal to the specified temperature ("AirAmb_Te_ActlFilt<=23.40385"), then the controller 26 activates the temperature change element 24 to provide cooling at step 224. However, at step 222 , if the controller 26 determines that the ambient temperature is greater than a certain temperature (“AirAmb_Te_ActlFilt>23.40385”), then at step 226 , the controller 26 does not enable the temperature change element 24 to provide cooling, or deactivates the temperature change element 24 if the controller 26 has already enabled the temperature change element 24 to provide cooling.

[0093] Now refer to Figure 7BAt step 186, if the controller 26 determines that the climate control system of the vehicle 10 is blowing air at a level greater than a certain level ("Front_Rear_Blower_Req>1.785714"), the controller 26 proceeds to step 228. At step 228, the controller 26 determines whether the temperature in the vehicle 10 ("InCarTemp") is greater than or less than the certain temperature. At step 228, if the controller 26 determines that the temperature in the vehicle 10 is less than or equal to the certain temperature ("InCarTemp<=25.14154"), then at step 230, the controller 26 does not activate the temperature change element 24 to provide cooling, or deactivates the temperature change element 24 if the controller 26 has already activated the temperature change element 24 to provide cooling. However, at step 228, if the controller 26 determines that the temperature in the vehicle 10 is greater than the certain temperature ("InCarTemp>25.14154"), the controller 26 proceeds to step 232. At step 232, the controller 26 determines whether the engine speed ("EngAout_N_Actl") is greater than or less than a specified value. At step 232, if the controller 26 determines that the engine speed is greater than the specified value ("EngAout_N_Actl>1359.044"), the controller 26 does not activate the temperature changing element 24 to provide cooling at step 234, or deactivates the temperature changing element 24 if the controller 26 has already activated the temperature changing element 24 to provide cooling. However, at step 232, if the controller 26 determines that the engine speed is less than or equal to the specified value ("EngAout_N_Actl<=1359.044"), the controller 26 proceeds to step 236. At step 236, the controller 26 determines whether the driver has activated the air conditioning function ("AC_Request"). At step 236, if the controller 26 determines that the driver has not enabled the air conditioning function ("AC_Request <= 0.8536586"), the controller 26 does not enable the temperature change element 24 to provide cooling at step 238, or deactivates the temperature change element 24 if the controller 26 has already enabled the temperature change element 24 to provide cooling. However, at step 236, if the controller 26 determines that the driver has enabled the air conditioning function ("AC_Request > 0.8536586"), the controller 26 proceeds to step 240. At step 240, the controller 26 determines whether the vehicle 10 speed ("Veh_V_ActlEng") is greater than or less than a specified value. At step 240, if the controller 26 determines that the vehicle 10 speed is greater than the specified value ("Veh_V_ActlEng > 66.09882"), the controller 26 enables the temperature change element 24 to provide cooling at step 242.However, at step 240, if the controller 26 determines that the vehicle 10 speed is less than or equal to a specific value ("Veh_V_ActlEng<=66.09882"), the controller 26 proceeds to step 244. At step 244, the controller 26 determines whether the level at which the climate control system of the vehicle 10 is blowing air ("Front_Rear_Blower_Req") is greater than or less than the specific level. At step 244, if the controller 26 determines that the climate control system of the vehicle 10 is blowing air at a level less than or equal to the specific level ("Front_Rear_Blower_Req<=3.236842"), the controller 26 proceeds to step 246. At step 246, the controller 26 determines whether the engine speed ("EngAout_N_Actl") is greater than or less than a specific value. At step 246, if the controller 26 determines that the engine speed is less than or equal to a specific value ("EngAout_N_Actl<=1314.136"), the controller 26 does not activate the temperature change element 24 to provide cooling at step 248, or if the controller 26 has already activated the temperature change element 24 to provide cooling, deactivates the temperature change element 24. However, at step 246, if the controller 26 determines that the engine speed is greater than the specific value ("EngAout_N_Actl>1314.136"), the controller 26 activates the temperature change element 24 to provide cooling at step 250. Referring back to step 244, if the controller 26 determines that the climate control system of the vehicle 10 is blowing air at a level greater than a specific level ("Front_Rear_Blower_Req>3.236842"), the controller 26 proceeds to step 252. At step 252 , the controller 26 determines whether the automatic windshield wipers are wiping due to sensing rain (“Smart_Wiper_Motor_Stat_UB”). At step 252 , if the controller 26 determines that the automatic windshield wipers are not wiping (“Smart_Wiper_Motor_Stat_UB<=0.8877551”), the controller 26 does not activate the temperature change element 24 to provide cooling at step 254 , or deactivates the temperature change element 24 if the controller 26 has already activated the temperature change element 24 to provide cooling. However, at step 252 , if the controller 26 determines that the automatic windshield wipers are wiping due to sensing rain (“Smart_Wiper_Motor_Stat_UB>0.8877551”), the controller 26 activates the temperature change element 24 to provide cooling at step 256 .

[0094] Another exemplary pre-established predictive activation model for cooling, developed from a C.50 program CART analysis of data obtained from a test vehicle associated with a specific identifiable condition, is described below. The pre-established predictive activation model for cooling describes rules governing when and whether the controller 26 activates / deactivates the temperature changing element 24 to apply cooling based on input data associated with a specific identifiable condition associated with the vehicle 10. The exemplary pre-established predictive activation model for cooling is:

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] The pre-established predictive activation model for cooling can be interpreted in the same manner as the model that preceded it. A value of "0" following the value of a particular identifiable condition, such as the "0" in "AirAmb_Te_ActlFilt>23.48864:0(82 / 22)", identifies that the controller 26 does not enable the temperature change element 24 to provide cooling, or deactivates the temperature change element 24 if the controller 26 has already enabled the temperature change element 24 to provide cooling. Similarly, a value of "1" following the value of a particular identifiable condition, such as the "1" in "Overriding_ModeReq>3.433735:1", identifies that the controller 26 enables the temperature change element 24 to provide cooling. A reference to a "SubTree" indicates the continuation of the tree from a reference point. For example, "[S7]" in "InCarTemp>25.34836:[S7]" refers to "SubTree[S7]", and the tree continues as if "SubTree" was incorporated by reference.

[0103] A pre-established predictive level model that establishes rules governing the level of temperature change of the temperature changing element 24 can be formed based on a neural network analysis or a multilayer perceptron classifier analysis of input data collected from the test vehicle as a whole or classified as explained above with respect to specific identifiable conditions. There are a variety of analyses that can provide useful results, including R (version 3.2.5) statistical programming software and multilayer perceptron classification via Weka (available from http: / / weka.sourceforge.net / doc.stable / weka / classifiers / functions / MultilayerPerceptron.html Other analyses are available, and this is not an exhaustive list. The rules of the pre-established predictive level model then govern, via the controller 26, the level at which the temperature changing element 24 changes the temperature within the first seat assembly 14 based on data input to the controller 26 relating to the specific identifiable conditions present in the vehicle 10.

[0104] The method may also include automatically deactivating the temperature change element 24 according to the pre-established predictive activation model after the temperature change element 24 is first automatically activated according to the pre-established predictive activation model if collected data related to a particular identifiable condition collected after the temperature change element 24 is first automatically activated satisfies the pre-established predictive activation model's rules for deactivating the temperature change element 24. For example, using the pre-established predictive activation model for heating that begins with "AirAmb_Te_Actl>12.17466" formed according to the C.50 procedure described above, if the ambient temperature is greater than 12.17466 degrees Celsius ("AirAmb_Te_Actl>12.17466") and the front passenger side temperature set point is greater than 154.9836 (i.e., approximately 77.3 degrees Fahrenheit) ("Front_Rt_Temp_Setpt>154.9836:1"), the controller 26 will initially automatically activate the temperature change element 24 to apply heat. However, if the controller 26 receives input that the ambient temperature is still greater than 12.17466 degrees Celsius (“AirAmb_Te_Actl>12.17466”) but the front passenger side temperature set point has been adjusted to less than or equal to 154.9836 (i.e., approximately 77.3 degrees Fahrenheit) (“Front_Rt_Temp_Setpt<=154.9836:0”), the controller 26 deactivates the temperature changing element 24.

[0105] The method of controlling the temperature change element 24 may further include, after automatically deactivating the temperature change element 24 according to the pre-established predictive activation model, automatically reactivating the temperature change element 24 according to the pre-established predictive activation model if data collected after deactivation of the temperature change element 24 relating to the specific identifiable condition again satisfies activation rules according to the pre-established predictive activation model. While an occupant is occupying the first seat assembly 14, the controller 26 may continue to collect data regarding the specific identifiable condition and compare the collected data to the rules of the pre-established predictive activation model. If the collected data again satisfies the rules of the pre-established predictive activation model for activating the temperature change element 24, the controller 26 may reactivate the temperature change element 24 accordingly. For example, again using the exemplary pre-established predictive activation model for heating described above beginning with “AirAmb_Te_Actl>12.17466,” when the controller 26 receives an ambient temperature greater than a certain temperature (“AirAmb_Te_Actl>12.17466”) and the front passenger side temperature set point is greater than 154.9836 (i.e., approximately 77.3 degrees Fahrenheit) (“Front_Rt_Temp_Setpt>154.9836:1”), the controller 26 automatically activates the temperature changing element 24 to apply heat. As explained above, when the controller 26 receives input that does not satisfy the rules regarding activation, such as the front passenger side temperature set point having changed to less than or equal to a specific temperature ("Front_Rt_Temp_Setpt<=154.9836:0), the controller 26 deactivates the temperature changing element 24. However, if the controller 26 subsequently receives input data related to a specific identifiable condition satisfying the rules regarding activation of the temperature changing element 24 of the pre-established predictive activation model, such as the front passenger side temperature set point being again set to greater than 154.9836:0, the controller 26 deactivates the temperature changing element 24. In other words, the controller 26 receives input data related to specific identifiable conditions in "real time," dynamically considers whether the input data satisfies any of the rules of the pre-established predictive activation model regarding activation or deactivation of the temperature change element 24, and controls activation / deactivation of the temperature change element 24 accordingly.

[0106] The method of controlling the temperature changing element 24 may also include the occupant of the first seat assembly 14 manually deactivating the temperature changing element 24 via the user interface 32 (see Figure 3AFor example, an occupant of the first seat assembly 14 may press a button 36 labeled "OFF" on the touchscreen display 34 to manually deactivate the temperature change element 24 of the first seat assembly 14, which the controller 26 had previously automatically activated based on a pre-established predictive activation model. The controller 26 accepts this interaction as input and deactivates the temperature change element 24 accordingly.

[0107] The method of controlling the temperature change element 24 may also include, upon manual deactivation of the temperature change element 24 by an occupant of the first seat assembly 14 via the user interface 32, recalibrating the pre-established predictive activation model to a new predictive activation model, thereby taking into account the collected data relating to the specific identifiable conditions that existed when the occupant manually deactivated the temperature change element 24, and thereby establishing new rules regarding activation and deactivation of the temperature change element 24. To this end, the controller 26 may be pre-loaded with software to perform classification and regression tree analysis, such as the referenced C.50 procedure. Manual deactivation by an occupant of a temperature change element 24 that has been automatically activated by the controller 26 in accordance with the pre-established predictive activation model constitutes a rejection by the occupant of the rule of the pre-established predictive activation model (and therefore a rejection of the specific identifiable condition that satisfied the rule) that the controller 26 relied upon to automatically activate the temperature change element 24. For example, using the rule of the pre-established predictive activation model regarding heating set forth above,

[0108]

[0109] If the controller 26 receives input regarding the following, the controller 26 will automatically activate the temperature change element 24 to apply heat: the ambient temperature is less than or equal to 12.17466 degrees Celsius ("AirAmb_Te_Actl<=12.17466"), the time of day is morning ("isMorning>0"), the occupant has not requested the vehicle 10 to heat the interior 12 at a high blower speed ("turnOnHeat3<=0"), and the temperature difference between the ambient temperature and the temperature in the vehicle 10 is greater than 3.670543 degrees Celsius ("tempDiff>3.670543:1"). However, if the controller 26 automatically activates the temperature change element 24 to apply heat and the occupant manually deactivates the temperature change element 24 via the user interface 32 to deny activation, the controller 26 recalibrates the pre-established predictive model to a new predictive activation model, thereby taking into account data related to the specific identifiable conditions that existed when the occupant manually deactivated the temperature change element 24. The new predictive engagement model can significantly weight the occupant's decision, such that if the specific identifiable conditions that existed when the occupant manually deactivated the temperature change element 24 reappear, the controller 26 will not activate the temperature change element 24 and will automatically deactivate the temperature change element 24 if it was already activated. In other words, the new predictive engagement model can include rules that deactivate or not activate the temperature change element 24 based on those specific identifiable conditions. Alternatively, the new predictive engagement model can weight data related to the specific identifiable conditions that existed when the occupant manually deactivated the temperature change element 24 with the same data collected from the test vehicle from which the pre-established predictive engagement model was initially derived. In any case, the new predictive engagement model will continue to utilize data collected from the test vehicle as well as data collected from the vehicle 10 related to the specific identifiable conditions that existed when the occupant manually deactivated the temperature change element 24.

[0110] The method of controlling the temperature change element 24 may also include an occupant manually activating the temperature change element 24 via the user interface 32. For example, the occupant of the first seat assembly 14 may press the button 44 labeled "ON" on the touchscreen display 34 to activate the temperature change element 24 of the first seat assembly 14. The controller 26 accepts this interaction as input and accordingly activates the temperature change element 24, which the controller 26 had previously deactivated or not activated based on the pre-established predictive activation model (or the new predictive activation model).

[0111] The method of controlling the temperature change element 24 may also include, after an occupant manually activates the temperature change element 24 via the user interface 32, recalibrating the new predictive activation model into an updated predictive activation model to take into account the collected data related to the specific identifiable conditions that existed when the occupant manually activated the temperature change element 24 and establish new rules for activating and / or deactivating the temperature change element 24. The controller 26 records the data related to the specific identifiable conditions that existed when the occupant manually activated the temperature change element 24 and uses the data to prepare the updated predictive activation model with the new rules for activation. Similarly, the updated predictive activation model may heavily weight the data related to the specific identifiable conditions that existed when the occupant manually activated the temperature change element 24 with the new rules, so that the controller 26 automatically activates the temperature change element 24 when those specific identifiable conditions re-exist. Alternatively, the updated predictive model may weight the data related to the specific identifiable conditions that existed when the occupant manually activated the temperature change element 24 with the other data previously relied upon to derive the pre-established predictive activation model. Generally speaking, the controller 26 continues to refine the predictive modeling (the pre-established predictive activation model and its subsequent recalibration) by performing new CART analyses based on data related to specific identifiable conditions each time an occupant of the first seat assembly 14 manually activates or deactivates the temperature change element 24. Ultimately, the predictive modeling will be refined based on the occupant's preferences, and the occupant will no longer need to manually activate or deactivate the temperature change element 24; the predictive modeling will automatically activate or deactivate the temperature change element 24 to satisfy the occupant's preferences.

[0112] By refining the pre-established predictive activation model into a new predictive activation model, the updated predictive activation model, and its subsequent refinement, will recognize occupant preferences, including situations where an occupant desires to activate the temperature change element 24 for reasons other than the temperature in the vehicle 10 or the ambient temperature. For example, an occupant may desire the temperature change element 24 to apply heat during the first few minutes of their commute to work for therapeutic reasons. As another example, during the spring months, an occupant of the first seat assembly 14 may desire the temperature change element 24 to apply cooling during the workday (to ensure occupant comfort) when the front passenger side setpoint temperature is above a certain temperature (to ensure occupant comfort) as a compensation for the hot air being blown in an attempt to meet the front passenger side setpoint temperature. CART analysis of the collected data related to specific identifiable conditions will learn this behavior and ultimately automatically activate and deactivate the temperature change element 24 accordingly. Therefore, CART analysis is a learning algorithm that provides high accuracy because it considers specific identifiable conditions throughout the entire history of the vehicle 10. Other possible non-learning methods, such as those involving weighted averages, will be less accurate and will not take time / date / season dependent behavior into account.

[0113] The method of controlling the temperature change element 24 may also include determining which of several different temperature change levels the controller 26 will automatically set for the temperature change element 24 first by comparing the collected data with the rules of a pre-established predictive level model, and automatically setting the temperature change element 24 to the determined level first. In other words, when the controller 26 determines to automatically activate the temperature change element 24 based on the pre-established predictive activation model (or a new predictive activation model or an updated predictive activation model), the controller 26 further determines which level (e.g., low, medium, or high) to set the temperature change element 24 to based on the pre-established predictive level model and data related to a specific identifiable condition. While the temperature change element 24 remains activated, the controller 26 dynamically compares the collected data with the rules of the pre-established predictive level model and adjusts the level of the temperature change element 24 accordingly. If, based on data collected after enabling temperature change element 24, the rules of the pre-established predictive level model indicate that the temperature level of temperature change element 24 is to be changed, controller 26 therefore causes temperature change element 24 to change temperature according to the level prescribed by the pre-established predictive level model.

[0114] The method of controlling the temperature change element 24 may also include the occupant of the first seat assembly 14 manually changing the temperature change level of the temperature change element 24 via the user interface 32. For example, based on a pre-established predictive temperature model, the controller 26 may have initially set the temperature change element 24 to change the temperature at level 3 (high). The occupant of the first seat assembly 14 may then press the button 42 labeled "low" on the touchscreen display 34 to cause the temperature change element 24 to change the temperature at a relatively low level. The controller 26 accepts this interaction as input and, accordingly, causes the temperature change element 24 to change the temperature at the relatively low level.

[0115] The method of controlling the temperature change element 24 may also include, after an occupant manually changes the temperature change level of the temperature change element 24 via the user interface 32, recalibrating the previously established predictive level model to a new predictive level model to account for data collected regarding specific identifiable conditions that existed when the occupant manually changed the temperature change level, and establishing new rules governing the temperature change level of the temperature change element 24 when the temperature change element 24 is automatically activated. The controller 26 records data regarding specific identifiable conditions that existed when the occupant manually changed the temperature change level and prepares a new predictive level model to account for these specific identifiable conditions. Generally speaking, the controller 26 continues to refine the predictive modeling governing the temperature change level by performing a new neural network analysis or a multilayer perceptron classifier analysis that includes data collected regarding specific identifiable conditions each time an occupant of the first seat assembly 14 manually changes the level of the temperature change element 24. Like the CART analysis, the multilayer perceptron classifier analysis is a learning algorithm that provides high accuracy because it accounts for specific identifiable conditions throughout the entire history of the vehicle 10. Other possible non-learning methods, such as those involving weighted averages, will be less accurate.

[0116] The method of controlling the temperature change element 24 may also include automatically deactivating the temperature change element 24 after the occupant manually changes the temperature change level, and then automatically reactivating the temperature change element 24. When the controller 26 automatically reactivates the temperature change element 24 based on a pre-established predictive activation model (or a recalibrated version thereof), the method may also include determining which of several different temperature change levels the controller 26 will automatically set for the temperature change element 24 first by comparing the collected data with the new predictive level model, and automatically setting the temperature change element 24 to the determined level. In other words, during subsequent stages of automatic activation of the temperature change element 24, the controller 26 utilizes the rules of the new predictive level model to determine the level at which the temperature change element 24 will be set.

[0117] The method of controlling the temperature change element 24 may also include removing an occupant from the first seat assembly 14, having a second occupant occupy the first seat assembly 14, and recognizing that the second occupant is different from the first occupant. The controller 26 may determine that the second occupant, different from the first occupant, is occupying the first seat assembly 14 in various ways, such as by having the second occupant's weight, as measured by the first seat assembly 14, differ from the first occupant's weight. Alternatively, the second occupant may indicate to the controller 26 via the user interface 32 (such as by selecting a user profile specific to the second occupant) that the second occupant, and not the first occupant, is occupying the first seat assembly 14.

[0118] The method of controlling the temperature change element 24 may also include collecting data related to a specific identifiable condition when the second occupant is occupying the first seat assembly 14, and determining whether the collected data satisfies the rules of the pre-established predictive activation model by comparing the data collected only when the second occupant is occupying the seat assembly 14, rather than the data collected when the first occupant is occupying the seat assembly 14, to the rules of the pre-established predictive activation model to automatically activate the temperature change element 24 first. In other words, the controller 26 recognizes that the second occupant is occupying the first seat assembly 14 and begins a new model using the pre-established predictive activation model, rather than recalibrating the predictive activation model to account for the first occupant's manual activation or deactivation of the temperature change element 24 (such as a new predictive activation model or a subsequent recalibrated version thereof). Thus, only manual activation and deactivation of the temperature change element 24 by the second occupant will cause the pre-established predictive activation model to be recalibrated to a subsequent predictive model. The method of controlling the temperature change element 24 may also include automatically activating the temperature change element 24 first when the second occupant is occupying the first seat assembly 14. In other words, the collected data relating to the particular identifiable condition is compared to the rules of the pre-established predictive activation model, and the controller 26 can then activate the temperature change element 24 to apply heat or cooling accordingly when the second occupant is occupying the first seat assembly 14, as indicated by the collected data and the rules of the pre-established predictive activation model.

[0119] This approach of controlling activation / deactivation of the temperature change element 24 based on a pre-established predictive activation model and controlling the temperature change level based on a pre-established predictive level model (and subsequent recalibration iterations thereof) provides advantages over other approaches of controlling all temperature control devices in a vehicle, such as blower levels, temperature set points, etc. For example, an occupant of the first seat assembly 14 may simply desire that the controller 26 exert automatic control over the temperature change element 24 in the first seat assembly 14 , but not automatically control the entire climate in the interior 12 of the vehicle 10 .

[0120] It will be understood that changes and modifications may be made to the above-mentioned structures without departing from the inventive concepts, and it will be further understood that these concepts are intended to be covered by the following claims unless the following claims expressly state otherwise in their language.

[0121] According to the present invention, a method for controlling a temperature change element within a seat assembly of a vehicle comprises: providing a vehicle, the vehicle comprising: a seat assembly, the seat assembly including a temperature change element; a controller, the controller communicating with the temperature change element, the controller comprising a pre-established predictive activation model, the model prescribing rules for governing activation of the temperature change element based on data relating to a specific identifiable condition; and a user interface configured to allow manual activation or deactivation of the temperature change element; having a first occupant occupy the seat assembly; collecting data relating to the specific identifiable condition while the first occupant is occupying the seat assembly; determining whether the collected data satisfies the rules of the pre-established predictive activation model by comparing the collected data with the rules of the pre-established predictive activation model so as to automatically activate the temperature change element first; and automatically activating the temperature change element.

[0122] According to one embodiment, the pre-established predictive engagement model is formed from classification and regression tree analysis of input data related to specific identifiable conditions collected from other drivers of other vehicles.

[0123] According to one embodiment, a pre-established predictive engagement model establishes rules that are a function of at least the following specific identifiable conditions: ambient temperature; a temperature set point for the vehicle's interior; time of day; whether the first occupant has requested the vehicle to heat the interior with a blower at a specific blower speed; the temperature of the vehicle's interior; and the temperature difference between the ambient temperature and the temperature in the vehicle.

[0124] According to one embodiment, a pre-established predictive activation model establishes rules that are a function of at least the following specific identifiable conditions: whether the windshield wipers are activated; whether the air conditioning is activated; the temperature set point of the vehicle's interior; the ambient temperature; the level of air blown by the climate control system in the vehicle; the engine speed; the vehicle speed; and the temperature in the vehicle.

[0125] According to one embodiment, the above invention is further characterized in that the pre-established predictive activation model establishes rules that are functions of at least the following specific identifiable conditions: the temperature in the vehicle; the ambient temperature; the level of air blown by the climate control system in the vehicle; whether the rear window defrost is activated; the vehicle speed; whether the air conditioning is activated; the engine speed; and whether the windshield wipers are activated.

[0126] According to one embodiment, the above invention is also characterized in that, when the ambient temperature is greater than a specific temperature, the controller automatically enables the temperature changing element based on a pre-established predictive enabling model according to data related to at least one other specific identifiable condition that does not include the ambient temperature; and wherein, when the ambient temperature is less than a specific temperature, the controller automatically enables the temperature changing element based on a pre-established predictive enabling model according to data related to at least one other specific identifiable condition that does not include the ambient temperature.

[0127] According to one embodiment, the above invention is further characterized in that the controller does not automatically activate the temperature change element based on a pre-established predictive activation model when the windshield wipers are already activated; and wherein, when the windshield wipers are not yet activated, the controller automatically activates the temperature change element based on the pre-established predictive activation model based on data related to at least one other specific identifiable condition that does not include whether the windshield wipers are already activated.

[0128] According to one embodiment, the above invention is further characterized in that, when the temperature in the vehicle is less than a specific temperature, the controller does not automatically enable the temperature changing element to provide cooling based on a pre-established predictive activation model; and wherein, when the temperature in the vehicle is greater than the specific temperature, the controller automatically enables the temperature changing element to provide cooling based on data related to at least one other specific identifiable condition that does not include the temperature in the vehicle based on a pre-established predictive activation model.

[0129] According to one embodiment, a pre-established predictive activation model establishes rules regarding activation of a temperature changing element to provide cooling, and the rules are a function of data related to at least the following specific identifiable conditions: ambient temperature; temperature in the vehicle; whether rear window defrost has been activated; and a temperature set point for the interior of the vehicle; wherein when the ambient temperature is less than a specific temperature and the temperature in the vehicle is greater than another specific temperature, the controller automatically activates the temperature changing element to provide cooling based on data related to at least one other specific identifiable condition including vehicle speed in accordance with the rules of the pre-established predictive activation model; wherein when the ambient temperature is greater than a specific temperature, the controller automatically activates the temperature changing element to provide cooling in accordance with data related to at least one other specific identifiable condition in accordance with the rules of the pre-established predictive activation model.

[0130] According to one embodiment, the above invention is also characterized in that after the temperature change element is first automatically enabled according to a pre-established predictive activation model, if the collected data related to the specific identifiable conditions collected after the temperature change element is first automatically enabled meets the rules of the pre-established predictive activation model regarding deactivation of the temperature change element, the temperature change element is automatically deactivated according to the pre-established predictive activation model.

[0131] According to one embodiment, the above invention is also characterized in that after the temperature change element is automatically deactivated according to a pre-established predictive activation model, if the collected data related to the specific identifiable condition collected after the deactivation of the temperature change element again meets the activation rules according to the pre-established predictive activation model, the temperature change element is automatically reactivated according to the pre-established predictive activation model.

[0132] According to one embodiment, the above invention is further characterized by a first occupant of the seat assembly manually deactivating the temperature changing element via a user interface.

[0133] According to one embodiment, the above invention is further characterized in that, after the first occupant manually deactivates the temperature changing element via the user interface, the pre-established predictive activation model is recalibrated to a new predictive activation model, thereby taking into account the collected data related to the specific identifiable conditions that existed when the occupant manually deactivated the temperature changing element, and establishing new rules regarding activation and / or deactivation of the temperature changing element.

[0134] According to one embodiment, the above invention is further characterized in that the first occupant manually activates the temperature changing element via the user interface.

[0135] According to one embodiment, the above invention is further characterized in that, after the first occupant manually activates the temperature change 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 that existed when the occupant manually activated the temperature change element, and establishing new rules regarding activation and / or deactivation of the temperature change element.

[0136] According to one embodiment, the above invention is also characterized in that the temperature changing element can be adjusted to several different temperature change levels; the controller also includes a pre-established predictive level model, which establishes rules for managing which of the several different temperature change levels the controller will automatically set for the temperature changing element first, and the rules of the pre-established predictive level model are a function of one or more of the specific identifiable conditions; and the user interface is also configured to allow the first occupant to manually select a level among the several different temperature change levels; the method also includes: determining which of the several different temperature change levels the controller will automatically set for the temperature changing element first by comparing the collected data with the rules of the pre-established predictive level model; and automatically setting the temperature changing element to the determined level first.

[0137] According to one embodiment, the above invention is further characterized in that the pre-established predictive level model is formed based on a multi-layer perceptron classifier analysis of input data related to specific identifiable conditions collected from other vehicles.

[0138] According to one embodiment, the above invention is further characterized in that the first occupant of the seat assembly manually changes the temperature change level of the temperature changing element via the user interface.

[0139] According to one embodiment, the above invention is also characterized in that, after the first occupant manually changes the temperature change level of the temperature changing element via the user interface, the pre-established predictive level model is recalibrated to a new predictive level model, thereby taking into account the collected data related to the specific identifiable conditions that existed when the occupant manually changed the temperature change level, and establishing new rules for managing the temperature change level of the temperature changing element when the temperature changing element is automatically enabled; automatically deactivating the temperature changing element; automatically reactivating the temperature changing element; determining which of several different temperature change levels the controller will automatically set for the temperature changing element first by comparing the collected data with the new predictive level model; and automatically setting the temperature changing element to the determined level.

[0140] According to one embodiment, the above invention is further characterized by removing the first occupant from the seat assembly; having a second occupant occupy the seat assembly; recognizing that the second occupant is different from the occupant; collecting data related to the identifiable condition while the second occupant is occupying the seat assembly; determining whether the collected data satisfies the rules of a pre-established predictive activation model by comparing the data collected only when the second occupant is occupying the seat assembly, rather than the data collected when the occupant is occupying the seat assembly, to the rules of a pre-established predictive activation model so as to automatically activate the temperature change element first; and automatically activating the temperature change element first when the second occupant is occupying the seat assembly.

Claims

1. A method of controlling a temperature changing element in a seat assembly of a vehicle, the method comprising: A vehicle is provided, the vehicle comprising: a seat assembly including a temperature changing element; a controller in communication with the temperature change element, the controller including a pre-established predictive activation model that specifies rules governing activation of the temperature change element based on data related to specific identifiable conditions; and a user interface configured to allow manual activation or deactivation of the temperature changing element; having a first occupant occupy the seat assembly; collecting data related to the specific identifiable condition while the first occupant is occupying the seat assembly; determining whether the collected data satisfies the rules of the pre-established predictive activation model by comparing the collected data with the rules of the pre-established predictive activation model to automatically activate the temperature changing element first; and automatically activating the temperature changing element; wherein the rules of the pre-established predictive engagement model are a function of at least the following specific identifiable conditions: (i) whether the windscreen wipers are activated and / or (ii) the level of air being blown by the climate control system in the vehicle; or At least one of the following in combination with (i) whether the windshield wipers are activated and / or (ii) the level at which a climate control system in the vehicle is blowing air: (iii) engine speed; (iv) vehicle speed; (v) time of day; (vi) whether rear window defrost is activated; and (vii) whether the air conditioning should be activated.

2. The method according to claim 1, wherein the pre-established predictive engagement model establishes rules that are a function of at least the following specific identifiable conditions: ambient temperature; a temperature set point for the interior of the vehicle; time of day; whether the first occupant has requested the vehicle to heat the interior with a blower at a specific blower speed; the temperature of the interior of the vehicle; and a temperature difference between the ambient temperature and the temperature in the vehicle.

3. The method according to claim 1, wherein the pre-established predictive activation model establishes rules that are a function of at least the following specific identifiable conditions: whether the windshield wipers are activated; whether the air conditioning is activated; the temperature set point of the interior of the vehicle; the ambient temperature; the level of air blown by the climate control system in the vehicle; the engine speed; the vehicle speed; and the temperature in the vehicle.

4. The method according to claim 2, wherein when the ambient temperature is greater than a specific temperature, the controller automatically activates the temperature changing element based on data related to at least one other specific identifiable condition other than the ambient temperature according to the pre-established predictive activation model; and When the ambient temperature is less than the specific temperature, the controller automatically activates the temperature changing element according to the pre-established predictive activation model based on data related to at least one other specific identifiable condition excluding the ambient temperature.

5. The method according to claim 3, Wherein when the windshield wipers are not already activated, the controller automatically activates the temperature changing element based on the pre-established predictive activation model based on data related to at least one other specific identifiable condition that does not include whether the windshield wipers are already activated.

6. The method according to claim 1, wherein the pre-established predictive activation model establishes rules that are a function of at least the following specific identifiable conditions: temperature in the vehicle; ambient temperature; the level of air blown by a climate control system in the vehicle; whether rear window defrost has been activated; Vehicle speed; whether air conditioning is enabled; Engine speed; and whether the windshield wipers are activated; wherein when the temperature in the vehicle is less than a specific temperature, the controller does not automatically activate the temperature changing element to provide cooling according to the pre-established predictive activation model; and When the temperature in the vehicle is greater than the specific temperature, the controller automatically activates the temperature changing element based on the pre-established predictive activation model based on data related to at least one other specific identifiable condition other than the temperature in the vehicle.

7. The method according to claim 1, wherein the pre-established predictive activation model establishes rules regarding activation of the temperature changing element to provide cooling, and wherein the rules are a function of data related to at least the following specific identifiable conditions: ambient temperature; temperature in the vehicle; whether rear window defrost is enabled; and a temperature set point for the interior of the vehicle; wherein when the ambient temperature is less than a specific temperature and the temperature in the vehicle is greater than another specific temperature, the controller automatically activates the temperature changing element to apply cooling based on data related to at least one other specific identifiable condition including vehicle speed in accordance with the rules of the pre-established predictive activation model; and When the ambient temperature is greater than the specific temperature, the controller automatically activates the temperature changing element to provide cooling according to the rules of the pre-established predictive activation model based on data related to at least one other specific identifiable condition.

8. The method of claim 1, further comprising: automatically deactivating the temperature change element in accordance with the pre-established predictive activation model after first automatically activating the temperature change element, if the collected data relating to the specific identifiable condition collected after first automatically activating the temperature change element satisfies the rule of the pre-established predictive activation model regarding deactivating the temperature change element; as well as After the temperature change element is automatically deactivated according to the pre-established predictive activation model, if the collected data related to the specific identifiable condition collected after the temperature change element is deactivated again satisfies the rule according to the pre-established predictive activation model, the temperature change element is automatically reactivated according to the pre-established predictive activation model.

9. The method of claim 8, further comprising: the first occupant of the seat assembly manually deactivating the temperature changing element via the user interface; as well as After the first occupant manually deactivates the temperature change element via the user interface, the pre-established predictive activation model is recalibrated to a new predictive activation model to account for the collected data related to the specific identifiable condition that existed when the occupant manually deactivated the temperature change element, and new rules regarding activation and / or deactivation of the temperature change element are established.

10. The method of claim 9, further comprising: the first occupant manually activating the temperature changing element via the user interface; as well as After the first occupant manually activates the temperature change element via the user interface, the new predictive activation model is recalibrated to an updated predictive activation model to account for the collected data related to the specific identifiable condition that existed when the occupant manually activated the temperature change element and establish new rules regarding activation and / or deactivation of the temperature change element.

11. The method according to claim 10, The temperature change element is adjustable to several different temperature change levels; the controller further comprising a pre-established predictive level model that establishes rules governing which of the several different temperature change levels the controller will automatically set for the temperature changing element first, the rules of the pre-established predictive level model being a function of one or more of the specific identifiable conditions; and The user interface is further configured to allow the first occupant to manually select the level among the several different levels of temperature change; The method further comprises: determining which of the several different temperature change levels the controller will automatically set for the temperature change element first by comparing the collected data to the rules of the pre-established predictive level model; and The temperature changing element is first automatically set to the determined level.

12. The method of claim 11, further comprising: the first occupant of the seat assembly manually changing the temperature change level associated with the temperature changing element via the user interface; as well as after the first occupant manually changes the temperature change level associated with the temperature change element via the user interface, recalibrating the pre-established predictive level model to a new predictive level model to account for the collected data associated with the specific identifiable condition that existed when the occupant manually changed the temperature change level, and establishing new rules governing the temperature change level associated with the temperature change element when the temperature change element is automatically activated; automatically deactivating the temperature changing element; automatically reactivating the temperature changing element; determining which of the several different temperature change levels the controller will automatically set for the temperature change element first by comparing the collected data to the rules of the new predictive level model; and The temperature changing element is automatically set to the determined level.

13. The method of claim 12, further comprising: removing the first occupant from the seat assembly; having a second occupant occupy the seat assembly; recognizing that the second occupant is different from the first occupant; collecting data related to the identifiable condition while the second occupant is occupying the seat assembly; determining whether the collected data satisfies the rules of the pre-established predictive engagement model to automatically engage the temperature changing element first by comparing the data collected only when the second occupant is occupying the seat assembly, but not when the occupant is occupying the seat assembly, to the rules of the pre-established predictive engagement model; as well as The temperature changing element is first automatically activated while the second occupant is occupying the seat assembly.

14. The method according to any one of claims 1 to 13, The pre-established predictive engagement model is formed based on classification and regression tree analysis of input data related to the specific identifiable condition collected from other drivers of other vehicles.

15. The method according to any one of claims 11 to 13, The pre-established predictive level model is formed based on a multi-layer perceptron classifier analysis of input data related to the specific identifiable condition collected from other vehicles.

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