Battery Cell Abnormal Voltage Detection from Dual Moving Averages
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Solution Overview
Problem
Current methods for diagnosing abnormal voltage in battery cells are inaccurate due to variations in temperature and State Of Health (SOH), making it difficult to differentiate between normal and abnormal voltage conditions using only cell voltage measurements.
Innovation Solution
A battery diagnosis apparatus and method that calculates short-term and long-term moving averages of cell voltage and detects abnormal voltage by analyzing the difference between these averages, using statistical adaptive thresholds and normalization to account for variations in temperature and SOH.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If cell voltage comparison method is used for abnormal voltage diagnosis, then diagnosis simplicity is maintained, but diagnosis accuracy deteriorates due to temperature and SOH variations
Solution Approach 1:
The patent transforms the diagnosis approach from using absolute cell voltage values to using voltage change rate parameters. By monitoring how voltage changes over time rather than absolute voltage levels, the system can identify abnormal cells regardless of temperature or SOH variations. This parameter transformation resolves the contradiction by maintaining diagnostic simplicity while improving accuracy through rate-based comparison.
Solution Approach 2:
The patent introduces dynamic monitoring of voltage changes over time using moving average calculations and voltage change rate analysis. Instead of static voltage comparison, the system continuously tracks voltage dynamics, calculating the rate of voltage change and comparing it against threshold values. This dynamic approach maintains simplicity while accurately detecting abnormal cells that exhibit unusual voltage change patterns.
2Measurement precision
If additional parameters (current, temperature, SOC) are used for diagnosis, then diagnosis accuracy is improved, but system complexity and diagnosis time increase
Solution Approach 1:
The patent extracts and isolates the voltage change rate as the critical diagnostic parameter, separating it from other cell parameters like current, temperature, and SOC. By focusing solely on voltage dynamics and its rate of change, the system achieves accurate abnormal cell detection without requiring measurement or processing of additional parameters, thus maintaining system simplicity while improving diagnostic accuracy.
Solution Approach 2:
The patent introduces voltage change rate as an intermediary parameter that mediates between raw voltage measurements and abnormal cell detection. This intermediary transformation allows the system to indirectly account for temperature and SOH effects without directly measuring or processing those parameters, resolving the contradiction by achieving improved accuracy through a simple intermediate calculation rather than through complex multi-parameter analysis.
3Measurement precision
If additional parameters (current, temperature, SOC) are used for diagnosis, then diagnosis accuracy is improved, but diagnosis time increases
Solution Approach 1:
The patent extracts voltage change rate as the sole diagnostic parameter, eliminating the need to measure and process additional parameters like current, temperature, and SOC. This extraction approach maintains high diagnostic accuracy by focusing on the most informative aspect (voltage dynamics) while significantly reducing diagnosis time by avoiding unnecessary measurements and calculations of other parameters.
Data Source
AI summary
A battery diagnosis apparatus for diagnosis of a cell group including a plurality of battery cells connected in series, includes a voltage sensing circuit configured to periodically generate a voltage signal indicating a cell voltage of each battery cell, and a control circuit configured to generate time series data indicating a change in cell voltage of each battery cell over time based on the voltage signal. The control circuit is configured to (i) determine a first average cell voltage and a second average cell voltage of each battery cell based on the time series data, wherein the first average cell voltage is a short term moving average, and the second average cell voltage is a long term moving average, and (ii) detect an abnormal voltage of each battery cell based on a difference between the first average cell voltage and the second average cell voltage.