Vehicle Seat Temperature Control via Predictive Occupant Modeling
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Solution Overview
Problem
Current vehicle seating systems require manual activation and deactivation of temperature altering elements, which can be inconvenient and may not accurately predict occupant preferences for heating and cooling based on varying conditions.
Innovation Solution
A method involving a predictive model that automatically controls the temperature altering element in a vehicle seating assembly using data from identifiable conditions, such as ambient temperature, vehicle settings, and occupant interactions, to optimize heating and cooling based on pre-established rules and real-time data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual activation and deactivation of temperature altering elements is used, then the system is simple to operate, but occupant comfort is reduced due to inconvenience and inability to predict preferences
Solution Approach 1:
The system uses predictive modeling to automatically control the temperature altering element without requiring manual activation or deactivation by the occupant. The controller predicts when heating or cooling is needed based on learned patterns from identifiable conditions, allowing the system to serve itself by making intelligent decisions about temperature control timing and duration
Solution Approach 2:
The system incorporates feedback mechanisms where the controller continuously monitors identifiable conditions and adjusts the temperature altering element accordingly. The predictive model learns from past occupant behavior and environmental conditions, using this feedback to improve future temperature control decisions and better align with occupant preferences
2Extent of automation
If predictive modeling is implemented to automatically control temperature, then occupant comfort is improved through prediction of preferences, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing predictive models before actual temperature control is needed. The controller pre-learns patterns from identifiable conditions and occupant behavior, so that when temperature control is required, the system can immediately act based on pre-computed predictions rather than requiring complex real-time analysis
Solution Approach 2:
The system manages complexity by changing parameters in a structured way - it monitors specific identifiable conditions (temperature, humidity, occupancy, time of day) and adjusts the temperature altering element based on predefined predictive rules. This parameter-based approach simplifies the control logic compared to analyzing all possible environmental factors
3Measurement precision
If the predictive model uses multiple identifiable conditions, then prediction accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant identifiable conditions needed for accurate prediction, rather than processing all possible data. The predictive model is configured to monitor specific parameters (ambient temperature, vehicle interior temperature, occupancy status, time of day) that have been identified as most influential in determining occupant temperature preferences, filtering out unnecessary data processing
Data Source
AI summary
A method of controlling a temperature altering element within a seating assembly of a vehicle comprising: presenting a vehicle including a seating assembly including a temperature altering element, a controller in communication with the temperature altering element, the controller including a Pre-established Predictive Activation Model setting forth rules governing the activation of the temperature altering element as a function of data relating to Certain Identifiable Conditions, and a user interface configured to allow the temperature altering element to be manually activated or deactivated; occupying the seating assembly with a first occupant; collecting data relating to the Certain Identifiable Conditions while the first occupant is occupying the seating assembly; determining, by comparing the collected data to the rules of the Pre-established Predictive Activation Model, whether the collected data satisfies the rules of the Pre-established Predictive Activation Model so as to activate the temperature altering element; and activating the temperature altering element.