Next Article in Journal
NaBH4-Poly(Ethylene Oxide) Composite Electrolyte for All-Solid-State Na-Ion Batteries
Next Article in Special Issue
General Machine Learning Approaches for Lithium-Ion Battery Capacity Fade Compared to Empirical Models
Previous Article in Journal
Physics-Based Equivalent Circuit Model Motivated by the Doyle–Fuller–Newman Model
Previous Article in Special Issue
State of Health Estimation for Lithium-Ion Battery Based on Sample Transfer Learning under Current Pulse Test
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Batteries
Volume 10
Issue 9
10.3390/batteries10090315
batteries-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

State of Health Estimation of Li-Ion Battery via Incremental Capacity Analysis and Internal Resistance Identification Based on Kolmogorov–Arnold Networks

by
Jun Peng
1,
Xuan Zhao
1,*,
Jian Ma
1,
Dean Meng
1,
Shuhai Jia
2,
Kai Zhang
1,
Chenyan Gu
1 and
Wenhao Ding
1
1
School of Automobile, Chang’an University, Xi’an 710064, China
2
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Batteries 2024, 10(9), 315; https://doi.org/10.3390/batteries10090315
Submission received: 16 July 2024 / Revised: 30 August 2024 / Accepted: 2 September 2024 / Published: 4 September 2024
(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries—2nd Edition)
Browse Figures
Versions Notes

Abstract

An accurate estimation of the state of health (SOH) of Li-ion batteries is critical for the efficient and safe operation of battery-powered systems. Traditional methods for SOH estimation, such as Coulomb counting, often struggle with sensitivity to measurement noise and time-consuming tests. This study addresses this issue by combining incremental capacity (IC) analysis and a novel neural network, Kolmogorov–Arnold Networks (KANs). Fifteen features were extracted from IC curves and a 2RC equivalent circuit model was used to identify the internal resistance of batteries. Recursive least squares were used to identify the parameters of the equivalent circuit model. IC features and internal resistance were considered as input variables to establish the SOH estimation model. Three commonly used machine learning methods (BP, LSTM, TCN) and two hybrid algorithms (LSTM-KAN and TCN-KAN) were used to establish the SOH estimation model. The performance of the five models was compared and analyzed. The results demonstrated that the hybrid models integrated with the KAN performed better than the conventional models, and the LSTM-KAN model had higher estimation accuracy than that of the other models. The model achieved a mean absolute error of less than 0.412% in SOH prediction in the test and validation dataset. The proposed model does not require complete charge and discharge data, which provides a promising tool for the accurate monitoring and fast detection of battery SOH.
Keywords: SOH estimation; KAN; incremental capacity analysis; internal resistance; Li-ion battery SOH estimation; KAN; incremental capacity analysis; internal resistance; Li-ion battery

Share and Cite

MDPI and ACS Style

Peng, J.; Zhao, X.; Ma, J.; Meng, D.; Jia, S.; Zhang, K.; Gu, C.; Ding, W. State of Health Estimation of Li-Ion Battery via Incremental Capacity Analysis and Internal Resistance Identification Based on Kolmogorov–Arnold Networks. Batteries 2024, 10, 315. https://doi.org/10.3390/batteries10090315

AMA Style

Peng J, Zhao X, Ma J, Meng D, Jia S, Zhang K, Gu C, Ding W. State of Health Estimation of Li-Ion Battery via Incremental Capacity Analysis and Internal Resistance Identification Based on Kolmogorov–Arnold Networks. Batteries. 2024; 10(9):315. https://doi.org/10.3390/batteries10090315

Chicago/Turabian Style

Peng, Jun, Xuan Zhao, Jian Ma, Dean Meng, Shuhai Jia, Kai Zhang, Chenyan Gu, and Wenhao Ding. 2024. "State of Health Estimation of Li-Ion Battery via Incremental Capacity Analysis and Internal Resistance Identification Based on Kolmogorov–Arnold Networks" Batteries 10, no. 9: 315. https://doi.org/10.3390/batteries10090315

APA Style

Peng, J., Zhao, X., Ma, J., Meng, D., Jia, S., Zhang, K., Gu, C., & Ding, W. (2024). State of Health Estimation of Li-Ion Battery via Incremental Capacity Analysis and Internal Resistance Identification Based on Kolmogorov–Arnold Networks. Batteries, 10(9), 315. https://doi.org/10.3390/batteries10090315

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop