Battery diagnosis apparatus, battery diagnosis method, and battery diagnosis system
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Solution Overview
Problem
Existing battery diagnosis methods based on time domain signals are prone to noise and measurement errors, leading to incomplete data analysis and inaccurate diagnosis of battery abnormalities.
Innovation Solution
A battery diagnosis apparatus and method that converts time-based data into frequency-based data using pre-processing techniques like time aggregation and domain conversion, calculating statistical values and error values to diagnose battery abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If time-based signal analysis is used for battery diagnosis, then the diagnosis method is simple and direct, but the measurement precision is reduced due to noise and measurement errors
Solution Approach 1:
The patent transforms battery diagnosis from time-domain analysis to frequency-domain analysis by applying Fast Fourier Transform (FFT). This dimensional change allows the system to analyze frequency components of battery signals, revealing patterns and abnormalities that are not visible in the time domain, thereby improving measurement precision while maintaining diagnostic capability
2Object-affected harmful factors
If filters are applied to time-based signals for long-term pattern analysis, then noise is reduced, but data loss occurs and measurement precision deteriorates
Solution Approach 1:
Instead of filtering time-domain signals which causes data loss, the patent applies FFT to transform the signals into the frequency domain. This allows comprehensive analysis of all frequency components without removing any data, preserving complete information for pattern analysis while effectively separating signal from noise through frequency decomposition
3Measurement precision
If frequency-based data transformation is applied to pre-processed data, then noise interference is reduced and hidden patterns are detected, but the device complexity increases
Solution Approach 1:
The patent replaces complex mechanical or hardware-based noise filtering systems with a computational approach using Fast Fourier Transform. This substitution achieves superior noise reduction and pattern detection capabilities through mathematical transformation rather than physical filtering, improving measurement precision without requiring additional complex hardware components
Data Source
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AI summary
According to some embodiments disclosed herein, a battery diagnosis apparatus includes a sensor configured to measure time-based first battery data from a diagnosis target battery and a controller configured to pre-process the first battery data to generate pre-processed data, generate frequency-based second battery data based on the pre-processed data, and diagnose, based on statistical data related to the second battery data, whether the diagnosis target battery is abnormal.