Automatic Climate Control Using Predictive Models
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Solution Overview
Problem
Vehicle climate control systems require extensive user interaction to achieve desired cabin comfort levels, and existing adaptive models do not effectively automate adjustments based on user behavior patterns and environmental conditions.
Innovation Solution
An automatic climate control system using onboard processors and sensors that apply predictive models to adjust HVAC settings, such as temperature and blower speed, based on user preferences and environmental data, allowing for continuous automation and reduced manual interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual control of climate system is provided, then user can adjust to desired comfort level, but user interaction is extensive and complex
Solution Approach 1:
The climate control system automatically adjusts temperature, humidity, and airflow settings based on sensor data and learned user preferences without requiring manual intervention. The system serves itself by continuously monitoring cabin conditions and making autonomous control decisions, eliminating the need for users to manually adjust climate controls while achieving desired comfort levels quickly.
Solution Approach 2:
The system incorporates multiple sensors that continuously monitor cabin temperature, humidity, and external environmental conditions. This feedback loop allows the system to detect current climate state, compare it with target conditions based on user preferences, and automatically adjust HVAC settings to maintain optimal comfort, reducing both user interaction and time to achieve desired conditions.
2Extent of automation
If adaptive models are used to automate climate control, then user interaction is reduced, but models must learn from extensive user behavior data
Solution Approach 1:
The system performs preliminary learning of user climate preferences during initial system operation and calibration periods. By collecting and analyzing user manual adjustments and comfort patterns in advance, the system builds predictive models that enable high-level automation from the outset, reducing the need for complex real-time decision-making while maintaining high automation capability.
Solution Approach 2:
The adaptive model is divided into separate functional modules: sensor data processing, user preference learning, predictive modeling, and control execution. Each module handles specific aspects of the automation task independently, reducing overall system complexity while enabling high程度的 automation through coordinated operation of these segmented components.
3Reliability
If climate control adjusts continuously based on environmental changes, then comfort is maintained, but system complexity increases
Solution Approach 1:
The climate control system performs adjustments at periodic intervals rather than continuously, triggered by threshold-based sensor inputs or scheduled updates. This periodic operation maintains reliable comfort levels by responding to significant environmental changes while avoiding the complexity of continuous real-time adjustment algorithms, reducing computational burden and system complexity.
Solution Approach 2:
The system maintains reliability by monitoring key environmental parameters (temperature, humidity, external conditions) and making discrete adjustments when parameters cross predefined thresholds or deviate from target ranges. This parameter-based control approach ensures consistent comfort maintenance through simple threshold comparisons rather than complex continuous control algorithms, reducing system complexity while maintaining reliability.
Data Source
AI summary
Systems and methods for controlling an automatic climate control system of a vehicle include, by one or more onboard processors in operative communication with one or more sensors, initiating the automatic climate control system. The processors receive from the one or more sensors at least a vehicle input and a user identifier input. A unique user climate control system actuation action is implemented according to the received vehicle input and user identifier input provided by applying at least one predictive model to a stored set of climate control system operating parameters. A unique user climate control system operating pattern is implemented according to the received vehicle input and user identifier input provided by applying at least one different predictive model to the stored set of climate control system operating parameters.