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SOH Estimation of Lithium Batteries Based on ICA and WOA-RBF Algorithm

Qi Wang1,2,3, Yandong Gu1,*, Tao Zhu1, Lantian Ge1, Yibo Huang1

1 School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China
2 Research and Development Department, Shuangdeng Group Co., Ltd., Taizhou, 225500, China
3 School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China

* Corresponding Author: Yandong Gu. Email: email

(This article belongs to the Special Issue: Advanced Modelling, Operation, Management and Diagnosis of Lithium Batteries)

Energy Engineering 2024, 121(11), 3221-3239. https://doi.org/10.32604/ee.2024.053758

Abstract

Accurately estimating the State of Health (SOH) of batteries is of great significance for the stable operation and safety of lithium batteries. This article proposes a method based on the combination of Capacity Incremental Curve Analysis (ICA) and Whale Optimization Algorithm-Radial Basis Function (WOA-RBF) neural network algorithm to address the issues of low accuracy and slow convergence speed in estimating State of Health of batteries. Firstly, preprocess the battery data to obtain the real battery SOH curve and Capacity-Voltage (Q-V) curve, convert the Q-V curve into an IC curve and denoise it, analyze the parameters in the IC curve that may serve as health features; Then, extract the constant current charging time of the battery and the horizontal and vertical coordinates of the two IC peaks as health features, and perform correlation analysis using Pearson correlation coefficient method; Finally, the WOA-RBF algorithm was used to estimate the battery SOH, and the training results of LSTM, RBF, and PSO-RBF algorithms were compared. The conclusion was drawn that the WOA-RBF algorithm has high accuracy, fast convergence speed, and the best linearity in estimating SOH. The absolute error of its SOH estimation can be controlled within 1%, and the relative error can be controlled within 2%.

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APA Style
Wang, Q., Gu, Y., Zhu, T., Ge, L., Huang, Y. (2024). SOH estimation of lithium batteries based on ICA and WOA-RBF algorithm. Energy Engineering, 121(11), 3221-3239. https://doi.org/10.32604/ee.2024.053758
Vancouver Style
Wang Q, Gu Y, Zhu T, Ge L, Huang Y. SOH estimation of lithium batteries based on ICA and WOA-RBF algorithm. Energ Eng. 2024;121(11):3221-3239 https://doi.org/10.32604/ee.2024.053758
IEEE Style
Q. Wang, Y. Gu, T. Zhu, L. Ge, and Y. Huang, “SOH Estimation of Lithium Batteries Based on ICA and WOA-RBF Algorithm,” Energ. Eng., vol. 121, no. 11, pp. 3221-3239, 2024. https://doi.org/10.32604/ee.2024.053758



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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