Virtual Battery Model Training for Short Circuit Detection

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Solution Overview

Problem

Existing battery management systems struggle to accurately detect short circuits in batteries, which can lead to safety issues and reduced battery performance.

Innovation Solution

A method and apparatus for training a short circuit detection model using virtual battery models with different parameter sets and applying constraints to simulate short circuit states, allowing the model to infer short circuit conditions from battery measurements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If virtual battery models with different parameter sets are generated and constraints are applied to simulate short circuit states, then the accuracy of short circuit detection is improved, but the complexity of the training process increases

Engineering Contradiction:

Improveshort circuit detection accuracyVSAvoidtraining process complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent creates virtual battery models that copy the behavior of physical batteries under normal conditions, then applies constraints to simulate short circuit states. These virtual models replicate battery parameters and responses without requiring actual physical short circuit testing, enabling accurate detection training while avoiding the complexity and danger of physical experimentation.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent systematically varies battery parameters (resistance, capacitance, voltage, current) across multiple virtual battery models to create diverse training data representing different short circuit scenarios. By changing parameters rather than physically modifying batteries, the system achieves comprehensive coverage of short circuit conditions without the complexity of physical manipulation.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If multiple virtual battery models with different parameter sets are generated, then the comprehensiveness of short circuit detection training is improved, but the computational resources required increase

Engineering Contradiction:

Improveshort circuit detection comprehensivenessVSAvoidcomputational energy consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent divides the training data into segments representing different battery states and short circuit scenarios by creating multiple virtual battery models with specific parameter sets. Each model handles a particular segment of the operational space, allowing comprehensive coverage while managing computational resources through structured organization of training data.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The virtual battery models are pre-generated with diverse parameters before actual training begins. This preliminary creation of varied models allows the system to prepare comprehensive training scenarios in advance, reducing the need for computationally intensive real-time simulations during the actual training process.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If constraints are applied to virtual battery models to simulate short circuit states, then the reliability of detection under varying resistance levels is improved, but the difficulty of model training increases

Engineering Contradiction:

Improvedetection reliability under varying resistance levelsVSAvoidmodel training difficulty

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies constraints to virtual battery models by systematically changing parameters such as resistance values to simulate different short circuit conditions. This parameter-based approach enables the model to learn detection across varying resistance levels without the complexity of physical constraint application, improving reliability while managing training difficulty through mathematical rather than physical constraints.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250124346A1Method and apparatus with training of short circuit detection model

Publication Date: 2025.04.17 SAMSUNG ELECTRONICS CO LTD

AI summary

A method and apparatus for training a short circuit detection model are disclosed. The method includes generating virtual battery models with different battery parameter sets, based on battery data measured by a real battery in a non-short circuit state, by applying a constraint corresponding to a short circuit state to the virtual battery models, generating a virtual test result of the short circuit state, and training a short circuit detection model configured to detect the short circuit state using the virtual test result.