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A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF

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  • Cui, Zhenhua
  • Kang, Le
  • Li, Liwei
  • Wang, Licheng
  • Wang, Kai

Abstract

Lithium-ion batteries have become the fastest-growing energy storage equipment available for extrinsic and intrinsic reasons. State of Charge (SOC) is one of the lithium-ion batteries' most critical performance indicators, reflecting the remaining capacity. An accurate and stable estimate of SOC is critical for any lithium-ion battery. This paper proposes a hybrid method to achieve stable and real-time battery SOC estimation at different temperatures, composed of an Improved Bidirectional Gated Recurrent Unit (IBGRU) network and Unscented Kalman filtering (UKF). The proposed method is experimentally validated using data from UDDS and US06 driving cycles. The verification results show that the method can adapt to various working conditions and obtain good estimation accuracy and robustness, with MAE and RMSE less than 0.83% and 1.12%, respectively. After transfer learning, the method can also be applied to new lithium-ion batteries and achieve good estimation performance at new temperature conditions. The maximum errors are 4.98% and 5.76% at 25 °C and −10 °C, respectively. Therefore, the IBGRU-UKF method can achieve a more accurate and stable SOC estimation with good expansion performance for different lithium-ion batteries.

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  • Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:energy:v:259:y:2022:i:c:s0360544222018345
    DOI: 10.1016/j.energy.2022.124933
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    Cited by:

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    5. García, Antonio & Monsalve-Serrano, Javier & Ponce-Mora, Alberto & Fogué-Robles, Álvaro, 2023. "Development of a calibration methodology for fitting the response of a lithium-ion cell P2D model using real driving cycles," Energy, Elsevier, vol. 271(C).
    6. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
    7. Mostafa Elshahed & Mohamed A. Tolba & Ali M. El-Rifaie & Ahmed Ginidi & Abdullah Shaheen & Shazly A. Mohamed, 2023. "An Artificial Rabbits’ Optimization to Allocate PVSTATCOM for Ancillary Service Provision in Distribution Systems," Mathematics, MDPI, vol. 11(2), pages 1-19, January.
    8. Kapil Deshpande & Philipp Möhl & Alexander Hämmerle & Georg Weichhart & Helmut Zörrer & Andreas Pichler, 2022. "Energy Management Simulation with Multi-Agent Reinforcement Learning: An Approach to Achieve Reliability and Resilience," Energies, MDPI, vol. 15(19), pages 1-35, October.
    9. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    10. Areeb Khalid & Syed Abdul Rahman Kashif & Noor Ul Ain & Muhammad Awais & Majid Ali Smieee & Jorge El Mariachet Carreño & Juan C. Vasquez & Josep M. Guerrero & Baseem Khan, 2023. "Comparison of Kalman Filters for State Estimation Based on Computational Complexity of Li-Ion Cells," Energies, MDPI, vol. 16(6), pages 1-20, March.
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    13. Ming Zhang & Yanshuo Liu & Dezhi Li & Xiaoli Cui & Licheng Wang & Liwei Li & Kai Wang, 2023. "Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries," Energies, MDPI, vol. 16(4), pages 1-16, February.
    14. Molla Shahadat Hossain Lipu & Tahia F. Karim & Shaheer Ansari & Md. Sazal Miah & Md. Siddikur Rahman & Sheikh T. Meraj & Rajvikram Madurai Elavarasan & Raghavendra Rajan Vijayaraghavan, 2022. "Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities," Energies, MDPI, vol. 16(1), pages 1-31, December.
    15. Lai, Xin & Yao, Yi & Tang, Xiaopeng & Zheng, Yuejiu & Zhou, Yuanqiang & Sun, Yuedong & Gao, Furong, 2023. "Voltage profile reconstruction and state of health estimation for lithium-ion batteries under dynamic working conditions," Energy, Elsevier, vol. 282(C).
    16. Julan Chen & Guangheng Qi & Kai Wang, 2023. "Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review," Energies, MDPI, vol. 16(17), pages 1-22, August.

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