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Determination of the battery pack capacity considering the estimation error using a Capacity–Quantity diagram

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  • Ouyang, Minggao
  • Gao, Shang
  • Lu, Languang
  • Feng, Xuning
  • Ren, Dongsheng
  • Li, Jianqiu
  • Zheng, Yuejiu
  • Shen, Ping

Abstract

Accurate estimation of the capacity of a battery pack is essential for the battery management system (BMS) in electric vehicles. The SOCs and capacities of individual cells are the prerequisites for accurately estimating the capacity of a battery pack. This paper proposes quantitative analysis on how the estimation errors of individual cells’ SOCs and capacities influence the estimation error of the battery pack capacity using an approach named Capacity–Quantity diagram (C–Q diagram). The analysis concludes that the estimation error of cell SOC has more influence on the estimation error of pack capacity than the estimation error of cell capacity does. The theoretical analysis is further validated by an experiment using six NCM batteries connected in series with different initial SOC variations. The results help to guide the determination of specifications, e.g., the estimation error of the SOC and that of the capacity, during the design process of a BMS.

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  • Ouyang, Minggao & Gao, Shang & Lu, Languang & Feng, Xuning & Ren, Dongsheng & Li, Jianqiu & Zheng, Yuejiu & Shen, Ping, 2016. "Determination of the battery pack capacity considering the estimation error using a Capacity–Quantity diagram," Applied Energy, Elsevier, vol. 177(C), pages 384-392.
    • Handle: RePEc:eee:appene:v:177:y:2016:i:c:p:384-392
    DOI: 10.1016/j.apenergy.2016.05.137
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    1. Deng Ma & Kai Gao & Yutao Mu & Ziqi Wei & Ronghua Du, 2022. "An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error," Energies, MDPI, vol. 15(10), pages 1-18, May.
    2. Zhang, Caiping & Jiang, Yan & Jiang, Jiuchun & Cheng, Gong & Diao, Weiping & Zhang, Weige, 2017. "Study on battery pack consistency evolutions and equilibrium diagnosis for serial- connected lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 510-519.
    3. Weng, Caihao & Feng, Xuning & Sun, Jing & Peng, Huei, 2016. "State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking," Applied Energy, Elsevier, vol. 180(C), pages 360-368.
    4. Lin, Yu-Hsiu & Shen, Ting-Yu, 2023. "Novel cell screening and prognosing based on neurocomputing-based multiday-ahead time-series forecasting for predictive maintenance of battery modules in frequency regulation-energy storage systems," Applied Energy, Elsevier, vol. 351(C).

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