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A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BP

Tianqing Yuan1,2, Na Li1,2, Hao Sun3, Sen Tan4,*

1 Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, 132012, China
2 Department of Electrical Engineering, Northeast Electric Power University, Jilin, 132012, China
3 College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, China
4 Center for Research on Microgrids, Department of Energy, Aalborg University, Aalborg, 9220, Denmark

* Corresponding Author: Sen Tan. Email: email

Computers, Materials & Continua 2024, 80(3), 4497-4512. https://doi.org/10.32604/cmc.2024.056061

Abstract

To improve the estimation accuracy of state of charge (SOC) and state of health (SOH) for lithium-ion batteries, in this paper, a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm (GA) combined with back propagation (BP) neural network is proposed, the research addresses the issue of data manipulation resulting from cyber-attacks. Firstly, anomalous data stemming from cyber-attacks are identified and eliminated using the isolated forest algorithm, followed by data restoration. Secondly, the incremental capacity (IC) curve is derived from the restored data using the Kalman filtering algorithm, with the peak of the IC curve (ICP) and its corresponding voltage serving as the health factor (HF). Thirdly, the GA-BP neural network is applied to map the relationship between HF, constant current charging time, and SOH, facilitating the estimation of SOH based on HF. Finally, SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time, and by updating the maximum available capacity. Experiments show that the root mean squared error of the joint estimation results does not exceed 1%, which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.

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APA Style
Yuan, T., Li, N., Sun, H., Tan, S. (2024). A joint estimation method of SOC and SOH for lithium-ion battery considering cyber-attacks based on GA-BP. Computers, Materials & Continua, 80(3), 4497-4512. https://doi.org/10.32604/cmc.2024.056061
Vancouver Style
Yuan T, Li N, Sun H, Tan S. A joint estimation method of SOC and SOH for lithium-ion battery considering cyber-attacks based on GA-BP. Comput Mater Contin. 2024;80(3):4497-4512 https://doi.org/10.32604/cmc.2024.056061
IEEE Style
T. Yuan, N. Li, H. Sun, and S. Tan "A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BP," Comput. Mater. Contin., vol. 80, no. 3, pp. 4497-4512. 2024. https://doi.org/10.32604/cmc.2024.056061



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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