Battery Cell Type Identification Using SOC-OCV and Resistance-Capacity
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Solution Overview
Problem
Existing technologies are unable to identify the type of unknown battery cells or packs, leading to performance discrepancies between expected and actual product capabilities, particularly in electric vehicles, which can only be revealed after prolonged use.
Innovation Solution
A battery analysis system that estimates SOC-OCV curves, resistance, and capacity products for each cell block, and identifies cell types by comparing these parameters with defined curves and products, allowing for accurate identification without disassembly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery performance is tested through long-term operation, then accurate performance data can be obtained, but the time required for testing increases significantly
Solution Approach 1:
The system performs preliminary identification of cell type and estimation of battery characteristics (SOC-OCV curves, resistance, capacity) before actual use or long-term testing. By analyzing voltage and current data from initial operation and comparing it with pre-stored reference data for various cell types, the system can predict performance characteristics without requiring extended testing periods.
2Adaptability or versatility
If the cell type of a battery pack is unknown, then the battery can be used flexibly, but performance prediction and analysis become impossible
Solution Approach 1:
The system continuously monitors battery voltage and current during operation, compares the measured data with expected characteristics for different cell types, and provides feedback to identify the cell type. This feedback mechanism enables the system to adapt to unknown battery packs by automatically determining their characteristics through real-time data analysis and comparison with reference databases.
3Adaptability or versatility
If battery characteristic data is not available for unknown cell types, then new battery configurations can be utilized, but performance under various conditions cannot be predicted
Solution Approach 1:
The system creates a digital model or copy of the unknown battery's characteristics by measuring its voltage-response-to-current behavior and comparing it with stored reference models of known cell types. Through this copying process, the system reproduces the characteristic SOC-OCV curves, resistance, and capacity values for the unknown cell type, enabling reliable performance prediction without requiring physical samples or extensive testing of the specific battery configuration.
Data Source
AI summary
A battery-specific characteristic generation unit estimates, for each battery pack, an SOC (State of Charge)-OCV (Open Circuit Voltage) curve of a cell block included in the battery pack, a resistance of the cell block, and a capacity of the cell block, and calculates a resistance capacity product derived from multiplying the resistance of the cell block by the capacity of the cell block. A cell type identification unit identifies a type of the cell included in an undefined battery pack based on a degree of agreement between the SOC-OCV curve of the cell block of the undefined battery pack and the SOC-OCV curve of an already-defined cell block and based on a degree of agreement between the resistance capacity product of the cell block of the undefined battery pack and the resistance capacity product of the already-defined cell block.