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Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation

Author

Listed:
  • Saeed Sepasi

    (Hawaii Natural Energy Institute, University of Hawaii at Manoa, 1680 East-West Road, Post 105, Honolulu, HI 96822, USA)

  • Leon R. Roose

    (Hawaii Natural Energy Institute, University of Hawaii at Manoa, 1680 East-West Road, Post 105, Honolulu, HI 96822, USA
    These authors contributed equally to this work.)

  • Marc M. Matsuura

    (Hawaii Natural Energy Institute, University of Hawaii at Manoa, 1680 East-West Road, Post 105, Honolulu, HI 96822, USA
    These authors contributed equally to this work.)

Abstract

As the world moves toward greenhouse gas reduction, there is increasingly active work around Li-ion chemistry-based batteries as an energy source for electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids. In these applications, the battery management system (BMS) requires an accurate online estimation of the state of charge (SOC) in a battery pack. This estimation is difficult, especially after substantial battery aging. In order to address this problem, this paper utilizes SOC estimation of Li-ion battery packs using a fuzzy-improved extended Kalman filter (fuzzy-IEKF) for Li-ion cells, regardless of their age. The proposed approach introduces a fuzzy method with a new class and associated membership function that determines an approximate initial value applied to SOC estimation. Subsequently, the EKF method is used by considering the single unit model for the battery pack to estimate the SOC for following periods of battery use. This approach uses an adaptive model algorithm to update the model for each single cell in the battery pack. To verify the accuracy of the estimation method, tests are done on a LiFePO 4 aged battery pack consisting of 120 cells connected in series with a nominal voltage of 432 V.

Suggested Citation

  • Saeed Sepasi & Leon R. Roose & Marc M. Matsuura, 2015. "Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:6:p:5217-5233:d:50604
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    References listed on IDEAS

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    16. Fang Zhou & Feng Xiao & Cheng Chang & Yulong Shao & Chuanxue Song, 2017. "Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-21, July.
    17. Dian Wang & Yun Bao & Jianjun Shi, 2017. "Online Lithium-Ion Battery Internal Resistance Measurement Application in State-of-Charge Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(9), pages 1-11, August.
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    19. Zengkai Wang & Shengkui Zeng & Jianbin Guo & Taichun Qin, 2018. "Remaining capacity estimation of lithium-ion batteries based on the constant voltage charging profile," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-22, July.
    20. S. M. Mezbahul Amin & Nazia Hossain & Molla Shahadat Hossain Lipu & Shabana Urooj & Asma Akter, 2023. "Development of a PV/Battery Micro-Grid for a Data Center in Bangladesh: Resilience and Sustainability Analysis," Sustainability, MDPI, vol. 15(22), pages 1-22, November.
    21. Tang, Xiaopeng & Liu, Boyang & Lv, Zhou & Gao, Furong, 2017. "Observer based battery SOC estimation: Using multi-gain-switching approach," Applied Energy, Elsevier, vol. 204(C), pages 1275-1283.
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    23. Bizhong Xia & Guanghao Chen & Jie Zhou & Yadi Yang & Rui Huang & Wei Wang & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2019. "Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization," Energies, MDPI, vol. 12(15), pages 1-20, July.

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