Battery Diagnosis Using Overpotential Compensation at High Stimulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery diagnosis methods using high electric stimulation suffer from inaccurate results due to overpotential noise, which prolongs diagnosis time and reduces accuracy, making it difficult to accurately assess charge/discharge performance.
Innovation Solution
A battery diagnosis apparatus and method that applies high electric stimulation to obtain charge/discharge information and uses a machine learning-based factor correction model to remove overpotential noise, generating an estimated full-cell profile by subtracting the overpotential profile from the first target full-cell profile, determining a first performance factor group, and determining a second performance factor group using a cell diagnosis logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low electric stimulation is applied to the battery during diagnosis, then diagnosis accuracy is improved, but diagnosis time is excessively long
Solution Approach 1:
The patent introduces an overpotential compensation model as an intermediary mechanism that mediates between the high electric stimulation measurement data and the actual battery performance assessment. This model compensates for the overpotential effects, enabling accurate diagnosis without requiring low electric stimulation conditions
Solution Approach 2:
The patent changes the electrical parameters by applying high electric stimulation (high current or voltage) during diagnosis instead of traditional low electric stimulation. Combined with overpotential compensation algorithms, this parameter change enables both rapid testing and accurate results
2Productivity
If high electric stimulation is applied to the battery to shorten diagnosis time, then diagnosis speed is improved, but diagnosis accuracy deteriorates due to overpotential noise
Solution Approach 1:
The patent converts the harmful overpotential effect into a beneficial diagnostic tool. By intentionally applying high electric stimulation that generates overpotential, and then using compensation models to remove the overpotential component, the system extracts accurate battery performance data while maintaining high diagnostic speed
Solution Approach 2:
The overpotential compensation model serves as an intermediary that processes the high electric stimulation data, separating the actual battery performance signals from the overpotential noise, thereby enabling accurate diagnosis under high stimulation conditions
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces diagnosis time and improves accuracy by correcting performance factors using a machine learning-based model, ensuring a high degree of consistency with actual battery performance.
Implementation Method 1
as the current flowing through the battery is greater, the polarization phenomenon is generated more, and overpotential is caused by the polarization phenomenon
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 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. The second performance factor group includes an estimation result of a positive electrode participation end point of the target cell.