Battery Cell Abnormality Detection Using Adaptive G-H Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting abnormal battery cells in battery management systems are inaccurate, costly, and require extensive data collection, leading to reliability issues and increased time and financial costs, especially due to their reliance on experimental models that struggle with scalability and adaptability.
Innovation Solution
A method using adaptive filters to calculate G and H parameters from real-time voltage and current measurements, allowing for the accurate detection of abnormal battery cells by determining their sensitivity and internal state without the need for extensive data collection, and can be integrated into battery management systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experimental models with extensive data collection are used to estimate battery internal state, then measurement precision is improved, but loss of time and manufacturing cost increase
Solution Approach 1:
The patent extracts only the essential parameters (voltage and current) needed for abnormal cell detection from the full set of battery operating conditions. By focusing on these two key measurable variables and using them to derive G and H parameters through adaptive filtering, the method eliminates the need for extensive multi-dimensional data collection across various SOC, temperature, and current conditions, thereby reducing time requirements while maintaining detection accuracy
Solution Approach 2:
The patent performs preliminary derivation of the relationship between measurable variables (voltage, current) and internal state parameters (G and H) through offline adaptive filtering algorithms. This preliminary action creates ready-to-use detection criteria that can be directly applied during battery operation without requiring real-time extensive data collection, thus reducing online measurement time while preserving estimation precision
2Measurement precision
If experimental models with extensive data collection are used to estimate battery internal state, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
The patent extracts only the essential parameters (voltage and current) needed for abnormal cell detection from the full set of battery operating conditions. By focusing on these two key measurable variables and using them to derive G and H parameters through adaptive filtering, the method eliminates the need for extensive multi-dimensional data collection across various SOC, temperature, and current conditions, thereby reducing time requirements while maintaining detection accuracy
Solution Approach 2:
The patent replaces expensive, time-consuming experimental data collection processes with a computationally efficient algorithmic approach. By using readily available voltage and current measurements combined with adaptive filtering algorithms, the method achieves accurate abnormal cell detection without requiring costly extensive testing campaigns, effectively substituting expensive physical experimentation with cheaper computational processing
3Reliability
If existing detection methods are used, then reliability is compromised due to sensor errors and estimation errors accumulation, but the system complexity remains low
Solution Approach 1:
The patent implements feedback through adaptive filtering algorithms that continuously update the G and H parameters based on real-time voltage and current measurements. This feedback mechanism allows the system to dynamically adjust to changing battery conditions and correct for sensor errors, thereby improving detection reliability. The adaptive nature of the filter enables error compensation without requiring complex additional hardware or sensors
Solution Approach 2:
The patent introduces G and H parameters as intermediary variables that mediate between directly measurable quantities (voltage and current) and the internal state of battery cells. These intermediary parameters serve as robust indicators of cell health that are less susceptible to sensor errors and estimation uncertainties. By detecting abnormalities through changes in G and H parameters rather than directly measuring internal states, the system achieves higher reliability with moderate algorithmic complexity
4Adaptability or versatility
If existing detection methods are used, then adaptability is reduced due to requirement of specific conditions and environment adjustments, but device complexity is low
Solution Approach 1:
The patent employs dynamic adaptive filtering algorithms that automatically adjust to different operating conditions without requiring manual reconfiguration or specific environmental adjustments. The adaptive nature of the filter allows it to learn and adapt to varying battery characteristics, temperatures, and usage patterns in real-time, thereby achieving high adaptability across diverse operating scenarios while maintaining relatively simple implementation through software-based algorithms
Data Source
AI summary
A method for detecting an abnormal battery cell includes periodically generating a voltage value and a current value for each of battery cells, updating, in real time by using an adaptive filter, a G parameter value and an H parameter value of each of battery cells, based on the voltage value and the current value, calculating a representative G parameter value and a representative H parameter value, and determining whether each of the battery cells is an abnormally deteriorated cell based on the G parameter value and the H parameter value of each of the battery cells, the representative G parameter value, and the representative H parameter value. The G parameter indicates sensitivity of voltage with respect to a change in current of the battery cell, and the H parameter indicates an effective potential determined by a local equilibrium potential distribution and a resistance distribution in the battery cell.