Battery Cell OCV Regression for Tab Disconnection Diagnosis
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Solution Overview
Problem
Existing battery management systems struggle to diagnose tab disconnection in battery cells without sudden changes in voltage, limiting their effectiveness in identifying abnormal behaviors.
Innovation Solution
A battery management apparatus and method that utilizes open circuit voltage (OCV) analysis through linear regression to calculate prediction models, allowing for the diagnosis of tab disconnection based on deviations and error rates even when voltage remains stable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If long-term/short-term moving average method is used to diagnose voltage abnormality, then real-time voltage monitoring is achieved, but system resources are increased and tab disconnection cannot be detected when voltage does not change suddenly
Solution Approach 1:
The patent performs OCV measurements after charging/discharging cycles are completed, before normal operation resumes. This preliminary action allows detection of tab disconnection based on OCV deviation from linear regression predictions, without requiring continuous real-time monitoring during operation, thus reducing system resource requirements while maintaining diagnostic reliability
Solution Approach 2:
The patent shifts from monitoring real-time voltage during operation to measuring open circuit voltage after charging/discharging cycles. By changing the measurement parameter from dynamic voltage to static OCV and using linear regression analysis on OCV values, the system can detect tab disconnection without requiring complex real-time monitoring resources
2Reliability
If real-time voltage monitoring is used to detect sudden voltage changes, then tab disconnection can be detected when it occurs, but system resources are increased and tab disconnection cannot be detected when voltage remains stable
Solution Approach 1:
The patent implements periodic OCV measurements at specific intervals (after charging/discharging cycles) rather than continuous real-time monitoring. This periodic approach reduces energy consumption and system resource usage while still effectively detecting tab disconnection by comparing OCV values against linear regression predictions established from normal cycling behavior
Solution Approach 2:
The patent extracts the diagnostic function from continuous real-time voltage monitoring and separates it into periodic OCV measurements taken after charging/discharging cycles. This extraction allows the system to maintain accurate tab disconnection detection while significantly reducing the energy and computational resources required, as OCV measurement is less resource-intensive than continuous real-time monitoring
Data Source
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AI summary
A battery management apparatus according to an embodiment disclosed herein includes an information obtaining unit configured to obtain an open circuit voltage of each of a plurality of battery cells and a controller configured to calculate an average open circuit voltage for the open circuit voltage of each of the plurality of battery cells, calculate a first linear regression function related to an open circuit voltage of a first battery cell among the plurality of battery cells, based on the open circuit voltage of the first battery cell and the average open circuit voltage, calculate a first error between a prediction value calculated based on the first linear regression function and the open circuit voltage of the first battery cell, and diagnose the first battery cell based on the first error.