Research on SOC Prediction of Lithium-Ion Batteries Based on OLHS-DBO-BP Neural Network

Genbao Wang, Yejian Xue (), Yafei Qiao, Chunyang Song, Qing Ming, Shuang Tian () and Yonggao Xia
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Genbao Wang: School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
Yejian Xue: College of New Energy, Ningbo University of Technology, Ningbo 315336, China
Yafei Qiao: Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
Chunyang Song: Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
Qing Ming: School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
Shuang Tian: Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
Yonggao Xia: Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China

Energies, 2024, vol. 17, issue 23, 1-16

Abstract: Accurately estimating the state of charge (SOC) of lithium-ion batteries is of great significance for extending battery lifespan and enhancing the efficiency of energy management. Regarding the issue of the relatively low estimation accuracy of SOC by the backpropagation neural network (BPNN), an enhanced dung beetle optimizer (DBO) algorithm is proposed to optimize the initial weights and thresholds of the BPNN. This overcomes the drawback of a single BP neural network being prone to local optimum and accelerates the convergence rate. Simulation analyses on the experimental data of NCM and A123 lithium batteries were conducted in Matlab R2022a. The results indicate that the proposed algorithm in this paper has an average SOC estimation error of less than 1.6% and a maximum error within 2.9%, demonstrating relatively high estimation accuracy and robustness, and it holds certain theoretical research significance.

Keywords: lithium-ion batteries; state of charge; BP neural network; dung beetle optimizer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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