Battery Self-Discharge Fault Detection With Criticality Assessment
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Solution Overview
Problem
Existing diagnostic and monitoring methods fail to effectively detect and assess the criticality of self-discharge faults in electrochemical device batteries, which can lead to thermal runaway and safety hazards due to varying causes such as internal and external short-circuit mechanisms, chemical reactions, and insulation degradation.
Innovation Solution
A method and apparatus that utilize operating variable curves to detect self-discharge faults by determining operational features, evaluating them against defined fault criteria, and signaling criticality, incorporating cell voltage, state of charge, and balancing data to differentiate between non-critical and critical faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing diagnostic methods are used to monitor battery operation, then basic monitoring is possible, but self-discharge faults cannot be effectively detected or assessed for criticality
Solution Approach 1:
The patent segments the fault detection process into multiple independent evaluation criteria: self-discharge rate calculation, operational feature analysis, and criticality assessment. Each criterion evaluates specific aspects of battery behavior separately, allowing precise detection of self-discharge faults without requiring a single complex monitoring system.
Solution Approach 2:
The patent introduces a new dimension of evaluation by calculating self-discharge rates as a separate operational feature and assessing criticality based on multiple criteria simultaneously. This multi-dimensional approach transforms the monitoring system from basic voltage/current tracking to a comprehensive diagnostic tool that evaluates temporal changes, operational patterns, and fault severity independently.
2Measurement precision
If detailed operational features are analyzed to detect self-discharge faults, then detection precision improves, but system complexity increases
Solution Approach 1:
The patent performs preliminary calculations of operational features (self-discharge rates, temporal derivatives, operational states) before fault detection. By pre-processing the battery data to extract meaningful features, the system reduces the complexity of the actual fault detection algorithm while maintaining high detection precision through structured feature evaluation.
Solution Approach 2:
The monitoring system uses the battery's own operational data to assess its health status. By calculating self-discharge rates and operational features from the battery's inherent voltage, current, and temperature measurements, the system achieves precise fault detection without requiring external diagnostic equipment or complex additional sensors.
3Reliability
If early detection of self-discharge faults is implemented, then safety against thermal runaway improves, but continuous monitoring increases energy consumption
Solution Approach 1:
The patent implements periodic evaluation of operational features at defined time intervals rather than continuous monitoring. By calculating self-discharge rates and assessing criticality periodically based on accumulated operational data, the system achieves early fault detection while minimizing energy consumption through interval-based rather than continuous processing.
Solution Approach 2:
The system uses feedback from operational feature evaluations to adjust monitoring intensity. When self-discharge rates and operational features indicate normal battery behavior, monitoring operates at baseline energy consumption. When anomalies are detected, the system intensifies evaluation frequency, optimizing the balance between safety and energy usage based on real-time battery conditions.
Data Source
AI summary
A method for detecting a self-discharge fault of a device battery of a technical device and its criticality includes (a) providing at least one operating variable curve of at least one operating variable of the device battery, (b) determining at least one operational feature based on the at least one operating variable curve, (c) detecting a self-discharge fault based on fault criteria depending on the at least one operating feature, (d) determining a criticality of the self-discharge fault depending on which of the fault criteria are met, and (e) signaling a self-discharge fault depending on its criticality.