Battery Cell Abnormality Diagnosis Using Frequency-Band Filtering
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Solution Overview
Problem
Accurate and reliable measurement of battery cell voltage in electric vehicles is challenging due to noise signals generated during movement, affecting the precision of abnormality diagnosis.
Innovation Solution
An apparatus and method that involves extracting specific frequency bands from measurement data using a low pass filter and generating diagnostic data, which is then compared to a predefined or dynamically adjusted condition threshold to determine battery cell abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voltage measurement is performed during electric vehicle movement, then real-time battery monitoring is achieved, but noise signals increase causing measurement precision degradation
Solution Approach 1:
The patent segments the voltage measurement process into multiple frequency components through Fast Fourier Transform (FFT), separating the useful voltage signal from noise signals in the frequency domain. This allows selective filtering of specific frequency ranges to improve measurement precision while maintaining real-time monitoring capability.
Solution Approach 2:
The patent introduces an intermediary processing system that includes FFT transformation, frequency domain filtering, and inverse FFT transformation. This intermediary process acts as a mediator between the raw voltage measurement and the final diagnosis result, removing noise signals while preserving the essential voltage information for reliable abnormality detection.
2Measurement precision
If frequency domain processing is applied to remove noise, then measurement precision improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary FFT transformation and frequency domain filtering before the actual voltage value extraction and abnormality diagnosis. By pre-processing the voltage signal to remove noise in the frequency domain, the subsequent diagnosis process works with cleaner data, improving precision without requiring complex real-time processing during critical decision-making.
Solution Approach 2:
The patent replaces direct time-domain noise filtering with frequency-domain processing using FFT and IFFT transformations. This substitution allows for more selective and effective noise removal by targeting specific frequency components, achieving better measurement precision with a systematic approach that manages computational complexity through structured signal processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves noise signal reduction, enabling accurate and reliable diagnosis of battery cell abnormalities by distinguishing normal and abnormal conditions.
Implementation Method 1
extracting specific frequency bands from measurement data using a low pass filter and generating diagnostic data
Data Source
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AI summary
An apparatus and method for diagnosing an abnormality of a battery cell according to embodiments of the present invention may obtain measurement data for at least one parameter of a battery cell, extract a signal of specific frequency bands from the measurement data to generate diagnostic data, and applying the generated diagnostic data to a predefined diagnosis algorithm to determine whether the battery cell has an abnormality, and thus, a noise signal can be improved, thereby accurately and reliably diagnosing whether the battery cell has an abnormality.