Battery Data Storage Segmentation for Abnormality Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery management systems face challenges in efficiently managing large volumes of battery data, which is crucial for diagnosing battery abnormalities, as the management method and type of data impact the analysis of causes for battery anomalies.
Innovation Solution
A battery data management apparatus and method that separates storage based on the importance of battery data, using a circular queue structure with buffers to temporarily store data, classifies it based on abnormality levels, and transmits data to external or internal servers for real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If battery data is stored in a single unified storage, then storage management is simple, but data analysis efficiency deteriorates due to inability to prioritize important data
Solution Approach 1:
The patent divides the storage system into multiple storage regions (first storage region for normal data, second storage region for abnormal data) and further segments data into different types (operation data, diagnosis data, error data). This segmentation allows important abnormal data to be stored separately and prioritized for analysis, improving data analysis efficiency while maintaining manageable storage organization through standardized region definitions.
2Device complexity
If all battery data is stored with equal priority, then storage allocation is simple, but abnormality analysis precision deteriorates due to inability to prioritize critical data
Solution Approach 1:
The patent applies local quality by assigning different storage priorities and characteristics to different storage regions and data types. The second storage region is specifically designed for abnormal data with higher priority, while the first storage region handles normal operation data. This localized differentiation ensures that critical abnormality data receives appropriate storage priority and analysis attention, improving abnormality analysis precision without requiring complex overall storage allocation mechanisms.
3Device complexity
If battery data storage is not separated by abnormality level, then storage structure is simple, but cause tracking efficiency deteriorates due to mixed data types
Solution Approach 1:
The patent segments battery data into distinct types (operation data, diagnosis data, error data) and stores them in different storage regions. Error data and abnormal data are specifically isolated in the second storage region, allowing rapid identification and tracking of abnormality causes without searching through mixed normal operation data. This segmentation structure enables efficient cause tracking while maintaining a standardized, manageable storage framework.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A battery data management apparatus according to an embodiment disclosed herein includes a controller configured to determine whether a battery is abnormal based on battery data, determine an abnormality level of the battery based on whether the battery is abnormal, and classify and store the battery data in a plurality of storages, based on the abnormality level of the battery and a memory including the plurality of storages and a temporary storage temporarily storing the battery data.