Battery OCV Deviation Diagnosis for Low-Capacity Cell Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional battery diagnosis methods using state of charge (SOC), current, and open circuit voltage (OCV) information are cumbersome and difficult to implement in battery management systems, especially when data is missing, increasing the risk of device damage due to battery failures.
Innovation Solution
A battery diagnosis apparatus and method utilizing open circuit voltage (OCV) data to calculate exponential moving average (EMA) deviations, simplifying the diagnosis process by using OCV data to detect battery abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional battery diagnosis methods use multiple factors (SOC, current, capacity, OCV), then diagnosis accuracy is improved, but system complexity and memory usage increase
Solution Approach 1:
The patent extracts and focuses solely on OCV (open circuit voltage) data from the conventional multi-parameter diagnosis approach. By taking out only the essential OCV factor and eliminating SOC, current, and capacity data requirements, the system achieves simplified diagnosis while maintaining effectiveness for detecting battery abnormalities such as short circuits and low capacity conditions.
2Measurement precision
If conventional battery diagnosis methods use multiple factors (SOC, current, capacity, OCV), then diagnosis accuracy is improved, but data availability requirements increase
Solution Approach 1:
The patent extracts and focuses solely on OCV (open circuit voltage) data from the conventional multi-parameter diagnosis approach. By taking out only the essential OCV factor and eliminating SOC, current, and capacity data requirements, the system achieves simplified diagnosis while maintaining effectiveness for detecting battery abnormalities such as short circuits and low capacity conditions.
3Reliability
If battery management system collects data from vehicle, then comprehensive battery monitoring is achieved, but data transmission and processing burden increase
Solution Approach 1:
The patent extracts and focuses solely on OCV (open circuit voltage) data from the conventional multi-parameter diagnosis approach. By taking out only the essential OCV factor and eliminating SOC, current, and capacity data requirements, the system achieves simplified diagnosis while maintaining effectiveness for detecting battery abnormalities such as short circuits and low capacity conditions.
Solution Approach 2:
The patent uses OCV data as a simplified copy or proxy for comprehensive battery state information. Instead of collecting and processing multiple parameters (SOC, current, capacity, OCV), the system uses only OCV measurements to infer battery health status, effectively creating a lightweight diagnostic model that reduces data transmission and processing requirements while maintaining diagnostic capability.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A battery diagnosis device according to an embodiment disclosed in the present document may comprise: an acquisition unit for acquiring open circuit voltage (OCV) data of multiple battery units; a deviation calculation unit for, on the basis of the OCV data, calculating an OCV change value of the multiple battery units in a designated time interval and calculating an OCV deviation value with respect to an average indicating a difference value between an average OCV change value of the multiple battery units and an OCV change value of a target battery unit among the multiple battery units; an exponential moving average (EMA) calculation unit for calculating an EMA deviation value by applying an EMA filter to the OCV deviation value with respect to the average of the target battery unit; and a diagnosis unit for diagnosing an abnormality of the target battery unit on the basis of the EMA deviation value.