Battery Cell Abnormality Diagnosis Using Frequency-Band Signal Extraction
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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 vehicle movement and varying environmental conditions, leading to unreliable diagnosis of battery cell abnormalities.
Innovation Solution
A method and apparatus that involves obtaining measurement data, extracting signals from specific frequency bands using appearance frequency counts, and generating diagnostic data through frequency and time domain conversions, with optional low-pass filtering and comparison to predefined or dynamically adjusted condition thresholds to determine battery cell abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voltage measurement is performed in a noisy environment during vehicle movement, then the measurement can be conducted under real operating conditions, but the measurement precision deteriorates due to noise signals
Solution Approach 1:
The patent segments the voltage measurement signal into multiple frequency bands using FFT (Fast Fourier Transform). By dividing the signal spectrum into different frequency components, the system can identify and isolate noise signals from actual battery voltage variations, thereby improving measurement precision without sacrificing diagnosis reliability under real operating conditions.
Solution Approach 2:
The patent introduces an intermediary processing system that includes FFT analysis, frequency band segmentation, and inverse FFT transformation. This intermediary layer between the raw measurement and the diagnosis algorithm filters out noise components while preserving the essential battery cell voltage information, resolving the contradiction between measurement precision and reliability.
2Measurement precision
If frequency domain analysis is applied to extract signals from measurement data, then noise reduction is improved, but the device complexity increases due to additional processing steps
Solution Approach 1:
The patent replaces complex hardware-based filtering mechanisms with software-based signal processing algorithms (FFT and inverse FFT). This substitution allows for effective noise reduction and signal extraction through computational methods, improving measurement precision while avoiding the need for additional physical components that would increase device complexity.
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
This approach improves noise reduction and enhances the accuracy and reliability of battery cell abnormality diagnosis by effectively distinguishing normal and abnormal conditions.
Implementation Method 1
converting the measurement data into frequency domain data; calculating the appearance frequency count for each frequency band appearing in the converted frequency domain data
Implementation Method 2
inversely transforming the extracted signal into time domain data to generate the diagnostic data
Implementation Method 3
correcting the generated diagnostic data using a low pass filter (LPF)
Data Source
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 having multiple frequency bands from the measurement data to generate diagnostic data, and analyzing the generated diagnostic data to determine whether the battery cell has an abnormality.