Electrochemical Element Diagnostics Using Voltage Deviation
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Solution Overview
Problem
Existing battery management systems (BMS) provide imprecise diagnostics that require extensive data processing and infrastructure upgrades, failing to identify and quantify anomalies at the component level, leading to inefficiencies and safety risks in electrochemical systems.
Innovation Solution
A diagnostic method that collects and processes conventional measurement data, such as voltage and current across terminals, to identify and quantify residual capacity and imbalance among electrochemical elements, allowing for precise anomaly detection and corrective actions without additional data requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing BMS data processing methods are used, then the system can operate with current infrastructure, but the diagnostic precision remains insufficient and cannot identify anomalies at component level
Solution Approach 1:
The patent segments the electrochemical system into individual components (electrochemical elements) and analyzes each element's voltage separately. By identifying the maximum and minimum voltages among elements and calculating their deviations from the mean voltage, the method enables component-level anomaly detection without requiring complex infrastructure upgrades. This segmentation approach transforms system-level monitoring into element-level diagnostics, improving measurement precision while maintaining manageable complexity.
2Reliability
If centralized real-time data collection from all electrochemical systems is implemented, then new diagnostic methods can be applied, but the network and computing infrastructure would need to be oversised
Solution Approach 1:
The patent extracts only the essential diagnostic data elements from each BMS: the maximum voltage, minimum voltage, and mean voltage of electrochemical elements. By extracting only these critical parameters rather than collecting all available measurement data, the method achieves reliable component-level diagnostics without requiring oversized network and computing infrastructure. This selective extraction approach reduces data transmission and processing requirements while maintaining diagnostic reliability.
3Measurement precision
If characterization cycles with specific stresses are applied to assess state of health, then accurate SOH measurement can be obtained, but the tested elements are degraded and become non-operational during testing
Solution Approach 1:
The patent enables electrochemical elements to serve themselves diagnostically by continuously monitoring their own voltage characteristics during normal operation. Each element's voltage is measured and compared against the mean voltage of all elements, allowing the system to self-diagnose component anomalies without external intervention or specialized testing. This self-service approach eliminates the need for separate characterization cycles, maintaining both measurement precision and operational availability.
4Ease of operation
If BMS outputs data for the system as a whole rather than individual elements, then data transmission is simplified, but component-level anomaly identification becomes impossible
Solution Approach 1:
The patent applies local quality analysis by examining the voltage characteristics of individual electrochemical elements rather than treating the system as a homogeneous whole. By identifying which specific element has the maximum or minimum voltage and calculating its deviation from the mean, the method provides component-level diagnostic precision. This local quality approach maintains data transmission simplicity while enabling precise anomaly identification at the element level.
Data Source
AI summary
A method for diagnosing an electrochemical system, including: (a) collecting timestamped measurement data, (b) deducing for each instant: a reference state of charge of the system, a state of charge deviation of the first element having the highest voltage, a state of charge deviation of the second element having the lowest voltage, (c) grouping the data sets by ranges of values of the reference state of charge, and (d) assigning to each data pair composed of the identifier of the electrochemical element and the corresponding state of charge deviation: a diagnostic function among pre-established functions.