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Volume 10
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Article

Fast Impedance Spectrum Construction for Lithium-Ion Batteries Using a Multi-Density Clustering Algorithm

by
Ling Zhu
1,
Jichang Peng
1,
Jinhao Meng
2,*,
Chenghao Sun
3,
Lei Cai
4 and
Zhizhu Qu
3
1
Smart Grid Research Institute, Nanjing Institute of Technology, Nanjing 211167, China
2
School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
3
Faculty of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
4
School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Batteries 2024, 10(3), 112; https://doi.org/10.3390/batteries10030112
Submission received: 14 February 2024 / Revised: 12 March 2024 / Accepted: 18 March 2024 / Published: 20 March 2024
(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries—2nd Edition)
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Abstract

Effectively extracting a lithium-ion battery’s impedance is of great importance for various onboard applications, which requires consideration of both the time consumption and accuracy of the measurement process. Although the pseudorandom binary sequence (PRBS) excitation signal can inject the superposition frequencies with high time efficiency and an easily implementable device, processing the data of the battery’s impedance measurement is still a challenge at present. This study proposes a fast impedance spectrum construction method for lithium-ion batteries, where a multi-density clustering algorithm was designed to effectively extract the useful impedance after PRBS injection. According to the distribution properties of the measurement points by PRBS, a density-based spatial clustering of applications with noise (DBSCAN) was used for processing the data of the lithium-ion battery’s impedance. The two key parameters of the DBSCAN were adjusted by a delicate workflow according to the frequency range. The validation of the proposed method was proved on a 3 Ah lithium-ion battery under nine different test conditions, considering both the SOC and temperature variations.
Keywords: lithium-ion battery; pseudorandom sequence; electrochemical impedance spectroscopy; signal denoising; density clustering lithium-ion battery; pseudorandom sequence; electrochemical impedance spectroscopy; signal denoising; density clustering

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MDPI and ACS Style

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

AMA Style

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 Style

Zhu, 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 Style

Zhu, 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

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