Battery Swelling Prediction Using Hierarchical Displacement Data
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Solution Overview
Problem
Existing battery systems face challenges in predicting and managing battery swelling, which can lead to performance deterioration and potential structural damage.
Innovation Solution
A method and apparatus for predicting battery swelling by obtaining displacement data from lower-level assemblies, using a prediction model to calculate swelling forces and predict displacement tendencies in upper-level assemblies, and determining whether nodes are at risk of breakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If displacement data from lower level assemblies is used to predict swelling in upper level assemblies, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The battery system is divided into hierarchical levels (cells, modules, packs) with discrete nodes at each level. The prediction model processes displacement data segment by segment through multiple processing stages: data collection from lower level nodes, swelling force calculation, upper level displacement prediction, and breakage risk assessment. This segmentation allows complex predictions to be broken into manageable computational steps.
Solution Approach 2:
The patent transitions from direct measurement at upper level assemblies to indirect prediction through lower level assembly data. By adding the dimensional aspect of hierarchical levels and introducing intermediate swelling force calculations, the system achieves accurate prediction without direct complex measurement at the target level.
2Reliability
If a comprehensive prediction model covering multiple levels is implemented, then reliability of battery system monitoring is improved, but ease of operation deteriorates
Solution Approach 1:
The prediction model operates autonomously by automatically collecting displacement data from lower level nodes, calculating swelling forces, predicting upper level displacements, and assessing breakage risks without manual intervention. The system self-manages the entire prediction workflow, improving reliability while maintaining operational simplicity through automation.
Solution Approach 2:
The system continuously monitors displacement data from lower level assemblies and uses this feedback to update swelling force calculations and predict future states of upper level assemblies. This closed-loop feedback mechanism ensures reliable monitoring while the automated nature maintains ease of operation.
Data Source
AI summary
In a method for predicting swelling of a battery, the method includes: obtaining displacement data of at least one first node of a lower level assembly for a first time; predicting swelling of the battery including the lower level assembly using the displacement data of the at least one first node obtained during the first time as input data of a prediction model; and generating a signal to control operation of the battery based on the prediction of the swelling.