Battery Diagnosis Using Overpotential Correction for Fast Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery diagnosis methods using high electric stimulation suffer from inaccurate results due to overpotential noise, leading to prolonged diagnosis times and discrepancies between diagnosed and actual charge/discharge performance.
Innovation Solution
A battery diagnosis apparatus and method that applies high electric stimulation to obtain charge/discharge information, removes overpotential noise using a machine learning-based factor correction model, and generates accurate performance factors through a cell diagnosis logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If high electric stimulation is applied to the battery to shorten diagnosis time, then diagnosis speed is improved, but measurement precision deteriorates due to overpotential noise
Solution Approach 1:
The patent extracts and removes the overpotential component from the measured battery voltage signal. By separating the overpotential noise from the OCV signal, the system can use high electric stimulation for fast diagnosis while maintaining measurement accuracy through selective signal extraction.
Solution Approach 2:
The patent introduces an intermediary processing step that uses machine learning models to estimate and subtract overpotential from the measured voltage. This intermediary process enables the system to reconcile the conflict between high stimulation intensity and measurement accuracy.
2Measurement precision
If low electric stimulation is applied to the battery to improve measurement precision, then charge/discharge performance accuracy is improved, but productivity deteriorates due to prolonged diagnosis time
Solution Approach 1:
The patent replaces the traditional mechanical approach of using low current for accurate measurements with an intelligent system that uses high current combined with machine learning-based overpotential compensation. This substitution enables fast diagnosis without sacrificing accuracy.
Solution Approach 2:
The patent changes the operating parameters by applying high electric stimulation instead of low stimulation, and compensates for the resulting overpotential through computational methods. This parameter change transforms the diagnostic approach from slow and accurate to fast and computationally compensated.
3Productivity
If high electric stimulation is applied to the battery, then diagnosis speed is improved, but reliability deteriorates due to significant gap between diagnosed and actual performance
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously learns from the relationship between applied stimulation, measured voltage, and estimated OCV. This feedback loop improves the accuracy of overpotential compensation and enhances diagnosis reliability over time.
Solution Approach 2:
The patent performs preliminary characterization of the battery's overpotential behavior through machine learning training before actual diagnosis. By pre-learning the overpotential-stimulation relationship, the system can accurately compensate during fast diagnosis without sacrificing reliability.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Provided is a battery diagnosis apparatus and a battery diagnosis method. The battery diagnosis apparatus includes a data obtaining unit configured to obtain a first target full-cell profile representing a correspondence between a capacity factor and a voltage of a target cell while a first electric stimulation is being applied to the target cell, and temperature information of the target cell, and a control circuit configured to generate an estimated full-cell profile based on the first target full-cell profile and an overpotential profile. The control circuit determines a first performance factor group as a primary estimation result for charge/discharge performance of the target cell by applying a cell diagnosis logic to the estimated full-cell profile. The control circuit determines a second performance factor group as a secondary estimation result for the charge/discharge performance of the target cell by applying a factor correction model to the first performance factor group and the temperature information.