Battery Block Anomaly Detection Using Aggregated Voltage Deviations
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Solution Overview
Problem
Existing methods for monitoring the behavior of battery blocks in a series-connected battery lack the ability to detect anomalous states based on readily available electrical parameters without relying on an ideal reference and do so in an unobtrusive manner, particularly for potentially safety-relevant or manufacturing defects.
Innovation Solution
A method that calculates deviation values from average voltage measurements of battery blocks, aggregates these over time, and compares them to self-generated reference values from structurally similar batteries to detect anomalous states, using voltage-charge characteristics and signal-to-noise ratios to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voltage measurements of battery blocks are monitored using existing methods, then basic voltage data is collected, but the ability to detect anomalous states is insufficient without relying on ideal reference batteries
Solution Approach 1:
The system uses each battery block's own historical voltage measurements to generate baseline patterns and deviation thresholds, eliminating the need for external ideal reference batteries. The monitoring system serves itself by using its own collected data to establish normal variation ranges and detect anomalies through statistical deviation analysis.
Solution Approach 2:
The system creates virtual reference patterns by copying and analyzing historical voltage measurement patterns from each battery block itself. Instead of requiring physical ideal reference batteries, the system generates digital baseline models from past performance data that serve as comparison references for detecting current anomalies.
2Measurement precision
If deviation values are calculated and aggregated over time frames, then detection accuracy for anomalous states is improved, but computational complexity increases
Solution Approach 1:
The system calculates deviation values for each battery block against its historical baseline and aggregates these deviations over time frames. By accumulating multiple deviation measurements rather than relying on single-point comparisons, the system achieves higher detection precision through statistical aggregation, accepting increased computational effort as necessary for reliable anomaly detection.
Solution Approach 2:
The system pre-calculates baseline voltage patterns and expected variation ranges from historical data before actual anomaly detection occurs. These preliminary baseline models are established during normal operation phases and stored for rapid comparison during monitoring, reducing real-time computational complexity while maintaining high detection precision.
3Ease of operation
If monitoring is performed using only readily available electrical parameters from battery management systems, then system invasiveness is reduced, but detection capability for subtle anomalies is limited
Solution Approach 1:
The system continuously feeds back historical voltage measurement data to refine baseline patterns and update deviation thresholds over time. By using feedback from accumulated measurement data, the system enhances its detection capability using only standard electrical parameters already available from battery management systems, without requiring additional sensors or invasive modifications.
Solution Approach 2:
The system detects anomalies by identifying significant changes in voltage parameter patterns over time rather than relying on absolute threshold values. By analyzing parameter changes and deviations from historical patterns, the system achieves reliable anomaly detection using only standard electrical parameters that are already measured by conventional battery management systems.
Data Source
AI summary
A system and a method of monitoring the behavior of battery blocks (b1, . . . , bn) connected in series. For each of a plurality of points in time (t0, t−1, t−2), and for each battery block, a deviation value (ΔQb1, . . . , ΔQbn) is calculated, being a voltage deviation of the voltage measurement from an average (M) of the voltage measurements (Ub1, . . . , Ubn) of the battery blocks, or a corresponding charge deviation. The deviation values (ΔQb1, . . . , ΔQbn) of the battery block are aggregated. The aggregated deviation values are evaluated with respect to a reference aggregated deviation value, to detect a potentially anomalous state of the battery block. The reference aggregated deviation value is obtained based on corresponding aggregated deviation values of a corresponding battery block of corresponding batteries.