Clutch Temperature Model Tuning Using Genetic Algorithms
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Solution Overview
Problem
The existing clutch temperature estimation models require a long time for assessment and tuning, and their accuracy is heavily dependent on the expertise of the workers, making it inefficient and time-consuming to achieve high accuracy.
Innovation Solution
A method and apparatus using a genetic algorithm to automatically tune the clutch temperature estimation model by generating and recombining tuning genes, calculating their accuracy, and regenerating new genes through mutation and recombination, significantly reducing manual effort and increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning of clutch temperature estimation model is performed by workers, then the model can be adjusted and optimized, but it takes a long time and requires high expertise leading to inconsistent results
Solution Approach 1:
The system performs automatic self-tuning of the clutch temperature estimation model using genetic algorithms and hill-climbing methods. The tuning apparatus independently evaluates multiple tuning values, calculates accuracies, and selects optimal parameters without requiring manual worker intervention, thereby eliminating the dependency on worker expertise and significantly reducing tuning time while maintaining high accuracy.
Solution Approach 2:
The patent replaces the manual mechanical tuning process (workers adjusting parameters) with an automated computational system. The tuning apparatus uses computer-based genetic algorithms and hill-climbing optimization to automatically search for and determine optimal tuning values, substituting human manual adjustment with algorithmic automation.
2Ease of operation
If the clutch temperature estimation model is tuned manually, then expertise of workers is utilized, but the tuning accuracy becomes highly dependent on individual capabilities leading to inconsistency
Solution Approach 1:
The tuning apparatus performs automatic self-tuning using genetic algorithms and hill-climbing methods, eliminating the need for worker expertise. The system independently evaluates multiple tuning values, calculates accuracies against reference data, and selects optimal parameters automatically, ensuring consistent high accuracy regardless of operator capability.
3Productivity
If genetic algorithm is used to automatically tune the model, then manual effort is reduced, but the complexity of the tuning system increases
Solution Approach 1:
The patent introduces a tuning apparatus as an intermediary system that bridges the clutch temperature estimation model and the optimization algorithms. This apparatus manages the complexity by providing a structured interface that generates tuning values, evaluates accuracies using reference data, and selects optimal parameters, thereby organizing the complex genetic algorithm and hill-climbing processes into a manageable systematic approach.
Data Source
AI summary
A tuning method for a clutch temperature estimation model may include, generating n tuning genes, calculating a tuning value corresponding to a tuning variable by using information of each of the n tuning genes, calculating a temperature estimation accuracy by applying the calculated tuning value to the clutch temperature estimation model, extracting n tuning genes of highest calculated accuracies, and regenerating m tuning genes through recombination of the extracted n tuning genes.