Fast Impedance Spectrum Construction for Lithium-Ion Batteries Using a Multi-Density Clustering Algorithm
Abstract
:1. Introduction
- (1)
- A density-based spatial clustering of applications with a noise-based (DBSCAN-based) impedance data processing method is proposed to utilize the distribution properties of the measurement points by PRBS excitation.
- (2)
- A parameter setting workflow was designed to properly cluster the data points of the impedance measured by DBSCAN, where the key parameters, that is, the neighborhood radius and the minimum number of points within the specific neighborhood, can be adjusted according to the frequency ranges.
- (3)
- The fast measurement of a battery’s impedance by integrating the PRBS and the data processing method was verified at various temperatures and SOCs on a 3 Ah lithium-ion battery.
2. The Principle of Rapid Measurement of a Battery’s Impedance Spectrum
2.1. PRBS Generation
2.2. Calculation of the Battery’s Impedance
3. Extraction of the Lithium-Ion Battery’s EIS Using Multi-Density Data Clustering
3.1. Characterization of the Distribution of Batteries’ Impedance Based on PRBS Excitation
3.2. Impedance Extraction Algorithm with DBSCAN
3.2.1. Parameters Setting for DBSCAN
- (1)
- Minpts
- (2)
- Eps
3.2.2. Clustering Impedance Data
- (a)
- Determination of the cluster cores based on clustering parameters: if a data point has more than MinPts points in its neighborhood with a radius of Eps, this data point is marked as a core point. We can then find all core points in the dataset. The circles of different colors in subfigures (a) and (b) represent the neighborhood of the point with radius Eps.
- (b)
- Formation of a neighborhood chain: for each core point, find all points that are densely accessible from that core point, forming a chain of neighborhoods.
- (c)
- Labeling the cluster classes and noise points: All points contained in a neighborhood chain are labeled as a cluster class, boundary points are assigned to the core point cluster class to which they are connected, and points that are not assigned to any cluster class are labeled as noise. The entire impedance test dataset is scanned and then labeled with noise points and several data clusters consisting of core and boundary points.
3.2.3. The Proposed Multi-Density Clustering Algorithm
Algorithm 1 The proposed multi-density clustering algorithm. |
Input: Dataset: D = {x1,x2, …,xm}, Eps:, MinPts |
1. Ω |
2. for j = 1,2,…, m do |
3. if then |
4. Ω = Ω ∪ {xj} |
5. end if |
6. end for |
7. k = 0 |
8. = D |
9. while do |
10. |
11. Q = <o> |
12. |
13. while do |
14. if then |
15. |
16. |
17. end if |
18. end while |
19.k = k+1, |
20. Ω = Ω\Ck |
21. end while |
Output: Clustering result: C = {C1,C2, …,Ck} |
4. Experimental Verification
4.1. Experimental Platform
4.2. Validation of the Impedance Measurement
4.3. Validation at Different SOCs and Temperatures
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Nominal Capacity | Voltage Range | Maximum Current |
---|---|---|
3000 mAh | 2.0–4.25 V | 30 A |
Frequencies | Times | MinPts | Eps |
---|---|---|---|
High frequency | 1 | 5 | 6.25 × 10−4 |
2 | 5.85 × 10−5 | ||
3 | 6.56 × 10−6 | ||
Mid-frequency | 1 | 4 | 2.65 × 10−4 |
2 | 6.30 × 10−5 | ||
3 | 9.64 × 10−6 | ||
Low frequency | 1 | 3 | 1.04 × 10−3 |
2 | 2.77 × 10−4 | ||
3 | 1.30 × 10−5 |
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Zhu, L.; Peng, J.; Meng, J.; Sun, C.; Cai, L.; Qu, Z. Fast Impedance Spectrum Construction for Lithium-Ion Batteries Using a Multi-Density Clustering Algorithm. Batteries 2024, 10, 112. https://doi.org/10.3390/batteries10030112
Zhu L, Peng J, Meng J, Sun C, Cai L, Qu Z. Fast Impedance Spectrum Construction for Lithium-Ion Batteries Using a Multi-Density Clustering Algorithm. Batteries. 2024; 10(3):112. https://doi.org/10.3390/batteries10030112
Chicago/Turabian StyleZhu, Ling, Jichang Peng, Jinhao Meng, Chenghao Sun, Lei Cai, and Zhizhu Qu. 2024. "Fast Impedance Spectrum Construction for Lithium-Ion Batteries Using a Multi-Density Clustering Algorithm" Batteries 10, no. 3: 112. https://doi.org/10.3390/batteries10030112
APA StyleZhu, L., Peng, J., Meng, J., Sun, C., Cai, L., & Qu, Z. (2024). Fast Impedance Spectrum Construction for Lithium-Ion Batteries Using a Multi-Density Clustering Algorithm. Batteries, 10(3), 112. https://doi.org/10.3390/batteries10030112