Series Battery Block Monitoring 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, particularly safety-relevant or manufacturing defects, without relying on an ideal reference battery and in an unobtrusive manner, using only readily available electrical parameters.
Innovation Solution
A method that calculates deviation values from average voltage measurements of battery blocks, aggregates these values over time, and compares them to self-generated and external reference aggregated deviation values to detect anomalous states, utilizing a battery management system for voltage measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voltage measurements are used to monitor battery blocks, then the monitoring can be done using readily available electrical parameters, but the ability to detect anomalous states is insufficient without an ideal reference battery
Solution Approach 1:
The system uses the battery blocks themselves to generate reference values. The average voltage of all battery blocks is calculated to create a self-generated reference, and corresponding aggregated deviation values from other batteries serve as external self-generated references. This eliminates the need for an ideal reference battery while maintaining detection reliability.
Solution Approach 2:
The system continuously calculates deviation values by comparing individual battery block voltages against the average voltage, aggregates these deviations over time, and evaluates them against reference values. This closed-loop feedback mechanism enables reliable detection of anomalous states using only operational voltage measurements.
2Reliability
If deviation values are calculated for each battery block, then anomalous states can be detected, but the method becomes complex requiring aggregation over time frames
Solution Approach 1:
The monitoring period is divided into discrete time frames, and deviation values are calculated and aggregated for each time frame. This segmentation allows the complex monitoring task to be broken into manageable steps: calculate deviations, aggregate over time frames, then evaluate against references, simplifying the overall process.
Solution Approach 2:
Instead of continuously monitoring all parameters at all times, the system calculates deviation values only at specific points in time and aggregates them over defined time frames. This partial action approach reduces complexity while maintaining sufficient detection capability for anomalous states.
3Ease of manufacture
If only voltage measurements are used, then the monitoring is unobtrusive and economical, but detecting manufacturing defects and safety issues becomes difficult
Solution Approach 1:
The system enhances the single-dimensional voltage measurements by adding the time dimension through aggregation over multiple time frames and multiple battery blocks. By analyzing voltage deviations across time and across the battery pack, the system extracts meaningful patterns that indicate manufacturing defects and safety issues from otherwise simple voltage data.
Solution Approach 2:
The system performs preliminary aggregation of deviation values over time frames before evaluating against reference values. This preliminary processing of raw voltage measurements transforms them into meaningful deviation metrics that can reliably indicate manufacturing defects and safety issues.
Data Source
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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 (10) are evaluated with respect to a reference aggregated deviation value (12), to detect a potentially anomalous state of the battery block. The reference aggregated deviation value (12) is obtained based on corresponding aggregated deviation values (10) of a corresponding battery block of corresponding batteries (14).