Battery Cell Parameter Prediction via Sensor Correlation
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Solution Overview
Problem
Conventional xEVs are functionally limited by their electric energy systems, which lack effective monitoring of cell conditions, particularly for cells without sensing units, leading to incomplete information about battery pack performance and safety.
Innovation Solution
A battery pack system that includes a battery management system capable of predicting cell parameter values for cells without sensing units by correlating conditions with cells that have sensing units, using a computer-implemented method to determine and generate parameter values for unmonitored cells based on monitored cell data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensing units are installed in all battery cells to monitor cell conditions, then measurement precision and reliability are improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent uses sensing units from a first plurality of cells as proxies to infer conditions in the second plurality of cells without direct sensing. By creating virtual copies of sensor data through correlation analysis, the system achieves comprehensive monitoring without physically installing sensors in every cell, thus reducing complexity while maintaining measurement precision.
Solution Approach 2:
The patent introduces correlation relationships as an intermediary mechanism between sensed and unsensed cells. Instead of direct measurement, the system uses parameters from monitored cells as mediators to infer conditions in unmonitored cells through statistical correlations, enabling indirect but accurate monitoring.
2Reliability
If sensing units are installed in all battery cells, then reliability of battery management is improved, but manufacturing cost increases
Solution Approach 1:
The system creates virtual sensor readings for cells without physical sensors by copying and adapting data from neighboring sensed cells through correlation analysis. This approach maintains reliability by inferring accurate cell conditions without the expense of installing physical sensing units in every cell.
Solution Approach 2:
The patent employs a selective sensing strategy where sensing units are installed only in a subset of cells (first plurality) rather than all cells. This cost-effective approach uses the limited sensor set to infer conditions across the entire battery pack through correlation-based prediction, reducing manufacturing costs while maintaining adequate reliability.
3Loss of information
If all cell conditions are directly monitored with sensors, then loss of information is reduced, but device complexity increases
Solution Approach 1:
The system recovers complete cell condition information by creating virtual copies of sensor data for unsensed cells. Through correlation analysis with sensed cells, the system generates accurate estimates of parameters such as temperature and voltage for the second plurality of cells, eliminating information loss without adding physical sensors.
Solution Approach 2:
The patent implements a feedback mechanism where the battery management system continuously monitors sensed cells and uses correlation relationships to infer conditions in unsensed cells. This closed-loop approach ensures that information about all cells is maintained and updated in real-time, reducing information loss through intelligent data processing rather than physical expansion of the sensor network.
Data Source
AI summary
A computer-implemented method for predicting a value of a cell parameter is provided, wherein the cell is one of a plurality of cells of a battery pack. The method includes determining which other different conditions of the cell and which similar and/or different conditions of any other cell of the plurality of cells correlate with the cell condition, determining values of one or more parameters from the same cell or any other cell of the plurality of cells that correspond to the determined conditions that correlate with the cell condition, and predicting the value of the cell parameter based on the determined values.