Battery Degradation Estimation Using Segmented Capacity Curve Fitting
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Solution Overview
Problem
Conventional methods for estimating the internal degradation state of secondary batteries face challenges in reproducibility and reliability due to the interdependence of fitting parameters between electromotive force curves, making it difficult to determine the cause of capacity degradation.
Innovation Solution
A method that involves obtaining a target charge curve and its derivative, and a reference capacity characteristic curve from a similar battery, with separate fitting operations in low and high current capacity regions to improve correlation and parameter identification, using a measurement system that includes a charger and an estimation apparatus to perform these calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional electromotive force curve fitting is performed to estimate capacity degradation, then the estimation process can be completed, but the fitting parameters are interdependent making it impossible to determine which parameters cause the degradation
Solution Approach 1:
The patent segments the fitting process into two distinct phases: first fitting the electromotive force curve shape parameters (a, b, c) without changing capacity parameters, then separately adjusting capacity parameters (Q1, Q2). This segmentation isolates the causality of each parameter, allowing determination of which parameters actually cause capacity degradation rather than merely correlating with it.
Solution Approach 2:
The patent performs preliminary fitting of the electromotive force curve shape before capacity degradation analysis. By first establishing the shape parameters (a, b, c) that define the curve's form, the method creates a stable baseline that separates shape variations from capacity variations, enabling subsequent accurate identification of capacity-causing parameters.
2Measurement precision
If fitting parameters are adjusted to improve fit accuracy, then the fitting precision improves, but the interdependence between parameters increases making results less reproducible
Solution Approach 1:
The patent divides the fitting parameters into two independent groups: shape parameters (a, b, c) and capacity parameters (Q1, Q2). Each group is fitted separately in sequential steps, preventing the interdependence that occurs when all parameters are adjusted simultaneously. This segmentation maintains both high fitting precision and result reproducibility.
Solution Approach 2:
The patent performs preliminary fitting of shape parameters before capacity parameters. This preliminary action establishes a stable foundation that prevents subsequent capacity parameter adjustments from inadvertently altering shape characteristics, thereby maintaining reproducibility while achieving precision.
3Ease of manufacture
If simple electromotive force curve fitting is used, then the method is easy to implement, but it cannot accurately identify the internal degradation state of the battery
Solution Approach 1:
The patent implements a segmented fitting approach that maintains computational simplicity while improving accuracy. By separating shape and capacity parameters into distinct fitting steps, the method remains easy to implement through sequential processing while accurately identifying internal degradation states through isolated parameter analysis.
Solution Approach 2:
The patent performs preliminary fitting of electromotive force curve shapes before degradation analysis. This preliminary action provides a simple yet effective foundation that prepares the data for accurate degradation state identification without requiring complex simultaneous fitting algorithms.
Data Source
AI summary
In a method of estimating an internal degradation state of a degraded cell, a measurement system calculates a target capacity characteristic curve by differentiating current capacity by voltage on a target charge curve of a target battery. Further, the method obtains changes of parameters of different types based on a fitting operation that fits the curve and a reference capacity characteristic curve calculated from reference data. In the fitting operation, whichever of low current capacity regions and high current capacity regions of the curve and the curve have a stronger correlation are fit first and whichever of them have a weaker correlation are fit later.