Traction Battery Insulation Monitoring for Early Deterioration Warning
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Solution Overview
Problem
Traditional insulation performance monitoring methods for traction batteries in electric vehicles cannot provide reliable and effective early warnings for insulation deterioration, often resulting in thermal runaway and other failures.
Innovation Solution
An insulation monitoring method and system that collects and analyzes insulation resistance values over a preset time period, using statistical analysis and regression fitting to predict the risk of insulation deterioration and output alarm information before actual deterioration occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional insulation monitoring methods are used to detect insulation deterioration, then the monitoring system can identify actual insulation deterioration states, but it cannot provide early warnings before failures occur
Solution Approach 1:
The patent applies preliminary action by performing regression fitting on historical insulation resistance data to predict future insulation deterioration trends. The system calculates regression coefficients and uses them to forecast when insulation resistance will fall below threshold values, enabling early warnings before actual deterioration occurs. This transforms the monitoring approach from reactive detection to proactive prediction.
Solution Approach 2:
The patent implements feedback by continuously monitoring insulation resistance values, comparing actual measurements with predicted values from regression models, and adjusting predictions based on deviations. The system uses feedback loops to update regression coefficients and generate real-time warnings when predicted insulation resistance indicates impending failure, creating a closed-loop predictive monitoring system.
2Measurement precision
If insulation resistance threshold detection is used, then the monitoring method is simple to implement, but it cannot provide accurate prediction of insulation deterioration risks
Solution Approach 1:
The patent applies parameter changes by transforming the monitoring approach from using single threshold values to using regression parameters (coefficients, correlation values) derived from historical data. The system calculates multiple statistical parameters including insulation resistance trends, regression coefficients, and prediction confidence levels, enabling accurate deterioration risk assessment while maintaining manageable system complexity through algorithmic processing.
3Reliability
If continuous monitoring of insulation resistance is performed, then the system can detect insulation deterioration, but it cannot distinguish between normal fluctuations and actual deterioration trends
Solution Approach 1:
The patent implements continuity of useful action by continuously collecting insulation resistance data and performing ongoing regression analysis. The system maintains continuous prediction models that process sequential data streams, enabling distinction between temporary fluctuations and sustained deterioration trends through continuous statistical evaluation rather than discrete threshold checks.
Solution Approach 2:
The patent uses regression analysis as an intermediary that processes raw insulation resistance measurements and transforms them into meaningful trend indicators. The regression model acts as a mediator between raw data and deterioration assessment, filtering out noise and highlighting genuine deterioration patterns through statistical relationships established from historical data.
Data Source
AI summary
An insulation monitoring method and system for a traction battery and an apparatus are proposed to solve the problem of how to accurately predict a risk of insulation deterioration of the traction battery before the insulation deterioration occurs on the traction battery, so as to provide an early warning about failures in the traction battery. In this method, data statistics on a large amount of insulation resistance values within a long period of time are collected, and whether the traction battery has the risk of insulation deterioration is determined by analysis based on a data statistical result; and if the traction battery has the risk of insulation deterioration, alarm information is output. In this method, based on data statistical analysis performed on the large amount of insulation resistance values within a long period of time, insulation deterioration can be predicted before the insulation deterioration occurs on the traction battery. This allows a user to perform battery maintenance in time before the insulation deterioration occurs on the traction battery, so as to prevent traction battery failures.