Battery Node Fault Detection Using AI Outlier Models
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Solution Overview
Problem
Traditional battery management systems rely on rule-based approaches that fail to generalize well across various conditions and usage patterns, leading to inadequate detection of faulty battery cells, which can result in catastrophic outcomes.
Innovation Solution
Implementing a data-driven AI-based approach using machine learning models, such as outlier detection models and neural networks, to analyze battery node diagnostic data and identify faulty cells, coupled with deterministic rules for improved safety monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional rule-based safety algorithms are implemented in battery management systems, then the system structure remains simple and easy to implement, but the detection accuracy and generalization capability across various conditions deteriorate
Solution Approach 1:
The patent replaces traditional rule-based mechanical decision-making systems with machine learning models that learn patterns from data. Specifically, neural networks and outlier detection algorithms analyze battery node diagnostic data to identify faulty cells, substituting the simple rule-based approach with data-driven intelligent analysis that achieves superior detection accuracy while maintaining systematic organization.
2Measurement precision
If machine learning models are implemented for battery safety monitoring, then detection accuracy and generalization capability improve, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the battery system into multiple battery nodes, each with its own diagnostic data. Machine learning models process data at the node level and compare against population-level representations, dividing the complex monitoring task into manageable segments. This segmentation allows sophisticated ML analysis without requiring the entire system to be overly complex, as each component handles a specific portion of the monitoring function.
3Use of energy by moving object
If traditional rule-based approaches are used, then implementation cost and computational resources are low, but the system fails to detect faulty cells under various usage patterns and conditions
Solution Approach 1:
The patent changes the fundamental parameters of the monitoring approach by transitioning from fixed rule-based thresholds to dynamic machine learning models that adapt to varying usage patterns and conditions. The system processes battery node diagnostic data through ML algorithms that learn optimal detection parameters from population-level data, enabling reliable fault detection across diverse operating conditions while managing computational resources through efficient model design and selective application.
Data Source
AI summary
Battery node diagnostic data may be received from a battery system. One or more outlier detection machine learning models may be selected based on profile information included in the battery node diagnostic data. The profile information may identify a battery node operation profile associated with some or all of the battery node diagnostic data. One or more of the battery nodes may be identified as outliers by applying the one or more outlier detection machine learning models to identify one or more differences between first diagnostic data for the designated subset of the battery nodes and a population-level representation of the battery node diagnostic data. Outcome values may be determined by applying one or more predetermined rules to the battery node diagnostic data. A battery node may be identified as exhibiting a fault based on the designated subset of the battery nodes and the plurality of outcome values.