Automotive Air Conditioner Control Using Morphed Model Patterns
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Solution Overview
Problem
Existing air conditioner control systems for automobiles, such as the TAO method and neural networks, require significant manpower and time to develop due to their complexity and need for multiple control coefficients, and they demand high-performance computers for processing, leading to increased development time and costs.
Innovation Solution
A method and device for controlling air conditioners using model control patterns and morphing techniques to simplify the processing-intensive algorithms, allowing for efficient control based on mandatory input variables by preparing and applying easily obtainable model control patterns, which reduces the need for complex learning processes and high-performance computing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network-based control system is used, then control accuracy is improved, but device complexity and installation cost increase due to requiring high-performance computers
Solution Approach 1:
The patent replaces expensive neural network systems with a simpler control device that can be implemented on ordinary computers. The control logic uses basic computational operations (multiplication and addition) rather than complex neural network algorithms, making the system economically viable for automotive applications where cost is a critical factor.
Solution Approach 2:
The patent substitutes the mechanical/mathematical complexity of neural networks with a streamlined computational approach. By using straightforward arithmetic operations and pre-stored control coefficients, the system achieves adequate control accuracy without requiring the sophisticated processing power of neural networks.
2Adaptability or versatility
If TAO method with multiple variables is used, then control capability is improved, but development time increases due to requiring considerable manpower for finding suitable values
Solution Approach 1:
The patent performs preliminary work by pre-calculating and storing control coefficients (Ta1 to Ta8) for different ambient temperature ranges and solar radiation conditions. This pre-computation approach eliminates the need for time-consuming real-time calculations and manual tuning during development, as the coefficients are prepared in advance and simply looked up during operation.
Solution Approach 2:
The patent divides the control system into discrete segments based on ambient temperature ranges (eight different ranges) and solar radiation conditions. Each segment has pre-determined control coefficients, allowing the system to handle multiple variables through segmentation rather than requiring complex continuous calculations, thereby reducing development time.
Data Source
AI summary
Combined control patterns corresponding to the actual control coordinates point (px) are formed by morphing a shape of the model control patterns PA, PB, PC having J pieces and corresponding to each morphing coordinates (pa, pb, pc), in the control pattern space CPS extended by the second type of input variable β and the output variable α. The morphing is performed according to weight between the morphing coordinates (pa, pb, pc) in the M-dimensional input space MPS and the actual control coordinates point (px). Combined control patterns Px are formed, and output variable value α corresponding to the input value (px) based on the combined control pattern Px is calculated.