Battery Insulation Fault Warning Using Resistance Trend Prediction
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Solution Overview
Problem
Existing vehicle management systems cannot provide early warnings for battery insulation faults, leading to potential short circuit accidents due to decreased insulation resistance values caused by issues like water ingress or electrolyte leakage.
Innovation Solution
A method and apparatus for early warning of battery insulation faults, which involves acquiring and analyzing insulation resistance values over time to extract transient and trend features, constructing a prediction model to identify abnormal conditions, and issuing warnings based on probability thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle management system waits for insulation failure to issue an alarm, then the alarm is simple and reliable, but the accident has already occurred and no early warning is provided
Solution Approach 1:
The system performs preliminary actions by continuously monitoring insulation resistance values and analyzing trends before actual insulation failure occurs. The prediction model proactively identifies batteries at risk of insulation failure, enabling early warning and preventive maintenance before accidents happen, thus resolving the contradiction between reliable alarming and timely warning.
2Loss of time
If the system continuously monitors insulation resistance values and uses prediction models, then early warning capability is improved, but the system complexity increases
Solution Approach 1:
The system segments the monitoring task by dividing batteries into different risk groups based on their insulation resistance trends and characteristics. Instead of uniformly monitoring all batteries with complex models, the system identifies high-risk batteries and focuses prediction resources on them, reducing overall system complexity while maintaining early warning capability.
Solution Approach 2:
The system changes parameters by using multiple insulation resistance measurement parameters (different time points, different conditions) and analyzing their variations. By monitoring parameter changes rather than absolute values, the system achieves early warning with relatively simple comparison logic, avoiding the need for overly complex prediction models.
3Measurement precision
If the system analyzes multiple features and uses prediction models for all batteries, then prediction accuracy is improved, but the computational resources and processing time increase
Solution Approach 1:
The system applies partial action by focusing detailed prediction model analysis only on batteries identified as high-risk through initial screening. For low-risk batteries, simpler monitoring methods are used. This selective approach maintains high detection accuracy for critical cases while improving overall processing efficiency by avoiding exhaustive analysis of all batteries.
Data Source
AI summary
The present disclosure provides a method, apparatus and storage medium for early warning of battery insulation fault. There is provided a method for establishing a model of early warning of battery insulation fault, including: acquiring an insulation resistance value of a battery which changes over time; constructing feature engineering for a set of insulation resistance values of each battery within a predetermined time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery, which is marked as normal or abnormal; and establishing a prediction model for predicting whether an insulation fault occurs in the battery at least based on the extracted at least one of the transient insulation feature and the trend insulation feature marked as normal or abnormal of each battery and a label of whether the insulation fault actually occurs in the battery.