Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

Yan Cheng, Xuesen Zhang, Xiaoqiang Wang and Jianhua Li
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Yan Cheng: Department of Automotive Engineering, Hebei Jiaotong Vocational and Technical College, Shijiazhuang 266100, China
Xuesen Zhang: School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 266100, China
Xiaoqiang Wang: School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 266100, China
Jianhua Li: School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 266100, China

Energies, 2022, vol. 15, issue 6, 1-16

Abstract: The traditional battery state of charge (SOC) estimation method, which is based on neural networks, directly uses terminal voltage and terminal current as the input data. Although it is convenient to implement, it produces a large estimation error when the current and voltage change drastically. To solve this problem, a new method, which uses a composite multiscale wavelet transform, is proposed to estimate the battery SOC. In the proposed method, a wavelet transform is applied to the input data, and this process obtains the approximate coefficients and detail coefficients of the input data at different scales. A neural network then uses these coefficients as inputs to estimate the SOC. The experimental results show that the proposed method can improve the accuracy of the battery SOC estimation without changing the neural network structure or algorithm.

Keywords: state of charge estimation; wavelet transform; multiscale; neural network (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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