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A Robust Kalman Filter-Based Approach for SoC Estimation of Lithium-Ion Batteries in Smart Homes

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  • Omid Rezaei

    (Electronic Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada
    School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran 13114-16846, Iran)

  • Reza Habibifar

    (School of Electrical Engineering, Sharif University of Technology (SUT), Tehran 14588-89694, Iran)

  • Zhanle Wang

    (Electronic Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada)

Abstract

Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertainty of solar energy from root-top photovoltaic, supplying energy during a power outage, and responding to dynamic electricity prices. For the safe and economic operation of batteries, an optimal battery-management system (BMS) is required. One of the most important features of a BMS is state-of-charge ( SoC ) estimation. This article presents a robust central-difference Kalman filter (CDKF) method for the SoC estimation of on-site lithium-ion batteries in smart homes. The state-space equations of the battery are derived based on the equivalent circuit model. The battery model includes two RC subnetworks to represent the fast and slow transient responses of the terminal voltage. Moreover, the model includes the nonlinear relationship between the open-circuit voltage ( OCV ) and SoC . The proposed robust CDKF method can accurately estimate the SoC in the presence of the time-varying model uncertainties and measurement noises. Being able to cope with model uncertainties and measurement noises is essential, since they can lead to inaccurate SoC estimations. An experiment test bench is developed, and various experiments are conducted to extract the battery model parameters. The experimental results show that the proposed method can more accurately estimate SoC compared with other Kalman filter-based methods. The proposed method can be used in optimal BMSs to promote battery performance and decrease battery operational costs in smart homes.

Suggested Citation

  • Omid Rezaei & Reza Habibifar & Zhanle Wang, 2022. "A Robust Kalman Filter-Based Approach for SoC Estimation of Lithium-Ion Batteries in Smart Homes," Energies, MDPI, vol. 15(10), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3768-:d:820322
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    References listed on IDEAS

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    1. Zhongwei Zhang & Dan Zhou & Neng Xiong & Qiao Zhu, 2021. "Non-Fragile H ? Nonlinear Observer for State of Charge Estimation of Lithium-Ion Battery Based on a Fractional-Order Model," Energies, MDPI, vol. 14(16), pages 1-17, August.
    2. Yang, Kuo & Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2022. "A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism," Energy, Elsevier, vol. 244(PB).
    3. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
    4. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
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    Cited by:

    1. Xinghao Zhang & Yan Huang & Zhaowei Zhang & Huipin Lin & Yu Zeng & Mingyu Gao, 2022. "A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter," Energies, MDPI, vol. 15(18), pages 1-26, September.
    2. Qi Wang & Tian Gao & Xingcan Li, 2022. "SOC Estimation of Lithium-Ion Battery Based on Equivalent Circuit Model with Variable Parameters," Energies, MDPI, vol. 15(16), pages 1-15, August.
    3. Mahyar Alinejad & Omid Rezaei & Reza Habibifar & Mahdi Azimian, 2022. "A Charge/Discharge Plan for Electric Vehicles in an Intelligent Parking Lot Considering Destructive Random Decisions, and V2G and V2V Energy Transfer Modes," Sustainability, MDPI, vol. 14(19), pages 1-22, October.

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