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Dual fuzzy-based adaptive extended Kalman filter for state of charge estimation of liquid metal battery

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  • Xu, Cheng
  • Zhang, E
  • Jiang, Kai
  • Wang, Kangli

Abstract

Liquid metal batteries (LMBs) are alternatives to conventional lithium-ion batteries due to their specific benefits including high current density and long cycle life. Accurate state of charge (SOC) estimation is an important evaluation index for the battery management system (BMS), which is of great significance to ensure the safe operation of batteries. However, the estimation accuracy of SOC is influenced by many factors, including self-aging and external operating environment changes. Therefore, an online battery model with real-time parameter updates is necessary for accurate SOC estimation. In this paper, a novel dual fuzzy-based adaptive extended Kalman filter (DFAEKF) method is proposed for the SOC estimation of LMBs. Firstly, a second-order RC equivalent circuit model is established to describe the battery's behavior. The forgetting factor recursive least squares (FFRLS) is utilized to identify the model parameters and reconstruct the battery open circuit voltage (OCV). Secondly, the dual adaptive extended Kalman filter (DAEKF) is derived from the battery model. And an intelligent noise estimator is designed based on a fuzzy inference system, which adaptively adjusts the length of the residual innovation sequence (RIS), to update the noise covariance. Finally, the DFAEKF algorithm is proposed for the battery SOC and parameter co-estimation. The online estimated ohmic resistance is employed to calculate the state of health (SOH) of the battery. The proposed DFAEKF is verified through different experiments and compared to conventional algorithms. Experimental results show that the DFAEKF has higher accuracy (error < 1 %) and stronger robustness. The proposed method can also be applied to other model-based state estimation areas.

Suggested Citation

  • Xu, Cheng & Zhang, E & Jiang, Kai & Wang, Kangli, 2022. "Dual fuzzy-based adaptive extended Kalman filter for state of charge estimation of liquid metal battery," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922013484
    DOI: 10.1016/j.apenergy.2022.120091
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    1. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    2. Xu, Maoshu & Zhang, E. & Wang, Sheng & Shen, Yi & Zou, Binchen & Li, Haomiao & Wan, Yiming & Wang, Kangli & Jiang, Kai, 2024. "Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures," Applied Energy, Elsevier, vol. 355(C).
    3. Zhan, Mingjing & Wu, Baigong & Xu, Guoqi & Li, Wenjuan & Liang, Darong & Zhang, Xiao, 2023. "Application of adaptive extended Kalman algorithm based on strong tracking fading factor in Stat-of-Charge estimation of lithium-ion battery," Energy, Elsevier, vol. 284(C).
    4. 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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