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

Author

Listed:
  • 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)

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).

Suggested Citation

  • Deyu Cui & Bizhong Xia & Ruifeng Zhang & Zhen Sun & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network," Energies, MDPI, vol. 11(4), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:995-:d:142155
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    References listed on IDEAS

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    1. Shifei Yuan & Hongjie Wu & Chengliang Yin, 2013. "State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model," Energies, MDPI, vol. 6(1), pages 1-27, January.
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    3. Lee, Seongjun & Kim, Jonghoon, 2015. "Discrete wavelet transform-based denoising technique for advanced state-of-charge estimator of a lithium-ion battery in electric vehicles," Energy, Elsevier, vol. 83(C), pages 462-473.
    4. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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    Cited by:

    1. Li, Tianyu & Liu, Huiying & Wang, Hui & Yao, Yongming, 2020. "Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles," Energy, Elsevier, vol. 198(C).
    2. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    3. Bizhong Xia & Guanyong Zhang & Huiyuan Chen & Yuheng Li & Zhuojun Yu & Yunchao Chen, 2022. "Verification Platform of SOC Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-20, April.
    4. Xu, Bin & Lee, Jinwoo & Kwon, Daeil & Kong, Lingxi & Pecht, Michael, 2021. "Mitigation strategies for Li-ion battery thermal runaway: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    5. Zuo, Hongyan & Zhang, Bin & Huang, Zhonghua & Wei, Kexiang & Zhu, Hong & Tan, Jiqiu, 2022. "Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation," Energy, Elsevier, vol. 238(PB).

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