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Compensation Method for Estimating the State of Charge of Li-Polymer Batteries Using Multiple Long Short-Term Memory Networks Based on the Extended Kalman Filter

Author

Listed:
  • Donghoon Shin

    (The School of Mechanical Engineering, Korea University of Technology and Education, Cheonan 31253, Korea)

  • Beomjin Yoon

    (The School of Mechanical Engineering, Korea University of Technology and Education, Cheonan 31253, Korea)

  • Seungryeol Yoo

    (The School of Mechanical Engineering, Korea University of Technology and Education, Cheonan 31253, Korea)

Abstract

Many battery state of charge (SOC) estimation methods have been studied for decades; however, it is still difficult to precisely estimate SOC because it is nonlinear and affected by many factors, including the battery state and charge–discharge conditions. The extended Kalman filter (EKF) is generally used for SOC estimation, however its accuracy can decrease owing to the uncertain and inaccurate parameters of battery models and various factors with different time scales affecting the SOC. Herein, a SOC estimation method based on the EKF is proposed to obtain robust accuracy, in which the errors are compensated by a long short-term memory (LSTM) network. The proposed approach trains the errors of the EKF results, and the accurate SOC is estimated by applying calibration values corresponding to the condition of the battery and its load profiles with the help of LSTM. Furthermore, a multi-LSTM structure is implemented, and it adopts the ensemble average to guarantee estimation accuracy. SOC estimation with a root mean square error of less than 1% was found to be close to the actual SOC calculated by coulomb counting. Moreover, once the EKF model was established and the network trained, it was possible to predict the SOC online.

Suggested Citation

  • Donghoon Shin & Beomjin Yoon & Seungryeol Yoo, 2021. "Compensation Method for Estimating the State of Charge of Li-Polymer Batteries Using Multiple Long Short-Term Memory Networks Based on the Extended Kalman Filter," Energies, MDPI, vol. 14(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:349-:d:477940
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    References listed on IDEAS

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    Cited by:

    1. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
    2. Jikai Bi & Jae-Cheon Lee & Hao Liu, 2022. "Performance Comparison of Long Short-Term Memory and a Temporal Convolutional Network for State of Health Estimation of a Lithium-Ion Battery using Its Charging Characteristics," Energies, MDPI, vol. 15(7), pages 1-24, March.

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