Vehicle Seat Heating Algorithm for Automatic Temperature Control
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Solution Overview
Problem
Current vehicle seat heating systems require manual adjustment of temperature settings, which can be counterintuitive and difficult for users to manage effectively, leading to inefficient temperature regulation and reduced comfort.
Innovation Solution
A method for automating the operation of vehicle seat heating systems by learning user behavior through algorithms that predict temperature adjustments based on activation time and initial temperature, allowing for self-contained operation without additional sensors or actuators, using existing heating element sensors to determine activation and operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual temperature regulation is implemented, then user control is provided, but ease of operation deteriorates due to counterintuitive adjustment requirements
Solution Approach 1:
The heating system automatically regulates its own operation by detecting temperature thresholds and adjusting power levels without requiring manual user intervention. The system serves itself by monitoring seat temperature and autonomously controlling heating element activation and intensity adjustments.
Solution Approach 2:
The system pre-establishes temperature thresholds and heating parameters before operation begins. By pre-configuring activation temperatures, deactivation temperatures, and power level transitions, the system eliminates the need for real-time manual adjustments during operation.
2Ease of operation
If automatic temperature regulation is implemented, then ease of operation improves, but adaptability deteriorates because manual adjustment capabilities are removed
Solution Approach 1:
The system dynamically adapts its heating strategy based on detected temperature conditions and operational history. It transitions between different power levels and activation thresholds based on real-time seat temperature measurements and usage patterns, allowing flexible adaptation without manual intervention.
Solution Approach 2:
The system continuously monitors seat temperature through sensors and uses this feedback to automatically adjust heating element power levels. The closed-loop control system compares actual temperature readings against target thresholds and modulates heating intensity accordingly, enabling adaptive regulation.
3Adaptability or versatility
If self-contained automatic operation is implemented, then adaptability improves through learning user behavior, but device complexity increases due to algorithm requirements
Solution Approach 1:
The system performs self-learning by automatically analyzing user interaction patterns and temperature preferences over time. It autonomously builds predictive models of user behavior and heating requirements without requiring external programming or complex configuration, enabling adaptive personalization through self-service learning.
4Device complexity
If existing sensors are used for automatic control, then device complexity is reduced by avoiding additional sensors, but measurement precision may deteriorate
Solution Approach 1:
The system makes the existing temperature sensors serve multiple functions: they simultaneously monitor seat temperature for comfort control and provide data for learning user preferences and predicting heating requirements. This multi-functional use of existing sensors eliminates the need for additional measurement devices.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system adapts to individual user preferences, automatically regulating heating intensity and duration to maintain comfort, reducing user interaction and technical complexity while enhancing driving comfort by anticipating changes in heating needs.
Implementation Method 1
a heating element, as described in document DE102014201545
Data Source
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AI summary
A method for the automatic control of a heating system (12) for the seat (13) of a vehicle (1), comprising the steps of: i) training an algorithm on the basis of at least one manual operation of the heating system (12) by the user (14), wherein training the algorithm comprises generating a function on the basis of an activation temperature (31) of the heating system (12) and an operating time (45) of the heating system (12); ii) finalising the training of the algorithm on the basis of a plurality of manual operations performed by the user (14); and iii) automatically controlling the seat (13) heating system (12), on the basis of the function generated, with a view to achieving a situation of self-contained operation of the intensity or power of the heating in the seat (13) of a vehicle (1).