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State of charge estimation for a parallel battery pack jointly by fuzzy-PI model regulator and adaptive unscented Kalman filter

Author

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  • Peng, Simin
  • Miao, Yifan
  • Xiong, Rui
  • Bai, Jiawei
  • Cheng, Mengzeng
  • Pecht, Michael

Abstract

Parallel battery pack (PBP) is an important unit for its application in electric vehicles and energy storage, and precise state of charge (SOC) is the basic parameter for battery efficient operation. However, the SOC is an internal hidden immeasurable variable, and the measurable battery parameters of the PBP are limited, which makes it difficult to precisely estimate SOC for the PBP. The main efforts are as follows: An improved equivalent circuit model of the PBP is first established on the basis of the fuzzy-proportional integral model regulator, which can accurately describe the influence of battery cell inconsistency on the PBP discharging characteristics. Under constant current and UDDS operating conditions, the battery model voltage can accurately capture the measured voltage during the discharging process, especially at the final stage of discharge with the maximum voltage absolute error below 0.12 V (about 3.2%). A model-based SOC prediction algorithm using an adaptive unscented Kalman filter (AUKF) with a sliding window noise estimator is developed for the PBP. It can adaptively achieve accurate process and measurement noise statistics of the PBP for the AUKF. The SOC of the PBP can be precisely estimated using the developed method with the absolute errors below 2% even if the noise statistics are randomly given respectively. Moreover, compared to the unimproved AUKF and the Sage-Husa method, the presented algorithm illustrates the highest SOC prediction precision with the lowest root mean square error of 1.12% and the minimum mean absolute error of 1.08%.

Suggested Citation

  • Peng, Simin & Miao, Yifan & Xiong, Rui & Bai, Jiawei & Cheng, Mengzeng & Pecht, Michael, 2024. "State of charge estimation for a parallel battery pack jointly by fuzzy-PI model regulator and adaptive unscented Kalman filter," Applied Energy, Elsevier, vol. 360(C).
    • Handle: RePEc:eee:appene:v:360:y:2024:i:c:s0306261924001909
    DOI: 10.1016/j.apenergy.2024.122807
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    References listed on IDEAS

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    1. Kim, Jonghoon & Cho, B.H., 2013. "Screening process-based modeling of the multi-cell battery string in series and parallel connections for high accuracy state-of-charge estimation," Energy, Elsevier, vol. 57(C), pages 581-599.
    2. Diao, Weiping & Xue, Nan & Bhattacharjee, Vikram & Jiang, Jiuchun & Karabasoglu, Orkun & Pecht, Michael, 2018. "Active battery cell equalization based on residual available energy maximization," Applied Energy, Elsevier, vol. 210(C), pages 690-698.
    3. Simin Peng & Liyang Zhu & Zhenlan Dou & Dandan Liu & Ruixin Yang & Michael Pecht, 2023. "Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources," Energies, MDPI, vol. 16(9), pages 1-13, May.
    4. Yu, Quanqing & Nie, Yuwei & Peng, Simin & Miao, Yifan & Zhai, Chengzhi & Zhang, Runfeng & Han, Jinsong & Zhao, Shuo & Pecht, Michael, 2023. "Evaluation of the safety standards system of power batteries for electric vehicles in China," Applied Energy, Elsevier, vol. 349(C).
    5. Hou, Jie & Liu, Jiawei & Chen, Fengwei & Li, Penghua & Zhang, Tao & Jiang, Jincheng & Chen, Xiaolei, 2023. "Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter," Energy, Elsevier, vol. 271(C).
    6. Xie, Yanxin & Wang, Shunli & Zhang, Gexiang & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2023. "Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 336(C).
    7. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    8. Peng, Simin & Sun, Yunxiang & Liu, Dandan & Yu, Quanqing & Kan, Jiarong & Pecht, Michael, 2023. "State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network," Energy, Elsevier, vol. 282(C).
    9. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    10. He, Lin & Wang, Yangyang & Wei, Yujiang & Wang, Mingwei & Hu, Xiaosong & Shi, Qin, 2022. "An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery," Energy, Elsevier, vol. 244(PA).
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