Intelligent Battery Management System for Anomaly Detection
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Solution Overview
Problem
Traditional battery fault detection methods lack accuracy and require offline data processing, failing to effectively manage lithium-ion batteries, which poses safety risks due to their widespread use in devices like smartphones and electric vehicles.
Innovation Solution
An intelligent battery management system that detects anomalies using a detection engine, identifies relevant data from reference charging data, modifies it to include anomalies, and re-trains an AI model for improved operational management, enhancing accuracy and safety through on-device learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional offline battery management methods are used, then data processing requirements are reduced, but detection accuracy and real-time monitoring capability deteriorate
Solution Approach 1:
The patent replaces traditional mechanical/offline data processing methods with an AI-based intelligent system. The AI model automatically detects anomalies, identifies affected data portions, modifies reference charging data, and retraines itself, substituting complex manual processing with automated intelligent algorithms that achieve higher accuracy without proportional increases in system complexity
Solution Approach 2:
The AI model performs self-learning and self-improvement by automatically detecting anomalies in battery data, identifying relevant portions of reference charging data, modifying the training data to include detected anomalies, and retraining itself. This self-service mechanism eliminates the need for external intervention in the complex processing pipeline, maintaining accuracy while managing complexity through automation
2Reliability
If periodic offline battery measurements are performed, then safety monitoring is simplified, but real-time fault detection capability deteriorates
Solution Approach 1:
The patent implements continuous real-time monitoring of battery operations using the AI model, replacing periodic offline measurements. The system continuously analyzes battery data streams, detects anomalies as they occur, and provides immediate feedback, ensuring uninterrupted safety monitoring and eliminating the time delays inherent in periodic checking
Solution Approach 2:
The AI model is pre-trained on reference charging data and continuously learns from new data, preparing the system to detect faults in advance. By maintaining an updated model that anticipates potential anomalies based on learned patterns, the system can detect faults immediately when they occur rather than waiting for periodic measurements
3Adaptability or versatility
If existing battery management methods are used, then system simplicity is maintained, but adaptability to new fault patterns deteriorates
Solution Approach 1:
The patent implements a dynamic system where the AI model continuously adapts to new fault patterns through self-retraining. As the model detects new types of anomalies and learns from modified reference charging data, it dynamically updates its detection capabilities, allowing the system to evolve and adapt to emerging fault patterns rather than being static like traditional methods
Solution Approach 2:
The AI model changes its internal parameters and decision boundaries through continuous learning and retraining on modified data that includes detected anomalies. This parameter adaptation allows the system to recognize new fault patterns by adjusting its detection thresholds and feature importance weights, providing versatility without requiring structural system changes
Data Source
AI summary
A method, for intelligent management of a battery is provided. The method includes detecting at least one anomaly associated with the battery. The at least one anomaly impacts one or more operations of the battery. The method includes identifying at least one portion of data from reference charging data to include the at least one anomaly for managing the one or more operations of the battery. The method further includes modifying at least one portion of data from the reference charging data based on a pre-determined logic to include the at least one anomaly. The method also includes retraining an Artificial Intelligence (AI) model based on the reference charging data upon modification for managing the one or more operations of the battery.