Battery Voltage Time Series Pattern Matching
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Solution Overview
Problem
Existing battery diagnostic methods require significant computational resources and time to detect abnormalities based on multiple parameters, and voltage-based detection only identifies abnormal voltage behavior without providing insights into the cause.
Innovation Solution
A battery diagnosing apparatus and method that compares input time series of battery voltage with reference time series associated with specific abnormal patterns, using dynamic time warping and Pearson correlation to identify the cause of abnormal voltage behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple parameters (voltage, current, temperature, etc.) are monitored for battery diagnosis, then the accuracy of abnormality detection is improved, but the computational burden and time required for diagnosis increase significantly
Solution Approach 1:
The patent extracts only the voltage parameter from the full set of battery parameters (voltage, current, temperature, etc.) for diagnostic comparison. By taking out only the essential voltage information and comparing it with reference voltage profiles, the system achieves accurate abnormality detection without the computational burden of processing all parameters simultaneously.
Solution Approach 2:
The patent segments the battery diagnosis process into two distinct stages: (1) voltage-based abnormality detection to identify batteries with abnormal voltage behavior, and (2) pattern matching with reference time series to determine the specific abnormal pattern. This segmentation allows efficient processing by handling only voltage data in the first stage while maintaining diagnostic accuracy.
2Productivity
If voltage-based abnormality detection is used, then the computational burden is reduced and detection speed is improved, but the ability to identify the cause of abnormality is lost
Solution Approach 1:
The patent performs preliminary action by pre-collecting and storing reference time series data for various abnormal patterns (such as overcharge, undercharge, aging, etc.) in a database. When voltage abnormalities are detected, the system quickly compares the abnormal voltage profile against these pre-prepared reference patterns to identify the specific cause, thereby recovering the cause identification capability without sacrificing detection speed.
Solution Approach 2:
The patent creates copies of reference voltage time series for different abnormal patterns and stores them in a database. The detected abnormal voltage profile is copied and compared against these reference copies to identify the matching pattern. This copying approach enables rapid cause identification by avoiding complex real-time analysis while maintaining the ability to distinguish between different abnormality types.
3Power
If the number of batteries in series is increased to meet high voltage requirements, then the system power output is improved, but the frequency of battery abnormalities increases
Solution Approach 1:
The patent enables the battery system to self-diagnose abnormalities by automatically comparing each battery's voltage profile against reference patterns. The system independently identifies abnormal patterns without requiring external intervention, allowing for immediate detection and response to failures in high-voltage series configurations, thereby maintaining system reliability despite the increased number of batteries.
Solution Approach 2:
The patent implements a feedback mechanism where voltage information from each battery is continuously monitored and compared with reference patterns of abnormal behavior. When deviations are detected, the system provides feedback about the specific abnormal pattern identified, enabling proactive management of battery health in high-voltage systems and preventing cascading failures.
Data Source
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AI summary
A battery diagnosing apparatus according to the present disclosure includes: a voltage measuring circuit for generating a voltage signal representing a battery voltage that is a voltage across both ends of each of a plurality of batteries; a database in which a plurality of abnormal patterns, a plurality of reference time series, and a predetermined corresponding relationship between the plurality of abnormal patterns and the plurality of reference time series are recorded; and a control unit for generating a plurality of input time series representing a change history of the battery voltage of each of the plurality of batteries, based on the voltage signal. The control unit extracts an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other. The control unit identifies an abnormal pattern of the abnormal input time series among the plurality of abnormal patterns by comparing the abnormal input time series with the plurality of reference time series one by one.