RESS Isolation Resistance Monitoring for Early Fault Detection
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Solution Overview
Problem
Existing rechargeable energy storage systems (RESS) onboard vehicles face challenges in detecting and predicting loss of isolation, which can lead to electrical safety issues and reduced system performance.
Innovation Solution
A system and method for detecting loss of isolation in RESSs using monitoring of isolation resistance signals, statistical models, machine learning, and physics-based algorithms to predict isolation loss and identify its type, such as water intrusion or cell corrosion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electrical isolation between battery packs is maintained to achieve desired operational levels, then system reliability is improved, but device complexity increases due to the need for continuous isolation monitoring and detection systems
Solution Approach 1:
The system uses the existing battery management infrastructure and isolation resistance measurement capabilities to perform self-diagnosis. The battery management controller utilizes built-in measurement circuits to continuously monitor isolation resistance between battery packs, eliminating the need for separate complex detection hardware while maintaining high reliability
Solution Approach 2:
The system implements continuous feedback monitoring of isolation resistance values. The battery management controller periodically measures isolation resistance between battery packs and compares values against predefined thresholds, enabling real-time detection of isolation degradation and triggering appropriate warnings or protective actions
2Measurement precision
If continuous monitoring of isolation resistance is implemented to detect loss of isolation, then measurement precision is improved, but use of energy increases due to continuous data collection and processing
Solution Approach 1:
The system performs isolation resistance measurements at periodic intervals rather than continuously. The battery management controller schedules measurement operations at appropriate intervals during vehicle operation, achieving sufficient detection precision while minimizing energy consumption by keeping measurement circuits dormant between sampling events
Solution Approach 2:
The monitoring frequency and intensity are dynamically adjusted based on operational conditions. The system increases measurement frequency when isolation resistance approaches critical thresholds or under specific operational conditions, and reduces monitoring intensity during normal stable operation, optimizing the balance between detection precision and energy consumption
3Reliability
If statistical models and machine learning algorithms are used to predict isolation loss, then reliability is improved through early warning, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system pre-establishes isolation resistance thresholds and prediction models during system initialization or offline analysis. Historical data from the fleet is analyzed beforehand to determine characteristic isolation resistance patterns and thresholds for different failure modes, enabling rapid real-time prediction without complex computational processing during vehicle operation
Solution Approach 2:
The system uses simplified prediction models and threshold-based detection rather than complex machine learning algorithms requiring significant computational resources. The approach uses straightforward comparisons of measured isolation resistance against predefined thresholds, achieving reliable prediction with minimal processing complexity and without requiring powerful onboard computational hardware
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively detects and predicts loss of isolation, providing accurate identification of isolation issues, proactive alerts, and enabling proactive management of vehicle operations to prevent potential electrical hazards.
Implementation Method 1
determining a loss of isolation based on measured isolation resistance values between the battery packs
Data Source
AI summary
An assessment for loss of isolation in a rechargeable energy storage systems (RESS). The assessment may include collecting isolation resistance data for a plurality of RESSs operating onboard a fleet of vehicles, applying statistical methods and engineering rules to remove erroneous peaks and duplicate values from the isolation resistance data, generating one or more isolation rules based on the isolation resistance data to represent thresholds for determining whether a loss of isolation has occurred for the RESS associated therewith, and identifying an isolation issue type for the RESSs determined to have the loss of isolation.