Automotive HVAC Adaptive Control with Weighted Crowd Data
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Solution Overview
Problem
Existing automotive climate control systems face challenges in optimizing HVAC operation for energy efficiency and customer comfort, particularly in electric and hybrid vehicles, due to limitations in characterizing the HVAC environment using onboard sensors, and remote weather information is not sufficient to predict the best levels of heating or cooling for preconditioning.
Innovation Solution
A centralized cloud computing system collects and distributes HVAC-related data from a crowd of vehicles with similar operational settings to individual vehicles, using peer parameters and fuzzy rules to generate command parameters for adapting HVAC operation based on crowd data with confidence weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If onboard sensors and remote weather information are used for HVAC control, then the system can operate with available data, but the prediction accuracy for defrosting and comfort levels is insufficient
Solution Approach 1:
The patent combines multiple data sources including onboard sensors, remote weather information, and crowd-sourced data from peer vehicles to create a comprehensive HVAC environment characterization. This merging of information sources resolves the contradiction by providing both sufficient data coverage and high prediction accuracy for defrosting and comfort levels.
Solution Approach 2:
The system introduces a cloud-based peer matching intermediary that collects and distributes crowd-sourced HVAC data from similar vehicles. This intermediary enables the requesting vehicle to access real-world HVAC environment data from peer vehicles, significantly improving prediction accuracy beyond what onboard sensors and remote weather information alone can provide.
2Reliability
If HVAC system operates with higher power to ensure comfort and defrosting, then customer satisfaction improves, but energy efficiency deteriorates
Solution Approach 1:
The system uses crowd-sourced feedback from peer vehicles about their HVAC operation outcomes to optimize the requesting vehicle's HVAC settings. By learning from real-world data about what HVAC power levels achieved comfort and defrosting in similar conditions, the system can determine the minimum necessary power consumption, resolving the contradiction between reliability and energy efficiency.
Solution Approach 2:
The system dynamically adjusts HVAC operation parameters based on crowd-sourced data about environmental conditions and peer vehicle responses. By changing operational parameters (temperature setpoints, fan speeds, defrost intensity) based on real-world feedback, the system achieves reliable comfort and defrosting outcomes while optimizing energy consumption rather than using fixed high-power settings.
3Reliability
If crowd data from multiple vehicles is collected and processed, then prediction reliability improves, but system complexity increases
Solution Approach 1:
The system segments the crowd data collection and processing function into separate components: peer vehicles independently collect and transmit their own HVAC data, the cloud server aggregates and processes the data, and the requesting vehicle receives processed results. This segmentation distributes system complexity across multiple independent elements rather than concentrating it in one complex system, enabling high prediction reliability with manageable individual component complexity.
Data Source
AI summary
A motor vehicle comprises an HVAC system including a climate control circuit coupled to onboard sensors, a human-machine interface, and climate actuators. The actuators are responsive to respective command parameters generated by the control circuit in response to the sensors and the human-machine interface. A wireless communication system transmits vehicle HVAC data to and receives crowd data from a remote server. The control circuit initiates a request for crowd data via the communication system to the remote server, wherein the request includes peer parameters for identifying a vehicle environment. The control circuit receives a response via the communication system from the remote server. The response comprises crowd data and at least one weight indicating a confidence level associated with the crowd data. The control circuit generates at least one command parameter using a set of fuzzy rules responsive to the crowd data and the weight from the response.