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A Novel Data-Driven Fast Capacity Estimation of Spent Electric Vehicle Lithium-ion Batteries

Author

Listed:
  • Caiping Zhang

    (National Active Distribution Network Center, Beijing Jiaotong University, Beijing 100044, China)

  • Jiuchun Jiang

    (National Active Distribution Network Center, Beijing Jiaotong University, Beijing 100044, China)

  • Weige Zhang

    (National Active Distribution Network Center, Beijing Jiaotong University, Beijing 100044, China)

  • Yukun Wang

    (National Active Distribution Network Center, Beijing Jiaotong University, Beijing 100044, China)

  • Suleiman M. Sharkh

    (School of Engineering and the Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK)

  • Rui Xiong

    (National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Fast capacity estimation is a key enabling technique for second-life of lithium-ion batteries due to the hard work involved in determining the capacity of a large number of used electric vehicle (EV) batteries. This paper tries to make three contributions to the existing literature through a robust and advanced algorithm: (1) a three layer back propagation artificial neural network (BP ANN) model is developed to estimate the battery capacity. The model employs internal resistance expressing the battery’s kinetics as the model input, which can realize fast capacity estimation; (2) an estimation error model is established to investigate the relationship between the robustness coefficient and regression coefficient. It is revealed that commonly used ANN capacity estimation algorithm is flawed in providing robustness of parameter measurement uncertainties; (3) the law of large numbers is used as the basis for a proposed robust estimation approach, which optimally balances the relationship between estimation accuracy and disturbance rejection. An optimal range of the threshold for robustness coefficient is also discussed and proposed. Experimental results demonstrate the efficacy and the robustness of the BP ANN model together with the proposed identification approach, which can provide an important basis for large scale applications of second-life of batteries.

Suggested Citation

  • Caiping Zhang & Jiuchun Jiang & Weige Zhang & Yukun Wang & Suleiman M. Sharkh & Rui Xiong, 2014. "A Novel Data-Driven Fast Capacity Estimation of Spent Electric Vehicle Lithium-ion Batteries," Energies, MDPI, vol. 7(12), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:12:p:8076-8094:d:42969
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    References listed on IDEAS

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    1. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
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    Cited by:

    1. Yang Zhang & Bo Guo, 2015. "Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine," Energies, MDPI, vol. 8(11), pages 1-19, November.
    2. Jinsong Yu & Baohua Mo & Diyin Tang & Jie Yang & Jiuqing Wan & Jingjing Liu, 2017. "Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use," Energies, MDPI, vol. 10(12), pages 1-19, December.
    3. Yong Tian & Qianyuan Dong & Jindong Tian & Xiaoyu Li, 2023. "Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks," Energies, MDPI, vol. 16(2), pages 1-18, January.
    4. Weiping Diao & Jiuchun Jiang & Hui Liang & Caiping Zhang & Yan Jiang & Leyi Wang & Biqiang Mu, 2016. "Flexible Grouping for Enhanced Energy Utilization Efficiency in Battery Energy Storage Systems," Energies, MDPI, vol. 9(7), pages 1-15, June.
    5. Sebastian Bräuer & Florian Plenter & Benjamin Klör & Markus Monhof & Daniel Beverungen & Jörg Becker, 2020. "Transactions for trading used electric vehicle batteries: theoretical underpinning and information systems design principles," Business Research, Springer;German Academic Association for Business Research, vol. 13(1), pages 311-342, April.
    6. Liu, Yisheng & Fan, Guodong & Zhou, Boru & Chen, Shun & Sun, Ziqiang & Wang, Yansong & Zhang, Xi, 2023. "Rapid and flexible battery capacity estimation using random short-time charging segments based on residual convolutional networks," Applied Energy, Elsevier, vol. 351(C).
    7. Xuning Feng & Caihao Weng & Xiangming He & Li Wang & Dongsheng Ren & Languang Lu & Xuebing Han & Minggao Ouyang, 2018. "Incremental Capacity Analysis on Commercial Lithium-Ion Batteries using Support Vector Regression: A Parametric Study," Energies, MDPI, vol. 11(9), pages 1-21, September.
    8. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.
    9. Hong Zhang & Li Zhao & Yong Chen, 2015. "A Lossy Counting-Based State of Charge Estimation Method and Its Application to Electric Vehicles," Energies, MDPI, vol. 8(12), pages 1-18, December.

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