Vehicle Key State Detection Using Frequency Transform and Neural Network

The patent transforms the detection approach by changing from direct voltage threshold comparison to frequency domain analysis using FFT. The system converts time-domain voltage samples into frequency-domain representations, extracting features like dominant frequency and spectral entropy that are more robust to battery aging and temporary voltage fluctuations. This parameter transformation resolves the contradiction by maintaining high detection accuracy without proportionally increasing system complexity.

The patent replaces the simple voltage threshold comparison mechanism with a neural network-based classification system. The neural network processes frequency-domain features and learns optimal decision boundaries from training data, substituting the mechanical threshold-based approach with an adaptive intelligent system that maintains accuracy despite battery degradation and transient conditions.

The patent implements a training phase before actual operation where the neural network learns from labeled voltage samples corresponding to known key-on and key-off states. This preliminary training action allows the system to adapt to specific battery characteristics and vehicle electrical system nuances, improving reliability by pre-configuring the detection algorithm with system-specific knowledge before deployment.

The system incorporates feedback mechanisms where the neural network continuously processes voltage samples and adjusts its predictions based on learned patterns. The feedback loop allows the system to refine its state detection by comparing predicted states with actual system behavior, improving reliability over time while managing complexity through adaptive learning rather than rigid complex logic.