A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network

Deyu Cui, Bizhong Xia, Ruifeng Zhang, Zhen Sun, Zizhou Lao, Wei Wang, Wei Sun, Yongzhi Lai and Mingwang Wang
Additional contact information
Deyu Cui: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Bizhong Xia: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Ruifeng Zhang: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Zhen Sun: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Zizhou Lao: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Wei Wang: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Wei Sun: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Yongzhi Lai: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
Mingwang Wang: Sunwoda Electronic Co. Ltd., Shenzhen 518108, China

Energies, 2018, vol. 11, issue 4, 1-18

Abstract: State of charge (SOC) estimation is becoming increasingly important, along with electric vehicle (EV) rapid development, while SOC is one of the most significant parameters for the battery management system, indicating remaining energy and ensuring the safety and reliability of EV. In this paper, a hybrid wavelet neural network (WNN) model combining the discrete wavelet transform (DWT) method and adaptive WNN is proposed to estimate the SOC of lithium-ion batteries. The WNN model is trained by Levenberg-Marquardt (L-M) algorithm, whose inputs are processed by discrete wavelet decomposition and reconstitution. Compared with back-propagation neural network (BPNN), L-M based BPNN (LMBPNN), L-M based WNN (LMWNN), DWT with L-M based BPNN (DWTLMBPNN) and extend Kalman filter (EKF), the proposed intelligent SOC estimation method is validated and proved to be effective. Under the New European Driving Cycle (NEDC), the mean absolute error and maximum error can be reduced to 0.59% and 3.13%, respectively. The characteristics of high accuracy and strong robustness of the proposed method are verified by comparison study and robustness evaluation results (e.g., measurement noise test and untrained driving cycle test).

Keywords: wavelet neural network; state of charge; wavelet analysis; discrete wavelet transform; lithium-ion battery (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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