Cell Group Health Assessment Using Predicted Voltage Disparity
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Solution Overview
Problem
Assessing the health of individual cells within a battery pack becomes increasingly challenging after assembly, as testing constraints tighten, and existing methods fail to accurately predict cell performance post-assembly.
Innovation Solution
A system and method utilizing sensors to obtain and analyze a series of average cell group voltages at different stages, calculating a predicted voltage based on a disparity factor and third-stage voltage, allowing for real-time monitoring and evaluation of cell health without disassembly, using a controller with a processor and memory to determine cell group acceptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional testing methods are used after battery pack assembly, then manufacturing constraints are reduced, but measurement precision and ability to detect defective cells deteriorates
Solution Approach 1:
The system performs preliminary voltage measurements and establishes baseline characteristics during manufacturing stages (first stage before assembly, second stage at assembly, third stage at end-of-line). This preliminary characterization enables continuous monitoring and comparison throughout the battery pack lifecycle, allowing detection of deviations that indicate cell degradation or defects while maintaining manufacturing flexibility.
Solution Approach 2:
The system implements continuous feedback by comparing predicted voltage values (based on historical data and disparity factors) with actual measured voltages at calibration events. This feedback mechanism enables real-time assessment of cell group health and identification of defective cells without requiring disassembly or invasive testing, thus maintaining manufacturing constraints while improving detection accuracy.
2Measurement precision
If invasive testing methods are used to assess cell health, then measurement precision improves, but device complexity and manufacturing difficulty increase
Solution Approach 1:
The battery pack system performs self-diagnosis by continuously monitoring its own voltage characteristics and comparing them against predicted values. The controller uses embedded algorithms to calculate disparity factors and assess cell group health without requiring external testing equipment or disassembly, thereby achieving high measurement precision while minimizing device complexity and manufacturing difficulty.
Solution Approach 2:
The voltage sensing system serves multiple functions: it monitors cell group health, detects defective cells, tracks degradation over time, and provides data for predictive analytics. This multi-functionality eliminates the need for separate dedicated testing equipment, reducing device complexity while maintaining high measurement precision through the same sensing infrastructure.
3Reliability
If continuous monitoring of all cells is implemented, then reliability of battery pack improves, but loss of energy and computational resources increases
Solution Approach 1:
The system divides the battery pack into cell groups and performs monitoring and assessment at the cell group level rather than individual cell level. This segmentation reduces the computational burden and data processing requirements while maintaining reliability through hierarchical monitoring. The controller calculates disparity factors and assesses health for each cell group independently, enabling scalable implementation without proportional increases in energy consumption or computational resources.
Solution Approach 2:
The system implements partial monitoring by focusing computational resources on cell groups that show deviations from predicted behavior or exhibit higher risk of failure. Rather than continuously analyzing all cell groups at maximum detail, the system dynamically adjusts monitoring intensity based on assessed risk levels, thereby improving reliability where needed while conserving computational and energy resources in stable cell groups.
Data Source
AI summary
A system for assessing health of a cell group within a module of a battery pack includes a controller having a processor and tangible, non-transitory memory on which instructions are recorded. One or more sensors are configured to obtain a series of respective average cell group voltages at different stages. The controller is adapted to obtain a measured voltage (VM) of the cell group at a calibration event occurring after the third stage. The controller is adapted to calculate a predicted voltage (VP) of the cell group based in part on a sum of a disparity factor (ΔV) and a third stage cell group voltage (V3). The cell group is controlled based at least partially on a difference between the measured voltage (VM) and the predicted voltage (VP).