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ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries

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  • Dai, Haifeng
  • Guo, Pingjing
  • Wei, Xuezhe
  • Sun, Zechang
  • Wang, Jiayuan

Abstract

A common drawback of the SOC (State of Charge) estimators of EV (electric vehicle) traction batteries nowadays is that they don't consider the difference among individual cells and employ the “averaged SOC” as the state of charge of the pack. Over-charge or over-discharge may happen to the weak cells with this SOC value in vehicular applications. In this study, a novel approach for online pack SOC estimation and correction is proposed, which combines a traditional SOC estimator and an ANFIS (adaptive neuro-fuzzy inference system). The traditional KF (Kalman filtering) based estimator is applied to firstly estimate the “averaged SOC” of the battery pack, and the ANFIS is then used to online correct the “averaged” SOC estimation with the information of cell differences and loading current. The influence of cell differences on SOC estimation is embodied in the fuzzy rules of the ANFIS, which is trained offline. Validation results by experiments show that, the proposed method has the potential to overcome the drawbacks of traditional SOC estimators caused by cell-to-cell variations in a battery pack, and the corrected SOC is more reasonable than the traditional “averaged SOC”.

Suggested Citation

  • Dai, Haifeng & Guo, Pingjing & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan, 2015. "ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries," Energy, Elsevier, vol. 80(C), pages 350-360.
    • Handle: RePEc:eee:energy:v:80:y:2015:i:c:p:350-360
    DOI: 10.1016/j.energy.2014.11.077
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    5. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
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    8. Yixing Chen & Deqing Huang & Qiao Zhu & Weiqun Liu & Congzhi Liu & Neng Xiong, 2017. "A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter," Energies, MDPI, vol. 10(9), pages 1-19, September.
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    12. Zahid, Taimoor & Xu, Kun & Li, Weimin & Li, Chenming & Li, Hongzhe, 2018. "State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles," Energy, Elsevier, vol. 162(C), pages 871-882.
    13. Martínez-Lao, Juan & Montoya, Francisco G. & Montoya, Maria G. & Manzano-Agugliaro, Francisco, 2017. "Electric vehicles in Spain: An overview of charging systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 970-983.
    14. Li, Xiaoyu & Wang, Zhenpo & Zhang, Lei, 2019. "Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 174(C), pages 33-44.
    15. Feyijimi Adegbohun & Annette von Jouanne & Kwang Y. Lee, 2019. "Autonomous Battery Swapping System and Methodologies of Electric Vehicles," Energies, MDPI, vol. 12(4), pages 1-14, February.
    16. Lin, Cheng & Mu, Hao & Xiong, Rui & Cao, Jiayi, 2017. "Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy," Applied Energy, Elsevier, vol. 194(C), pages 560-568.
    17. Zheng Chen & Xiaoyu Li & Jiangwei Shen & Wensheng Yan & Renxin Xiao, 2016. "A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles," Energies, MDPI, vol. 9(9), pages 1-15, September.
    18. Tao, Laifa & Ma, Jian & Cheng, Yujie & Noktehdan, Azadeh & Chong, Jin & Lu, Chen, 2017. "A review of stochastic battery models and health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 716-732.
    19. Firouz, Y. & Relan, R. & Timmermans, J.M. & Omar, N. & Van den Bossche, P. & Van Mierlo, J., 2016. "Advanced lithium ion battery modeling and nonlinear analysis based on robust method in frequency domain: Nonlinear characterization and non-parametric modeling," Energy, Elsevier, vol. 106(C), pages 602-617.
    20. Deng, Zhongwei & Yang, Lin & Cai, Yishan & Deng, Hao & Sun, Liu, 2016. "Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery," Energy, Elsevier, vol. 112(C), pages 469-480.
    21. Mahmoudzadeh Andwari, Amin & Pesiridis, Apostolos & Rajoo, Srithar & Martinez-Botas, Ricardo & Esfahanian, Vahid, 2017. "A review of Battery Electric Vehicle technology and readiness levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 414-430.

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