Battery Status Visualization via Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional battery management systems (BMS) struggle to accurately indicate battery status and attribute changes due to reliance on numerical data analysis, limiting the ability to efficiently and accurately monitor battery health and events.
Innovation Solution
A method is introduced to create visual images of battery status models by collecting and processing data values of characteristic parameters over time, forming characteristic and variant images, and using machine learning to analyze these images, allowing for efficient and accurate indication of battery status and events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerical data analysis is used to determine battery status, then the battery management system can calculate parameters such as SOC and SOH, but the accuracy and reliability of battery status indication deteriorates due to limited user ability to analyze complex numerical data
Solution Approach 1:
The patent replaces manual numerical data analysis with automated image processing and pattern recognition algorithms. The control unit automatically captures numerical parameters, transforms them into visual images, performs pattern recognition, and generates analysis results, substituting the mechanical process of manual numerical analysis with an automated visual analysis system.
Solution Approach 2:
The patent introduces visual images as an intermediary between raw numerical battery parameters and human understanding. Instead of directly presenting complex numerical data, the system transforms parameters into visual representations that are easier to interpret, with the images serving as a mediator that bridges the gap between numerical data and user comprehension.
2Loss of information
If various algorithms are used to calculate battery parameters, then more comprehensive data can be obtained, but the complexity of the battery management system increases
Solution Approach 1:
The patent merges multiple battery parameter visualizations into a single comprehensive visual image. Instead of displaying separate graphs for voltage, current, temperature, and other parameters, the system combines them into one integrated visual representation that shows all parameters and their relationships simultaneously, reducing system complexity while maintaining information completeness.
Solution Approach 2:
The patent transforms one-dimensional numerical data into two-dimensional visual images, adding a spatial dimension to the data representation. This dimensional transformation allows multiple parameters to be visualized simultaneously in a single image, reducing the complexity of data presentation while preserving all information.
3Productivity
If manual analysis of numerical battery data is performed, then the system structure can be kept simple, but the productivity and efficiency of battery status monitoring deteriorates
Solution Approach 1:
The patent implements a self-service system where the control unit automatically performs image generation, pattern recognition, and analysis without requiring manual intervention. The system captures numerical data, transforms it into images, analyzes the images using embedded algorithms, and generates conclusions automatically, enabling the battery management system to monitor its own status efficiently without external assistance.
Data Source
AI summary
A method for establishing visual images of models of battery status transforms data of one or more characteristic parameters collected in a period of time into multiple images composed by normalized pixels corresponding to the data. The multiple images are superimposed with one another to form variant image(s) with variant areas and further moving trends of the variant areas. The visualization of data and superimposition of the visualized images facilitates correlation and variation analysis among characteristic parameters of the battery module. The present invention transform data analysis to image analysis, and therefore, the present invention facilitates following analysis and model training, which can adapt to environmental changes, and indicate battery status and battery events efficiently and accurately.