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State-of-charge estimation for second-life lithium-ion batteries based on cell difference model and adaptive fading unscented Kalman filter algorithm

Author

Listed:
  • Banghua Du
  • Zhang Yu
  • Shuhao Yi
  • Yanlin He
  • Yulin Luo

Abstract

Lithium-ion batteries retired from electric vehicles can provide considerable economic benefits when they are retired for secondary use. However, retired batteries after screening and restructuring still face the problem of inaccurate battery pack state-of-charge (SOC) estimation due to the existence of extreme inconsistency. To solve this problem, an adaptive fading unscented Kalman filtering (AFUKF) algorithm based on the cell difference model (CDM) is proposed in this paper for improving the accuracy of SOC estimation of retired lithium-ion battery packs. Firstly, an improved CDM based on a hypothetical Rint model is developed based on a second-order resistor/capacitor equivalent circuit model. Secondly, an AFUKF algorithm is developed to improve the adaptability and robustness of local state estimation against process modelling errors. Finally, characteristic data are obtained by conducting discharge tests on the screened retired lithium-ion batteries under specific operating conditions. The proposed method can improve the accuracy of SOC estimation of retired lithium-ion battery packs and provide a new idea for SOC estimation of retired lithium-ion battery packs, as shown by the simulated real experimental data.

Suggested Citation

  • Banghua Du & Zhang Yu & Shuhao Yi & Yanlin He & Yulin Luo, 2021. "State-of-charge estimation for second-life lithium-ion batteries based on cell difference model and adaptive fading unscented Kalman filter algorithm," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 16(3), pages 927-939.
    • Handle: RePEc:oup:ijlctc:v:16:y:2021:i:3:p:927-939.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctab019
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    Citations

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    Cited by:

    1. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures," Renewable Energy, Elsevier, vol. 198(C), pages 1328-1340.
    2. Fengming Chu & Wen Lu & Dailong Zhai & Guozhen Xiao & Guoan Yang, 2022. "Mass transfer behavior in electrode and battery performance analysis of organic flow battery [Control system design for micro-tubular solid oxide fuel cells]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 494-505.
    3. Zafar, Muhammad Hamza & Khan, Noman Mujeeb & Houran, Mohamad Abou & Mansoor, Majad & Akhtar, Naureen & Sanfilippo, Filippo, 2024. "A novel hybrid deep learning model for accurate state of charge estimation of Li-Ion batteries for electric vehicles under high and low temperature," Energy, Elsevier, vol. 292(C).

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