Battery Module Strain Estimation from Stress and Swelling Force
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Solution Overview
Problem
Current methods fail to accurately estimate strain in battery modules due to the lack of a direct correspondence between stress and strain, which can lead to deformation and potential damage from swelling pressures, especially in modular battery systems.
Innovation Solution
A module strain estimating apparatus and method that uses a learning module to derive a correspondence between stress and strain based on numerical information from the battery module, generating profiles to estimate strain under various forces and stresses, facilitating easier design and monitoring of battery modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to determine battery module deformation, then the measurement process becomes complex and time-consuming, but the accuracy of strain estimation remains insufficient
Solution Approach 1:
The patent replaces complex mechanical measurement systems with a machine learning-based computational approach. The processor uses trained neural network models to estimate strain directly from stress data and battery module parameters, eliminating the need for complex physical measurement apparatus while improving accuracy
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between stress measurement and strain calculation. This intermediary processes the relationship between stress and strain through learned patterns from training data, providing accurate strain estimation without direct complex measurement
2Measurement precision
If direct stress-strain measurement methods are used, then measurement precision improves, but the device complexity and time consumption increase significantly
Solution Approach 1:
The patent performs preliminary training of machine learning models offline using extensive datasets before actual strain estimation. This preliminary action creates pre-trained models that can quickly estimate strain in real-time applications, separating the time-consuming training phase from the rapid inference phase
Solution Approach 2:
The patent substitutes time-consuming direct measurement methods with a computational model that provides rapid strain estimation once trained, significantly reducing measurement time while maintaining precision
3Reliability
If traditional deformation determination methods are applied, then the process becomes cumbersome and difficult to implement, but reliable strain data is needed for safety assessment
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically learns the complex stress-strain relationships from training data and performs strain estimation without requiring complex manual measurement procedures. The system serves itself by using input parameters and stress data to generate reliable strain outputs
Solution Approach 2:
The patent replaces cumbersome mechanical measurement and calculation methods with an automated computational approach using machine learning, significantly improving ease of implementation while maintaining data reliability
Data Source
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AI summary
A module strain estimating apparatus according to an embodiment of the present disclosure includes: a module information obtaining unit configured to obtain a plurality of numerical information for a battery module; and a processor configured to receive the plurality of numerical information from the module information obtaining unit, generate a first profile representing a correspondence between a force applied from the inside of the battery module to the outside and a strain of the battery module and a second profile representing a correspondence between the force and a stress of the battery module based on a preset learning module and the plurality of numerical information, generate a third profile representing a correspondence between the strain and the stress of the battery module based on the first profile and the second profile, and estimate a strain of the battery module according to a stress of the battery module based on the third profile.