Battery OCV Diagnosis for Lower-Cost Abnormality Detection
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Solution Overview
Problem
Existing battery management systems face high processing costs and memory usage when detecting battery abnormalities due to the collection and analysis of multiple factors, which can lead to reduced accuracy and increased risk of battery failures.
Innovation Solution
A battery diagnosis apparatus that utilizes open circuit voltage (OCV) data to calculate OCV deviations, variances, and moving averages, applying weighted averages to diagnose battery abnormalities, reducing processing costs and memory usage while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple factors (SOC, current, capacity, OCV) are used to detect battery abnormalities, then detection accuracy is improved, but processing cost and memory usage are excessively increased
Solution Approach 1:
The patent extracts and focuses on the most critical factor (OCV) for battery abnormality detection, removing less important factors (SOC, current, capacity) from the analysis. This extraction principle reduces the number of parameters processed while maintaining detection accuracy by concentrating on the dominant indicator of battery health and abnormalities.
Solution Approach 2:
The patent uses a simplified, computationally inexpensive OCV-based diagnosis method that can be quickly executed, replacing complex multi-factor analysis. This approach uses readily available OCV data that is already collected by the battery management system, avoiding the need for additional sensors or complex calculations, thereby reducing processing costs and memory usage.
2Reliability
If multiple factors are collected and processed, then comprehensive battery monitoring is achieved, but processing time and computational resources are increased
Solution Approach 1:
The patent extracts the essential monitoring function by focusing solely on OCV variations, removing the need to process multiple other factors. This extraction maintains reliable battery monitoring by concentrating on the most indicative parameter while dramatically reducing processing time and computational resource requirements.
3Loss of information
If numerous factors are analyzed, then more detailed battery insights are obtained, but memory usage is excessively increased
Solution Approach 1:
The patent extracts only the critical diagnostic information contained in OCV data, removing the need to store and process multiple other battery parameters. This approach retains essential battery health insights while significantly reducing memory usage by focusing on a single key indicator rather than maintaining comprehensive multi-factor data sets.
Data Source
AI summary
The technology generally relates to a battery diagnosis approach where an abnormality of a battery may be detected using battery OCV information, reducing the processing cost and memory usage involved in diagnosing batteries while maintaining or improving accuracy. Battery abnormalities may be diagnosed in shorter periods of time, reducing the chance of fires occurring due to the battery abnormality.