Battery OCV Estimation From Filtered Current-Voltage Data
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Solution Overview
Problem
Existing methods for determining characteristic parameters of rechargeable batteries, such as open-circuit voltage and capacity, are limited by the inability to measure these parameters directly during field operation and require calibration phases that disrupt normal operation.
Innovation Solution
A method involving filtering current-voltage pairs using specific criteria to determine a subset for estimating open-circuit voltage, followed by determining the open-circuit voltage characteristic and battery capacity using Coulomb counting and regression techniques, with machine learning algorithms for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibration phases are used to measure battery parameters directly, then measurement precision is improved, but productivity deteriorates due to disrupted normal operation
Solution Approach 1:
The system performs self-diagnosis during normal operation by continuously monitoring current-voltage pairs and automatically filtering data to estimate open-circuit voltage and capacity without requiring external calibration phases or disrupting battery usage
Solution Approach 2:
The battery parameter estimation proceeds continuously during normal charge-discharge cycles by selectively using current-voltage pairs that meet filtering criteria, maintaining both operational continuity and measurement accuracy without interruption
2Productivity
If all current-voltage pairs are used for estimation, then productivity is improved by continuous operation, but measurement precision deteriorates due to noisy data
Solution Approach 1:
The system extracts only the useful subset of current-voltage pairs that satisfy specific filtering criteria (current magnitude threshold, time interval threshold, and dV/dt threshold) from the complete measurement data, discarding noisy or irrelevant data points while maintaining continuous operation
Solution Approach 2:
Different filtering criteria are applied selectively to different data points based on local conditions (current magnitude, time interval, voltage change rate), allowing precise estimation by treating each data point according to its specific quality characteristics rather than uniform processing
Data Source
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AI summary
Described are techniques for ascertaining one or more characteristic values of batteries. For example, the state of charge (SOC), the static voltage or open-circuit voltage (OCV) can be ascertained. The capacitance of the battery can also be ascertained.